Bringing consistency to simulation of population models--Poisson simulation as a bridge between micro and macro simulation.

PubMed

Gustafsson, Leif; Sternad, Mikael

2007-10-01

Population models concern collections of discrete entities such as atoms, cells, humans, animals, etc., where the focus is on the number of entities in a population. Because of the complexity of such models, simulation is usually needed to reproduce their complete dynamic and stochastic behaviour. Two main types of simulation models are used for different purposes, namely micro-simulation models, where each individual is described with its particular attributes and behaviour, and macro-simulation models based on stochastic differential equations, where the population is described in aggregated terms by the number of individuals in different states. Consistency between micro- and macro-models is a crucial but often neglected aspect. This paper demonstrates how the Poisson Simulation technique can be used to produce a population macro-model consistent with the corresponding micro-model. This is accomplished by defining Poisson Simulation in strictly mathematical terms as a series of Poisson processes that generate sequences of Poisson distributions with dynamically varying parameters. The method can be applied to any population model. It provides the unique stochastic and dynamic macro-model consistent with a correct micro-model. The paper also presents a general macro form for stochastic and dynamic population models. In an appendix Poisson Simulation is compared with Markov Simulation showing a number of advantages. Especially aggregation into state variables and aggregation of many events per time-step makes Poisson Simulation orders of magnitude faster than Markov Simulation. Furthermore, you can build and execute much larger and more complicated models with Poisson Simulation than is possible with the Markov approach.

The Dynamics of HPV Infection and Cervical Cancer Cells.

PubMed

Asih, Tri Sri Noor; Lenhart, Suzanne; Wise, Steven; Aryati, Lina; Adi-Kusumo, F; Hardianti, Mardiah S; Forde, Jonathan

2016-01-01

The development of cervical cells from normal cells infected by human papillomavirus into invasive cancer cells can be modeled using population dynamics of the cells and free virus. The cell populations are separated into four compartments: susceptible cells, infected cells, precancerous cells and cancer cells. The model system of differential equations also has a free virus compartment in the system, which infect normal cells. We analyze the local stability of the equilibrium points of the model and investigate the parameters, which play an important role in the progression toward invasive cancer. By simulation, we investigate the boundary between initial conditions of solutions, which tend to stable equilibrium point, representing controlled infection, and those which tend to unbounded growth of the cancer cell population. Parameters affected by drug treatment are varied, and their effect on the risk of cancer progression is explored.

Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.

PubMed

Poleszczuk, Jan; Macklin, Paul; Enderling, Heiko

2016-01-01

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

Cancer heterogeneity and multilayer spatial evolutionary games.

PubMed

Świerniak, Andrzej; Krześlak, Michał

2016-10-13

Evolutionary game theory (EGT) has been widely used to simulate tumour processes. In almost all studies on EGT models analysis is limited to two or three phenotypes. Our model contains four main phenotypes. Moreover, in a standard approach only heterogeneity of populations is studied, while cancer cells remain homogeneous. A multilayer approach proposed in this paper enables to study heterogeneity of single cells. In the extended model presented in this paper we consider four strategies (phenotypes) that can arise by mutations. We propose multilayer spatial evolutionary games (MSEG) played on multiple 2D lattices corresponding to the possible phenotypes. It enables simulation and investigation of heterogeneity on the player-level in addition to the population-level. Moreover, it allows to model interactions between arbitrary many phenotypes resulting from the mixture of basic traits. Different equilibrium points and scenarios (monomorphic and polymorphic populations) have been achieved depending on model parameters and the type of played game. However, there is a possibility of stable quadromorphic population in MSEG games for the same set of parameters like for the mean-field game. The model assumes an existence of four possible phenotypes (strategies) in the population of cells that make up tumour. Various parameters and relations between cells lead to complex analysis of this model and give diverse results. One of them is a possibility of stable coexistence of different tumour cells within the population, representing almost arbitrary mixture of the basic phenotypes. This article was reviewed by Tomasz Lipniacki, Urszula Ledzewicz and Jacek Banasiak.

Evaluation of Simulated Microgravity Environments Induced by Diamagnetic Levitation of Plant Cell Suspension Cultures

NASA Astrophysics Data System (ADS)

Kamal, Khaled Y.; Herranz, Raúl; van Loon, Jack J. W. A.; Christianen, Peter C. M.; Medina, F. Javier

2016-06-01

Ground-Based Facilities (GBF) are essetial tools to understand the physical and biological effects of the absence of gravity and they are necessary to prepare and complement space experiments. It has been shown previously that a real microgravity environment induces the dissociation of cell proliferation from cell growth in seedling root meristems, which are limited populations of proliferating cells. Plant cell cultures are large and homogeneous populations of proliferating cells, so that they are a convenient model to study the effects of altered gravity on cellular mechanisms regulating cell proliferation and associated cell growth. Cell suspension cultures of the Arabidopsis thaliana cell line MM2d were exposed to four altered gravity and magnetic field environments in a magnetic levitation facility for 3 hours, including two simulated microgravity and Mars-like gravity levels obtained with different magnetic field intensities. Samples were processed either by quick freezing, to be used in flow cytometry for cell cycle studies, or by chemical fixation for microscopy techniques to measure parameters of the nucleolus. Although the trend of the results was the same as those obtained in real microgravity on meristems (increased cell proliferation and decreased cell growth), we provide a technical discussion in the context of validation of proper conditions to achieve true cell levitation inside a levitating droplet. We conclude that the use of magnetic levitation as a simulated microgravity GBF for cell suspension cultures is not recommended.

Modeling Transformation and Conjugation in Bacteria Populations

NASA Astrophysics Data System (ADS)

Russo, John; Dong, J. J.

The rise of antibiotic resistance in bacteria populations is a growing threat to medical treatment of diseases. Transformation, where a cell absorbs a plasmid from its environment, and conjugation, direct transfer of a plasmid from one cell to another, are the two main mechanisms of emergence of antibiotic resistance. We model the processes using a combined approach of Kinetic Monte Carlo simulation and differential equations to describe the plasmid-carrying and plasmid-free populations. Through analysis of our results, we characterize the conditions that lead to dominance of the antibiotic resistant population. NSF-DMR #1248387.

Computational Model of Population Dynamics Based on the Cell Cycle and Local Interactions

NASA Astrophysics Data System (ADS)

Oprisan, Sorinel Adrian; Oprisan, Ana

2005-03-01

Our study bridges cellular (mesoscopic) level interactions and global population (macroscopic) dynamics of carcinoma. The morphological differences and transitions between well and smooth defined benign tumors and tentacular malignat tumors suggest a theoretical analysis of tumor invasion based on the development of mathematical models exhibiting bifurcations of spatial patterns in the density of tumor cells. Our computational model views the most representative and clinically relevant features of oncogenesis as a fight between two distinct sub-systems: the immune system of the host and the neoplastic system. We implemented the neoplastic sub-system using a three-stage cell cycle: active, dormant, and necrosis. The second considered sub-system consists of cytotoxic active (effector) cells — EC, with a very broad phenotype ranging from NK cells to CTL cells, macrophages, etc. Based on extensive numerical simulations, we correlated the fractal dimensions for carcinoma, which could be obtained from tumor imaging, with the malignat stage. Our computational model was able to also simulate the effects of surgical, chemotherapeutical, and radiotherapeutical treatments.

A Mathematical Tumor Model with Immune Resistance and Drug Therapy: An Optimal Control Approach

DOE PAGES

De Pillis, L. G.; Radunskaya, A.

2001-01-01

We present a competition model of cancer tumor growth that includes both the immune system response and drug therapy. This is a four-population model that includes tumor cells, host cells, immune cells, and drug interaction. We analyze the stability of the drug-free equilibria with respect to the immune response in order to look for target basins of attraction. One of our goals was to simulate qualitatively the asynchronous tumor-drug interaction known as “Jeffs phenomenon.” The model we develop is successful in generating this asynchronous response behavior. Our other goal was to identify treatment protocols that could improve standard pulsed chemotherapymore » regimens. Using optimal control theory with constraints and numerical simulations, we obtain new therapy protocols that we then compare with traditional pulsed periodic treatment. The optimal control generated therapies produce larger oscillations in the tumor population over time. However, by the end of the treatment period, total tumor size is smaller than that achieved through traditional pulsed therapy, and the normal cell population suffers nearly no oscillations.« less

A Mathematical Tumor Model with Immune Resistance and Drug Therapy: An Optimal Control Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Pillis, L. G.; Radunskaya, A.

We present a competition model of cancer tumor growth that includes both the immune system response and drug therapy. This is a four-population model that includes tumor cells, host cells, immune cells, and drug interaction. We analyze the stability of the drug-free equilibria with respect to the immune response in order to look for target basins of attraction. One of our goals was to simulate qualitatively the asynchronous tumor-drug interaction known as “Jeffs phenomenon.” The model we develop is successful in generating this asynchronous response behavior. Our other goal was to identify treatment protocols that could improve standard pulsed chemotherapymore » regimens. Using optimal control theory with constraints and numerical simulations, we obtain new therapy protocols that we then compare with traditional pulsed periodic treatment. The optimal control generated therapies produce larger oscillations in the tumor population over time. However, by the end of the treatment period, total tumor size is smaller than that achieved through traditional pulsed therapy, and the normal cell population suffers nearly no oscillations.« less

A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells

NASA Astrophysics Data System (ADS)

Vagos, Márcia R.; Arevalo, Hermenegild; de Oliveira, Bernardo Lino; Sundnes, Joakim; Maleckar, Mary M.

2017-09-01

Models of cardiac cell electrophysiology are complex non-linear systems which can be used to gain insight into mechanisms of cardiac dynamics in both healthy and pathological conditions. However, the complexity of cardiac models can make mechanistic insight difficult. Moreover, these are typically fitted to averaged experimental data which do not incorporate the variability in observations. Recently, building populations of models to incorporate inter- and intra-subject variability in simulations has been combined with sensitivity analysis (SA) to uncover novel ionic mechanisms and potentially clarify arrhythmogenic behaviors. We used the Koivumäki human atrial cell model to create two populations, representing normal Sinus Rhythm (nSR) and chronic Atrial Fibrillation (cAF), by varying 22 key model parameters. In each population, 14 biomarkers related to the action potential and dynamic restitution were extracted. Populations were calibrated based on distributions of biomarkers to obtain reasonable physiological behavior, and subjected to SA to quantify correlations between model parameters and pro-arrhythmia markers. The two populations showed distinct behaviors under steady state and dynamic pacing. The nSR population revealed greater variability, and more unstable dynamic restitution, as compared to the cAF population, suggesting that simulated cAF remodeling rendered cells more stable to parameter variation and rate adaptation. SA revealed that the biomarkers depended mainly on five ionic currents, with noted differences in sensitivities to these between nSR and cAF. Also, parameters could be selected to produce a model variant with no alternans and unaltered action potential morphology, highlighting that unstable dynamical behavior may be driven by specific cell parameter settings. These results ultimately suggest that arrhythmia maintenance in cAF may not be due to instability in cell membrane excitability, but rather due to tissue-level effects which promote initiation and maintenance of reentrant arrhythmia.

Dispersal leads to spatial autocorrelation in species distributions: A simulation model

USGS Publications Warehouse

Bahn, V.; Krohn, W.B.; O'Connor, R.J.

2008-01-01

Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.

Computational Fluid Dynamics-Population Balance Model Simulation of Effects of Cell Design and Operating Parameters on Gas-Liquid Two-Phase Flows and Bubble Distribution Characteristics in Aluminum Electrolysis Cells

NASA Astrophysics Data System (ADS)

Zhan, Shuiqing; Wang, Junfeng; Wang, Zhentao; Yang, Jianhong

2018-02-01

The effects of different cell design and operating parameters on the gas-liquid two-phase flows and bubble distribution characteristics under the anode bottom regions in aluminum electrolysis cells were analyzed using a three-dimensional computational fluid dynamics-population balance model. These parameters include inter-anode channel width, anode-cathode distance (ACD), anode width and length, current density, and electrolyte depth. The simulations results show that the inter-anode channel width has no significant effect on the gas volume fraction, electrolyte velocity, and bubble size. With increasing ACD, the above values decrease and more uniform bubbles can be obtained. Different effects of the anode width and length can be concluded in different cell regions. With increasing current density, the gas volume fraction and electrolyte velocity increase, but the bubble size keeps nearly the same. Increasing electrolyte depth decreased the gas volume fraction and bubble size in particular areas and the electrolyte velocity increased.

Methods for Stem Cell Production and Therapy

NASA Technical Reports Server (NTRS)

Valluri, Jagan V. (Inventor); Claudio, Pier Paolo (Inventor)

2015-01-01

The present invention relates to methods for rapidly expanding a stem cell population with or without culture supplements in simulated microgravity conditions. The present invention relates to methods for rapidly increasing the life span of stem cell populations without culture supplements in simulated microgravity conditions. The present invention also relates to methods for increasing the sensitivity of cancer stem cells to chemotherapeutic agents by culturing the cancer stem cells under microgravity conditions and in the presence of omega-3 fatty acids. The methods of the present invention can also be used to proliferate cancer cells by culturing them in the presence of omega-3 fatty acids. The present invention also relates to methods for testing the sensitivity of cancer cells and cancer stem cells to chemotherapeutic agents by culturing the cancer cells and cancer stem cells under microgravity conditions. The methods of the present invention can also be used to produce tissue for use in transplantation by culturing stem cells or cancer stem cells under microgravity conditions. The methods of the present invention can also be used to produce cellular factors and growth factors by culturing stem cells or cancer stem cells under microgravity conditions. The methods of the present invention can also be used to produce cellular factors and growth factors to promote differentiation of cancer stem cells under microgravity conditions.

ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data.

PubMed

Salehi, Sohrab; Steif, Adi; Roth, Andrew; Aparicio, Samuel; Bouchard-Côté, Alexandre; Shah, Sohrab P

2017-03-01

Next-generation sequencing (NGS) of bulk tumour tissue can identify constituent cell populations in cancers and measure their abundance. This requires computational deconvolution of allelic counts from somatic mutations, which may be incapable of fully resolving the underlying population structure. Single cell sequencing (SCS) is a more direct method, although its replacement of NGS is impeded by technical noise and sampling limitations. We propose ddClone, which analytically integrates NGS and SCS data, leveraging their complementary attributes through joint statistical inference. We show on real and simulated datasets that ddClone produces more accurate results than can be achieved by either method alone.

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zuwei; Zhao, Haibo, E-mail: klinsmannzhb@163.com; Zheng, Chuguang

2015-01-15

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule providesmore » a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.« less

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model.

PubMed

Mina, Petros; Tsaneva-Atanasova, Krasimira; Bernardo, Mario di

2016-07-15

We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

DOE PAGES

Hagos, Samson; Feng, Zhe; Plant, Robert S.; ...

2018-02-20

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii)more » the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. Finally, in addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.« less

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

NASA Astrophysics Data System (ADS)

Hagos, Samson; Feng, Zhe; Plant, Robert S.; Houze, Robert A.; Xiao, Heng

2018-02-01

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagos, Samson; Feng, Zhe; Plant, Robert S.

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii)more » the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. Finally, in addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.« less

Destruction of solid tumors by immune cells

NASA Astrophysics Data System (ADS)

López, Álvaro G.; Seoane, Jesús M.; Sanjuán, Miguel A. F.

2017-03-01

The fractional cell kill is a mathematical expression describing the rate at which a certain population of cells is reduced to a fraction of itself. In order to investigate the fractional cell kill that governs the rate at which a solid tumor is lysed by a cell population of cytotoxic CD8+ T cells (CTLs), we present several in silico simulations and mathematical analyses. When the CTLs eradicate efficiently the tumor cells, the models predict a correlation between the morphology of the tumors and the rate at which they are lysed. However, when the effectiveness of the immune cells is decreased, the mathematical function fails to reproduce the process of lysis. This limit is thoroughly discussed and a new fractional cell kill is proposed.

Recombination enhances HIV-1 envelope diversity by facilitating the survival of latent genomic fragments in the plasma virus population

DOE Office of Scientific and Technical Information (OSTI.GOV)

Immonen, Taina T.; Conway, Jessica M.; Romero-Severson, Ethan O.

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation processmore » including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Furthermore, our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.« less

Recombination enhances HIV-1 envelope diversity by facilitating the survival of latent genomic fragments in the plasma virus population

DOE PAGES

Immonen, Taina T.; Conway, Jessica M.; Romero-Severson, Ethan O.; ...

2015-12-22

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation processmore » including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Furthermore, our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.« less

Division of Labor, Bet Hedging, and the Evolution of Mixed Biofilm Investment Strategies.

PubMed

Lowery, Nick Vallespir; McNally, Luke; Ratcliff, William C; Brown, Sam P

2017-08-08

Bacterial cells, like many other organisms, face a tradeoff between longevity and fecundity. Planktonic cells are fast growing and fragile, while biofilm cells are often slower growing but stress resistant. Here we ask why bacterial lineages invest simultaneously in both fast- and slow-growing types. We develop a population dynamic model of lineage expansion across a patchy environment and find that mixed investment is favored across a broad range of environmental conditions, even when transmission is entirely via biofilm cells. This mixed strategy is favored because of a division of labor where exponentially dividing planktonic cells can act as an engine for the production of future biofilm cells, which grow more slowly. We use experimental evolution to test our predictions and show that phenotypic heterogeneity is persistent even under selection for purely planktonic or purely biofilm transmission. Furthermore, simulations suggest that maintenance of a biofilm subpopulation serves as a cost-effective hedge against environmental uncertainty, which is also consistent with our experimental findings. IMPORTANCE Cell types specialized for survival have been observed and described within clonal bacterial populations for decades, but why are these specialists continually produced under benign conditions when such investment comes at a high reproductive cost? Conversely, when survival becomes an imperative, does it ever benefit the population to maintain a pool of rapidly growing but vulnerable planktonic cells? Using a combination of mathematical modeling, simulations, and experiments, we find that mixed investment strategies are favored over a broad range of environmental conditions and rely on a division of labor between cell types, where reproductive specialists amplify survival specialists, which can be transmitted through the environment with a limited mortality rate. We also show that survival specialists benefit rapidly growing populations by serving as a hedge against unpredictable changes in the environment. These results help to clarify the general evolutionary and ecological forces that can generate and maintain diverse subtypes within clonal bacterial populations. Copyright © 2017 Lowery et al.

Model Simulation of Diurnal Vertical Migration Patterns of Different-Sized Colonies of Microcystis Employing a Particle Trajectory Approach.

PubMed

Chien, Yu Ching; Wu, Shian Chee; Chen, Wan Ching; Chou, Chih Chung

2013-04-01

Microcystis , a genus of potentially harmful cyanobacteria, is known to proliferate in stratified freshwaters due to its capability to change cell density and regulate buoyancy. In this study, a trajectory model was developed to simulate the cell density change and spatial distribution of Microcystis cells with nonuniform colony sizes. Simulations showed that larger colonies migrate to the near-surface water layer during the night to effectively capture irradiation and become heavy enough to sink during daytime. Smaller-sized colonies instead took a longer time to get to the surface. Simulation of the diurnally varying Microcystis population profile matched the observed pattern in the field when the radii of the multisized colonies were in a beta distribution. This modeling approach is able to take into account the history of cells by keeping track of their positions and properties, such as cell density and the sizes of colonies. It also serves as the basis for further developmental modeling of phytoplanktons that are forming colonies and changing buoyancy.

Mathematical modelling of phenotypic plasticity and conversion to a stem-cell state under hypoxia

NASA Astrophysics Data System (ADS)

Dhawan, Andrew; Madani Tonekaboni, Seyed Ali; Taube, Joseph H.; Hu, Stephen; Sphyris, Nathalie; Mani, Sendurai A.; Kohandel, Mohammad

2016-02-01

Hypoxia, or oxygen deficiency, is known to be associated with breast tumour progression, resistance to conventional therapies and poor clinical prognosis. The epithelial-mesenchymal transition (EMT) is a process that confers invasive and migratory capabilities as well as stem cell properties to carcinoma cells thus promoting metastatic progression. In this work, we examined the impact of hypoxia on EMT-associated cancer stem cell (CSC) properties, by culturing transformed human mammary epithelial cells under normoxic and hypoxic conditions, and applying in silico mathematical modelling to simulate the impact of hypoxia on the acquisition of CSC attributes and the transitions between differentiated and stem-like states. Our results indicate that both the heterogeneity and the plasticity of the transformed cell population are enhanced by exposure to hypoxia, resulting in a shift towards a more stem-like population with increased EMT features. Our findings are further reinforced by gene expression analyses demonstrating the upregulation of EMT-related genes, as well as genes associated with therapy resistance, in hypoxic cells compared to normoxic counterparts. In conclusion, we demonstrate that mathematical modelling can be used to simulate the role of hypoxia as a key contributor to the plasticity and heterogeneity of transformed human mammary epithelial cells.

A Hybrid Computer Simulation to Generate the DNA Distribution of a Cell Population.

ERIC Educational Resources Information Center

Griebling, John L.; Adams, William S.

1981-01-01

Described is a method of simulating the formation of a DNA distribution, on which statistical results and experimentally measured parameters from DNA distribution and percent-labeled mitosis studies are combined. An EAI-680 and DECSystem-10 Hybrid Computer configuration are used. (Author/CS)

Skin Stem Cell Hypotheses and Long Term Clone Survival – Explored Using Agent-based Modelling

PubMed Central

Li, X.; Upadhyay, A. K.; Bullock, A. J.; Dicolandrea, T.; Xu, J.; Binder, R. L.; Robinson, M. K.; Finlay, D. R.; Mills, K. J.; Bascom, C. C.; Kelling, C. K.; Isfort, R. J.; Haycock, J. W.; MacNeil, S.; Smallwood, R. H.

2013-01-01

Epithelial renewal in skin is achieved by the constant turnover and differentiation of keratinocytes. Three popular hypotheses have been proposed to explain basal keratinocyte regeneration and epidermal homeostasis: 1) asymmetric division (stem-transit amplifying cell); 2) populational asymmetry (progenitor cell with stochastic fate); and 3) populational asymmetry with stem cells. In this study, we investigated lineage dynamics using these hypotheses with a 3D agent-based model of the epidermis. The model simulated the growth and maintenance of the epidermis over three years. The offspring of each proliferative cell was traced. While all lineages were preserved in asymmetric division, the vast majority were lost when assuming populational asymmetry. The third hypothesis provided the most reliable mechanism for self-renewal by preserving genetic heterogeneity in quiescent stem cells, and also inherent mechanisms for skin ageing and the accumulation of genetic mutation. PMID:23712735

Skin stem cell hypotheses and long term clone survival--explored using agent-based modelling.

PubMed

Li, X; Upadhyay, A K; Bullock, A J; Dicolandrea, T; Xu, J; Binder, R L; Robinson, M K; Finlay, D R; Mills, K J; Bascom, C C; Kelling, C K; Isfort, R J; Haycock, J W; MacNeil, S; Smallwood, R H

2013-01-01

Epithelial renewal in skin is achieved by the constant turnover and differentiation of keratinocytes. Three popular hypotheses have been proposed to explain basal keratinocyte regeneration and epidermal homeostasis: 1) asymmetric division (stem-transit amplifying cell); 2) populational asymmetry (progenitor cell with stochastic fate); and 3) populational asymmetry with stem cells. In this study, we investigated lineage dynamics using these hypotheses with a 3D agent-based model of the epidermis. The model simulated the growth and maintenance of the epidermis over three years. The offspring of each proliferative cell was traced. While all lineages were preserved in asymmetric division, the vast majority were lost when assuming populational asymmetry. The third hypothesis provided the most reliable mechanism for self-renewal by preserving genetic heterogeneity in quiescent stem cells, and also inherent mechanisms for skin ageing and the accumulation of genetic mutation.

Effects of the distant population density on spatial patterns of demographic dynamics

NASA Astrophysics Data System (ADS)

Tamura, Kohei; Masuda, Naoki

2017-08-01

Spatio-temporal patterns of population changes within and across countries have various implications. Different geographical, demographic and econo-societal factors seem to contribute to migratory decisions made by individual inhabitants. Focusing on internal (i.e. domestic) migration, we ask whether individuals may take into account the information on the population density in distant locations to make migratory decisions. We analyse population census data in Japan recorded with a high spatial resolution (i.e. cells of size 500×500 m) for the entirety of the country, and simulate demographic dynamics induced by the gravity model and its variants. We show that, in the census data, the population growth rate in a cell is positively correlated with the population density in nearby cells up to a distance of 20 km as well as that of the focal cell. The ordinary gravity model does not capture this empirical observation. We then show that the empirical observation is better accounted for by extensions of the gravity model such that individuals are assumed to perceive the attractiveness, approximated by the population density, of the source or destination cell of migration as the spatial average over a circle of radius ≈1 km.

Effects of the distant population density on spatial patterns of demographic dynamics.

PubMed

Tamura, Kohei; Masuda, Naoki

2017-08-01

Spatio-temporal patterns of population changes within and across countries have various implications. Different geographical, demographic and econo-societal factors seem to contribute to migratory decisions made by individual inhabitants. Focusing on internal (i.e. domestic) migration, we ask whether individuals may take into account the information on the population density in distant locations to make migratory decisions. We analyse population census data in Japan recorded with a high spatial resolution (i.e. cells of size 500×500  m ) for the entirety of the country, and simulate demographic dynamics induced by the gravity model and its variants. We show that, in the census data, the population growth rate in a cell is positively correlated with the population density in nearby cells up to a distance of 20 km as well as that of the focal cell. The ordinary gravity model does not capture this empirical observation. We then show that the empirical observation is better accounted for by extensions of the gravity model such that individuals are assumed to perceive the attractiveness, approximated by the population density, of the source or destination cell of migration as the spatial average over a circle of radius ≈1 km.

Effects of the distant population density on spatial patterns of demographic dynamics

PubMed Central

2017-01-01

Spatio-temporal patterns of population changes within and across countries have various implications. Different geographical, demographic and econo-societal factors seem to contribute to migratory decisions made by individual inhabitants. Focusing on internal (i.e. domestic) migration, we ask whether individuals may take into account the information on the population density in distant locations to make migratory decisions. We analyse population census data in Japan recorded with a high spatial resolution (i.e. cells of size 500×500 m) for the entirety of the country, and simulate demographic dynamics induced by the gravity model and its variants. We show that, in the census data, the population growth rate in a cell is positively correlated with the population density in nearby cells up to a distance of 20 km as well as that of the focal cell. The ordinary gravity model does not capture this empirical observation. We then show that the empirical observation is better accounted for by extensions of the gravity model such that individuals are assumed to perceive the attractiveness, approximated by the population density, of the source or destination cell of migration as the spatial average over a circle of radius ≈1 km. PMID:28878987

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagos, Samson; Feng, Zhe; Plant, Robert S.

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The approach used follows the non-equilibrium statistical mechanical approach through a master equation. The aim is to represent the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics: (i) the probability of growth, (ii) the probability of decay, and (iii)more » the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and mass flux is a non-linear function of convective cell area, mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated mass flux variability under diurnally varying forcing. Besides its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to be capable of providing alternative, non-equilibrium, closure formulations for spectral mass flux parameterizations.« less

Modeling and experimental methods to predict oxygen distribution in bone defects following cell transplantation.

PubMed

Heylman, Christopher M; Santoso, Sharon; Krebs, Melissa D; Saidel, Gerald M; Alsberg, Eben; Muschler, George F

2014-04-01

We have developed a mathematical model that allows simulation of oxygen distribution in a bone defect as a tool to explore the likely effects of local changes in cell concentration, defect size or geometry, local oxygen delivery with oxygen-generating biomaterials (OGBs), and changes in the rate of oxygen consumption by cells within a defect. Experimental data for the oxygen release rate from an OGB and the oxygen consumption rate of a transplanted cell population are incorporated into the model. With these data, model simulations allow prediction of spatiotemporal oxygen concentration within a given defect and the sensitivity of oxygen tension to changes in critical variables. This information may help to minimize the number of experiments in animal models that determine the optimal combinations of cells, scaffolds, and OGBs in the design of current and future bone regeneration strategies. Bone marrow-derived nucleated cell data suggest that oxygen consumption is dependent on oxygen concentration. OGB oxygen release is shown to be a time-dependent function that must be measured for accurate simulation. Simulations quantify the dependency of oxygen gradients in an avascular defect on cell concentration, cell oxygen consumption rate, OGB oxygen generation rate, and OGB geometry.

Effect of Dedifferentiation on Time to Mutation Acquisition in Stem Cell-Driven Cancers

PubMed Central

Jilkine, Alexandra; Gutenkunst, Ryan N.

2014-01-01

Accumulating evidence suggests that many tumors have a hierarchical organization, with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells. It is unclear, however, whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells. To address this, we mathematically modeled the effect of dedifferentiation on carcinogenesis. We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors, including dedifferentiation of progenitor cells to a stem cell-like state. We performed exact computer simulations of the emergence of tumor subpopulations with two mutations, and we derived semi-analytical estimates for the waiting time distribution to fixation. Our results suggest that dedifferentiation may play an important role in carcinogenesis, depending on how stem cell homeostasis is maintained. If the stem cell population size is held strictly constant (due to all divisions being asymmetric), we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis. If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates (allowing both symmetric and asymmetric divisions), we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population. Thus, dedifferentiation may play a crucial role, the common modeling assumption of constant stem cell population size may not be adequate, and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis. PMID:24603301

Wiring Together Synthetic Bacterial Consortia to Create a Biological Integrated Circuit.

PubMed

Perry, Nicolas; Nelson, Edward M; Timp, Gregory

2016-12-16

The promise of adapting biology to information processing will not be realized until engineered gene circuits, operating in different cell populations, can be wired together to express a predictable function. Here, elementary biological integrated circuits (BICs), consisting of two sets of transmitter and receiver gene circuit modules with embedded memory placed in separate cell populations, were meticulously assembled using live cell lithography and wired together by the mass transport of quorum-sensing (QS) signal molecules to form two isolated communication links (comlinks). The comlink dynamics were tested by broadcasting "clock" pulses of inducers into the networks and measuring the responses of functionally linked fluorescent reporters, and then modeled through simulations that realistically captured the protein production and molecular transport. These results show that the comlinks were isolated and each mimicked aspects of the synchronous, sequential networks used in digital computing. The observations about the flow conditions, derived from numerical simulations, and the biofilm architectures that foster or silence cell-to-cell communications have implications for everything from decontamination of drinking water to bacterial virulence.

Infection Spread and Virus Release in Vitro in Cell Populations as a System with Percolation

NASA Astrophysics Data System (ADS)

Ochoa, Juan G. Diaz

The comprehension of the innate immune system of cell populations is not only of interest to understand systems in vivo but also in vitro, for example, in the control of the release of viral particles for the production of vaccines. In this report I introduce a model, based on dynamical networks, that simulates the cell signaling responsible for this innate immune response and its effect on the infection spread and virus production. The central motivation is to represent a cell population that is constantly mixed in a bio-reactor where there is a cell-to-cell signaling of cytokines (which are proteins responsible for the activation of the antiviral response inside the cell). Such signaling allows the definition of clusters of linked immune cells. Additionally, depending on the density of links, it is possible to identify critical threshold parameters associated to a percolation phase transition. I show that the control of this antiviral response is equivalent to a percolation process.

A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine.

PubMed

Bonin, Carla R B; Fernandes, Guilherme C; Dos Santos, Rodrigo W; Lobosco, Marcelo

2018-05-25

Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever. This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies. In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine. This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.

TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data

PubMed Central

Roth, Andrew; Khattra, Jaswinder; Ho, Julie; Yap, Damian; Prentice, Leah M.; Melnyk, Nataliya; McPherson, Andrew; Bashashati, Ali; Laks, Emma; Biele, Justina; Ding, Jiarui; Le, Alan; Rosner, Jamie; Shumansky, Karey; Marra, Marco A.; Gilks, C. Blake; Huntsman, David G.; McAlpine, Jessica N.; Aparicio, Samuel

2014-01-01

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN. PMID:25060187

High-throughput full-length single-cell mRNA-seq of rare cells.

PubMed

Ooi, Chin Chun; Mantalas, Gary L; Koh, Winston; Neff, Norma F; Fuchigami, Teruaki; Wong, Dawson J; Wilson, Robert J; Park, Seung-Min; Gambhir, Sanjiv S; Quake, Stephen R; Wang, Shan X

2017-01-01

Single-cell characterization techniques, such as mRNA-seq, have been applied to a diverse range of applications in cancer biology, yielding great insight into mechanisms leading to therapy resistance and tumor clonality. While single-cell techniques can yield a wealth of information, a common bottleneck is the lack of throughput, with many current processing methods being limited to the analysis of small volumes of single cell suspensions with cell densities on the order of 107 per mL. In this work, we present a high-throughput full-length mRNA-seq protocol incorporating a magnetic sifter and magnetic nanoparticle-antibody conjugates for rare cell enrichment, and Smart-seq2 chemistry for sequencing. We evaluate the efficiency and quality of this protocol with a simulated circulating tumor cell system, whereby non-small-cell lung cancer cell lines (NCI-H1650 and NCI-H1975) are spiked into whole blood, before being enriched for single-cell mRNA-seq by EpCAM-functionalized magnetic nanoparticles and the magnetic sifter. We obtain high efficiency (> 90%) capture and release of these simulated rare cells via the magnetic sifter, with reproducible transcriptome data. In addition, while mRNA-seq data is typically only used for gene expression analysis of transcriptomic data, we demonstrate the use of full-length mRNA-seq chemistries like Smart-seq2 to facilitate variant analysis of expressed genes. This enables the use of mRNA-seq data for differentiating cells in a heterogeneous population by both their phenotypic and variant profile. In a simulated heterogeneous mixture of circulating tumor cells in whole blood, we utilize this high-throughput protocol to differentiate these heterogeneous cells by both their phenotype (lung cancer versus white blood cells), and mutational profile (H1650 versus H1975 cells), in a single sequencing run. This high-throughput method can help facilitate single-cell analysis of rare cell populations, such as circulating tumor or endothelial cells, with demonstrably high-quality transcriptomic data.

Modeling cell adhesion and proliferation: a cellular-automata based approach.

PubMed

Vivas, J; Garzón-Alvarado, D; Cerrolaza, M

Cell adhesion is a process that involves the interaction between the cell membrane and another surface, either a cell or a substrate. Unlike experimental tests, computer models can simulate processes and study the result of experiments in a shorter time and lower costs. One of the tools used to simulate biological processes is the cellular automata, which is a dynamic system that is discrete both in space and time. This work describes a computer model based on cellular automata for the adhesion process and cell proliferation to predict the behavior of a cell population in suspension and adhered to a substrate. The values of the simulated system were obtained through experimental tests on fibroblast monolayer cultures. The results allow us to estimate the cells settling time in culture as well as the adhesion and proliferation time. The change in the cells morphology as the adhesion over the contact surface progress was also observed. The formation of the initial link between cell and the substrate of the adhesion was observed after 100 min where the cell on the substrate retains its spherical morphology during the simulation. The cellular automata model developed is, however, a simplified representation of the steps in the adhesion process and the subsequent proliferation. A combined framework of experimental and computational simulation based on cellular automata was proposed to represent the fibroblast adhesion on substrates and changes in a macro-scale observed in the cell during the adhesion process. The approach showed to be simple and efficient.

Tumor-volume simulation during radiotherapy for head-and-neck cancer using a four-level cell population model.

PubMed

Chvetsov, Alexei V; Dong, Lei; Palta, Jantinder R; Amdur, Robert J

2009-10-01

To develop a fast computational radiobiologic model for quantitative analysis of tumor volume during fractionated radiotherapy. The tumor-volume model can be useful for optimizing image-guidance protocols and four-dimensional treatment simulations in proton therapy that is highly sensitive to physiologic changes. The analysis is performed using two approximations: (1) tumor volume is a linear function of total cell number and (2) tumor-cell population is separated into four subpopulations: oxygenated viable cells, oxygenated lethally damaged cells, hypoxic viable cells, and hypoxic lethally damaged cells. An exponential decay model is used for disintegration and removal of oxygenated lethally damaged cells from the tumor. We tested our model on daily volumetric imaging data available for 14 head-and-neck cancer patients treated with an integrated computed tomography/linear accelerator system. A simulation based on the averaged values of radiobiologic parameters was able to describe eight cases during the entire treatment and four cases partially (50% of treatment time) with a maximum 20% error. The largest discrepancies between the model and clinical data were obtained for small tumors, which may be explained by larger errors in the manual tumor volume delineation procedure. Our results indicate that the change in gross tumor volume for head-and-neck cancer can be adequately described by a relatively simple radiobiologic model. In future research, we propose to study the variation of model parameters by fitting to clinical data for a cohort of patients with head-and-neck cancer and other tumors. The potential impact of other processes, like concurrent chemotherapy, on tumor volume should be evaluated.

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

PubMed Central

Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G.

2016-01-01

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512

Comparing a discrete and continuum model of the intestinal crypt

PubMed Central

Murray, Philip J.; Walter, Alex; Fletcher, Alex G.; Edwards, Carina M.; Tindall, Marcus J.; Maini, Philip K.

2011-01-01

The integration of processes at different scales is a key problem in the modelling of cell populations. Owing to increased computational resources and the accumulation of data at the cellular and subcellular scales, the use of discrete, cell-level models, which are typically solved using numerical simulations, has become prominent. One of the merits of this approach is that important biological factors, such as cell heterogeneity and noise, can be easily incorporated. However, it can be difficult to efficiently draw generalisations from the simulation results, as, often, many simulation runs are required to investigate model behaviour in typically large parameter spaces. In some cases, discrete cell-level models can be coarse-grained, yielding continuum models whose analysis can lead to the development of insight into the underlying simulations. In this paper we apply such an approach to the case of a discrete model of cell dynamics in the intestinal crypt. An analysis of the resulting continuum model demonstrates that there is a limited region of parameter space within which steady-state (and hence biologically realistic) solutions exist. Continuum model predictions show good agreement with corresponding results from the underlying simulations and experimental data taken from murine intestinal crypts. PMID:21411869

Voronoi Based Nanocrystalline Generation Algorithm for Atomistic Simulations

DTIC Science & Technology

2016-12-22

the  time  for reviewing instructions, searching existing data sources, gathering and maintaining the  data needed, and completing and reviewing the...taken when generating nanocrystals (left to right): populating cell with grain centers, sphere of atoms with defined crystal structure centered at...nanocrystals (left to right): populating cell with grain centers, sphere of atoms with defined crystal structure centered at each grain center, identifying atoms

Effects of acute hypoxia/acidosis on intracellular pH in differentiating neural progenitor cells.

PubMed

Nordström, Tommy; Jansson, Linda C; Louhivuori, Lauri M; Akerman, Karl E O

2012-06-21

The response of differentiating mouse neural progenitor cells, migrating out from neurospheres, to conditions simulating ischemia (hypoxia and extracellular or intracellular acidosis) was studied. We show here, by using BCECF and single cell imaging to monitor intracellular pH (pH(i)), that two main populations can be distinguished by exposing migrating neural progenitor cells to low extracellular pH or by performing an acidifying ammonium prepulse. The cells dominating at the periphery of the neurosphere culture, which were positive for neuron specific markers MAP-2, calbindin and NeuN had lower initial resting pH(i) and could also easily be further acidified by lowering the extracellular pH. Moreover, in this population, a more profound acidification was seen when the cells were acidified using the ammonium prepulse technique. However, when the cell population was exposed to depolarizing potassium concentrations no alterations in pH(i) took place in this population. In contrast, depolarization caused an increase in pH(i) (by 0.5 pH units) in the cell population closer to the neurosphere body, which region was positive for the radial cell marker (GLAST). This cell population, having higher resting pH(i) (pH 6.9-7.1) also responded to acute hypoxia. During hypoxic treatment the resting pH(i) decreased by 0.1 pH units and recovered rapidly after reoxygenation. Our results show that migrating neural progenitor cells are highly sensitive to extracellular acidosis and that irreversible damage becomes evident at pH 6.2. Moreover, our results show that a response to acidosis clearly distinguishes two individual cell populations probably representing neuronal and radial cells. Copyright © 2012 Elsevier B.V. All rights reserved.

Epidermal Homeostasis and Radiation Responses in a Multiscale Tissue Modeling Framework

NASA Technical Reports Server (NTRS)

Hu, Shaowen; Cucinotta, Francis A.

2013-01-01

The surface of skin is lined with several thin layers of epithelial cells that are maintained throughout life time by a small population of stem cells. High dose radiation exposures could injure and deplete the underlying proliferative cells and induce cutaneous radiation syndrome. In this work we propose a multiscale computational model for skin epidermal dynamics that links phenomena occurring at the subcellular, cellular, and tissue levels of organization, to simulate the experimental data of the radiation response of swine epidermis, which is closely similar to human epidermis. Incorporating experimentally measured histological and cell kinetic parameters, we obtain results of population kinetics and proliferation indexes comparable to observations in unirradiated and acutely irradiated swine experiments. At the sub-cellular level, several recently published Wnt signaling controlled cell-cycle models are applied and the roles of key components and parameters are analyzed. Based on our simulation results, we demonstrate that a moderate increase of proliferation rate for the survival proliferative cells is sufficient to fully repopulate the area denuded by high dose radiation, as long as the integrity of underlying basement membrane is maintained. Our work highlights the importance of considering proliferation kinetics as well as the spatial organization of tissues when conducting in vivo investigations of radiation responses. This integrated model allow us to test the validity of several basic biological rules at the cellular level and sub-cellular mechanisms by qualitatively comparing simulation results with published research, and enhance our understanding of the pathophysiological effects of ionizing radiation on skin.

Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry.

PubMed

García, Míriam R; Vázquez, José A; Teixeira, Isabel G; Alonso, Antonio A

2017-01-01

A realistic description of the variability in bacterial growth and division is critical to produce reliable predictions of safety risks along the food chain. Individual-based modeling of bacteria provides the theoretical framework to deal with this variability, but it requires information about the individual behavior of bacteria inside populations. In this work, we overcome this problem by estimating the individual behavior of bacteria from population statistics obtained with flow cytometry. For this objective, a stochastic individual-based modeling framework is defined based on standard assumptions during division and exponential growth. The unknown single-cell parameters required for running the individual-based modeling simulations, such as cell size growth rate, are estimated from the flow cytometry data. Instead of using directly the individual-based model, we make use of a modified Fokker-Plank equation. This only equation simulates the population statistics in function of the unknown single-cell parameters. We test the validity of the approach by modeling the growth and division of Pediococcus acidilactici within the exponential phase. Estimations reveal the statistics of cell growth and division using only data from flow cytometry at a given time. From the relationship between the mother and daughter volumes, we also predict that P. acidilactici divide into two successive parallel planes.

From innervation density to tactile acuity: 1. Spatial representation.

PubMed

Brown, Paul B; Koerber, H Richard; Millecchia, Ronald

2004-06-11

We tested the hypothesis that the population receptive field representation (a superposition of the excitatory receptive field areas of cells responding to a tactile stimulus) provides spatial information sufficient to mediate one measure of static tactile acuity. In psychophysical tests, two-point discrimination thresholds on the hindlimbs of adult cats varied as a function of stimulus location and orientation, as they do in humans. A statistical model of the excitatory low threshold mechanoreceptive fields of spinocervical, postsynaptic dorsal column and spinothalamic tract neurons was used to simulate the population receptive field representations in this neural population of the one- and two-point stimuli used in the psychophysical experiments. The simulated and observed thresholds were highly correlated. Simulated and observed thresholds' relations to physiological and anatomical variables such as stimulus location and orientation, receptive field size and shape, map scale, and innervation density were strikingly similar. Simulated and observed threshold variations with receptive field size and map scale obeyed simple relationships predicted by the signal detection model, and were statistically indistinguishable from each other. The population receptive field representation therefore contains information sufficient for this discrimination.

Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells

PubMed Central

Mina, Petros; di Bernardo, Mario; Savery, Nigel J.; Tsaneva-Atanasova, Krasimira

2013-01-01

Population-level measurements of phenotypic behaviour in biological systems may not necessarily reflect individual cell behaviour. To assess qualitative changes in the behaviour of a single cell, when alone and when part of a community, we developed an agent-based model describing the metabolic states of a population of quorum-coupled cells. The modelling is motivated by published experimental work of a synthetic genetic regulatory network (GRN) used in Escherichia coli cells that exhibit oscillatory behaviour across the population. To decipher the mechanisms underlying oscillations in the system, we investigate the behaviour of the model via numerical simulation and bifurcation analysis. In particular, we study the effect of an increase in population size as well as the spatio-temporal behaviour of the model. Our results demonstrate that oscillations are possible only in the presence of a high concentration of the coupling chemical and are due to a time scale separation in key regulatory components of the system. The model suggests that the population establishes oscillatory behaviour as the system's preferred stable state. This is achieved via an effective increase in coupling across the population. We conclude that population effects in GRN design need to be taken into consideration and be part of the design process. This is important in planning intervention strategies or designing specific cell behaviours. PMID:23135248

Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells.

PubMed

Mina, Petros; di Bernardo, Mario; Savery, Nigel J; Tsaneva-Atanasova, Krasimira

2013-01-06

Population-level measurements of phenotypic behaviour in biological systems may not necessarily reflect individual cell behaviour. To assess qualitative changes in the behaviour of a single cell, when alone and when part of a community, we developed an agent-based model describing the metabolic states of a population of quorum-coupled cells. The modelling is motivated by published experimental work of a synthetic genetic regulatory network (GRN) used in Escherichia coli cells that exhibit oscillatory behaviour across the population. To decipher the mechanisms underlying oscillations in the system, we investigate the behaviour of the model via numerical simulation and bifurcation analysis. In particular, we study the effect of an increase in population size as well as the spatio-temporal behaviour of the model. Our results demonstrate that oscillations are possible only in the presence of a high concentration of the coupling chemical and are due to a time scale separation in key regulatory components of the system. The model suggests that the population establishes oscillatory behaviour as the system's preferred stable state. This is achieved via an effective increase in coupling across the population. We conclude that population effects in GRN design need to be taken into consideration and be part of the design process. This is important in planning intervention strategies or designing specific cell behaviours.

A comparison between the stability properties in a DDE model for leukemia and the modified fractional counterpart

NASA Astrophysics Data System (ADS)

Rǎdulescu, I. R.; Cândea, D.; Kaslik, E.

2017-01-01

In this paper, a delay differential equations (DDEs) model of leukemia is introduced and its dynamical properties are investigated in comparison with the modified fractional-order system where the Caputo's derivative is used. The model takes into account three types of division that a stem-like cell can undergo and cell competition between healthy and leukemia cell populations. The action of the immune system on the leukemic cell populations is also considered. The stability properties of the equilibrium points are established through numerical results and the differences between the two types of approaches are discussed. Medical conclusions are drawn in view of the obtained numerical simulations.

Modelling the collective response of heterogeneous cell populations to stationary gradients and chemical signal relay

NASA Astrophysics Data System (ADS)

Pineda, M.; Eftimie, R.

2017-12-01

The directed motion of cell aggregates toward a chemical source occurs in many relevant biological processes. Understanding the mechanisms that control this complex behavior is of great relevance for our understanding of developmental biological processes and many diseases. In this paper, we consider a self-propelled particle model for the movement of heterogeneous subpopulations of chemically interacting cells towards an imposed stable chemical gradient. Our simulations show explicitly how self-organisation of cell populations (which could lead to engulfment or complete cell segregation) can arise from the heterogeneity of chemotactic responses alone. This new result complements current theoretical and experimental studies that emphasise the role of differential cell-cell adhesion on self-organisation and spatial structure of cellular aggregates. We also investigate how the speed of individual cell aggregations increases with the chemotactic sensitivity of the cells, and decreases with the number of cells inside the aggregates

Differentiated cell behavior: a multiscale approach using measure theory.

PubMed

Colombi, Annachiara; Scianna, Marco; Tosin, Andrea

2015-11-01

This paper deals with the derivation of a collective model of cell populations out of an individual-based description of the underlying physical particle system. By looking at the spatial distribution of cells in terms of time-evolving measures, rather than at individual cell paths, we obtain an ensemble representation stemming from the phenomenological behavior of the single component cells. In particular, as a key advantage of our approach, the scale of representation of the system, i.e., microscopic/discrete vs. macroscopic/continuous, can be chosen a posteriori according only to the spatial structure given to the aforesaid measures. The paper focuses in particular on the use of different scales based on the specific functions performed by cells. A two-population hybrid system is considered, where cells with a specialized/differentiated phenotype are treated as a discrete population of point masses while unspecialized/undifferentiated cell aggregates are represented by a continuous approximation. Numerical simulations and analytical investigations emphasize the role of some biologically relevant parameters in determining the specific evolution of such a hybrid cell system.

OncoSimulR: genetic simulation with arbitrary epistasis and mutator genes in asexual populations.

PubMed

Diaz-Uriarte, Ramon

2017-06-15

OncoSimulR implements forward-time genetic simulations of biallelic loci in asexual populations with special focus on cancer progression. Fitness can be defined as an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, restrictions in the order of accumulation of mutations, and order effects. Mutation rates can differ among genes, and can be affected by (anti)mutator genes. Also available are sampling from simulations (including single-cell sampling), plotting the genealogical relationships of clones and generating and plotting fitness landscapes. Implemented in R and C ++, freely available from BioConductor for Linux, Mac and Windows under the GNU GPL license. Version 2.5.9 or higher available from: http://www.bioconductor.org/packages/devel/bioc/html/OncoSimulR.html . GitHub repository at: https://github.com/rdiaz02/OncoSimul. ramon.diaz@iib.uam.es. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.

PubMed

Ha, Gavin; Roth, Andrew; Khattra, Jaswinder; Ho, Julie; Yap, Damian; Prentice, Leah M; Melnyk, Nataliya; McPherson, Andrew; Bashashati, Ali; Laks, Emma; Biele, Justina; Ding, Jiarui; Le, Alan; Rosner, Jamie; Shumansky, Karey; Marra, Marco A; Gilks, C Blake; Huntsman, David G; McAlpine, Jessica N; Aparicio, Samuel; Shah, Sohrab P

2014-11-01

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN. © 2014 Ha et al.; Published by Cold Spring Harbor Laboratory Press.

Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology.

PubMed

Colosimo, Alfredo

2018-01-01

This contribution reports on the simulation of some dynamical events observed in the collective behavior of different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS) paradigm as incorporated in the NetLogo™ interpreter, is a direct consequence of the cornerstone that simple, individual actions within a population of interacting agents often give rise to complex, collective behavior.The discussion will mainly focus on the emergence and spreading of synchronous activities within the population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network models. In such a general framework some recent applications taken from the direct experience of the author, and exploring the activation patterns characteristic of specific brain functional states, are described, and their impact on the Systems-Biology universe underlined.

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

PubMed

Haghverdi, Laleh; Lun, Aaron T L; Morgan, Michael D; Marioni, John C

2018-06-01

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.

Bet-hedging in bacteriocin producing Escherichia coli populations: the single cell perspective

NASA Astrophysics Data System (ADS)

Bayramoglu, Bihter; Toubiana, David; van Vliet, Simon; Inglis, R. Fredrik; Shnerb, Nadav; Gillor, Osnat

2017-02-01

Production of public goods in biological systems is often a collaborative effort that may be detrimental to the producers. It is therefore sustainable only if a small fraction of the population shoulders the cost while the majority reap the benefits. We modelled this scenario using Escherichia coli populations producing colicins, an antibiotic that kills producer cells’ close relatives. Colicin expression is a costly trait, and it has been proposed that only a small fraction of the population actively expresses the antibiotic. Colicinogenic populations were followed at the single-cell level using time-lapse microscopy, and showed two distinct, albeit dynamic, subpopulations: the majority silenced colicin expression, while a small fraction of elongated, slow-growing cells formed colicin-expressing hotspots, placing a significant burden on expressers. Moreover, monitoring lineages of individual colicinogenic cells showed stochastic switching between expressers and non-expressers. Hence, colicin expressers may be engaged in risk-reducing strategies—or bet-hedging—as they balance the cost of colicin production with the need to repel competitors. To test the bet-hedging strategy in colicin-mediated interactions, competitions between colicin-sensitive and producer cells were simulated using a numerical model, demonstrating a finely balanced expression range that is essential to sustaining the colicinogenic population.

Computation models simulating notochordal cell extinction during early ageing of an intervertebral disc.

PubMed

Louman-Gardiner, K M; Coombe, D; Hunter, C J

2011-12-01

Lower back pain due to intervertebral disc (IVD) degeneration is a prevalent problem which drastically affects the quality of life of millions of sufferers. Healthy IVDs begin with high populations of notochordal cells in the nucleus pulposus, while by the second stage of degeneration, these cells will be replaced by chondrocyte-like cells. Because the IVD is avascular, these cells rely on passive diffusion of nutrients to survive. It is thought that this transition in cell phenotype causes the shift of the IVD's physical properties, which impede the flow of nutrients. Our computational model of the IVD illustrates its ability to simulate the evolving chemical and mechanical environments occurring during the early ageing process. We demonstrate that, due to the insufficient nutrient supply and accompanying changes in physical properties of the IVD, there was a resultant exponential decay in the number of notochordal cells over time.

Cell Culture in Microgravity: Opening the Door to Space Cell Biology

NASA Technical Reports Server (NTRS)

Pellis, Neal R.; Dawson, David L. (Technical Monitor)

1999-01-01

Adaptational response of human cell populations to microgravity is investigated using simulation, short-term Shuttle experiments, and long-term microgravity. Simulation consists of a clinostatically-rotated cell culture system. The system is a horizontally-rotated cylinder completely filled with culture medium. Low speed rotation results in continuous-fall of the cells through the fluid medium. In this setting, cells: 1) aggregate, 2) propagate in three dimensions, 3) synthesize matrix, 4) differentiate, and 5) form sinusoids that facilitate mass transfer. Space cell culture is conducted in flight bioreactors and in static incubators. Cells grown in microgravity are: bovine cartilage, promyelocytic leukemia, kidney proximal tubule cells, adrenal medulla, breast and colon cancer, and endothelium. Cells were cultured in space to test specific hypotheses. Cartilage cells were used to determine structural differences in cartilage grown in space compared to ground-based bioreactors. Results from a 130-day experiment on Mir revealed that cartilage grown in space was substantially more compressible due to insufficient glycosaminoglycan in the matrix. Interestingly, earth-grown cartilage conformed better to the dimensions of the scaffolding material, while the Mir specimens were spherical. The other cell populations are currently being analyzed for cell surface properties, gene expression, and differentiation. Results suggest that some cells spontaneously differentiate in microgravity. Additionally, vast changes in gene expression may occur in response to microgravity. In conclusion, the transition to microgravity may constitute a physical perturbation in cells resulting in unique gene expressions, the consequences of which may be useful in tissue engineering, disease modeling, and space cell biology.

Range expansion of heterogeneous populations.

PubMed

Reiter, Matthias; Rulands, Steffen; Frey, Erwin

2014-04-11

Risk spreading in bacterial populations is generally regarded as a strategy to maximize survival. Here, we study its role during range expansion of a genetically diverse population where growth and motility are two alternative traits. We find that during the initial expansion phase fast-growing cells do have a selective advantage. By contrast, asymptotically, generalists balancing motility and reproduction are evolutionarily most successful. These findings are rationalized by a set of coupled Fisher equations complemented by stochastic simulations.

A multiphase model for tissue construct growth in a perfusion bioreactor.

PubMed

O'Dea, R D; Waters, S L; Byrne, H M

2010-06-01

The growth of a cell population within a rigid porous scaffold in a perfusion bioreactor is studied, using a three-phase continuum model of the type presented by Lemon et al. (2006, Multiphase modelling of tissue growth using the theory of mixtures. J. Math. Biol., 52, 571-594) to represent the cell population (and attendant extracellular matrix), culture medium and porous scaffold. The bioreactor system is modelled as a 2D channel containing the cell-seeded rigid porous scaffold (tissue construct) which is perfused with culture medium. The study concentrates on (i) the cell-cell and cell-scaffold interactions and (ii) the impact of mechanotransduction mechanisms on construct composition. A numerical and analytical analysis of the model equations is presented and, depending upon the relative importance of cell aggregation and repulsion, markedly different cell movement is revealed. Additionally, mechanotransduction effects due to cell density, pressure and shear stress-mediated tissue growth are shown to generate qualitative differences in the composition of the resulting construct. The results of our simulations indicate that this model formulation (in conjunction with appropriate experimental data) has the potential to provide a means of identifying the dominant regulatory stimuli in a cell population.

In-vitro analysis of APA microcapsules for oral delivery of live bacterial cells.

PubMed

Chen, H; Ouyang, W; Jones, M; Haque, T; Lawuyi, B; Prakash, S

2005-08-01

Oral administration of microcapsules containing live bacterial cells has potential as an alternative therapy for several diseases. This article evaluates the suitability of the alginate-poly-L-lysine-alginate (APA) microcapsules for oral delivery of live bacterial cells, in-vitro, using a dynamic simulated human gastro-intestinal (GI) model. Results showed that the APA microcapsules were morphologically stable in the simulated stomach conditions, but did not retain their structural integrity after a 3-day exposure in simulated human GI media. The microbial populations of the tested bacterial cells and the activities of the tested enzymes in the simulated human GI suspension were not substantially altered by the presence of the APA microcapsules, suggesting that there were no significant adverse effects of oral administration of the APA microcapsules on the flora of the human gastrointestinal tract. When the APA microcapsules containing Lactobacillus plantarum 80 (LP80) were challenged in the simulated gastric medium (pH = 2.0), 80.0% of the encapsulated cells remained viable after a 5-min incubation; however, the viability decreased considerably (8.3%) after 15 min and dropped to 2.6% after 30 min and lower than 0.2% after 60 min, indicating the limitations of the currently obtainable APA membrane for oral delivery of live bacteria. Further in-vivo studies are required before conclusions can be made concerning the inadequacy of APA microcapsules for oral delivery of live bacterial cells.

Representation of Perceptual Color Space in Macaque Posterior Inferior Temporal Cortex (the V4 Complex)

PubMed Central

Bohon, Kaitlin S.; Hermann, Katherine L.; Hansen, Thorsten

2016-01-01

Abstract The lateral geniculate nucleus is thought to represent color using two populations of cone-opponent neurons [L vs M; S vs (L + M)], which establish the cardinal directions in color space (reddish vs cyan; lavender vs lime). How is this representation transformed to bring about color perception? Prior work implicates populations of glob cells in posterior inferior temporal cortex (PIT; the V4 complex), but the correspondence between the neural representation of color in PIT/V4 complex and the organization of perceptual color space is unclear. We compared color-tuning data for populations of glob cells and interglob cells to predictions obtained using models that varied in the color-tuning narrowness of the cells, and the color preference distribution across the populations. Glob cells were best accounted for by simulated neurons that have nonlinear (narrow) tuning and, as a population, represent a color space designed to be perceptually uniform (CIELUV). Multidimensional scaling and representational similarity analyses showed that the color space representations in both glob and interglob populations were correlated with the organization of CIELUV space, but glob cells showed a stronger correlation. Hue could be classified invariant to luminance with high accuracy given glob responses and above-chance accuracy given interglob responses. Luminance could be read out invariant to changes in hue in both populations, but interglob cells tended to prefer stimuli having luminance contrast, regardless of hue, whereas glob cells typically retained hue tuning as luminance contrast was modulated. The combined luminance/hue sensitivity of glob cells is predicted for neurons that can distinguish two colors of the same hue at different luminance levels (orange/brown). PMID:27595132

Modelling Spatially Regulated β-Catenin Dynamics and Invasion in Intestinal Crypts

PubMed Central

Murray, Philip J.; Kang, Jun-Won; Mirams, Gary R.; Shin, Sung-Young; Byrne, Helen M.; Maini, Philip K.; Cho, Kwang-Hyun

2010-01-01

Experimental data (e.g., genetic lineage and cell population studies) on intestinal crypts reveal that regulatory features of crypt behavior, such as control via morphogen gradients, are remarkably well conserved among numerous organisms (e.g., from mouse and rat to human) and throughout the different regions of the small and large intestines. In this article, we construct a partial differential equation model of a single colonic crypt that describes the spatial distribution of Wnt pathway proteins along the crypt axis. The novelty of our continuum model is that it is based upon assumptions that can be directly related to processes at the cellular and subcellular scales. We use the model to predict how the distributions of Wnt pathway proteins are affected by mutations. The model is then extended to investigate how mutant cell populations can invade neighboring crypts. The model simulations suggest that cell crowding caused by increased proliferation and decreased cell loss may be sufficient for a mutant cell population to colonize a neighboring healthy crypt. PMID:20682248

Influence of simulated microgravity on the longevity of insect-cell culture

NASA Technical Reports Server (NTRS)

Cowger, N. L.; O'Connor, K. C.; Bivins, J. E.

1997-01-01

Simulated microgravity within the NASA High Aspect Rotating-Wall Vessel (HARV) provides a quiescent environment to culture fragile insect cells. In this vessel, the duration of stationary and death phase for cultures of Spodoptera frugiperda cells was greatly extended over that achieved in shaker-flask controls. For both HARV and control cultures, S. frugiperda cells grew to concentrations in excess of 1 x 10(7) viable cells ml-1 with viabilities greater than 90%. In the HARV, stationary phase was maintained 9-15 days in contrast to 4-5 days in the shaker flask. Furthermore, the rate of cell death was reduced in the HARV by a factor of 20-90 relative to the control culture and was characterized with a death rate constant of 0.01-0.02 day-1. Beginning in the stationary phase and continuing in the death phase, there was a significant decrease in population size in the HARV versus an increase in the shaker flask. This phenomenon could represent cell adaptation to simulated microgravity and/or a change in the ratio of apoptotic to necrotic cells. Differences observed in this research between the HARV and its control were attributed to a reduction in hydrodynamic forces in the microgravity vessel.

How the tooth got its stripes: patterning via strain-cued motility

PubMed Central

Cox, Brian N.

2013-01-01

We hypothesize that a population of migrating cells can form patterns when changes in local strains owing to relative cell motions induce changes in cell motility. That the mechanism originates in competing rates of motion distinguishes it from mechanisms involving strain energy gradients, e.g. those generated by surface energy effects or eigenstrains among cells, and diffusion–reaction mechanisms involving chemical signalling factors. The theory is tested by its ability to reproduce the morphological characteristics of enamel in the mouse incisor. Dental enamel is formed during amelogenesis by a population of ameloblasts that move about laterally within an expanding curved sheet, subject to continuously evolving spatial and temporal gradients in strain. Discrete-cell simulations of this process compute the changing strain environment of all cells and predict cell trajectories by invoking simple rules for the motion of an individual cell in response to its strain environment. The rules balance a tendency for cells to enhance relative sliding motion against a tendency to maintain uniform cell–cell separation. The simulations account for observed waviness in the enamel microstructure, the speed and shape of the ‘commencement front’ that separates domains of migrating secretory-stage ameloblasts from those that are not yet migrating, the initiation and sustainment of layered, fracture-resistant decussation patterns (cross-plied microstructure) and the transition from decussating inner enamel to non-decussating outer enamel. All these characteristics can be correctly predicted with the use of a single scalar adjustable parameter. PMID:23614945

Analysis and IbM simulation of the stages in bacterial lag phase: basis for an updated definition.

PubMed

Prats, Clara; Giró, Antoni; Ferrer, Jordi; López, Daniel; Vives-Rego, Josep

2008-05-07

The lag phase is the initial phase of a culture that precedes exponential growth and occurs when the conditions of the culture medium differ from the pre-inoculation conditions. It is usually defined by means of cell density because the number of individuals remains approximately constant or slowly increases, and it is quantified with the lag parameter lambda. The lag phase has been studied through mathematical modelling and by means of specific experiments. In recent years, Individual-based Modelling (IbM) has provided helpful insights into lag phase studies. In this paper, the definition of lag phase is thoroughly examined. Evolution of the total biomass and the total number of bacteria during lag phase is tackled separately. The lag phase lasts until the culture reaches a maximum growth rate both in biomass and cell density. Once in the exponential phase, both rates are constant over time and equal to each other. Both evolutions are split into an initial phase and a transition phase, according to their growth rates. A population-level mathematical model is presented to describe the transitional phase in cell density. INDividual DIScrete SIMulation (INDISIM) is used to check the outcomes of this analysis. Simulations allow the separate study of the evolution of cell density and total biomass in a batch culture, they provide a depiction of different observed cases in lag evolution at the individual-cell level, and are used to test the population-level model. The results show that the geometrical lag parameter lambda is not appropriate as a universal definition for the lag phase. Moreover, the lag phase cannot be characterized by a single parameter. For the studied cases, the lag phases of both the total biomass and the population are required to fully characterize the evolution of bacterial cultures. The results presented prove once more that the lag phase is a complex process that requires a more complete definition. This will be possible only after the phenomena governing the population dynamics at an individual level of description, and occurring during the lag and exponential growth phases, are well understood.

Simulated conditions of microgravity suppress progesterone production by luteal cells of the pregnant rat

NASA Technical Reports Server (NTRS)

Bhat, G. K.; Yang, H.; Sridaran, R.

2001-01-01

The purpose of this study was to assess whether simulated conditions of microgravity induce changes in the production of progesterone by luteal cells of the pregnant rat ovary using an in vitro model system. The microgravity environment was simulated using either a high aspect ratio vessel (HARV) bioreactor with free fall or a clinostat without free fall of cells. A mixed population of luteal cells isolated from the corpora lutea of day 8 pregnant rats was attached to cytodex microcarrier beads (cytodex 3). These anchorage dependent cells were placed in equal numbers in the HARV or a spinner flask control vessel in culture conditions. It was found that HARV significantly reduced the daily production of progesterone from day 1 through day 8 compared to controls. Scanning electron microscopy showed that cells attached to the microcarrier beads throughout the duration of the experiment in both types of culture vessels. Cells cultured in chamber slide flasks and placed in a clinostat yielded similar results when compared to those in the HARV. Also, when they were stained by Oil Red-O for lipid droplets, the clinostat flasks showed a larger number of stained cells compared to control flasks at 48 h. Further, the relative amount of Oil Red-O staining per milligram of protein was found to be higher in the clinostat than in the control cells at 48 h. It is speculated that the increase in the level of lipid content in cells subjected to simulated conditions of microgravity may be due to a disruption in cholesterol transport and/or lesions in the steroidogenic pathway leading to a fall in the synthesis of progesterone. Additionally, the fall in progesterone in simulated conditions of microgravity could be due to apoptosis of luteal cells.

Detecting the gravitational sensitivity of Paramecium caudatum using magnetic forces

NASA Astrophysics Data System (ADS)

Guevorkian, Karine; Valles, James M., Jr.

2006-03-01

Under normal conditions, Paramecium cells regulate their swimming speed in response to the pN level mechanical force of gravity. This regulation, known as gravikinesis, is more pronounced when the external force is increased by methods such as centrifugation. Here we present a novel technique that simulates gravity fields using the interactions between strong inhomogeneous magnetic fields and cells. We are able to achieve variable gravities spanning from 10xg to -8xg; where g is earth's gravity. Our experiments show that the swimming speed regulation of Paramecium caudatum to magnetically simulated gravity is a true physiological response. In addition, they reveal a maximum propulsion force for paramecia. This advance establishes a general technique for applying continuously variable forces to cells or cell populations suitable for exploring their force transduction mechanisms.

Multi-scale modeling in cell biology

PubMed Central

Meier-Schellersheim, Martin; Fraser, Iain D. C.; Klauschen, Frederick

2009-01-01

Biomedical research frequently involves performing experiments and developing hypotheses that link different scales of biological systems such as, for instance, the scales of intracellular molecular interactions to the scale of cellular behavior and beyond to the behavior of cell populations. Computational modeling efforts that aim at exploring such multi-scale systems quantitatively with the help of simulations have to incorporate several different simulation techniques due to the different time and space scales involved. Here, we provide a non-technical overview of how different scales of experimental research can be combined with the appropriate computational modeling techniques. We also show that current modeling software permits building and simulating multi-scale models without having to become involved with the underlying technical details of computational modeling. PMID:20448808

Single-cell copy number variation detection

PubMed Central

2011-01-01

Detection of chromosomal aberrations from a single cell by array comparative genomic hybridization (single-cell array CGH), instead of from a population of cells, is an emerging technique. However, such detection is challenging because of the genome artifacts and the DNA amplification process inherent to the single cell approach. Current normalization algorithms result in inaccurate aberration detection for single-cell data. We propose a normalization method based on channel, genome composition and recurrent genome artifact corrections. We demonstrate that the proposed channel clone normalization significantly improves the copy number variation detection in both simulated and real single-cell array CGH data. PMID:21854607

Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion

DOE PAGES

Golkaram, Mahdi; Hellander, Stefan; Drawert, Brian; ...

2016-11-28

We seek to elucidate the role of macromolecular crowding in transcription and translation. It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population. Recent experimental observations by Tan et al. have improved our understanding of the functional role of macromolecular crowding. It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression, resulting in a more homogeneous cell population. We introduce a spatial stochastic model to provide insight into this process. Our results show that macromolecular crowding reduces noise (as measured by themore » kurtosis of the mRNA distribution) in a cell population by limiting the diffusion of transcription factors (i.e. removing the unstable intermediate states), and that crowding by large molecules reduces noise more efficiently than crowding by small molecules. Finally, our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population« less

Model-Based Phenotypic Signatures Governing the Dynamics of the Stem and Semi-differentiated Cell Populations in Dysplastic Colonic Crypts.

PubMed

Nikolov, Svetoslav; Santos, Guido; Wolkenhauer, Olaf; Vera, Julio

2018-02-01

Mathematical modeling of cell differentiated in colonic crypts can contribute to a better understanding of basic mechanisms underlying colonic tissue organization, but also its deregulation during carcinogenesis and tumor progression. Here, we combined bifurcation analysis to assess the effect that time delay has in the complex interplay of stem cells and semi-differentiated cells at the niche of colonic crypts, and systematic model perturbation and simulation to find model-based phenotypes linked to cancer progression. The models suggest that stem cell and semi-differentiated cell population dynamics in colonic crypts can display chaotic behavior. In addition, we found that clinical profiling of colorectal cancer correlates with the in silico phenotypes proposed by the mathematical model. Further, potential therapeutic targets for chemotherapy resistant phenotypes are proposed, which in any case will require experimental validation.

Cell mechanics and human disease states

NASA Astrophysics Data System (ADS)

Suresh, Subra

2006-03-01

This presentation will provide summary of our very recent studies exploring the effects of biochemical factors, influenced by foreign organisms or in vivo processes, on intracellular structural reorganization, single-cell mechanical response and motility of a population of cells in the context of two human diseases: malaria induced by Plasmodium falciparum merozoites that invade red blood cells, and gastrointestinal cancer metastasis involving epithelial cells. In both cases, particular attention will be devoted to systematic changes induced in specific molecular species in response to controlled alterations in disease state. The role of critical proteins in influencing the mechanical response of human red bloods during the intra-erythrocytic development of P. falciparum merozoites has also been assessed quantitatively using specific protein knock-out experiments by recourse to gene inactivation methods. Single-cell mechanical response characterization entails such tools as optical tweezers and mechanical plate stretchers whereas cell motility assays and cell-population biorheology characterization involves microfluidic channels. The experimental studies are accompanied by three-dimensional computational simulations at the continuum and mesoscopic scales of cell deformation. An outcome of such combined experimental and computational biophysical studies is the realization of how chemical factors influence single-cell mechanical response, cytoadherence, the biorheology of a large population of cells through microchannels representative of in vivo conditions, and the onset and progression of disease states.

Dynamical System Modeling to Simulate Donor T Cell Response to Whole Exome Sequencing-Derived Recipient Peptides Demonstrates Different Alloreactivity Potential in HLA-Matched and -Mismatched Donor-Recipient Pairs.

PubMed

Abdul Razzaq, Badar; Scalora, Allison; Koparde, Vishal N; Meier, Jeremy; Mahmood, Musa; Salman, Salman; Jameson-Lee, Max; Serrano, Myrna G; Sheth, Nihar; Voelkner, Mark; Kobulnicky, David J; Roberts, Catherine H; Ferreira-Gonzalez, Andrea; Manjili, Masoud H; Buck, Gregory A; Neale, Michael C; Toor, Amir A

2016-05-01

Immune reconstitution kinetics and subsequent clinical outcomes in HLA-matched recipients of allogeneic stem cell transplantation (SCT) are variable and difficult to predict. Considering SCT as a dynamical system may allow sequence differences across the exomes of the transplant donors and recipients to be used to simulate an alloreactive T cell response, which may allow better clinical outcome prediction. To accomplish this, whole exome sequencing was performed on 34 HLA-matched SCT donor-recipient pairs (DRPs) and the nucleotide sequence differences translated to peptides. The binding affinity of the peptides to the relevant HLA in each DRP was determined. The resulting array of peptide-HLA binding affinity values in each patient was considered as an operator modifying a hypothetical T cell repertoire vector, in which each T cell clone proliferates in accordance with the logistic equation of growth. Using an iterating system of matrices, each simulated T cell clone's growth was calculated with the steady-state population being proportional to the magnitude of the binding affinity of the driving HLA-peptide complex. Incorporating competition between T cell clones responding to different HLA-peptide complexes reproduces a number of features of clinically observed T cell clonal repertoire in the simulated repertoire, including sigmoidal growth kinetics of individual T cell clones and overall repertoire, Power Law clonal frequency distribution, increase in repertoire complexity over time with increasing clonal diversity, and alteration of clonal dominance when a different antigen array is encountered, such as in SCT. The simulated, alloreactive T cell repertoire was markedly different in HLA-matched DRPs. The patterns were differentiated by rate of growth and steady-state magnitude of the simulated T cell repertoire and demonstrate a possible correlation with survival. In conclusion, exome wide sequence differences in DRPs may allow simulation of donor alloreactive T cell response to recipient antigens and may provide a quantitative basis for refining donor selection and titration of immunosuppression after SCT. Copyright © 2016 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Phototactic orientation mechanism in the ciliate Fabrea salina, as inferred from numerical simulations.

PubMed

Marangoni, R; Preosti, G; Colombetti, G

2000-02-01

The marine ciliate Fabrea salina shows a clear positive phototaxis, but the mechanism by which a single cell is able to detect the direction of light and orient its swimming accordingly is still unknown. A simple model of phototaxis is that of a biased random walk, where the bias due to light can affect one or more of the parameters that characterize a random walk, i.e., the mean speed, the frequency distribution of the angles of directional changes and the frequency of directional changes. Since experimental evidence has shown no effect of light on the mean speed of Fabrea salina, we have excluded models depending on this parameter. We have, therefore, investigated the phototactic orientation of Fabrea salina by computer simulation of two simple models, the first where light affects the frequency distribution of the angles of directional changes (model M1) and the second where the light bias modifies the frequency of directional changes (model M2). Simulated M1 cells directly orient their swimming towards the direction of light, regardless of their current swimming orientation; simulated M2 cells, on the contrary, are unable to actively orient their motion, but remain locked along the light direction once they find it by chance. The simulations show that these two orientation models lead to different macroscopic behaviours of the simulated cell populations. By comparing the results of the simulations with the experimental ones, we have found that the phototactic behaviour of real cells is more similar to that of the M2 model.

Scaffold-free Tissue Formation Under Real and Simulated Microgravity Conditions.

PubMed

Aleshcheva, Ganna; Bauer, Johann; Hemmersbach, Ruth; Slumstrup, Lasse; Wehland, Markus; Infanger, Manfred; Grimm, Daniela

2016-10-01

Scaffold-free tissue formation in microgravity is a new method in regenerative medicine and an important topic in Space Medicine. In this MiniReview, we focus on recent findings in the field of tissue engineering that were observed by exposing cells to real microgravity in space or to devices simulating to at least some extent microgravity conditions on Earth (ground-based facilities). Under both conditions - real and simulated microgravity - a part of the cultured cells of various populations detaches from the bottom of a culture flask. The cells form three-dimensional (3D) aggregates resembling the organs from which the cells have been derived. As spaceflights are rare and extremely expensive, cell culture under simulated microgravity allows more comprehensive and frequent studies on the scaffold-free 3D tissue formation in some aspects, as a number of publications have proven during the last two decades. In this MiniReview, we summarize data from our own studies and work from various researchers about tissue engineering of multi-cellular spheroids formed by cancer cells, tube formation by endothelial cells and cartilage formation by exposing the cells to ground-based facilities such as the 3D Random Positioning Machine (RPM), the 2D Fast-Rotating Clinostat (FRC) or the Rotating Wall Vessel (RWV). Subsequently, we investigated self-organization of 3D aggregates without scaffolds pursuing to enhance the frequency of 3D formation and to enlarge the size of the organ-like aggregates. The density of the monolayer exposed to real or simulated microgravity as well as the composition of the culture media revealed an impact on the results. Genomic and proteomic alterations were induced by simulated microgravity. Under microgravity conditions, adherent cells expressed other genes than cells grown in spheroids. In this MiniReview, the recent improvements in scaffold-free tissue formation are summarized and relationships between phenotypic and molecular appearance are highlighted. © 2016 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

Effect of electrical stimulation on beta-adrenergic receptor population and cyclic amp production in chicken and rat skeletal muscle cell cultures

NASA Technical Reports Server (NTRS)

Young, R. B.; Bridge, K. Y.; Strietzel, C. J.

2000-01-01

Expression of the beta-adrenergic receptor (betaAR) and its coupling to cyclic AMP (cAMP) synthesis are important components of the signaling system that controls muscle atrophy and hypertrophy, and the goal of this study was to determine if electrical stimulation in a pattern simulating slow muscle contraction would alter the betaAR response in primary cultures of avian and mammalian skeletal muscle cells. Specifically, chicken skeletal muscle cells and rat skeletal muscle cells that had been grown for 7 d in culture were subjected to electrical stimulation for an additional 2 d at a pulse frequency of 0.5 pulses/sec and a pulse duration of 200 msec. In chicken skeletal muscle cells, the betaAR population was not significantly affected by electrical stimulation; however, the ability of these cells to synthesize cyclic AMP was reduced by approximately one-half. In contrast, the betaAR population in rat muscle cells was increased slightly but not significantly by electrical stimulation, and the ability of these cells to synthesize cyclic AMP was increased by almost twofold. The basal levels of intracellular cyclic AMP in neither rat muscle cells nor chicken muscle cells were affected by electrical stimulation.

Effect of Electrical Stimulation on Beta-Adrenergic Receptor Population and Cyclic AMP Production in Chicken and Rat Skeletal Muscle Cell Cultures

NASA Technical Reports Server (NTRS)

Young, Ronald B.; Bridge, Kristin Y.; Strietzel, Catherine J.

2000-01-01

Expression of the beta-adrenergic receptor (PAR) and its coupling to Adenosine 3'5' Cyclic Monophosphate (cAMP) synthesis are important components of the signaling system that controls muscle atrophy and hypertrophy and the goal of this study was to determine if electrical stimulation in a pattern simulating slow muscle contraction would alter the PAR response in primary cultures of avian and mammalian skeletal muscle cells. Specifically chicken skeletal muscle cells and rat skeletal muscle cells that had been grown for 7 d in culture, were subjected to electrical stimulation for an additional 2 d at a pulse frequency of 0.5 pulses/sec and a pulse duration of 200 msec. In chicken skeletal muscle cells, the PAR population was not significantly affected by electrical stimulation; however, the ability, of these cells to synthesize cyclic AMP was reduced by approximately one-half. In contrast, the PAR population in rat muscle cells was increased slightly but not significantly by electrical stimulation, and the ability of these cells to synthesize cyclic AMP was increased by almost twofold. The basal levels of intracellular cyclic AMP in neither rat muscle cells nor chicken muscle cells were affected by electrical stimulation.

Modeling the Population Dynamics of Antibiotic-Resistant Bacteria:. AN Agent-Based Approach

NASA Astrophysics Data System (ADS)

Murphy, James T.; Walshe, Ray; Devocelle, Marc

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.

Sorting cells of the microalga Chlorococcum littorale with increased triacylglycerol productivity.

PubMed

Cabanelas, Iago Teles Dominguez; van der Zwart, Mathijs; Kleinegris, Dorinde M M; Wijffels, René H; Barbosa, Maria J

2016-01-01

Despite extensive research in the last decades, microalgae are still only economically feasible for high valued markets. Strain improvement is a strategy to increase productivities, hence reducing costs. In this work, we focus on microalgae selection: taking advantage of the natural biological variability of species to select variations based on desired characteristics. We focused on triacylglycerol (TAG), which have applications ranging from biodiesel to high-value omega-3 fatty-acids. Hence, we demonstrated a strategy to sort microalgae cells with increased TAG productivity. 1. We successfully identified sub-populations of cells with increased TAG productivity using Fluorescence assisted cell sorting (FACS). 2. We sequentially sorted cells after repeated cycles of N-starvation, resulting in five sorted populations (S1-S5). 3. The comparison between sorted and original populations showed that S5 had the highest TAG productivity [0.34 against 0.18 g l(-1) day(-1) (original), continuous light]. 4. Original and S5 were compared in lab-scale reactors under simulated summer conditions confirming the increased TAG productivity of S5 (0.4 against 0.2 g l(-1) day(-1)). Biomass composition analyses showed that S5 produced more biomass under N-starvation because of an increase only in TAG content and, flow cytometry showed that our selection removed cells with lower efficiency in producing TAGs. All combined, our results present a successful strategy to improve the TAG productivity of Chlorococcum littorale, without resourcing to genetic manipulation or random mutagenesis. Additionally, the improved TAG productivity of S5 was confirmed under simulated summer conditions, highlighting the industrial potential of S5 for microalgal TAG production.

The prisoner's dilemma as a cancer model.

PubMed

West, Jeffrey; Hasnain, Zaki; Mason, Jeremy; Newton, Paul K

2016-09-01

Tumor development is an evolutionary process in which a heterogeneous population of cells with different growth capabilities compete for resources in order to gain a proliferative advantage. What are the minimal ingredients needed to recreate some of the emergent features of such a developing complex ecosystem? What is a tumor doing before we can detect it? We outline a mathematical model, driven by a stochastic Moran process, in which cancer cells and healthy cells compete for dominance in the population. Each are assigned payoffs according to a Prisoner's Dilemma evolutionary game where the healthy cells are the cooperators and the cancer cells are the defectors. With point mutational dynamics, heredity, and a fitness landscape controlling birth and death rates, natural selection acts on the cell population and simulated 'cancer-like' features emerge, such as Gompertzian tumor growth driven by heterogeneity, the log-kill law which (linearly) relates therapeutic dose density to the (log) probability of cancer cell survival, and the Norton-Simon hypothesis which (linearly) relates tumor regression rates to tumor growth rates. We highlight the utility, clarity, and power that such models provide, despite (and because of) their simplicity and built-in assumptions.

Computational design of nanoparticle drug delivery systems for selective targeting

NASA Astrophysics Data System (ADS)

Duncan, Gregg A.; Bevan, Michael A.

2015-09-01

Ligand-functionalized nanoparticles capable of selectively binding to diseased versus healthy cell populations are attractive for improved efficacy of nanoparticle-based drug and gene therapies. However, nanoparticles functionalized with high affinity targeting ligands may lead to undesired off-target binding to healthy cells. In this work, Monte Carlo simulations were used to quantitatively determine net surface interactions, binding valency, and selectivity between targeted nanoparticles and cell surfaces. Dissociation constant, KD, and target membrane protein density, ρR, are explored over a range representative of healthy and cancerous cell surfaces. Our findings show highly selective binding to diseased cell surfaces can be achieved with multiple, weaker affinity targeting ligands that can be further optimized by varying the targeting ligand density, ρL. Using the approach developed in this work, nanomedicines can be optimally designed for exclusively targeting diseased cells and tissues.Ligand-functionalized nanoparticles capable of selectively binding to diseased versus healthy cell populations are attractive for improved efficacy of nanoparticle-based drug and gene therapies. However, nanoparticles functionalized with high affinity targeting ligands may lead to undesired off-target binding to healthy cells. In this work, Monte Carlo simulations were used to quantitatively determine net surface interactions, binding valency, and selectivity between targeted nanoparticles and cell surfaces. Dissociation constant, KD, and target membrane protein density, ρR, are explored over a range representative of healthy and cancerous cell surfaces. Our findings show highly selective binding to diseased cell surfaces can be achieved with multiple, weaker affinity targeting ligands that can be further optimized by varying the targeting ligand density, ρL. Using the approach developed in this work, nanomedicines can be optimally designed for exclusively targeting diseased cells and tissues. Electronic supplementary information (ESI) available: Movie showing simulation renderings of targeted (ρL = 1820/μm2, KD = 120 μM) nanoparticle selective binding to cancer (ρR = 256/μm2) vs. healthy (ρR = 64/μm2) cell surfaces. Target membrane proteins have linear color scale depending on binding energy ranging from white when unbound (URL = 0) to red when tightly bound (URL = UM). See DOI: 10.1039/c5nr03691g

Individual based simulations of bacterial growth on agar plates

NASA Astrophysics Data System (ADS)

Ginovart, M.; López, D.; Valls, J.; Silbert, M.

2002-03-01

The individual based simulator, INDividual DIScrete SIMulations (INDISIM) has been used to study the behaviour of the growth of bacterial colonies on a finite dish. The simulations reproduce the qualitative trends of pattern formation that appear during the growth of Bacillus subtilis on an agar plate under different initial conditions of nutrient peptone concentration, the amount of agar on the plate, and the temperature. The simulations are carried out by imposing closed boundary conditions on a square lattice divided into square spatial cells. The simulator studies the temporal evolution of the bacterial population possible by setting rules of behaviour for each bacterium, such as its uptake, metabolism and reproduction, as well as rules for the medium in which the bacterial cells grow, such as concentration of nutrient particles and their diffusion. The determining factors that characterize the structure of the bacterial colony patterns in the presents simulations, are the initial concentrations of nutrient particles, that mimic the amount of peptone in the experiments, and the set of values for the microscopic diffusion parameter related, in the experiments, to the amount of the agar medium.

A High-Performance Cellular Automaton Model of Tumor Growth with Dynamically Growing Domains

PubMed Central

Poleszczuk, Jan; Enderling, Heiko

2014-01-01

Tumor growth from a single transformed cancer cell up to a clinically apparent mass spans many spatial and temporal orders of magnitude. Implementation of cellular automata simulations of such tumor growth can be straightforward but computing performance often counterbalances simplicity. Computationally convenient simulation times can be achieved by choosing appropriate data structures, memory and cell handling as well as domain setup. We propose a cellular automaton model of tumor growth with a domain that expands dynamically as the tumor population increases. We discuss memory access, data structures and implementation techniques that yield high-performance multi-scale Monte Carlo simulations of tumor growth. We discuss tumor properties that favor the proposed high-performance design and present simulation results of the tumor growth model. We estimate to which parameters the model is the most sensitive, and show that tumor volume depends on a number of parameters in a non-monotonic manner. PMID:25346862

Modelling spatially regulated beta-catenin dynamics and invasion in intestinal crypts.

PubMed

Murray, Philip J; Kang, Jun-Won; Mirams, Gary R; Shin, Sung-Young; Byrne, Helen M; Maini, Philip K; Cho, Kwang-Hyun

2010-08-04

Experimental data (e.g., genetic lineage and cell population studies) on intestinal crypts reveal that regulatory features of crypt behavior, such as control via morphogen gradients, are remarkably well conserved among numerous organisms (e.g., from mouse and rat to human) and throughout the different regions of the small and large intestines. In this article, we construct a partial differential equation model of a single colonic crypt that describes the spatial distribution of Wnt pathway proteins along the crypt axis. The novelty of our continuum model is that it is based upon assumptions that can be directly related to processes at the cellular and subcellular scales. We use the model to predict how the distributions of Wnt pathway proteins are affected by mutations. The model is then extended to investigate how mutant cell populations can invade neighboring crypts. The model simulations suggest that cell crowding caused by increased proliferation and decreased cell loss may be sufficient for a mutant cell population to colonize a neighboring healthy crypt. 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Adaptive therapy.

PubMed

Gatenby, Robert A; Silva, Ariosto S; Gillies, Robert J; Frieden, B Roy

2009-06-01

A number of successful systemic therapies are available for treatment of disseminated cancers. However, tumor response is often transient, and therapy frequently fails due to emergence of resistant populations. The latter reflects the temporal and spatial heterogeneity of the tumor microenvironment as well as the evolutionary capacity of cancer phenotypes to adapt to therapeutic perturbations. Although cancers are highly dynamic systems, cancer therapy is typically administered according to a fixed, linear protocol. Here we examine an adaptive therapeutic approach that evolves in response to the temporal and spatial variability of tumor microenvironment and cellular phenotype as well as therapy-induced perturbations. Initial mathematical models find that when resistant phenotypes arise in the untreated tumor, they are typically present in small numbers because they are less fit than the sensitive population. This reflects the "cost" of phenotypic resistance such as additional substrate and energy used to up-regulate xenobiotic metabolism, and therefore not available for proliferation, or the growth inhibitory nature of environments (i.e., ischemia or hypoxia) that confer resistance on phenotypically sensitive cells. Thus, in the Darwinian environment of a cancer, the fitter chemosensitive cells will ordinarily proliferate at the expense of the less fit chemoresistant cells. The models show that, if resistant populations are present before administration of therapy, treatments designed to kill maximum numbers of cancer cells remove this inhibitory effect and actually promote more rapid growth of the resistant populations. We present an alternative approach in which treatment is continuously modulated to achieve a fixed tumor population. The goal of adaptive therapy is to enforce a stable tumor burden by permitting a significant population of chemosensitive cells to survive so that they, in turn, suppress proliferation of the less fit but chemoresistant subpopulations. Computer simulations show that this strategy can result in prolonged survival that is substantially greater than that of high dose density or metronomic therapies. The feasibility of adaptive therapy is supported by in vivo experiments. [Cancer Res 2009;69(11):4894-903] Major FindingsWe present mathematical analysis of the evolutionary dynamics of tumor populations with and without therapy. Analytic solutions and numerical simulations show that, with pretreatment, therapy-resistant cancer subpopulations are present due to phenotypic or microenvironmental factors; maximum dose density chemotherapy hastens rapid expansion of resistant populations. The models predict that host survival can be maximized if "treatment-for-cure strategy" is replaced by "treatment-for-stability." Specifically, the models predict that an optimal treatment strategy will modulate therapy to maintain a stable population of chemosensitive cells that can, in turn, suppress the growth of resistant populations under normal tumor conditions (i.e., when therapy-induced toxicity is absent). In vivo experiments using OVCAR xenografts treated with carboplatin show that adaptive therapy is feasible and, in this system, can produce long-term survival.

Cells as strain-cued automata

NASA Astrophysics Data System (ADS)

Cox, Brian N.; Snead, Malcolm L.

2016-02-01

We argue in favor of representing living cells as automata and review demonstrations that autonomous cells can form patterns by responding to local variations in the strain fields that arise from their individual or collective motions. An autonomous cell's response to strain stimuli is assumed to be effected by internally-generated, internally-powered forces, which generally move the cell in directions other than those implied by external energy gradients. Evidence of cells acting as strain-cued automata have been inferred from patterns observed in nature and from experiments conducted in vitro. Simulations that mimic particular cases of pattern forming share the idealization that cells are assumed to pass information among themselves solely via mechanical boundary conditions, i.e., the tractions and displacements present at their membranes. This assumption opens three mechanisms for pattern formation in large cell populations: wavelike behavior, kinematic feedback in cell motility that can lead to sliding and rotational patterns, and directed migration during invasions. Wavelike behavior among ameloblast cells during amelogenesis (the formation of dental enamel) has been inferred from enamel microstructure, while strain waves in populations of epithelial cells have been observed in vitro. One hypothesized kinematic feedback mechanism, "enhanced shear motility", accounts successfully for the spontaneous formation of layered patterns during amelogenesis in the mouse incisor. Directed migration is exemplified by a theory of invader cells that sense and respond to the strains they themselves create in the host population as they invade it: analysis shows that the strain fields contain positional information that could aid the formation of cell network structures, stabilizing the slender geometry of branches and helping govern the frequency of branch bifurcation and branch coalescence (the formation of closed networks). In simulations of pattern formation in homogeneous populations and network formation by invaders, morphological outcomes are governed by the ratio of the rates of two competing time dependent processes, one a migration velocity and the other a relaxation velocity related to the propagation of strain information. Relaxation velocities are approximately constant for different species and organs, whereas cell migration rates vary by three orders of magnitude. We conjecture that developmental processes use rapid cell migration to achieve certain outcomes, and slow migration to achieve others. We infer from analysis of host relaxation during network formation that a transition exists in the mechanical response of a host cell from animate to inanimate behavior when its strain changes at a rate that exceeds 10-4-10-3 s-1. The transition has previously been observed in experiments conducted in vitro.

Whistler anisotropy instabilities as the source of banded chorus: Van Allen Probes observations and particle-in-cell simulations

DOE PAGES

Fu, Xiangrong; Cowee, Misa M.; Friedel, Reinhard H.; ...

2014-10-22

Magnetospheric banded chorus is enhanced whistler waves with frequencies ω r < Ω e, where Ω e is the electron cyclotron frequency, and a characteristic spectral gap at ω r ≃ Ω e/2. This paper uses spacecraft observations and two-dimensional particle-in-cell simulations in a magnetized, homogeneous, collisionless plasma to test the hypothesis that banded chorus is due to local linear growth of two branches of the whistler anisotropy instability excited by two distinct, anisotropic electron components of significantly different temperatures. The electron densities and temperatures are derived from Helium, Oxygen, Proton, and Electron instrument measurements on the Van Allen Probesmore » A satellite during a banded chorus event on 1 November 2012. The observations are consistent with a three-component electron model consisting of a cold (a few tens of eV) population, a warm (a few hundred eV) anisotropic population, and a hot (a few keV) anisotropic population. The simulations use plasma and field parameters as measured from the satellite during this event except for two numbers: the anisotropies of the warm and the hot electron components are enhanced over the measured values in order to obtain relatively rapid instability growth. The simulations show that the warm component drives the quasi-electrostatic upper band chorus and that the hot component drives the electromagnetic lower band chorus; the gap at ~Ω e/2 is a natural consequence of the growth of two whistler modes with different properties.« less

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

PubMed Central

Barron, Martin; Zhang, Siyuan

2018-01-01

Abstract Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data. PMID:29140455

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

PubMed Central

Zuckerman, Daniel M.; Chong, Lillian T.

2018-01-01

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling—the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes—protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation. PMID:28301772

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software.

PubMed

Zuckerman, Daniel M; Chong, Lillian T

2017-05-22

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling-the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes-protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.

Modeling and Analysis of a Nonlinear Age-Structured Model for Tumor Cell Populations with Quiescence

NASA Astrophysics Data System (ADS)

Liu, Zijian; Chen, Jing; Pang, Jianhua; Bi, Ping; Ruan, Shigui

2018-05-01

We present a nonlinear first-order hyperbolic partial differential equation model to describe age-structured tumor cell populations with proliferating and quiescent phases at the avascular stage in vitro. The division rate of the proliferating cells is assumed to be nonlinear due to the limitation of the nutrient and space. The model includes a proportion of newborn cells that enter directly the quiescent phase with age zero. This proportion can reflect the effect of treatment by drugs such as erlotinib. The existence and uniqueness of solutions are established. The local and global stabilities of the trivial steady state are investigated. The existence and local stability of the positive steady state are also analyzed. Numerical simulations are performed to verify the results and to examine the impacts of parameters on the nonlinear dynamics of the model.

Emergent multicellular life cycles in filamentous bacteria owing to density-dependent population dynamics.

PubMed

Rossetti, Valentina; Filippini, Manuela; Svercel, Miroslav; Barbour, A D; Bagheri, Homayoun C

2011-12-07

Filamentous bacteria are the oldest and simplest known multicellular life forms. By using computer simulations and experiments that address cell division in a filamentous context, we investigate some of the ecological factors that can lead to the emergence of a multicellular life cycle in filamentous life forms. The model predicts that if cell division and death rates are dependent on the density of cells in a population, a predictable cycle between short and long filament lengths is produced. During exponential growth, there will be a predominance of multicellular filaments, while at carrying capacity, the population converges to a predominance of short filaments and single cells. Model predictions are experimentally tested and confirmed in cultures of heterotrophic and phototrophic bacterial species. Furthermore, by developing a formulation of generation time in bacterial populations, it is shown that changes in generation time can alter length distributions. The theory predicts that given the same population growth curve and fitness, species with longer generation times have longer filaments during comparable population growth phases. Characterization of the environmental dependence of morphological properties such as length, and the number of cells per filament, helps in understanding the pre-existing conditions for the evolution of developmental cycles in simple multicellular organisms. Moreover, the theoretical prediction that strains with the same fitness can exhibit different lengths at comparable growth phases has important implications. It demonstrates that differences in fitness attributed to morphology are not the sole explanation for the evolution of life cycles dominated by multicellularity.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua E.; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

2016-09-01

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of ``stiff'' equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Kinetic modeling of multi-feed simultaneous saccharification and co-fermentation of pretreated birch to ethanol.

PubMed

Wang, Ruifei; Koppram, Rakesh; Olsson, Lisbeth; Franzén, Carl Johan

2014-11-01

Fed-batch simultaneous saccharification and fermentation (SSF) is a feasible option for bioethanol production from lignocellulosic raw materials at high substrate concentrations. In this work, a segregated kinetic model was developed for simulation of fed-batch simultaneous saccharification and co-fermentation (SSCF) of steam-pretreated birch, using substrate, enzymes and cell feeds. The model takes into account the dynamics of the cellulase-cellulose system and the cell population during SSCF, and the effects of pre-cultivation of yeast cells on fermentation performance. The model was cross-validated against experiments using different feed schemes. It could predict fermentation performance and explain observed differences between measured total yeast cells and dividing cells very well. The reproducibility of the experiments and the cell viability were significantly better in fed-batch than in batch SSCF at 15% and 20% total WIS contents. The model can be used for simulation of fed-batch SSCF and optimization of feed profiles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impacts of high resolution data on traveler compliance levels in emergency evacuation simulations

DOE PAGES

Lu, Wei; Han, Lee D.; Liu, Cheng; ...

2016-05-05

In this article, we conducted a comparison study of evacuation assignment based on Traffic Analysis Zones (TAZ) and high resolution LandScan USA Population Cells (LPC) with detailed real world roads network. A platform for evacuation modeling built on high resolution population distribution data and activity-based microscopic traffic simulation was proposed. This platform can be extended to any cities in the world. The results indicated that evacuee compliance behavior affects evacuation efficiency with traditional TAZ assignment, but it did not significantly compromise the performance with high resolution LPC assignment. The TAZ assignment also underestimated the real travel time during evacuation. Thismore » suggests that high data resolution can improve the accuracy of traffic modeling and simulation. The evacuation manager should consider more diverse assignment during emergency evacuation to avoid congestions.« less

Optimising Cell Aggregate Expansion in a Perfused Hollow Fibre Bioreactor via Mathematical Modelling

PubMed Central

Chapman, Lloyd A. C.; Shipley, Rebecca J.; Whiteley, Jonathan P.; Ellis, Marianne J.; Byrne, Helen M.; Waters, Sarah L.

2014-01-01

The need for efficient and controlled expansion of cell populations is paramount in tissue engineering. Hollow fibre bioreactors (HFBs) have the potential to meet this need, but only with improved understanding of how operating conditions and cell seeding strategy affect cell proliferation in the bioreactor. This study is designed to assess the effects of two key operating parameters (the flow rate of culture medium into the fibre lumen and the fluid pressure imposed at the lumen outlet), together with the cell seeding distribution, on cell population growth in a single-fibre HFB. This is achieved using mathematical modelling and numerical methods to simulate the growth of cell aggregates along the outer surface of the fibre in response to the local oxygen concentration and fluid shear stress. The oxygen delivery to the cell aggregates and the fluid shear stress increase as the flow rate and pressure imposed at the lumen outlet are increased. Although the increased oxygen delivery promotes growth, the higher fluid shear stress can lead to cell death. For a given cell type and initial aggregate distribution, the operating parameters that give the most rapid overall growth can be identified from simulations. For example, when aggregates of rat cardiomyocytes that can tolerate shear stresses of up to are evenly distributed along the fibre, the inlet flow rate and outlet pressure that maximise the overall growth rate are predicted to be in the ranges to (equivalent to to ) and to (or 15.6 psi to 15.7 psi) respectively. The combined effects of the seeding distribution and flow on the growth are also investigated and the optimal conditions for growth found to depend on the shear tolerance and oxygen demands of the cells. PMID:25157635

Density-Dependent Recycling Promotes the Long-Term Survival of Bacterial Populations during Periods of Starvation.

PubMed

Takano, Sotaro; Pawlowska, Bogna J; Gudelj, Ivana; Yomo, Tetsuya; Tsuru, Saburo

2017-02-07

The amount of natural resources in the Earth's environment is in flux, which can trigger catastrophic collapses of ecosystems. How populations survive under nutrient-poor conditions is a central question in ecology. Curiously, some bacteria persist for a long time in nutrient-poor environments. Although this survival may be accomplished through cell death and the recycling of dead cells, the importance of these processes and the mechanisms underlying the survival of the populations have not been quantitated. Here, we use microbial laboratory experiments and mathematical models to demonstrate that death and recycling are essential activities for the maintenance of cell survival. We also show that the behavior of the survivors is governed by population density feedback, wherein growth is limited not only by the available resources but also by the population density. The numerical simulations suggest that population density-dependent recycling could be an advantageous behavior under starvation conditions. How organisms survive after exhaustion of resources is a central question in ecology. Starving Escherichia coli constitute a model system to understand survival mechanisms during long-term starvation. Although death and the recycling of dead cells might play a key role in the maintenance of long-term survival, their mechanisms and importance have not been quantitated. Here, we verified the significance of social recycling of dead cells for long-term survival. We also show that the survivors restrained their recycling and did not use all available nutrients released from dead cells, which may be advantageous under starvation conditions. These results indicate that not only the utilization of dead cells but also restrained recycling coordinate the effective utilization of limited resources for long-term survival under starvation. Copyright © 2017 Takano et al.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

Experimental studies of protozoan response to intense magnetic fields and forces

NASA Astrophysics Data System (ADS)

Guevorkian, Karine

Intense static magnetic fields of up to 31 Tesla were used as a novel tool to manipulate the swimming mechanics of unicellular organisms. It is shown that homogenous magnetic fields alter the swimming trajectories of the single cell protozoan Paramecium caudatum, by aligning them parallel to the applied field. Immobile neutrally buoyant paramecia also oriented in magnetic fields with similar rates as the motile ones. It was established that the magneto-orientation is mostly due to the magnetic torques acting on rigid structures in the cell body and therefore the response is a non-biological, passive response. From the orientation rate of paramecia in various magnetic field strengths, the average anisotropy of the diamagnetic susceptibility of the cell was estimated. It has also been demonstrated that magnetic forces can be used to create increased, decreased and even inverted simulated gravity environments for the investigation of the gravi-responses of single cells. Since the mechanisms by which Earth's gravity affects cell functioning are still not fully understood, a number of methods to simulate different strength gravity environments, such as centrifugation, have been employed. Exploiting the ability to exert magnetic forces on weakly diamagnetic constituents of the cells, we were able to vary the gravity from -8 g to 10 g, where g is Earth's gravity. Investigations of the swimming response of paramecia in these simulated gravities revealed that they actively regulate their swimming speed to oppose the external force. This result is in agreement with centrifugation experiments, confirming the credibility of the technique. Moreover, the Paramecium's swimming ceased in simulated gravity of 10 g, indicating a maximum possible propulsion force of 0.7 nN. The magnetic force technique to simulate gravity is the only earthbound technique that can create increased and decreased simulated gravities in the same experimental setup. These findings establish a general technique for applying continuously variable forces to cells or cell populations suitable for exploring their force transduction mechanisms.

Bioinspired decision architectures containing host and microbiome processing units.

PubMed

Heyde, K C; Gallagher, P W; Ruder, W C

2016-09-27

Biomimetic robots have been used to explore and explain natural phenomena ranging from the coordination of ants to the locomotion of lizards. Here, we developed a series of decision architectures inspired by the information exchange between a host organism and its microbiome. We first modeled the biochemical exchanges of a population of synthetically engineered E. coli. We then built a physical, differential drive robot that contained an integrated, onboard computer vision system. A relay was established between the simulated population of cells and the robot's microcontroller. By placing the robot within a target-containing a two-dimensional arena, we explored how different aspects of the simulated cells and the robot's microcontroller could be integrated to form hybrid decision architectures. We found that distinct decision architectures allow for us to develop models of computation with specific strengths such as runtime efficiency or minimal memory allocation. Taken together, our hybrid decision architectures provide a new strategy for developing bioinspired control systems that integrate both living and nonliving components.

Toxic Alexandrium blooms in the western Gulf of Maine: The plume advection hypothesis revisited

USGS Publications Warehouse

Anderson, D.M.; Keafer, B.A.; Geyer, W.R.; Signell, R.P.; Loder, T.C.

2005-01-01

The plume advection hypothesis links blooms of the toxic dinoflagellate Alexandrium fundyense in the western Gulf of Maine (GOM) to a buoyant plume derived from river outflows. This hypothesis was examined with cruise and moored-instrument observations in 1993 when levels of paralytic shellfish poisoning (PSP) toxins were high, and in 1994 when toxicity was low. A coupled physical-biological model simulated hydrography and A. fundyense distributions. Initial A. fundyense populations were restricted to low-salinity nearshore waters near Casco Bay, but also occurred in higher salinity waters along the plume boundary. This suggests two sources of cells - those from shallow-water cyst populations and those transported to shore from offshore blooms in the eastern segment of the Maine coastal current (EMCC). Observations confirm the role of the plume in A. fundyense transport and growth. Downwelling-favorable winds in 1993 transported the plume and its cells rapidly alongshore, enhancing toxicity and propagating PSP to the south. In 1994, sustained upwelling moved the plume offshore, resulting in low toxicity in intertidal shellfish. A. fundyense blooms were likely nutrient limited, leading to low growth rates and moderate cell abundances. These observations and mechanisms were reproduced by coupled physical-biological model simulations. The plume advection hypothesis provides a viable explanation for outbreaks of PSP in the western GOM, but should be refined to include two sources for cells that populate the plume and two major pathways for transport: one within the low-salinity plume and another where A. fundyense cells originating in the EMCC are transported along the outer boundary of the plume front with the western segment of the Maine coastal current.

Neutral Evolution and Dispersal Limitation Produce Biogeographic Patterns in Microcystis aeruginosa Populations of Lake Systems.

PubMed

Shirani, Sahar; Hellweger, Ferdi L

2017-08-01

Molecular observations reveal substantial biogeographic patterns of cyanobacteria within systems of connected lakes. An important question is the relative role of environmental selection and neutral processes in the biogeography of these systems. Here, we quantify the effect of genetic drift and dispersal limitation by simulating individual cyanobacteria cells using an agent-based model (ABM). In the model, cells grow (divide), die, and migrate between lakes. Each cell has a full genome that is subject to neutral mutation (i.e., the growth rate is independent of the genome). The model is verified by simulating simplified lake systems, for which theoretical solutions are available. Then, it is used to simulate the biogeography of the cyanobacterium Microcystis aeruginosa in a number of real systems, including the Great Lakes, Klamath River, Yahara River, and Chattahoochee River. Model output is analyzed using standard bioinformatics tools (BLAST, MAFFT). The emergent patterns of nucleotide divergence between lakes are dynamic, including gradual increases due to accumulation of mutations and abrupt changes due to population takeovers by migrant cells (coalescence events). The model predicted nucleotide divergence is heterogeneous within systems, and for weakly connected lakes, it can be substantial. For example, Lakes Superior and Michigan are predicted to have an average genomic nucleotide divergence of 8200 bp or 0.14%. The divergence between more strongly connected lakes is much lower. Our results provide a quantitative baseline for future biogeography studies. They show that dispersal limitation can be an important factor in microbe biogeography, which is contrary to the common belief, and could affect how a system responds to environmental change.

The Formation of Tight Tumor Clusters Affects the Efficacy of Cell Cycle Inhibitors: A Hybrid Model Study

PubMed Central

Kim, MunJu; Reed, Damon; Rejniak, Katarzyna A.

2014-01-01

Cyclin-dependent kinases (CDKs) are vital in regulating cell cycle progression, and, thus, in highly proliferating tumor cells CDK inhibitors are gaining interest as potential anticancer agents. Clonogenic assay experiments are frequently used to determine drug efficacy against the survival and proliferation of cancer cells. While the anticancer mechanisms of drugs are usually described at the intracellular single-cell level, the experimental measurements are sampled from the entire cancer cell population. This approach may lead to discrepancies between the experimental observations and theoretical explanations of anticipated drug mechanisms. To determine how individual cell responses to drugs that inhibit CDKs affect the growth of cancer cell populations, we developed a spatially explicit hybrid agent-based model. In this model, each cell is equipped with internal cell cycle regulation mechanisms, but it is also able to interact physically with its neighbors. We model cell cycle progression, focusing on the G1 and G2/M cell cycle checkpoints, as well as on related essential components, such as CDK1, CDK2, cell size, and DNA damage. We present detailed studies of how the emergent properties (e.g., cluster formation) of an entire cell population depend on altered physical and physiological parameters. We analyze the effects of CDK1 and CKD2 inhibitors on population growth, time-dependent changes in cell cycle distributions, and the dynamic evolution of spatial cell patterns. We show that cell cycle inhibitors that cause cell arrest at different cell cycle phases are not necessarily synergistically super-additive. Finally, we demonstrate that the physical aspects of cell population growth, such as the formation of tight cell clusters versus dispersed colonies, alter the efficacy of cell cycle inhibitors, both in 2D and 3D simulations. This finding may have implications for interpreting the treatment efficacy results of in vitro experiments, in which treatment is applied before the cells can grow to produce clusters, especially because in vivo tumors, in contrast, form large masses before they are detected and treated. PMID:24607745

Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis.

PubMed

Lee, Sang-Woo; Morishita, Yoshihiro

2017-07-01

Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated. With the progress in identification of related molecules, some reports described the relevance of cell mechanics during elimination. Furthermore, recent live imaging studies have shown that even in tissues composed of genetically identical cells, a non-negligible number of cells are eliminated during growth. Thus, mechanical cell elimination (MCE) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon. Here, we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis. First, we propose two quantitative measures, cell and tissue fitness, to evaluate cellular competitiveness and tissue growth efficiency, respectively. By mechanical tissue simulation in a pure population where all cells have the same mechanical traits, we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters. In particular, we found that geometrical (specifically, cell size) and mechanical (stress magnitude) heterogeneities are common determinants of the elimination rate. Based on these results, we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Moreover, when cells with different mechanical traits are mixed (e.g., in the presence of phenotypic variation), we show that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits, i.e. intra-tissue "evolution". Along with the improvement of growth efficiency, cell density, stress state, and phenotype (mechanical traits) were also shown to be homogenized through growth. More theoretically, we propose a mathematical model that approximates cell competition dynamics, by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model.

Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis

PubMed Central

2017-01-01

Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated. With the progress in identification of related molecules, some reports described the relevance of cell mechanics during elimination. Furthermore, recent live imaging studies have shown that even in tissues composed of genetically identical cells, a non-negligible number of cells are eliminated during growth. Thus, mechanical cell elimination (MCE) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon. Here, we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis. First, we propose two quantitative measures, cell and tissue fitness, to evaluate cellular competitiveness and tissue growth efficiency, respectively. By mechanical tissue simulation in a pure population where all cells have the same mechanical traits, we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters. In particular, we found that geometrical (specifically, cell size) and mechanical (stress magnitude) heterogeneities are common determinants of the elimination rate. Based on these results, we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Moreover, when cells with different mechanical traits are mixed (e.g., in the presence of phenotypic variation), we show that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits, i.e. intra-tissue “evolution”. Along with the improvement of growth efficiency, cell density, stress state, and phenotype (mechanical traits) were also shown to be homogenized through growth. More theoretically, we propose a mathematical model that approximates cell competition dynamics, by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model. PMID:28704373

Effectiveness of nanoencapsulated methotrexate against osteosarcoma cells: in vitro cytotoxicity under dynamic conditions.

PubMed

Mitxelena-Iribarren, O; Hisey, C L; Errazquin-Irigoyen, M; González-Fernández, Y; Imbuluzqueta, E; Mujika, M; Blanco-Prieto, M J; Arana, S

2017-06-01

Cancer is a leading cause of mortality in the world, with osteosarcoma being one of the most common types among children between 1 and 14 years old. Current treatments including preoperative chemotherapy, surgery and postoperative chemotherapy produce several side effects with limited effectiveness. The use of lipid nanoparticles as biodegradable shells for controlled drug delivery shows promise as a more effective and targeted tumor treatment. However, in vitro validation of these vehicles is limited due to fluid stagnation in current techniques, in which nanoparticles sediment onto the bottom of the wells killing the cells by asphyxiation. In the current series of experiments, results obtained with methotrexate-lipid nanoparticles under dynamic assay conditions are presented as a promising alternative to current free drug based therapies. Effects on the viability of the U-2 OS osteosarcoma cell line of recirculation of cell media, free methotrexate and blank and methotrexate containing lipid nanoparticles in a 11 μM concentration were successfully assessed. In addition, several designs for the microfluidic platform used were simulated using COMSOL-Multiphysics, optimized devices were fabricated using soft-lithography and simulated parameters were experimentally validated. Nanoparticles did not sediment to the bottom of the platform, demonstrating the effectiveness of the proposed system. Moreover, encapsulated methotrexate was the most effective treatment, as after 72 h the cell population was reduced nearly 40% while under free methotrexate circulation the cell population doubled. Overall, these results indicate that methotrexate-lipid nanoparticles are a promising targeted therapy for osteosarcoma treatment.

Ca-Pri a Cellular Automata Phenomenological Research Investigation: Simulation Results

NASA Astrophysics Data System (ADS)

Iannone, G.; Troisi, A.

2013-05-01

Following the introduction of a phenomenological cellular automata (CA) model capable to reproduce city growth and urban sprawl, we develop a toy model simulation considering a realistic framework. The main characteristic of our approach is an evolution algorithm based on inhabitants preferences. The control of grown cells is obtained by means of suitable functions which depend on the initial condition of the simulation. New born urban settlements are achieved by means of a logistic evolution of the urban pattern while urban sprawl is controlled by means of the population evolution function. In order to compare model results with a realistic urban framework we have considered, as the area of study, the island of Capri (Italy) in the Mediterranean Sea. Two different phases of the urban evolution on the island have been taken into account: a new born initial growth as induced by geographic suitability and the simulation of urban spread after 1943 induced by the population evolution after this date.

Synchronization of glycolytic oscillations in a yeast cell population.

PubMed

Danø, S; Hynne, F; De Monte, S; d'Ovidio, F; Sørensen, P G; Westerhoff, H

2001-01-01

The mechanism of active phase synchronization in a suspension of oscillatory yeast cells has remained a puzzle for almost half a century. The difficulty of the problem stems from the fact that the synchronization phenomenon involves the entire metabolic network of glycolysis and fermentation, and consequently it cannot be addressed at the level of a single enzyme or a single chemical species. In this paper it is shown how this system in a CSTR (continuous flow stirred tank reactor) can be modelled quantitatively as a population of Stuart-Landau oscillators interacting by exchange of metabolites through the extracellular medium, thus reducing the complexity of the problem without sacrificing the biochemical realism. The parameters of the model can be derived by a systematic expansion from any full-scale model of the yeast cell kinetics with a supercritical Hopf bifurcation. Some parameter values can also be obtained directly from analysis of perturbation experiments. In the mean-field limit, equations for the study of populations having a distribution of frequencies are used to simulate the effect of the inherent variations between cells.

Treatment cost and life expectancy of diffuse large B-cell lymphoma (DLBCL): a discrete event simulation model on a UK population-based observational cohort.

PubMed

Wang, Han-I; Smith, Alexandra; Aas, Eline; Roman, Eve; Crouch, Simon; Burton, Cathy; Patmore, Russell

2017-03-01

Diffuse large B-cell lymphoma (DLBCL) is the commonest non-Hodgkin lymphoma. Previous studies examining the cost of treating DLBCL have generally focused on a specific first-line therapy alone; meaning that their findings can neither be extrapolated to the general patient population nor to other points along the treatment pathway. Based on empirical data from a representative population-based patient cohort, the objective of this study was to develop a simulation model that could predict costs and life expectancy of treating DLBCL. All patients newly diagnosed with DLBCL in the UK's population-based Haematological Malignancy Research Network ( www.hmrn.org ) in 2007 were followed until 2013 (n = 271). Mapped treatment pathways, alongside cost information derived from the National Tariff 2013/14, were incorporated into a patient-level simulation model in order to reflect the heterogeneities of patient characteristics and treatment options. The NHS and social services perspective was adopted, and all outcomes were discounted at 3.5 % per annum. Overall, the expected total medical costs were £22,122 for those treated with curative intent, and £2930 for those managed palliatively. For curative chemotherapy, the predicted medical costs were £14,966, £23,449 and £7376 for first-, second- and third-line treatments, respectively. The estimated annual cost for treating DLBCL across the UK was around £88-92 million. This is the first cost modelling study using empirical data to provide 'real world' evidence throughout the DLBCL treatment pathway. Future application of the model could include evaluation of new technologies/treatments to support healthcare decision makers, especially in the era of personalised medicine.

Mesoscale Simulation of Blood Flow in Small Vessels

PubMed Central

Bagchi, Prosenjit

2007-01-01

Computational modeling of blood flow in microvessels with internal diameter 20–500 μm is a major challenge. It is because blood in such vessels behaves as a multiphase suspension of deformable particles. A continuum model of blood is not adequate if the motion of individual red blood cells in the suspension is of interest. At the same time, multiple cells, often a few thousands in number, must also be considered to account for cell-cell hydrodynamic interaction. Moreover, the red blood cells (RBCs) are highly deformable. Deformation of the cells must also be considered in the model, as it is a major determinant of many physiologically significant phenomena, such as formation of a cell-free layer, and the Fahraeus-Lindqvist effect. In this article, we present two-dimensional computational simulation of blood flow in vessels of size 20–300 μm at discharge hematocrit of 10–60%, taking into consideration the particulate nature of blood and cell deformation. The numerical model is based on the immersed boundary method, and the red blood cells are modeled as liquid capsules. A large RBC population comprising of as many as 2500 cells are simulated. Migration of the cells normal to the wall of the vessel and the formation of the cell-free layer are studied. Results on the trajectory and velocity traces of the RBCs, and their fluctuations are presented. Also presented are the results on the plug-flow velocity profile of blood, the apparent viscosity, and the Fahraeus-Lindqvist effect. The numerical results also allow us to investigate the variation of apparent blood viscosity along the cross-section of a vessel. The computational results are compared with the experimental results. To the best of our knowledge, this article presents the first simulation to simultaneously consider a large ensemble of red blood cells and the cell deformation. PMID:17208982

Accounting for Space—Quantification of Cell-To-Cell Transmission Kinetics Using Virus Dynamics Models.

PubMed

Kumberger, Peter; Durso-Cain, Karina; Uprichard, Susan L; Dahari, Harel; Graw, Frederik

2018-04-17

Mathematical models based on ordinary differential equations (ODE) that describe the population dynamics of viruses and infected cells have been an essential tool to characterize and quantify viral infection dynamics. Although an important aspect of viral infection is the dynamics of viral spread, which includes transmission by cell-free virions and direct cell-to-cell transmission, models used so far ignored cell-to-cell transmission completely, or accounted for this process by simple mass-action kinetics between infected and uninfected cells. In this study, we show that the simple mass-action approach falls short when describing viral spread in a spatially-defined environment. Using simulated data, we present a model extension that allows correct quantification of cell-to-cell transmission dynamics within a monolayer of cells. By considering the decreasing proportion of cells that can contribute to cell-to-cell spread with progressing infection, our extension accounts for the transmission dynamics on a single cell level while still remaining applicable to standard population-based experimental measurements. While the ability to infer the proportion of cells infected by either of the transmission modes depends on the viral diffusion rate, the improved estimates obtained using our novel approach emphasize the need to correctly account for spatial aspects when analyzing viral spread.

Enhancing dendritic cell immunotherapy for melanoma using a simple mathematical model.

PubMed

Castillo-Montiel, E; Chimal-Eguía, J C; Tello, J Ignacio; Piñon-Zaráte, G; Herrera-Enríquez, M; Castell-Rodríguez, A E

2015-06-09

The immunotherapy using dendritic cells (DCs) against different varieties of cancer is an approach that has been previously explored which induces a specific immune response. This work presents a mathematical model of DCs immunotherapy for melanoma in mice based on work by Experimental Immunotherapy Laboratory of the Medicine Faculty in the Universidad Autonoma de Mexico (UNAM). The model is a five delay differential equation (DDEs) which represents a simplified view of the immunotherapy mechanisms. The mathematical model takes into account the interactions between tumor cells, dendritic cells, naive cytotoxic T lymphocytes cells (inactivated cytotoxic cells), effector cells (cytotoxic T activated cytotoxic cells) and transforming growth factor β cytokine (T G F-β). The model is validated comparing the computer simulation results with biological trial results of the immunotherapy developed by the research group of UNAM. The results of the growth of tumor cells obtained by the control immunotherapy simulation show a similar amount of tumor cell population than the biological data of the control immunotherapy. Moreover, comparing the increase of tumor cells obtained from the immunotherapy simulation and the biological data of the immunotherapy applied by the UNAM researchers obtained errors of approximately 10 %. This allowed us to use the model as a framework to test hypothetical treatments. The numerical simulations suggest that by using more doses of DCs and changing the infusion time, the tumor growth decays compared with the current immunotherapy. In addition, a local sensitivity analysis is performed; the results show that the delay in time " τ", the maximal growth rate of tumor "r" and the maximal efficiency of tumor cytotoxic cells rate "aT" are the most sensitive model parameters. By using this mathematical model it is possible to simulate the growth of the tumor cells with or without immunotherapy using the infusion protocol of the UNAM researchers, to obtain a good approximation of the biological trials data. It is worth mentioning that by manipulating the different parameters of the model the effectiveness of the immunotherapy may increase. This last suggests that different protocols could be implemented by the Immunotherapy Laboratory of UNAM in order to improve their results.

A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor

PubMed Central

Kaul, Himanshu; Cui, Zhanfeng; Ventikos, Yiannis

2013-01-01

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications. PMID:23555740

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

NASA Astrophysics Data System (ADS)

Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

2009-12-01

Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Multiscale Modeling of Microbial Communities

NASA Astrophysics Data System (ADS)

Blanchard, Andrew

Although bacteria are single-celled organisms, they exist in nature primarily in the form of complex communities, participating in a vast array of social interactions through regulatory gene networks. The social interactions between individual cells drive the emergence of community structures, resulting in an intricate relationship across multiple spatiotemporal scales. Here, I present my work towards developing and applying the tools necessary to model the complex dynamics of bacterial communities. In Chapter 2, I utilize a reaction-diffusion model to determine the population dynamics for a population with two species. One species (CDI+) utilizes contact dependent inhibition to kill the other sensitive species (CDI-). The competition can produce diverse patterns, including extinction, coexistence, and localized aggregation. The emergence, relative abundance, and characteristic features of these patterns are collectively determined by the competitive benefit of CDI and its growth disadvantage for a given rate of population diffusion. The results provide a systematic and statistical view of CDI-based bacterial population competition, expanding the spectrum of our knowledge about CDI systems and possibly facilitating new experimental tests for a deeper understanding of bacterial interactions. In the following chapter, I present a systematic computational survey on the relationship between social interaction types and population structures for two-species communities by developing and utilizing a hybrid computational framework that combines discrete element techniques with reaction-diffusion equations. The impact of deleterious and beneficial interactions on the community are quantified. Deleterious interactions generate an increased variance in relative abundance, a drastic decrease in surviving lineages, and a rough expanding front. In contrast, beneficial interactions contribute to a reduced variance in relative abundance, an enhancement in lineage number, and a smooth expanding front. More specifically, mutualism promotes spatial homogeneity and population robustness while competition increases spatial segregation and population fluctuations. To examine the generality of these findings, a large set of initial conditions with varying density and species abundance was tested and analyzed. The results and the computational framework presented provide the basis for further explorations of individual based simulations of bacterial communities. For Chapter 4, I consider the role of gene regulation in shaping the outcome of competition between a bacteriocin (i.e. toxin) producing and sensitive strain. In natural systems, bacteriocin production is often conditional, governed by underlying quorum sensing regulatory circuitry. By developing an ordinary differential equation (ODE) model integrating population dynamics with molecular regulation, we find that the ecological contribution of bacteriocin production can be positive or negative, determined by the tradeoff between the benefit of bacteriocins in mediating competition and the fitness cost due to metabolic load. Interestingly, under the naturally occurring scenario where bacteriocin production has a high cost, density-dependent synthesis is more advantageous than constitutive synthesis, which offers a quantitative interpretation for the wide prevalence of density-related bacteriocin production in nature. By incorporating the modeling framework presented in Chapter 3, the results of the ODE model were extended to the spatial setting, providing ecological insights into the costs and benefits of bacteriocin synthesis in competitive environments. For the final research chapter, I consider the impact of growth coupling on protein production at both the single cell and population scales. The same machinery (e.g. ribosomes) and resources (e.g. amino acids and ATP) are used within cells to produce both endogenous (host) and exogenous (circuit) proteins. Thus, the introduction of a gene circuit generates a metabolic burden on the cell which can slow its growth rate relative to the wild type. Building off of the computational framework introduced in Chapter 3 with single cell resolution, I utilize deterministic and stochastic simulations to characterize the changes in protein production due to host-circuit coupling for a simple gene regulatory architecture. Analytical arguments and simulation results show that incorporating growth can lead to drastic changes in both the steady state and time scales for protein production at the single cell and population level. Furthermore, host-circuit coupling can induce bimodality at the population level well outside the bistable region for single cell dynamics.

Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling

PubMed Central

Narayanan, Manikandan; Martins, Andrew J.; Tsang, John S.

2016-01-01

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies. PMID:27438699

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domanskyi, Sergii; Schilling, Joshua E.; Privman, Vladimir, E-mail: privman@clarkson.edu

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model wemore » describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of “stiff” equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.« less

Drug scheduling of cancer chemotherapy based on natural actor-critic approach.

PubMed

Ahn, Inkyung; Park, Jooyoung

2011-11-01

Recently, reinforcement learning methods have drawn significant interests in the area of artificial intelligence, and have been successfully applied to various decision-making problems. In this paper, we study the applicability of the NAC (natural actor-critic) approach, a state-of-the-art reinforcement learning method, to the drug scheduling of cancer chemotherapy for an ODE (ordinary differential equation)-based tumor growth model. ODE-based cancer dynamics modeling is an active research area, and many different mathematical models have been proposed. Among these, we use the model proposed by de Pillis and Radunskaya (2003), which considers the growth of tumor cells and their interaction with normal cells and immune cells. The NAC approach is applied to this ODE model with the goal of minimizing the tumor cell population and the drug amount while maintaining the adequate population levels of normal cells and immune cells. In the framework of the NAC approach, the drug dose is regarded as the control input, and the reward signal is defined as a function of the control input and the cell populations of tumor cells, normal cells, and immune cells. According to the control policy found by the NAC approach, effective drug scheduling in cancer chemotherapy for the considered scenarios has turned out to be close to the strategy of continuing drug injection from the beginning until an appropriate time. Also, simulation results showed that the NAC approach can yield better performance than conventional pulsed chemotherapy. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology

PubMed Central

Bonin, Carla Rezende Barbosa; Fernandes, Guilherme Cortes; dos Santos, Rodrigo Weber; Lobosco, Marcelo

2017-01-01

ABSTRACT New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus. PMID:28027002

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology.

PubMed

Bonin, Carla Rezende Barbosa; Fernandes, Guilherme Cortes; Dos Santos, Rodrigo Weber; Lobosco, Marcelo

2017-02-01

New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.

Sequential CD34 cell fractionation by magnetophoresis in a magnetic dipole flow sorter.

PubMed

Schneider, Thomas; Karl, Stephan; Moore, Lee R; Chalmers, Jeffrey J; Williams, P Stephen; Zborowski, Maciej

2010-01-01

Cell separation and fractionation based on fluorescent and magnetic labeling procedures are common tools in contemporary research. These techniques rely on binding of fluorophores or magnetic particles conjugated to antibodies to target cells. Cell surface marker expression levels within cell populations vary with progression through the cell cycle. In an earlier work we showed the reproducible magnetic fractionation (single pass) of the Jurkat cell line based on the population distribution of CD45 surface marker expression. Here we present a study on magnetic fractionation of a stem and progenitor cell (SPC) population using the established acute myelogenous leukemia cell line KG-1a as a cell model. The cells express a CD34 cell surface marker associated with the hematopoietic progenitor cell activity and the progenitor cell lineage commitment. The CD34 expression level is approximately an order of magnitude lower than that of the CD45 marker, which required further improvements of the magnetic fractionation apparatus. The cells were immunomagnetically labeled using a sandwich of anti-CD34 antibody-phycoerythrin (PE) conjugate and anti-PE magnetic nanobead and fractionated into eight components using a continuous flow dipole magnetophoresis apparatus. The CD34 marker expression distribution between sorted fractions was measured by quantitative PE flow cytometry (using QuantiBRITE PE calibration beads), and it was shown to be correlated with the cell magnetophoretic mobility distribution. A flow outlet addressing scheme based on the concept of the transport lamina thickness was used to control cell distribution between the eight outlet ports. The fractional cell distributions showed good agreement with numerical simulations of the fractionation based on the cell magnetophoretic mobility distribution in the unsorted sample.

Evolutionary dynamics of imatinib-treated leukemic cells by stochastic approach

NASA Astrophysics Data System (ADS)

Pizzolato, Nicola; Valenti, Davide; Adorno, Dominique Persano; Spagnolo, Bernardo

2009-09-01

The evolutionary dynamics of a system of cancerous cells in a model of chronic myeloid leukemia (CML) is investigated by a statistical approach. Cancer progression is explored by applying a Monte Carlo method to simulate the stochastic behavior of cell reproduction and death in a population of blood cells which can experience genetic mutations. In CML front line therapy is represented by the tyrosine kinase inhibitor imatinib which strongly affects the reproduction of leukemic cells only. In this work, we analyze the effects of a targeted therapy on the evolutionary dynamics of normal, first-mutant and cancerous cell populations. Several scenarios of the evolutionary dynamics of imatinib-treated leukemic cells are described as a consequence of the efficacy of the different modelled therapies. We show how the patient response to the therapy changes when a high value of the mutation rate from healthy to cancerous cells is present. Our results are in agreement with clinical observations. Unfortunately, development of resistance to imatinib is observed in a fraction of patients, whose blood cells are characterized by an increasing number of genetic alterations. We find that the occurrence of resistance to the therapy can be related to a progressive increase of deleterious mutations.

Fas/APO-1 protein is increased in spaceflown lymphocytes (Jurkat)

NASA Technical Reports Server (NTRS)

Cubano, L. A.; Lewis, M. L.

2000-01-01

Human lymphocytes flown on the Space Shuttle respond poorly to mitogen stimulation and populations of the lymphoblastoid T cell line, Jurkat, manifest growth arrest, increase in apoptosis and time- and microgravity-dependent increases in the soluble form of the cell death factor, Fas/APO-1 (sFas). The potential role of apoptosis in population dynamics of space-flown lymphocytes has not been investigated previously. We flew Jurkat cells on Space Transportation System (STS)-80 and STS-95 to determine whether apoptosis and the apparent microgravity-related release of sFas are characteristic of lymphocytes in microgravity. The effects of spaceflight and ground-based tests simulating spaceflight experimental conditions, including high cell density and low serum concentration, were assessed. Immunofluorescence microscopy showed increased cell associated Fas in flown cells. Results of STS-80 and STS-95 confirmed increase in apoptosis during spaceflight and the release of sFas as a repeatable, time-dependent and microgravity-related response. Ground-based tests showed that holding cells at 1.5 million/ml in medium containing 2% serum before launch did not increase sFas. Reports of increased Fas in cells of the elderly and the increases in spaceflown cells suggest possible similarities between aging and spaceflight effects on lymphocytes.

A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast

PubMed Central

Almquist, Joachim; Bendrioua, Loubna; Adiels, Caroline Beck; Goksör, Mattias; Hohmann, Stefan; Jirstrand, Mats

2015-01-01

The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME) modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS) approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient response of Mig1 tend to be faster, more extended, and displays an increased cell-to-cell variability. PMID:25893847

Lipid droplets fusion in adipocyte differentiated 3T3-L1 cells: A Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boschi, Federico, E-mail: federico.boschi@univr.it; Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona; Rizzatti, Vanni

Several human worldwide diseases like obesity, type 2 diabetes, hepatic steatosis, atherosclerosis and other metabolic pathologies are related to the excessive accumulation of lipids in cells. Lipids accumulate in spherical cellular inclusions called lipid droplets (LDs) whose sizes range from fraction to one hundred of micrometers in adipocytes. It has been suggested that LDs can grow in size due to a fusion process by which a larger LD is obtained with spherical shape and volume equal to the sum of the progenitors’ ones. In this study, the size distribution of two populations of LDs was analyzed in immature and maturemore » (5-days differentiated) 3T3-L1 adipocytes (first and second populations, respectively) after Oil Red O staining. A Monte Carlo simulation of interaction between LDs has been developed in order to quantify the size distribution and the number of fusion events needed to obtain the distribution of the second population size starting from the first one. Four models are presented here based on different kinds of interaction: a surface weighted interaction (R2 Model), a volume weighted interaction (R3 Model), a random interaction (Random model) and an interaction related to the place where the LDs are born (Nearest Model). The last two models mimic quite well the behavior found in the experimental data. This work represents a first step in developing numerical simulations of the LDs growth process. Due to the complex phenomena involving LDs (absorption, growth through additional neutral lipid deposition in existing droplets, de novo formation and catabolism) the study focuses on the fusion process. The results suggest that, to obtain the observed size distribution, a number of fusion events comparable with the number of LDs themselves is needed. Moreover the MC approach results a powerful tool for investigating the LDs growth process. Highlights: • We evaluated the role of the fusion process in the synthesis of the lipid droplets. • We compared the size distribution of the lipid droplets in immature and mature cells. • We used the Monte Carlo simulation approach, simulating 10 thousand of fusion events. • Four different interaction models between the lipid droplets were tested. • The best model which mimics the experimental measures was selected.« less

Effect of Electrical Stimulation on Beta-Adrenergic Receptor Population and Coupling Efficiency in Chicken and Rat Skeleton Muscle Cell Cultures

NASA Technical Reports Server (NTRS)

Young, Ronald B.; Bridge, Kristin Y.; Strietzel, Catherine J.

1999-01-01

Expression of the beta-adrenergic receptor (bAR) and its coupling to cyclic AMP (cAMP) synthesis are important components of the signaling system that controls muscle atrophy and hypertrophy, and the goal of this study was to determine if electrical stimulation in a pattern simulating slow muscle contraction would alter the bAR response in primary cultures of avian and mammalian skeletal muscle cells. Specifically, chicken skeletal muscle cells and rat skeletal muscle cells that had been grown for seven days in culture were subjected to electrical stimulation for an additional two days at a pulse frequency of 0.5 pulses/sec and a pulse duration of 200 msec. In chicken skeletal muscle cells, the bAR population was not significantly affected by electrical stimulation; however, the ability of these cells to synthesize cyclic AMP was reduced by approximately one-half. Thus, in chicken muscle cells an enhanced level of contraction reduced the coupling efficiency of bAR for cyclic AMP production by approximately 55% compared to controls. In contrast, the bAR population in rat muscle cells was increased by approximately 25% by electrical stimulation, and the ability of these cells to synthesize cyclic AMP was also increased by almost two-fold. Thus, in rat muscle cells an enhanced level of contraction increased the coupling efficiency of bAR for cyclic AMP production by approximately 50% compared to controls. The basal levels of intracellular cyclic AMP in both rat muscle cells and chicken muscle cells were not affected by electrical stimulation.

Growth Mechanism of Microbial Colonies

NASA Astrophysics Data System (ADS)

Zhu, Minhui; Martini, K. Michael; Kim, Neil H.; Sherer, Nicholas; Lee, Jia Gloria; Kuhlman, Thomas; Goldenfeld, Nigel

Experiments on nutrient-limited E. coli colonies, growing on agar gel from single cells reveal a power-law distribution of sizes, both during the growth process and in the final stage when growth has ceased. We developed a Python simulation to study the growth mechanism of the bacterial population and thus understand the broad details of the experimental findings. The simulation takes into account nutrient uptake, metabolic function, growth and cell division. Bacteria are modeled in two dimensions as hard circle-capped cylinders with steric interactions and elastic stress dependent growth characteristics. Nutrient is able to diffuse within and between the colonies. The mechanism of microbial colony growth involves reproduction of cells within the colonies and the merging of different colonies. We report results on the dynamic scaling laws and final state size distribution, that capture in semi-quantitative detail the trends observed in experiment. Supported by NSF Grant 0822613.

An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size

NASA Astrophysics Data System (ADS)

Gao, Shangce; Wang, Rong-Long; Ishii, Masahiro; Tang, Zheng

This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.

Efficient Analysis of Systems Biology Markup Language Models of Cellular Populations Using Arrays.

PubMed

Watanabe, Leandro; Myers, Chris J

2016-08-19

The Systems Biology Markup Language (SBML) has been widely used for modeling biological systems. Although SBML has been successful in representing a wide variety of biochemical models, the core standard lacks the structure for representing large complex regular systems in a standard way, such as whole-cell and cellular population models. These models require a large number of variables to represent certain aspects of these types of models, such as the chromosome in the whole-cell model and the many identical cell models in a cellular population. While SBML core is not designed to handle these types of models efficiently, the proposed SBML arrays package can represent such regular structures more easily. However, in order to take full advantage of the package, analysis needs to be aware of the arrays structure. When expanding the array constructs within a model, some of the advantages of using arrays are lost. This paper describes a more efficient way to simulate arrayed models. To illustrate the proposed method, this paper uses a population of repressilator and genetic toggle switch circuits as examples. Results show that there are memory benefits using this approach with a modest cost in runtime.

VERDE Analytic Modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

2008-01-15

The Verde Analytic Modules permit the user to ingest openly available data feeds about phenomenology (storm tracks, wind, precipitation, earthquake, wildfires, and similar natural and manmade power grid disruptions and forecast power outages, restoration times, customers outaged, and key facilities that will lose power. Damage areas are predicted using historic damage criteria of the affected area. The modules use a cellular automata approach to estimating the distribution circuits assigned to geo-located substations. Population estimates served within the service areas are located within 1 km grid cells and converted to customer counts by conversion through demographic estimation of households and commercialmore » firms within the population cells. Restoration times are estimated by agent-based simulation of restoration crews working according to utility published prioritization calibrated by historic performance.« less

Fundamental limits on dynamic inference from single-cell snapshots

PubMed Central

Weinreb, Caleb; Tusi, Betsabeh K.; Socolovsky, Merav

2018-01-01

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements. PMID:29463712

Controlling the Flow of Visual Information through the Lateral Geniculate Nucleus: From Single Cells to Neural Networks.

DTIC Science & Technology

1991-10-31

in my laboratory, Drs. Dan Kammen, Ernst Niebur and Florentin Worg6tter, as well as with three outside collaborators, Prof. John Kulli from the...also for experimentally observed cortical column structures ( Niebur and Worg6tter, 1990a,b). Temporal Dynamics of Interacting Neuronal Populations We...Connection Machine to simulate a 128 by 128 grid of 16,384 cells under a variety of stimulation patterns ( Niebur , Kammen & Koch, 1991). To explore

A differential equation model of HIV infection of CD4+ T-cells with cure rate

NASA Astrophysics Data System (ADS)

Zhou, Xueyong; Song, Xinyu; Shi, Xiangyun

2008-06-01

A differential equation model of HIV infection of CD4+ T-cells with cure rate is studied. We prove that if the basic reproduction number R0<1, the HIV infection is cleared from the T-cell population and the disease dies out; if R0>1, the HIV infection persists in the host. We find that the chronic disease steady state is globally asymptotically stable if R0>1. Furthermore, we also obtain the conditions for which the system exists an orbitally asymptotically stable periodic solution. Numerical simulations are presented to illustrate the results.

Survival of Deinococcus geothermalis in Biofilms under Desiccation and Simulated Space and Martian Conditions

NASA Astrophysics Data System (ADS)

Frösler, Jan; Panitz, Corinna; Wingender, Jost; Flemming, Hans-Curt; Rettberg, Petra

2017-05-01

Biofilm formation represents a successful survival strategy for bacteria. In biofilms, cells are embedded in a matrix of extracellular polymeric substances (EPS). As they are often more stress-tolerant than single cells, biofilm cells might survive the conditions present in space and on Mars. To investigate this topic, the bacterium Deinococcus geothermalis was chosen as a model organism due to its tolerance toward desiccation and radiation. Biofilms cultivated on membranes and, for comparison, planktonically grown cells deposited on membranes were air-dried and exposed to individual stressors that included prolonged desiccation, extreme temperatures, vacuum, simulated martian atmosphere, and UV irradiation, and they were exposed to combinations of stressors that simulate space (desiccation + vacuum + UV) or martian (desiccation + Mars atmosphere + UV) conditions. The effect of sulfatic Mars regolith simulant on cell viability during stress was investigated separately. The EPS produced by the biofilm cells contained mainly polysaccharides and proteins. To detect viable but nonculturable (VBNC) cells, cultivation-independent viability indicators (membrane integrity, ATP, 16S rRNA) were determined in addition to colony counts. Desiccation for 2 months resulted in a decrease of culturability with minor changes of membrane integrity in biofilm cells and major loss of membrane integrity in planktonic bacteria. Temperatures between -25°C and +60°C, vacuum, and Mars atmosphere affected neither culturability nor membrane integrity in both phenotypes. Monochromatic (254 nm; ≥1 kJ m-2) and polychromatic (200-400 nm; >5.5 MJ m-2 for planktonic cells and >270 MJ m-2 for biofilms) UV irradiation significantly reduced the culturability of D. geothermalis but did not affect cultivation-independent viability markers, indicating the induction of a VBNC state in UV-irradiated cells. In conclusion, a substantial proportion of the D. geothermalis population remained viable under all stress conditions tested, and in most cases the biofilm form proved advantageous for surviving space and Mars-like conditions.

Multicellular automaticity of cardiac cell monolayers: effects of density and spatial distribution of pacemaker cells

NASA Astrophysics Data System (ADS)

Elber Duverger, James; Boudreau-Béland, Jonathan; Le, Minh Duc; Comtois, Philippe

2014-11-01

Self-organization of pacemaker (PM) activity of interconnected elements is important to the general theory of reaction-diffusion systems as well as for applications such as PM activity in cardiac tissue to initiate beating of the heart. Monolayer cultures of neonatal rat ventricular myocytes (NRVMs) are often used as experimental models in studies on cardiac electrophysiology. These monolayers exhibit automaticity (spontaneous activation) of their electrical activity. At low plated density, cells usually show a heterogeneous population consisting of PM and quiescent excitable cells (QECs). It is therefore highly probable that monolayers of NRVMs consist of a heterogeneous network of the two cell types. However, the effects of density and spatial distribution of the PM cells on spontaneous activity of monolayers remain unknown. Thus, a simple stochastic pattern formation algorithm was implemented to distribute PM and QECs in a binary-like 2D network. A FitzHugh-Nagumo excitable medium was used to simulate electrical spontaneous and propagating activity. Simulations showed a clear nonlinear dependency of spontaneous activity (occurrence and amplitude of spontaneous period) on the spatial patterns of PM cells. In most simulations, the first initiation sites were found to be located near the substrate boundaries. Comparison with experimental data obtained from cardiomyocyte monolayers shows important similarities in the position of initiation site activity. However, limitations in the model that do not reflect the complex beat-to-beat variation found in experiments indicate the need for a more realistic cardiomyocyte representation.

Macroenvironmental regulation of hair cycling and collective regenerative behavior.

PubMed

Plikus, Maksim V; Chuong, Cheng-Ming

2014-01-01

The hair follicle (HF) regeneration paradigm provides a unique opportunity for studying the collective behavior of stem cells in living animals. Activation of HF stem cells depends on the core inhibitory BMP and activating WNT signals operating within the HF microenvironment. Additionally, HFs receive multilayered signaling inputs from the extrafollicular macroenvironment, which includes dermis, adipocytes, neighboring HFs, hormones, and external stimuli. These activators/inhibitors are integrated across multiple stem-cell niches to produce dynamic hair growth patterns. Because of their pigmentation, these patterns can be easily studied on live shaved animals. Comparing to autonomous regeneration of one HF, populations of HFs display coupled decision making, allowing for more robust and adaptable regenerative behavior to occur collectively. The generic cellular automata model used to simulate coordinated HF cycling here can be extended to study population-level behavior of other complex biological systems made of cycling elements.

Macroenvironmental Regulation of Hair Cycling and Collective Regenerative Behavior

PubMed Central

Plikus, Maksim V.; Chuong, Cheng-Ming

2014-01-01

The hair follicle (HF) regeneration paradigm provides a unique opportunity for studying the collective behavior of stem cells in living animals. Activation of HF stem cells depends on the core inhibitory BMP and activating WNT signals operating within the HF microenvironment. Additionally, HFs receive multilayered signaling inputs from the extrafollicular macroenvironment, which includes dermis, adipocytes, neighboring HFs, hormones, and external stimuli. These activators/inhibitors are integrated across multiple stem-cell niches to produce dynamic hair growth patterns. Because of their pigmentation, these patterns can be easily studied on live shaved animals. Comparing to autonomous regeneration of one HF, populations of HFs display coupled decision making, allowing for more robust and adaptable regenerative behavior to occur collectively. The generic cellular automata model used to simulate coordinated HF cycling here can be extended to study population-level behavior of other complex biological systems made of cycling elements. PMID:24384813

Continuum-level modelling of cellular adhesion and matrix production in aggregates.

PubMed

Geris, Liesbet; Ashbourn, Joanna M A; Clarke, Tim

2011-05-01

Key regulators in tissue-engineering processes such as cell culture and cellular organisation are the cell-cell and cell-matrix interactions. As mathematical models are increasingly applied to investigate biological phenomena in the biomedical field, it is important, for some applications, that these models incorporate an adequate description of cell adhesion. This study describes the development of a continuum model that represents a cell-in-gel culture system used in bone-tissue engineering, namely that of a cell aggregate embedded in a hydrogel. Cell adhesion is modelled through the use of non-local (integral) terms in the partial differential equations. The simulation results demonstrate that the effects of cell-cell and cell-matrix adhesion are particularly important for the survival and growth of the cell population and the production of extracellular matrix by the cells, concurring with experimental observations in the literature.

Cell Division Induces and Switches Coherent Angular Motion within Bounded Cellular Collectives.

PubMed

Siedlik, Michael J; Manivannan, Sriram; Kevrekidis, Ioannis G; Nelson, Celeste M

2017-06-06

Collective cell migration underlies many biological processes, including embryonic development, wound healing, and cancer progression. In the embryo, cells have been observed to move collectively in vortices using a mode of collective migration known as coherent angular motion (CAM). To determine how CAM arises within a population and changes over time, here, we study the motion of mammary epithelial cells within engineered monolayers, in which the cells move collectively about a central axis in the tissue. Using quantitative image analysis, we find that CAM is significantly reduced when mitosis is suppressed. Particle-based simulations recreate the observed trends, suggesting that cell divisions drive the robust emergence of CAM and facilitate switches in the direction of collective rotation. Our simulations predict that the location of a dividing cell, rather than the orientation of the division axis, facilitates the onset of this motion. These predictions agree with experimental observations, thereby providing, to our knowledge, new insight into how cell divisions influence CAM within a tissue. Overall, these findings highlight the dynamic nature of CAM and suggest that regulating cell division is crucial for tuning emergent collective migratory behaviors, such as vortical motions observed in vivo. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Population Density and Moment-based Approaches to Modeling Domain Calcium-mediated Inactivation of L-type Calcium Channels.

PubMed

Wang, Xiao; Hardcastle, Kiah; Weinberg, Seth H; Smith, Gregory D

2016-03-01

We present a population density and moment-based description of the stochastic dynamics of domain [Formula: see text]-mediated inactivation of L-type [Formula: see text] channels. Our approach accounts for the effect of heterogeneity of local [Formula: see text] signals on whole cell [Formula: see text] currents; however, in contrast with prior work, e.g., Sherman et al. (Biophys J 58(4):985-995, 1990), we do not assume that [Formula: see text] domain formation and collapse are fast compared to channel gating. We demonstrate the population density and moment-based modeling approaches using a 12-state Markov chain model of an L-type [Formula: see text] channel introduced by Greenstein and Winslow (Biophys J 83(6):2918-2945, 2002). Simulated whole cell voltage clamp responses yield an inactivation function for the whole cell [Formula: see text] current that agrees with the traditional approach when domain dynamics are fast. We analyze the voltage-dependence of [Formula: see text] inactivation that may occur via slow heterogeneous domain [[Formula: see text

The cell-stretcher: A novel device for the mechanical stimulation of cell populations

NASA Astrophysics Data System (ADS)

Seriani, S.; Del Favero, G.; Mahaffey, J.; Marko, D.; Gallina, P.; Long, C. S.; Mestroni, L.; Sbaizero, O.

2016-08-01

Mechanical stimulation appears to be a critical modulator for many aspects of biology, both of living tissue and cells. The cell-stretcher, a novel device for the mechanical uniaxial stimulation of populations of cells, is described. The system is based on a variable stroke cam-lever-tappet mechanism which allows the delivery of cyclic stimuli with frequencies of up to 10 Hz and deformation between 1% and 20%. The kinematics is presented and a simulation of the dynamics of the system is shown, in order to compute the contact forces in the mechanism. The cells, following cultivation and preparation, are plated on an ad hoc polydimethylsiloxane membrane which is then loaded on the clamps of the cell-stretcher via force-adjustable magnetic couplings. In order to show the viability of the experimentation and biocompatibility of the cell-stretcher, a set of two in vitro tests were performed. Human epithelial carcinoma cell line A431 and Adult Mouse Ventricular Fibroblasts (AMVFs) from a dual reporter mouse were subject to 0.5 Hz, 24 h cyclic stretching at 15% strain, and to 48 h stimulation at 0.5 Hz and 15% strain, respectively. Visual analysis was performed on A431, showing definite morphological changes in the form of cellular extroflections in the direction of stimulation compared to an unstimulated control. A cytometric analysis was performed on the AMVF population. Results show a post-stimulation live-dead ratio deviance of less than 6% compared to control, which proves that the environment created by the cell-stretcher is suitable for in vitro experimentation.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

PubMed

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J

2014-07-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

Modeling Political Populations with Bacteria

NASA Astrophysics Data System (ADS)

Cleveland, Chris; Liao, David

2011-03-01

Results from lattice-based simulations of micro-environments with heterogeneous nutrient resources reveal that competition between wild-type and GASP rpoS819 strains of E. Coli offers mutual benefit, particularly in nutrient deprived regions. Our computational model spatially maps bacteria populations and energy sources onto a set of 3D lattices that collectively resemble the topology of North America. By implementing Wright-Fishcer re- production into a probabilistic leap-frog scheme, we observe populations of wild-type and GASP rpoS819 cells compete for resources and, yet, aid each other's long term survival. The connection to how spatial political ideologies map in a similar way is discussed.

Kinetic Simulations of Type II Radio Burst Emission Processes

NASA Astrophysics Data System (ADS)

Ganse, U.; Spanier, F. A.; Vainio, R. O.

2011-12-01

The fundamental emission process of Type II Radio Bursts has been under discussion for many decades. While analytic deliberations point to three wave interaction as the source for fundamental and harmonic radio emissions, sparse in-situ observational data and high computational demands for kinetic simulations have not allowed for a definite conclusion to be reached. A popular model puts the radio emission into the foreshock region of a coronal mass ejection's shock front, where shock drift acceleration can create eletrcon beam populations in the otherwise quiescent foreshock plasma. Beam-driven instabilities are then assumed to create waves, forming the starting point of three wave interaction processes. Using our kinetic particle-in-cell code, we have studied a number of emission scenarios based on electron beam populations in a CME foreshock, with focus on wave-interaction microphysics on kinetic scales. The self-consistent, fully kinetic simulations with completely physical mass-ratio show fundamental and harmonic emission of transverse electromagnetic waves and allow for detailled statistical analysis of all contributing wavemodes and their couplings.

ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast.

PubMed

Boczko, Erik M; Gedeon, Tomas; Stowers, Chris C; Young, Todd R

2010-07-01

Biologists have long observed periodic-like oxygen consumption oscillations in yeast populations under certain conditions, and several unsatisfactory explanations for this phenomenon have been proposed. These ‘autonomous oscillations’ have often appeared with periods that are nearly integer divisors of the calculated doubling time of the culture. We hypothesize that these oscillations could be caused by a form of cell cycle synchronization that we call clustering. We develop some novel ordinary differential equation models of the cell cycle. For these models, and for random and stochastic perturbations, we give both rigorous proofs and simulations showing that both positive and negative growth rate feedback within the cell cycle are possible agents that can cause clustering of populations within the cell cycle. It occurs for a variety of models and for a broad selection of parameter values. These results suggest that the clustering phenomenon is robust and is likely to be observed in nature. Since there are necessarily an integer number of clusters, clustering would lead to periodic-like behaviour with periods that are nearly integer divisors of the period of the cell cycle. Related experiments have shown conclusively that cell cycle clustering occurs in some oscillating yeast cultures.

An "age"-structured model of hematopoietic stem cell organization with application to chronic myeloid leukemia.

PubMed

Roeder, Ingo; Herberg, Maria; Horn, Matthias

2009-04-01

Previously, we have modeled hematopoietic stem cell organization by a stochastic, single cell-based approach. Applications to different experimental systems demonstrated that this model consistently explains a broad variety of in vivo and in vitro data. A major advantage of the agent-based model (ABM) is the representation of heterogeneity within the hematopoietic stem cell population. However, this advantage comes at the price of time-consuming simulations if the systems become large. One example in this respect is the modeling of disease and treatment dynamics in patients with chronic myeloid leukemia (CML), where the realistic number of individual cells to be considered exceeds 10(6). To overcome this deficiency, without losing the representation of the inherent heterogeneity of the stem cell population, we here propose to approximate the ABM by a system of partial differential equations (PDEs). The major benefit of such an approach is its independence from the size of the system. Although this mean field approach includes a number of simplifying assumptions compared to the ABM, it retains the key structure of the model including the "age"-structure of stem cells. We show that the PDE model qualitatively and quantitatively reproduces the results of the agent-based approach.

Variance in binary stellar population synthesis

NASA Astrophysics Data System (ADS)

Breivik, Katelyn; Larson, Shane L.

2016-03-01

In the years preceding LISA, Milky Way compact binary population simulations can be used to inform the science capabilities of the mission. Galactic population simulation efforts generally focus on high fidelity models that require extensive computational power to produce a single simulated population for each model. Each simulated population represents an incomplete sample of the functions governing compact binary evolution, thus introducing variance from one simulation to another. We present a rapid Monte Carlo population simulation technique that can simulate thousands of populations in less than a week, thus allowing a full exploration of the variance associated with a binary stellar evolution model.

Studying Variance in the Galactic Ultra-compact Binary Population

NASA Astrophysics Data System (ADS)

Larson, Shane L.; Breivik, Katelyn

2017-01-01

In the years preceding LISA, Milky Way compact binary population simulations can be used to inform the science capabilities of the mission. Galactic population simulation efforts generally focus on high fidelity models that require extensive computational power to produce a single simulated population for each model. Each simulated population represents an incomplete sample of the functions governing compact binary evolution, thus introducing variance from one simulation to another. We present a rapid Monte Carlo population simulation technique that can simulate thousands of populations on week-long timescales, thus allowing a full exploration of the variance associated with a binary stellar evolution model.

SIApopr: a computational method to simulate evolutionary branching trees for analysis of tumor clonal evolution.

PubMed

McDonald, Thomas O; Michor, Franziska

2017-07-15

SIApopr (Simulating Infinite-Allele populations) is an R package to simulate time-homogeneous and inhomogeneous stochastic branching processes under a very flexible set of assumptions using the speed of C ++. The software simulates clonal evolution with the emergence of driver and passenger mutations under the infinite-allele assumption. The software is an application of the Gillespie Stochastic Simulation Algorithm expanded to a large number of cell types and scenarios, with the intention of allowing users to easily modify existing models or create their own. SIApopr is available as an R library on Github ( https://github.com/olliemcdonald/siapopr ). Supplementary data are available at Bioinformatics online. michor@jimmy.harvard.edu. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Modeling and fabrication of scale-like cantilever for cell capturing

NASA Astrophysics Data System (ADS)

Liu, Boyin; Fu, Jing; Muradoglu, Murat

2013-12-01

The micro-domain provides excellent conditions for performing biological experiments on small populations of cells and has given rise to the proliferation of so-called lab-on-a-chip devices. In order to fully utilize the benefits of cell assays, means of retaining cells at defined locations over time are required. Here, the creation of scale-like cantilevers, inspired by biomimetics, on planar silicon nitride (Si3N4) film using focused ion beam machining is described. Using SEM imaging, regular tilting of the cantilever with almost no warping of the cantilever was uncovered. Finite element analysis showed that the scale-like cantilever was best at limiting stress concentration without difficulty in manufacture and having stresses more evenly distributed along the edge. It also had a major advantage in that the degree of deflection could be simply altered by changing the central angle. From a piling simulation conducted, it was found that a random delivery of simulated particles on to the scale-like obstacle should create a triangular collection. In the experimental trapping of polystyrene beads in suspension, the basic triangular piling structure was observed, but with extended tails and a fanning out around the obstacle. This was attributed to the aggregation tendency of polystyrene beads that acted on top of the piling behavior. In the experiment with bacterial cells, triangular pile up behind the cantilever was absent and the bacteria cells were able to slip inside the cantilever's opening despite the size of the bacteria being larger than the gap. Overall, the fabricated scale-like cantilever architectures offer a viable way to trap small populations of material in suspension.

Importance of the predator's ecological neighborhood in modeling predation on migrating prey

USGS Publications Warehouse

DeAngelis, Donald L.; Petersen, James H.

2001-01-01

Most mathematical descriptions of predator-prey interactions fail to take into account the spatio-temporal structures of the populations, which can lead to errors or misinterpretations. For example, a compact pulse of prey migrating through a field of quasi-stationary predators may not be well described by standard predator-prey models, because the predators and prey are unlikely to be well mixed; that is, the prey may be exposed to only a fraction of the predator population at a time. This underscores the importance of properly accounting for the ecological neighborhood, or effective feeding range, of predators in models. We illustrate this situation with a series of models of salmon smolts migrating through a reservoir arrayed with predators. The reservoir is divided into a number of longitudinal compartments or spatial cells, the length of each cell representing the upstream-downstream range over which predators can forage. In this series of models a 100-km-long reservoir is divided, successively into 2, 5, 10, 25, 50, 100, 200, and 400 cells, with respective cell lengths of 50, 20, 10, 4, 2, 1, 0.5, and 0.25 km. We used a detailed individual-based simulation model at first, but to ensure robustness of results we supplemented this with a simple analytic model. Both models showed sharp differences in the predicted mortality to a compact pulse of smolt prey moving through the reservoir, depending on the number of spatial cells in the model. In particular, models with fewer than about 10 cells vastly overpredicted the amount of mortality due to predators with activity ranges of not more than a few kilometers. These results corroborate recent theoretical and simulation studies on the importance of spatial scale and behavior in modeling predator-prey dynamics.

LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons.

PubMed

Lindén, Henrik; Hagen, Espen; Lęski, Szymon; Norheim, Eivind S; Pettersen, Klas H; Einevoll, Gaute T

2013-01-01

Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

Cell adhesion heterogeneity reinforces tumour cell dissemination: novel insights from a mathematical model.

PubMed

Reher, David; Klink, Barbara; Deutsch, Andreas; Voss-Böhme, Anja

2017-08-11

Cancer cell invasion, dissemination, and metastasis have been linked to an epithelial-mesenchymal transition (EMT) of individual tumour cells. During EMT, adhesion molecules like E-cadherin are downregulated and the decrease of cell-cell adhesion allows tumour cells to dissociate from the primary tumour mass. This complex process depends on intracellular cues that are subject to genetic and epigenetic variability, as well as extrinsic cues from the local environment resulting in a spatial heterogeneity in the adhesive phenotype of individual tumour cells. Here, we use a novel mathematical model to study how adhesion heterogeneity, influenced by intrinsic and extrinsic factors, affects the dissemination of tumour cells from an epithelial cell population. The model is a multiscale cellular automaton that couples intracellular adhesion receptor regulation with cell-cell adhesion. Simulations of our mathematical model indicate profound effects of adhesion heterogeneity on tumour cell dissemination. In particular, we show that a large variation of intracellular adhesion receptor concentrations in a cell population reinforces cell dissemination, regardless of extrinsic cues mediated through the local cell density. However, additional control of adhesion receptor concentration through the local cell density, which can be assumed in healthy cells, weakens the effect. Furthermore, we provide evidence that adhesion heterogeneity can explain the remarkable differences in adhesion receptor concentrations of epithelial and mesenchymal phenotypes observed during EMT and might drive early dissemination of tumour cells. Our results suggest that adhesion heterogeneity may be a universal trigger to reinforce cell dissemination in epithelial cell populations. This effect can be at least partially compensated by a control of adhesion receptor regulation through neighbouring cells. Accordingly, our findings explain how both an increase in intra-tumour adhesion heterogeneity and the loss of control through the local environment can promote tumour cell dissemination. This article was reviewed by Hanspeter Herzel, Thomas Dandekar and Marek Kimmel.

Limiting Energy Dissipation Induces Glassy Kinetics in Single-Cell High-Precision Responses

PubMed Central

Das, Jayajit

2016-01-01

Single cells often generate precise responses by involving dissipative out-of-thermodynamic-equilibrium processes in signaling networks. The available free energy to fuel these processes could become limited depending on the metabolic state of an individual cell. How does limiting dissipation affect the kinetics of high-precision responses in single cells? I address this question in the context of a kinetic proofreading scheme used in a simple model of early-time T cell signaling. Using exact analytical calculations and numerical simulations, I show that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-to-cell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and ergodicity breaking. Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, can create a fundamental difficulty in determining single-cell kinetics from cell-population results. PMID:26958894

Multicellular Computing Using Conjugation for Wiring

PubMed Central

Goñi-Moreno, Angel; Amos, Martyn; de la Cruz, Fernando

2013-01-01

Recent efforts in synthetic biology have focussed on the implementation of logical functions within living cells. One aim is to facilitate both internal “re-programming” and external control of cells, with potential applications in a wide range of domains. However, fundamental limitations on the degree to which single cells may be re-engineered have led to a growth of interest in multicellular systems, in which a “computation” is distributed over a number of different cell types, in a manner analogous to modern computer networks. Within this model, individual cell type perform specific sub-tasks, the results of which are then communicated to other cell types for further processing. The manner in which outputs are communicated is therefore of great significance to the overall success of such a scheme. Previous experiments in distributed cellular computation have used global communication schemes, such as quorum sensing (QS), to implement the “wiring” between cell types. While useful, this method lacks specificity, and limits the amount of information that may be transferred at any one time. We propose an alternative scheme, based on specific cell-cell conjugation. This mechanism allows for the direct transfer of genetic information between bacteria, via circular DNA strands known as plasmids. We design a multi-cellular population that is able to compute, in a distributed fashion, a Boolean XOR function. Through this, we describe a general scheme for distributed logic that works by mixing different strains in a single population; this constitutes an important advantage of our novel approach. Importantly, the amount of genetic information exchanged through conjugation is significantly higher than the amount possible through QS-based communication. We provide full computational modelling and simulation results, using deterministic, stochastic and spatially-explicit methods. These simulations explore the behaviour of one possible conjugation-wired cellular computing system under different conditions, and provide baseline information for future laboratory implementations. PMID:23840385

Oncolytic potency and reduced virus tumor-specificity in oncolytic virotherapy. A mathematical modelling approach.

PubMed

Mahasa, Khaphetsi Joseph; Eladdadi, Amina; de Pillis, Lisette; Ouifki, Rachid

2017-01-01

In the present paper, we address by means of mathematical modeling the following main question: How can oncolytic virus infection of some normal cells in the vicinity of tumor cells enhance oncolytic virotherapy? We formulate a mathematical model describing the interactions between the oncolytic virus, the tumor cells, the normal cells, and the antitumoral and antiviral immune responses. The model consists of a system of delay differential equations with one (discrete) delay. We derive the model's basic reproductive number within tumor and normal cell populations and use their ratio as a metric for virus tumor-specificity. Numerical simulations are performed for different values of the basic reproduction numbers and their ratios to investigate potential trade-offs between tumor reduction and normal cells losses. A fundamental feature unravelled by the model simulations is its great sensitivity to parameters that account for most variation in the early or late stages of oncolytic virotherapy. From a clinical point of view, our findings indicate that designing an oncolytic virus that is not 100% tumor-specific can increase virus particles, which in turn, can further infect tumor cells. Moreover, our findings indicate that when infected tissues can be regenerated, oncolytic viral infection of normal cells could improve cancer treatment.

Sporadic on/off switching of HTLV-1 Tax expression is crucial to maintain the whole population of virus-induced leukemic cells

PubMed Central

Mahgoub, Mohamed; Iwami, Shingo; Nakaoka, Shinji; Koizumi, Yoshiki; Shimura, Kazuya; Matsuoka, Masao

2018-01-01

Viruses causing chronic infection artfully manipulate infected cells to enable viral persistence in vivo under the pressure of immunity. Human T-cell leukemia virus type 1 (HTLV-1) establishes persistent infection mainly in CD4+ T cells in vivo and induces leukemia in this subset. HTLV-1–encoded Tax is a critical transactivator of viral replication and a potent oncoprotein, but its significance in pathogenesis remains obscure due to its very low level of expression in vivo. Here, we show that Tax is expressed in a minor fraction of leukemic cells at any given time, and importantly, its expression spontaneously switches between on and off states. Live cell imaging revealed that the average duration of one episode of Tax expression is ∼19 hours. Knockdown of Tax rapidly induced apoptosis in most cells, indicating that Tax is critical for maintaining the population, even if its short-term expression is limited to a small subpopulation. Single-cell analysis and computational simulation suggest that transient Tax expression triggers antiapoptotic machinery, and this effect continues even after Tax expression is diminished; this activation of the antiapoptotic machinery is the critical event for maintaining the population. In addition, Tax is induced by various cytotoxic stresses and also promotes HTLV-1 replication. Thus, it seems that Tax protects infected cells from apoptosis and increases the chance of viral transmission at a critical moment. Keeping the expression of Tax minimal but inducible on demand is, therefore, a fundamental strategy of HTLV-1 to promote persistent infection and leukemogenesis. PMID:29358408

A two-scale model for correlation between B cell VDJ usage in zebrafish

NASA Astrophysics Data System (ADS)

Pan, Keyao; Deem, Michael

2011-03-01

The zebrafish (Danio rerio) is one of the model animals for study of immunology. The dynamics of the adaptive immune system in zebrafish is similar to that in higher animals. In this work, we built a two-scale model to simulate the dynamics of B cells in primary and secondary immune reactions in zebrafish and to explain the reported correlation between VDJ usage of B cell repertoires in distinct zebrafish. The first scale of the model consists of a generalized NK model to simulate the B cell maturation process in the 10-day primary immune response. The second scale uses a delay ordinary differential equation system to model the immune responses in the 6-month lifespan of zebrafish. The generalized NK model shows that mature B cells specific to one antigen mostly possess a single VDJ recombination. The probability that mature B cells in two zebrafish have the same VDJ recombination increases with the B cell population size or the B cell selection intensity and decreases with the B cell hypermutation rate. The ODE model shows a distribution of correlation in the VDJ usage of the B cell repertoires in two six-month-old zebrafish that is highly similar to that from experiment. This work presents a simple theory to explain the experimentally observed correlation in VDJ usage of distinct zebrafish B cell repertoires after an immune response.

Slow and fast dynamics model of a Malaria with Sickle-Cell genetic disease with multi-stage infections of the mosquitoes population

NASA Astrophysics Data System (ADS)

Dewi Siawanta, Shanti; Adi-Kusumo, Fajar; Irwan Endrayanto, Aluicius

2018-03-01

Malaria, which is caused by Plasmodium, is a common disease in tropical areas. There are three types of Plasmodium i.e. Plasmodium Vivax, Plasmodium Malariae, and Plasmodium Falciparum. The most dangerous cases of the Malaria are mainly caused by the Plasmodium Falciparum. One of the important characteristics for the Plasmodium infection is due to the immunity of erythrocyte that contains HbS (Haemoglobin Sickle-cell) genes. The individuals who has the HbS gene has better immunity against the disease. In this paper, we consider a model that shows the spread of malaria involving the interaction between the mosquitos population, the human who has HbS genes population and the human with normal gene population. We do some analytical and numerical simulation to study the basic reproduction ratio and the slow-fast dynamics of the phase-portrait. The slow dynamics in our model represents the response of the human population with HbS gene to the Malaria disease while the fast dynamics show the response of the human population with the normal gene to the disease. The slow and fast dynamics phenomena are due to the fact that the population of the individuals who have HbS gene is much smaller than the individuals who has normal genes.

Population Pharmacokinetic and Pharmacodynamic Model-Based Comparability Assessment of a Recombinant Human Epoetin Alfa and the Biosimilar HX575

PubMed Central

Yan, Xiaoyu; Lowe, Philip J.; Fink, Martin; Berghout, Alexander; Balser, Sigrid; Krzyzanski, Wojciech

2012-01-01

The aim of this study was to develop an integrated pharmacokinetic and pharmacodynamic (PK/PD) model and assess the comparability between epoetin alfa HEXAL/Binocrit (HX575) and a comparator epoetin alfa by a model-based approach. PK/PD data—including serum drug concentrations, reticulocyte counts, red blood cells, and hemoglobin levels—were obtained from 2 clinical studies. In sum, 149 healthy men received multiple intravenous or subcutaneous doses of HX575 (100 IU/kg) and the comparator 3 times a week for 4 weeks. A population model based on pharmacodynamics-mediated drug disposition and cell maturation processes was used to characterize the PK/PD data for the 2 drugs. Simulations showed that due to target amount changes, total clearance may increase up to 2.4-fold as compared with the baseline. Further simulations suggested that once-weekly and thrice-weekly subcutaneous dosing regimens would result in similar efficacy. The findings from the model-based analysis were consistent with previous results using the standard noncompartmental approach demonstrating PK/PD comparability between HX575 and comparator. However, due to complexity of the PK/PD model, control of random effects was not straightforward. Whereas population PK/PD model-based analyses are suited for studying complex biological systems, such models have their limitations (statistical), and their comparability results should be interpreted carefully. PMID:22162538

Dynamics of tissue topology during cancer invasion and metastasis

NASA Astrophysics Data System (ADS)

Munn, Lance L.

2013-12-01

During tumor progression, cancer cells mix with other cell populations including epithelial and endothelial cells. Although potentially important clinically as well as for our understanding of basic tumor biology, the process of mixing is largely a mystery. Furthermore, there is no rigorous, analytical measure available for quantifying the mixing of compartments within a tumor. I present here a mathematical model of tissue repair and tumor growth based on collective cell migration that simulates a wide range of observed tumor behaviors with correct tissue compartmentalization and connectivity. The resulting dynamics are analyzed in light of the Euler characteristic number (χ), which describes key topological features such as fragmentation, looping and cavities. The analysis predicts a number of regimes in which the cancer cells can encapsulate normal tissue, form a co-interdigitating mass, or become fragmented and encapsulated by endothelial or epithelial structures. Key processes that affect the topological changes are the production of provisional matrix in the tumor, and the migration of endothelial or epithelial cells on this matrix. Furthermore, the simulations predict that topological changes during tumor invasion into blood vessels may contribute to metastasis. The topological analysis outlined here could be useful for tumor diagnosis or monitoring response to therapy and would only require high resolution, 3D image data to resolve and track the various cell compartments.

Invasion emerges from cancer cell adaptation to competitive microenvironments: Quantitative predictions from multiscale mathematical models

PubMed Central

Rejniak, Katarzyna A.; Gerlee, Philip

2013-01-01

Summary In this review we summarize our recent efforts using mathematical modeling and computation to simulate cancer invasion, with a special emphasis on the tumor microenvironment. We consider cancer progression as a complex multiscale process and approach it with three single-cell based mathematical models that examine the interactions between tumor microenvironment and cancer cells at several scales. The models exploit distinct mathematical and computational techniques, yet they share core elements and can be compared and/or related to each other. The overall aim of using mathematical models is to uncover the fundamental mechanisms that lend cancer progression its direction towards invasion and metastasis. The models effectively simulate various modes of cancer cell adaptation to the microenvironment in a growing tumor. All three point to a general mechanism underlying cancer invasion: competition for adaptation between distinct cancer cell phenotypes, driven by a tumor microenvironment with scarce resources. These theoretical predictions pose an intriguing experimental challenge: test the hypothesis that invasion is an emergent property of cancer cell populations adapting to selective microenvironment pressure, rather than culmination of cancer progression producing cells with the “invasive phenotype”. In broader terms, we propose that fundamental insights into cancer can be achieved by experimentation interacting with theoretical frameworks provided by computational and mathematical modeling. PMID:18524624

Solving the puzzle of yeast survival in ephemeral nectar systems: exponential growth is not enough.

PubMed

Hausmann, Sebastian L; Tietjen, Britta; Rillig, Matthias C

2017-12-01

Flower nectar is a sugar-rich ephemeral habitat for microorganisms. Nectar-borne yeasts are part of the microbial community and can affect pollination by changing nectar chemistry, attractiveness to pollinators or flower temperature if yeast population densities are high. Pollinators act as dispersal agents in this system; however, pollination events lead potentially to shrinking nectar yeast populations. We here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. We found that cell densities only reached an ecologically relevant size for an intermediate pollination probability. Too few pollination events reduce yeast inoculation rate and too many reduce yeast population size strongly. In addition, nectar yeasts need a trait combination of at least an intermediate growth rate and an intermediate remaining fraction to compensate for highly frequent decimations. Our results can be used to predict nectar yeast dispersal, growth and consequently their ecological effects. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Biomimicry of quorum sensing using bacterial lifecycle model.

PubMed

Niu, Ben; Wang, Hong; Duan, Qiqi; Li, Li

2013-01-01

Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems.

Biomimicry of quorum sensing using bacterial lifecycle model

PubMed Central

2013-01-01

Background Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. Results In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Conclusions Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems. PMID:23815296

Human Perivascular Stem Cell-Based Bone Graft Substitute Induces Rat Spinal Fusion

PubMed Central

Chung, Choon G.; James, Aaron W.; Asatrian, Greg; Chang, Le; Nguyen, Alan; Le, Khoi; Bayani, Georgina; Lee, Robert; Stoker, David; Zhang, Xinli

2014-01-01

Adipose tissue is an attractive source of mesenchymal stem cells (MSCs) because of its abundance and accessibility. We have previously defined a population of native MSCs termed perivascular stem cells (PSCs), purified from diverse human tissues, including adipose tissue. Human PSCs (hPSCs) are a bipartite cell population composed of pericytes (CD146+CD34−CD45−) and adventitial cells (CD146−CD34+CD45−), isolated by fluorescence-activated cell sorting and with properties identical to those of culture identified MSCs. Our previous studies showed that hPSCs exhibit improved bone formation compared with a sample-matched unpurified population (termed stromal vascular fraction); however, it is not known whether hPSCs would be efficacious in a spinal fusion model. To investigate, we evaluated the osteogenic potential of freshly sorted hPSCs without culture expansion and differentiation in a rat model of posterolateral lumbar spinal fusion. We compared increasing dosages of implanted hPSCs to assess for dose-dependent efficacy. All hPSC treatment groups induced successful spinal fusion, assessed by manual palpation and microcomputed tomography. Computerized biomechanical simulation (finite element analysis) further demonstrated bone fusion with hPSC treatment. Histological analyses showed robust endochondral ossification in hPSC-treated samples. Finally, we confirmed that implanted hPSCs indeed differentiated into osteoblasts and osteocytes; however, the majority of the new bone formation was of host origin. These results suggest that implanted hPSCs positively regulate bone formation via direct and paracrine mechanisms. In summary, hPSCs are a readily available MSC population that effectively forms bone without requirements for culture or predifferentiation. Thus, hPSC-based products show promise for future efforts in clinical bone regeneration and repair. PMID:25154782

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

PubMed Central

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J.

2014-01-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity. PMID:24992156

An accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems using gradient-based diffusion and tau-leaping.

PubMed

Koh, Wonryull; Blackwell, Kim T

2011-04-21

Stochastic simulation of reaction-diffusion systems enables the investigation of stochastic events arising from the small numbers and heterogeneous distribution of molecular species in biological cells. Stochastic variations in intracellular microdomains and in diffusional gradients play a significant part in the spatiotemporal activity and behavior of cells. Although an exact stochastic simulation that simulates every individual reaction and diffusion event gives a most accurate trajectory of the system's state over time, it can be too slow for many practical applications. We present an accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems designed to improve the speed of simulation by reducing the number of time-steps required to complete a simulation run. This method is unique in that it employs two strategies that have not been incorporated in existing spatial stochastic simulation algorithms. First, diffusive transfers between neighboring subvolumes are based on concentration gradients. This treatment necessitates sampling of only the net or observed diffusion events from higher to lower concentration gradients rather than sampling all diffusion events regardless of local concentration gradients. Second, we extend the non-negative Poisson tau-leaping method that was originally developed for speeding up nonspatial or homogeneous stochastic simulation algorithms. This method calculates each leap time in a unified step for both reaction and diffusion processes while satisfying the leap condition that the propensities do not change appreciably during the leap and ensuring that leaping does not cause molecular populations to become negative. Numerical results are presented that illustrate the improvement in simulation speed achieved by incorporating these two new strategies.

Peptide crystal simulations reveal hidden dynamics

PubMed Central

Janowski, Pawel A.; Cerutti, David S.; Holton, James; Case, David A.

2013-01-01

Molecular dynamics simulations of biomolecular crystals at atomic resolution have the potential to recover information on dynamics and heterogeneity hidden in the X-ray diffraction data. We present here 9.6 microseconds of dynamics in a small helical peptide crystal with 36 independent copies of the unit cell. The average simulation structure agrees with experiment to within 0.28 Å backbone and 0.42 Å all-atom rmsd; a model refined against the average simulation density agrees with the experimental structure to within 0.20 Å backbone and 0.33 Å all-atom rmsd. The R-factor between the experimental structure factors and those derived from this unrestrained simulation is 23% to 1.0 Å resolution. The B-factors for most heavy atoms agree well with experiment (Pearson correlation of 0.90), but B-factors obtained by refinement against the average simulation density underestimate the coordinate fluctuations in the underlying simulation where the simulation samples alternate conformations. A dynamic flow of water molecules through channels within the crystal lattice is observed, yet the average water density is in remarkable agreement with experiment. A minor population of unit cells is characterized by reduced water content, 310 helical propensity and a gauche(−) side-chain rotamer for one of the valine residues. Careful examination of the experimental data suggests that transitions of the helices are a simulation artifact, although there is indeed evidence for alternate valine conformers and variable water content. This study highlights the potential for crystal simulations to detect dynamics and heterogeneity in experimental diffraction data, as well as to validate computational chemistry methods. PMID:23631449

cgCorrect: a method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

NASA Astrophysics Data System (ADS)

Blasi, Thomas; Buettner, Florian; Strasser, Michael K.; Marr, Carsten; Theis, Fabian J.

2017-06-01

Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue is differences in cell size, which introduce additional variability into the data and for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell-growth-corrected mRNA transcript number given the measured, cell size-dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportionally to the cell’s volume during the cell cycle. cgCorrect can be used for both data normalization and to analyze the steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time polymerase chain reaction (PCR) data from mouse blood stem and progenitor cells (and to quantitative single-cell RNA-sequencing data obtained from mouse embryonic stem cells). We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.

Collective dynamics of cell migration and cell rearrangements

NASA Astrophysics Data System (ADS)

Kabla, Alexandre

Understanding multicellular processes such as embryo development or cancer metastasis requires to decipher the contributions of local cell autonomous behaviours and long range interactions with the tissue environment. A key question in this context concerns the emergence of large scale coordination in cell behaviours, a requirement for collective cell migration or convergent extension. I will present a few examples where physical and mechanical aspects play a significant role in driving tissue scale dynamics.

Microfluidic device capable of medium recirculation for non-adherent cell culture

PubMed Central

Dixon, Angela R.; Rajan, Shrinidhi; Kuo, Chuan-Hsien; Bersano, Tom; Wold, Rachel; Futai, Nobuyuki; Takayama, Shuichi; Mehta, Geeta

2014-01-01

We present a microfluidic device designed for maintenance and culture of non-adherent mammalian cells, which enables both recirculation and refreshing of medium, as well as easy harvesting of cells from the device. We demonstrate fabrication of a novel microfluidic device utilizing Braille perfusion for peristaltic fluid flow to enable switching between recirculation and refresh flow modes. Utilizing fluid flow simulations and the human promyelocytic leukemia cell line, HL-60, non-adherent cells, we demonstrate the utility of this RECIR-REFRESH device. With computer simulations, we profiled fluid flow and concentration gradients of autocrine factors and found that the geometry of the cell culture well plays a key role in cell entrapping and retaining autocrine and soluble factors. We subjected HL-60 cells, in the device, to a treatment regimen of 1.25% dimethylsulfoxide, every other day, to provoke differentiation and measured subsequent expression of CD11b on day 2 and day 4 and tumor necrosis factor-alpha (TNF-α) on day 4. Our findings display perfusion sensitive CD11b expression, but not TNF-α build-up, by day 4 of culture, with a 1:1 ratio of recirculation to refresh flow yielding the greatest increase in CD11b levels. RECIR-REFRESH facilitates programmable levels of cell differentiation in a HL-60 non-adherent cell population and can be expanded to other types of non-adherent cells such as hematopoietic stem cells. PMID:24753733

Sporadic on/off switching of HTLV-1 Tax expression is crucial to maintain the whole population of virus-induced leukemic cells.

PubMed

Mahgoub, Mohamed; Yasunaga, Jun-Ichirou; Iwami, Shingo; Nakaoka, Shinji; Koizumi, Yoshiki; Shimura, Kazuya; Matsuoka, Masao

2018-02-06

Viruses causing chronic infection artfully manipulate infected cells to enable viral persistence in vivo under the pressure of immunity. Human T-cell leukemia virus type 1 (HTLV-1) establishes persistent infection mainly in CD4+ T cells in vivo and induces leukemia in this subset. HTLV-1-encoded Tax is a critical transactivator of viral replication and a potent oncoprotein, but its significance in pathogenesis remains obscure due to its very low level of expression in vivo. Here, we show that Tax is expressed in a minor fraction of leukemic cells at any given time, and importantly, its expression spontaneously switches between on and off states. Live cell imaging revealed that the average duration of one episode of Tax expression is ∼19 hours. Knockdown of Tax rapidly induced apoptosis in most cells, indicating that Tax is critical for maintaining the population, even if its short-term expression is limited to a small subpopulation. Single-cell analysis and computational simulation suggest that transient Tax expression triggers antiapoptotic machinery, and this effect continues even after Tax expression is diminished; this activation of the antiapoptotic machinery is the critical event for maintaining the population. In addition, Tax is induced by various cytotoxic stresses and also promotes HTLV-1 replication. Thus, it seems that Tax protects infected cells from apoptosis and increases the chance of viral transmission at a critical moment. Keeping the expression of Tax minimal but inducible on demand is, therefore, a fundamental strategy of HTLV-1 to promote persistent infection and leukemogenesis. Copyright © 2018 the Author(s). Published by PNAS.

Sensitization prevalence, antibody cross-reactivity and immunogenic peptide profile of Api g 2, the non-specific lipid transfer protein 1 of celery.

PubMed

Gadermaier, Gabriele; Hauser, Michael; Egger, Matthias; Ferrara, Rosetta; Briza, Peter; Santos, Keity Souza; Zennaro, Danila; Girbl, Tamara; Zuidmeer-Jongejan, Laurian; Mari, Adriano; Ferreira, Fatima

2011-01-01

Celery (Apium graveolens) represents a relevant allergen source that can elicit severe reactions in the adult population. To investigate the sensitization prevalence and cross-reactivity of Api g 2 from celery stalks in a Mediterranean population and in a mouse model. 786 non-randomized subjects from Italy were screened for IgE reactivity to rApi g 2, rArt v 3 (mugwort pollen LTP) and nPru p 3 (peach LTP) using an allergen microarray. Clinical data of 32 selected patients with reactivity to LTP under investigation were evaluated. Specific IgE titers and cross-inhibitions were performed in ELISA and allergen microarray. Balb/c mice were immunized with purified LTPs; IgG titers were determined in ELISA and mediator release was examined using RBL-2H3 cells. Simulated endolysosomal digestion was performed using microsomes obtained from human DCs. IgE testing showed a sensitization prevalence of 25.6% to Api g 2, 18.6% to Art v 3, and 28.6% to Pru p 3 and frequent co-sensitization and correlating IgE-reactivity was observed. 10/32 patients suffering from LTP-related allergy reported symptoms upon consumption of celery stalks which mainly presented as OAS. Considerable IgE cross-reactivity was observed between Api g 2, Art v 3, and Pru p 3 with varying inhibition degrees of individual patients' sera. Simulating LTP mono-sensitization in a mouse model showed development of more congruent antibody specificities between Api g 2 and Art v 3. Notably, biologically relevant murine IgE cross-reactivity was restricted to the latter and diverse from Pru p 3 epitopes. Endolysosomal processing of LTP showed generation of similar clusters, which presumably represent T-cell peptides. Api g 2 represents a relevant celery stalk allergen in the LTP-sensitized population. The molecule displays common B cell epitopes and endolysosomal peptides that encompass T cell epitopes with pollen and plant-food derived LTP.

The virtual slice setup.

PubMed

Lytton, William W; Neymotin, Samuel A; Hines, Michael L

2008-06-30

In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. The virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. The virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. By default, a summed population display is shown during a run to indicate the level of activity and no states are saved. Simulations can run for hours of model time, therefore it is not practical to save all of the state variables. These, in any case, are primarily of interest at discrete times when experiments are being run: the simulation can be stopped momentarily at such times to save activity patterns. The virtual slice setup maintains an automated notebook showing shocks and parameter changes as well as user comments. We demonstrate how interaction with a continuously running simulation encourages experimental prototyping and can suggest additional dynamical features such as ligand wash-in and wash-out-alternatives to typical instantaneous parameter change. The virtual slice setup currently uses event-driven cells and runs at approximately 2 min/h on a laptop.

Dual-modality wide-field photothermal quantitative phase microscopy and depletion of cell populations

NASA Astrophysics Data System (ADS)

Turko, Nir A.; Barnea, Itay; Blum, Omry; Korenstein, Rafi; Shaked, Natan T.

2015-03-01

We review our dual-modality technique for quantitative imaging and selective depletion of populations of cells based on wide-field photothermal (PT) quantitative phase imaging and simultaneous PT cell extermination. The cells are first labeled by plasmonic gold nanoparticles, which evoke local plasmonic resonance when illuminated by light in a wavelength corresponding to their specific plasmonic resonance peak. This reaction creates changes of temperature, resulting in changes of phase. This phase changes are recorded by a quantitative phase microscope (QPM), producing specific imaging contrast, and enabling bio-labeling in phase microscopy. Using this technique, we have shown discrimination of EGFR over-expressing (EGFR+) cancer cells from EGFR under-expressing (EGFR-) cancer cells. Then, we have increased the excitation power in order to evoke greater temperatures, which caused specific cell death, all under real-time phase acquisition using QPM. Close to 100% of all EGFR+ cells were immediately exterminated when illuminated with the strong excitation beam, while all EGFR- cells survived. For the second experiment, in order to simulate a condition where circulating tumor cells (CTCs) are present in blood, we have mixed the EGFR+ cancer cells with white blood cells (WBCs) from a healthy donor. Here too, we have used QPM to observe and record the phase of the cells as they were excited for selective visualization and then exterminated. The WBCs survival rate was over 95%, while the EGFR+ survival rate was under 5%. The technique may be the basis for real-time detection and controlled treatment of CTCs.

Examining the Relationship between Pre-Malignant Breast Lesions, Carcinogenesis and Tumor Evolution in the Mammary Epithelium Using an Agent-Based Model.

PubMed

Chapa, Joaquin; An, Gary; Kulkarni, Swati A

2016-01-01

Breast cancer, the product of numerous rare mutational events that occur over an extended time period, presents numerous challenges to investigators interested in studying the transformation from normal breast epithelium to malignancy using traditional laboratory methods, particularly with respect to characterizing transitional and pre-malignant states. Dynamic computational modeling can provide insight into these pathophysiological dynamics, and as such we use a previously validated agent-based computational model of the mammary epithelium (the DEABM) to investigate the probabilistic mechanisms by which normal populations of ductal cells could transform into states replicating features of both pre-malignant breast lesions and a diverse set of breast cancer subtypes. The DEABM consists of simulated cellular populations governed by algorithms based on accepted and previously published cellular mechanisms. Cells respond to hormones, undergo mitosis, apoptosis and cellular differentiation. Heritable mutations to 12 genes prominently implicated in breast cancer are acquired via a probabilistic mechanism. 3000 simulations of the 40-year period of menstrual cycling were run in wild-type (WT) and BRCA1-mutated groups. Simulations were analyzed by development of hyperplastic states, incidence of malignancy, hormone receptor and HER-2 status, frequency of mutation to particular genes, and whether mutations were early events in carcinogenesis. Cancer incidence in WT (2.6%) and BRCA1-mutated (45.9%) populations closely matched published epidemiologic rates. Hormone receptor expression profiles in both WT and BRCA groups also closely matched epidemiologic data. Hyperplastic populations carried more mutations than normal populations and mutations were similar to early mutations found in ER+ tumors (telomerase, E-cadherin, TGFB, RUNX3, p < .01). ER- tumors carried significantly more mutations and carried more early mutations in BRCA1, c-MYC and genes associated with epithelial-mesenchymal transition. The DEABM generates diverse tumors that express tumor markers consistent with epidemiologic data. The DEABM also generates non-invasive, hyperplastic populations, analogous to atypia or ductal carcinoma in situ (DCIS), via mutations to genes known to be present in hyperplastic lesions and as early mutations in breast cancers. The results demonstrate that agent-based models are well-suited to studying tumor evolution through stages of carcinogenesis and have the potential to be used to develop prevention and treatment strategies.

Discrete stochastic simulation methods for chemically reacting systems.

PubMed

Cao, Yang; Samuels, David C

2009-01-01

Discrete stochastic chemical kinetics describe the time evolution of a chemically reacting system by taking into account the fact that, in reality, chemical species are present with integer populations and exhibit some degree of randomness in their dynamical behavior. In recent years, with the development of new techniques to study biochemistry dynamics in a single cell, there are increasing studies using this approach to chemical kinetics in cellular systems, where the small copy number of some reactant species in the cell may lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. This chapter reviews the fundamental theory related to stochastic chemical kinetics and several simulation methods based on that theory. We focus on nonstiff biochemical systems and the two most important discrete stochastic simulation methods: Gillespie's stochastic simulation algorithm (SSA) and the tau-leaping method. Different implementation strategies of these two methods are discussed. Then we recommend a relatively simple and efficient strategy that combines the strengths of the two methods: the hybrid SSA/tau-leaping method. The implementation details of the hybrid strategy are given here and a related software package is introduced. Finally, the hybrid method is applied to simple biochemical systems as a demonstration of its application.

Optical control demonstrates switch-like PIP3 dynamics underlying the initiation of immune cell migration

PubMed Central

Karunarathne, W. K. Ajith; Giri, Lopamudra; Patel, Anilkumar K.; Venkatesh, Kareenhalli V.; Gautam, N.

2013-01-01

There is a dearth of approaches to experimentally direct cell migration by continuously varying signal input to a single cell, evoking all possible migratory responses and quantitatively monitoring the cellular and molecular response dynamics. Here we used a visual blue opsin to recruit the endogenous G-protein network that mediates immune cell migration. Specific optical inputs to this optical trigger of signaling helped steer migration in all possible directions with precision. Spectrally selective imaging was used to monitor cell-wide phosphatidylinositol (3,4,5)-triphosphate (PIP3), cytoskeletal, and cellular dynamics. A switch-like PIP3 increase at the cell front and a decrease at the back were identified, underlying the decisive migratory response. Migration was initiated at the rapidly increasing switch stage of PIP3 dynamics. This result explains how a migratory cell filters background fluctuations in the intensity of an extracellular signal but responds by initiating directionally sensitive migration to a persistent signal gradient across the cell. A two-compartment computational model incorporating a localized activator that is antagonistic to a diffusible inhibitor was able to simulate the switch-like PIP3 response. It was also able simulate the slow dissipation of PIP3 on signal termination. The ability to independently apply similar signaling inputs to single cells detected two cell populations with distinct thresholds for migration initiation. Overall the optical approach here can be applied to understand G-protein–coupled receptor network control of other cell behaviors. PMID:23569254

Optical control demonstrates switch-like PIP3 dynamics underlying the initiation of immune cell migration.

PubMed

Karunarathne, W K Ajith; Giri, Lopamudra; Patel, Anilkumar K; Venkatesh, Kareenhalli V; Gautam, N

2013-04-23

There is a dearth of approaches to experimentally direct cell migration by continuously varying signal input to a single cell, evoking all possible migratory responses and quantitatively monitoring the cellular and molecular response dynamics. Here we used a visual blue opsin to recruit the endogenous G-protein network that mediates immune cell migration. Specific optical inputs to this optical trigger of signaling helped steer migration in all possible directions with precision. Spectrally selective imaging was used to monitor cell-wide phosphatidylinositol (3,4,5)-triphosphate (PIP3), cytoskeletal, and cellular dynamics. A switch-like PIP3 increase at the cell front and a decrease at the back were identified, underlying the decisive migratory response. Migration was initiated at the rapidly increasing switch stage of PIP3 dynamics. This result explains how a migratory cell filters background fluctuations in the intensity of an extracellular signal but responds by initiating directionally sensitive migration to a persistent signal gradient across the cell. A two-compartment computational model incorporating a localized activator that is antagonistic to a diffusible inhibitor was able to simulate the switch-like PIP3 response. It was also able simulate the slow dissipation of PIP3 on signal termination. The ability to independently apply similar signaling inputs to single cells detected two cell populations with distinct thresholds for migration initiation. Overall the optical approach here can be applied to understand G-protein-coupled receptor network control of other cell behaviors.

Multi-scale modeling of the CD8 immune response

NASA Astrophysics Data System (ADS)

Barbarroux, Loic; Michel, Philippe; Adimy, Mostafa; Crauste, Fabien

2016-06-01

During the primary CD8 T-Cell immune response to an intracellular pathogen, CD8 T-Cells undergo exponential proliferation and continuous differentiation, acquiring cytotoxic capabilities to address the infection and memorize the corresponding antigen. After cleaning the organism, the only CD8 T-Cells left are antigen-specific memory cells whose role is to respond stronger and faster in case they are presented this very same antigen again. That is how vaccines work: a small quantity of a weakened pathogen is introduced in the organism to trigger the primary response, generating corresponding memory cells in the process, giving the organism a way to defend himself in case it encounters the same pathogen again. To investigate this process, we propose a non linear, multi-scale mathematical model of the CD8 T-Cells immune response due to vaccination using a maturity structured partial differential equation. At the intracellular scale, the level of expression of key proteins is modeled by a delay differential equation system, which gives the speeds of maturation for each cell. The population of cells is modeled by a maturity structured equation whose speeds are given by the intracellular model. We focus here on building the model, as well as its asymptotic study. Finally, we display numerical simulations showing the model can reproduce the biological dynamics of the cell population for both the primary response and the secondary responses.

Multi-scale modeling of the CD8 immune response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbarroux, Loic, E-mail: loic.barbarroux@doctorant.ec-lyon.fr; Ecole Centrale de Lyon, 36 avenue Guy de Collongue, 69134 Ecully; Michel, Philippe, E-mail: philippe.michel@ec-lyon.fr

During the primary CD8 T-Cell immune response to an intracellular pathogen, CD8 T-Cells undergo exponential proliferation and continuous differentiation, acquiring cytotoxic capabilities to address the infection and memorize the corresponding antigen. After cleaning the organism, the only CD8 T-Cells left are antigen-specific memory cells whose role is to respond stronger and faster in case they are presented this very same antigen again. That is how vaccines work: a small quantity of a weakened pathogen is introduced in the organism to trigger the primary response, generating corresponding memory cells in the process, giving the organism a way to defend himself inmore » case it encounters the same pathogen again. To investigate this process, we propose a non linear, multi-scale mathematical model of the CD8 T-Cells immune response due to vaccination using a maturity structured partial differential equation. At the intracellular scale, the level of expression of key proteins is modeled by a delay differential equation system, which gives the speeds of maturation for each cell. The population of cells is modeled by a maturity structured equation whose speeds are given by the intracellular model. We focus here on building the model, as well as its asymptotic study. Finally, we display numerical simulations showing the model can reproduce the biological dynamics of the cell population for both the primary response and the secondary responses.« less

E. coli chemotaxis and super-diffusion

NASA Astrophysics Data System (ADS)

Dobnikar, Jure; Matthäus, Franziska; Jagodic, Marko

2010-03-01

The bacteria E. coli actively propel by switching between clockwise and anti-clockwise rotation of the flagella attached to their cell membranes. This results in two modes of motion: tumbling and swimming. The switching between the two modes is coupled to the ligand sensing through the chemotactic signalling pathway inside the cell. We modelled the signalling pathway and performed numerical simulations of the chemotactic motion of a large number of E. coli bacteria under various external conditions. We have shown that under certain conditions the thermal noise in the level of receptor-bound CheR (an enzyme responsible for methylation of the receptor sites) leads to super-diffusive behaviour (L'evy walk) which is advantageous for the bacterial populations in environments with scarce food. Exerting external pressure we might observe evolution of the wild-type to the super-diffusive populations.

Role of differential physical properties in emergent behavior of 3D cell co-cultures

NASA Astrophysics Data System (ADS)

Kolbman, Dan; Das, Moumita

2015-03-01

The biophysics of binary cell populations is of great interest in many biological processes, whether the formation of embryos or the initiation of tumors. During these processes, cells are surrounded by other cell types with different physical properties, often with important consequences. For example, recent experiments on a co-culture of breast cancer cells and healthy breast epithelial cells suggest that the mechanical mismatch between the two cell types may contribute to enhanced migration of the cancer cells. Here we explore how the differential physical properties of different cell types may influence cell-cell interaction, aggregation, and migration. To this end, we study a proof of concept model- a three-dimensional binary system of interacting, active, and deformable particles with different physical properties such as elastic stiffness, contractility, and particle-particle adhesion, using Langevin Dynamics simulations. Our results may provide insights into emergent behavior such as segregation and differential migration in cell co-cultures in three dimensions.

The Impact of Prophage on the Equilibria and Stability of Phage and Host

NASA Astrophysics Data System (ADS)

Yu, Pei; Nadeem, Alina; Wahl, Lindi M.

2017-06-01

In this paper, we present a bacteriophage model that includes prophage, that is, phage genomes that are incorporated into the host cell genome. The general model is described by an 18-dimensional system of ordinary differential equations. This study focuses on asymptotic behaviour of the model, and thus the system is reduced to a simple six-dimensional model, involving uninfected host cells, infected host cells and phage. We use dynamical system theory to explore the dynamic behaviour of the model, studying in particular the impact of prophage on the equilibria and stability of phage and host. We employ bifurcation and stability theory, centre manifold and normal form theory to show that the system has multiple equilibrium solutions which undergo a series of bifurcations, finally leading to oscillating motions. Numerical simulations are presented to illustrate and confirm the analytical predictions. The results of this study indicate that in some parameter regimes, the host cell population may drive the phage to extinction through diversification, that is, if multiple types of host emerge; this prediction holds even if the phage population is likewise diverse. This parameter regime is restricted, however, if infecting phage are able to recombine with prophage sequences in the host cell genome.

20170312 - In Silico Dynamics: computer simulation in a ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or bioche

In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

The waiting time problem in a model hominin population.

PubMed

Sanford, John; Brewer, Wesley; Smith, Franzine; Baumgardner, John

2015-09-17

Functional information is normally communicated using specific, context-dependent strings of symbolic characters. This is true within the human realm (texts and computer programs), and also within the biological realm (nucleic acids and proteins). In biology, strings of nucleotides encode much of the information within living cells. How do such information-bearing nucleotide strings arise and become established? This paper uses comprehensive numerical simulation to understand what types of nucleotide strings can realistically be established via the mutation/selection process, given a reasonable timeframe. The program Mendel's Accountant realistically simulates the mutation/selection process, and was modified so that a starting string of nucleotides could be specified, and a corresponding target string of nucleotides could be specified. We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50% selective elimination). Random point mutations were generated within the starting string. Whenever an instance of the target string arose, all individuals carrying the target string were assigned a specified reproductive advantage. When natural selection had successfully amplified an instance of the target string to the point of fixation, the experiment was halted, and the waiting time statistics were tabulated. Using this methodology we tested the effect of mutation rate, string length, fitness benefit, and population size on waiting time to fixation. Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. To establish a string of two nucleotides required on average 84 million years. To establish a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive. We show that the waiting time problem is a significant constraint on the macroevolution of the classic hominin population. Routine establishment of specific beneficial strings of two or more nucleotides becomes very problematic.

Improving dynamic phytoplankton reserve-utilization models with an indirect proxy for internal nitrogen.

PubMed

Malerba, Martino E; Heimann, Kirsten; Connolly, Sean R

2016-09-07

Ecologists have often used indirect proxies to represent variables that are difficult or impossible to measure directly. In phytoplankton, the internal concentration of the most limiting nutrient in a cell determines its growth rate. However, directly measuring the concentration of nutrients within cells is inaccurate, expensive, destructive, and time-consuming, substantially impairing our ability to model growth rates in nutrient-limited phytoplankton populations. The red chlorophyll autofluorescence (hereafter "red fluorescence") signal emitted by a cell is highly correlated with nitrogen quota in nitrogen-limited phytoplankton species. The aim of this study was to evaluate the reliability of including flow cytometric red fluorescence as a proxy for internal nitrogen status to model phytoplankton growth rates. To this end, we used the classic Quota model and designed three approaches to calibrate its model parameters to data: where empirical observations on cell internal nitrogen quota were used to fit the model ("Nitrogen-Quota approach"), where quota dynamics were inferred only from changes in medium nutrient depletion and population density ("Virtual-Quota approach"), or where red fluorescence emission of a cell was used as an indirect proxy for its internal nitrogen quota ("Fluorescence-Quota approach"). Two separate analyses were carried out. In the first analysis, stochastic model simulations were parameterized from published empirical relationships and used to generate dynamics of phytoplankton communities reared under nitrogen-limited conditions. Quota models were fitted to the dynamics of each simulated species with the three different approaches and the performance of each model was compared. In the second analysis, we fit Quota models to laboratory time-series and we calculate the ability of each calibration approach to describe the observed trajectories of internal nitrogen quota in the culture. Results from both analyses concluded that the Fluorescence-Quota approach including per-cell red fluorescence as a proxy of internal nitrogen substantially improved the ability of Quota models to describe phytoplankton dynamics, while still accounting for the biologically important process of cell nitrogen storage. More broadly, many population models in ecology implicitly recognize the importance of accounting for storage mechanisms to describe the dynamics of individual organisms. Hence, the approach documented here with phytoplankton dynamics may also be useful for evaluating the potential of indirect proxies in other ecological systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Need for speed: An optimized gridding approach for spatially explicit disease simulations.

PubMed

Sellman, Stefan; Tsao, Kimberly; Tildesley, Michael J; Brommesson, Peter; Webb, Colleen T; Wennergren, Uno; Keeling, Matt J; Lindström, Tom

2018-04-01

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.

Need for speed: An optimized gridding approach for spatially explicit disease simulations

PubMed Central

Tildesley, Michael J.; Brommesson, Peter; Webb, Colleen T.; Wennergren, Uno; Lindström, Tom

2018-01-01

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power. PMID:29624574

Rapid Monte Carlo Simulation of Gravitational Wave Galaxies

NASA Astrophysics Data System (ADS)

Breivik, Katelyn; Larson, Shane L.

2015-01-01

With the detection of gravitational waves on the horizon, astrophysical catalogs produced by gravitational wave observatories can be used to characterize the populations of sources and validate different galactic population models. Efforts to simulate gravitational wave catalogs and source populations generally focus on population synthesis models that require extensive time and computational power to produce a single simulated galaxy. Monte Carlo simulations of gravitational wave source populations can also be used to generate observation catalogs from the gravitational wave source population. Monte Carlo simulations have the advantes of flexibility and speed, enabling rapid galactic realizations as a function of galactic binary parameters with less time and compuational resources required. We present a Monte Carlo method for rapid galactic simulations of gravitational wave binary populations.

Limiting Energy Dissipation Induces Glassy Kinetics in Single-Cell High-Precision Responses.

PubMed

Das, Jayajit

2016-03-08

Single cells often generate precise responses by involving dissipative out-of-thermodynamic-equilibrium processes in signaling networks. The available free energy to fuel these processes could become limited depending on the metabolic state of an individual cell. How does limiting dissipation affect the kinetics of high-precision responses in single cells? I address this question in the context of a kinetic proofreading scheme used in a simple model of early-time T cell signaling. Using exact analytical calculations and numerical simulations, I show that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-to-cell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and ergodicity breaking. Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, can create a fundamental difficulty in determining single-cell kinetics from cell-population results. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Determination of optimal parameters for dual-layer cathode of polymer electrolyte fuel cell using computational intelligence-aided design.

PubMed

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference η and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs.

A Multiscale Computational Model of the Response of Swine Epidermis After Acute Irradiation

NASA Technical Reports Server (NTRS)

Hu, Shaowen; Cucinotta, Francis A.

2012-01-01

Radiation exposure from Solar Particle Events can lead to very high skin dose for astronauts on exploration missions outside the protection of the Earth s magnetic field [1]. Assessing the detrimental effects to human skin under such adverse conditions could be predicted by conducting territorial experiments on animal models. In this study we apply a computational approach to simulate the experimental data of the radiation response of swine epidermis, which is closely similar to human epidermis [2]. Incorporating experimentally measured histological and cell kinetic parameters into a multiscale tissue modeling framework, we obtain results of population kinetics and proliferation index comparable to unirradiated and acutely irradiated swine experiments [3]. It is noted the basal cell doubling time is 10 to 16 days in the intact population, but drops to 13.6 hr in the regenerating populations surviving irradiation. This complex 30-fold variation is proposed to be attributed to the shortening of the G1 phase duration. We investigate this radiation induced effect by considering at the sub-cellular level the expression and signaling of TGF-beta, as it is recognized as a key regulatory factor of tissue formation and wound healing [4]. This integrated model will allow us to test the validity of various basic biological rules at the cellular level and sub-cellular mechanisms by qualitatively comparing simulation results with published research, and should lead to a fuller understanding of the pathophysiological effects of ionizing radiation on the skin.

Viability and Isolation of Marine Bacteria by Dilution Culture: Theory, Procedures, and Initial Results

PubMed Central

Button, D. K.; Schut, Frits; Quang, Pham; Martin, Ravonna; Robertson, Betsy R.

1993-01-01

Dilution culture, a method for growing the typical small bacteria from natural aquatic assemblages, has been developed. Each of 11 experimental trials of the technique was successful. Populations are measured, diluted to a small and known number of cells, inoculated into unamended sterilized seawater, and examined three times for the presence of 104 or more cells per ml over a 9-week interval. Mean viability for assemblage members is obtained from the frequency of growth, and many of the cultures produced are pure. Statistical formulations for determining viability and the frequency of pure culture production are derived. Formulations for associated errors are derived as well. Computer simulations of experiments agreed with computed values within the expected error, which verified the formulations. These led to strategies for optimizing viability determinations and pure culture production. Viabilities were usually between 2 and 60% and decreased with >5 mg of amino acids per liter as carbon. In view of difficulties in growing marine oligobacteria, these high values are noteworthy. Significant differences in population characteristics during growth, observed by high-resolution flow cytometry, suggested substantial population diversity. Growth of total populations as well as of cytometry-resolved subpopulations sometimes were truncated at levels of near 104 cells per ml, showing that viable cells could escape detection. Viability is therefore defined as the ability to grow to that population; true viabilities could be even higher. Doubling times, based on whole populations as well as individual subpopulations, were in the 1-day to 1-week range. Data were examined for changes in viability with dilution suggesting cell-cell interactions, but none could be confirmed. The frequency of pure culture production can be adjusted by inoculum size if the viability is known. These apparently pure cultures produced retained the size and apparent DNA-content characteristic of the bulk of the organisms in the parent seawater. Three cultures are now available, two of which have been carried for 3 years. The method is thus seen as a useful step for improving our understanding of typical aquatic organisms. PMID:16348896

Cancer stem cells in solid tumors: is 'evading apoptosis' a hallmark of cancer?

PubMed

Enderling, Heiko; Hahnfeldt, Philip

2011-08-01

Conventional wisdom has long held that once a cancer cell has developed it will inevitably progress to clinical disease. Updating this paradigm, it has more recently become apparent that the tumor interacts with its microenvironment and that some environmental bottlenecks, such as the angiogenic switch, must be overcome for the tumor to progress. In parallel, attraction has been drawn to the concept that there is a minority population of cells - the cancer stem cells - bestowed with the exclusive ability to self-renew and regenerate the tumor. With therapeutic targeting issues at stake, much attention has shifted to the identification of cancer stem cells, the thinking being that the remaining non-stem population, already fated to die, will play a negligible role in tumor development. In fact, the newly appreciated importance of intercellular interactions in cancer development also extends in a unique and unexpected way to interactions between the stem and non-stem compartments of the tumor. Here we discuss recent findings drawn from a hybrid mathematical-cellular automaton model that simulates growth of a heterogeneous solid tumor comprised of cancer stem cells and non-stem cancer cells. The model shows how the introduction of cell fate heterogeneity paradoxically influences the tumor growth dynamic in response to apoptosis, to reveal yet another bottleneck to tumor progression potentially exploitable for disease control. Copyright © 2011 Elsevier Ltd. All rights reserved.

Effects of Fluid Shear Stress on Cancer Stem Cell Viability

NASA Astrophysics Data System (ADS)

Sunday, Brittney; Triantafillu, Ursula; Domier, Ria; Kim, Yonghyun

2014-11-01

Cancer stem cells (CSCs), which are believed to be the source of tumor formation, are exposed to fluid shear stress as a result of blood flow within the blood vessels. It was theorized that CSCs would be less susceptible to cell death than non-CSCs after both types of cell were exposed to a fluid shear stress, and that higher levels of fluid shear stress would result in lower levels of cell viability for both cell types. To test this hypothesis, U87 glioblastoma cells were cultured adherently (containing smaller populations of CSCs) and spherically (containing larger populations of CSCs). They were exposed to fluid shear stress in a simulated blood flow through a 125-micrometer diameter polyetheretherketone (PEEK) tubing using a syringe pump. After exposure, cell viability data was collected using a BioRad TC20 Automated Cell Counter. Each cell type was tested at three physiological shear stress values: 5, 20, and 60 dynes per centimeter squared. In general, it was found that the CSC-enriched U87 sphere cells had higher cell viability than the CSC-depleted U87 adherent cancer cells. Interestingly, it was also observed that the cell viability was not negatively affected by the higher fluid shear stress values in the tested range. In future follow-up studies, higher shear stresses will be tested. Furthermore, CSCs from different tumor origins (e.g. breast tumor, prostate tumor) will be tested to determine cell-specific shear sensitivity. National Science Foundation Grant #1358991 supported the first author as an REU student.

Population Simulation, AKA: Grahz, Rahbitz and Fawkzes

NASA Technical Reports Server (NTRS)

Bangert, Tyler R.

2008-01-01

In an effort to give students a more visceral experience of science and instill a deeper working knowledge of concepts, activities that utilize hands-on, laboratory and simulated experiences are recommended because these activities have a greater impact on student learning, especially for Native American students. Because it is not usually feasible to take large and/or multiple classes of high school science students into the field to count numbers of organisms of a particular species, especially over a long period of time and covering a large area of an environment, the population simulation presented in this paper was created to aid students in understanding population dynamics by working with a simulated environment, which can be done in the classroom. Students create an environment and populate the environment with imaginary species. Then, using a sequence of "rules" that allow organisms to eat, reproduce, move and age, students see how the population of a species changes over time. In particular, students practice collecting data, summarizing information, plotting graphs, and interpreting graphs for such information as carrying capacity, predator prey relationships, and how specific species factors impact population and the environment. Students draw conclusions from their results and suggest further research, which may involve changes in simulation parameters, prediction of outcomes, and testing predictions. The population Simulation has demonstrated success in the above student activities using a "board game" version of the population simulation. A computer version of the population simulation needs more testing, but preliminary runs are promising. A second - and more complicated - computer simulation will simulate the same things and will add simulated population genetics.

Population pharmacokinetic-pharmacodynamic modeling and model-based prediction of docetaxel-induced neutropenia in Japanese patients with non-small cell lung cancer.

PubMed

Fukae, Masato; Shiraishi, Yoshimasa; Hirota, Takeshi; Sasaki, Yuka; Yamahashi, Mika; Takayama, Koichi; Nakanishi, Yoichi; Ieiri, Ichiro

2016-11-01

Docetaxel is used to treat many cancers, and neutropenia is the dose-limiting factor for its clinical use. A population pharmacokinetic-pharmacodynamic (PK-PD) model was introduced to predict the development of docetaxel-induced neutropenia in Japanese patients with non-small cell lung cancer (NSCLC). Forty-seven advanced or recurrent Japanese patients with NSCLC were enrolled. Patients received 50 or 60 mg/m 2 docetaxel as monotherapy, and blood samples for a PK analysis were collected up to 24 h after its infusion. Laboratory tests including absolute neutrophil count data and demographic information were used in population PK-PD modeling. The model was built by NONMEM 7.2 with a first-order conditional estimation using an interaction method. Based on the final model, a Monte Carlo simulation was performed to assess the impact of covariates on and the predictability of neutropenia. A three-compartment model was employed to describe PK data, and the PK model adequately described the docetaxel concentrations observed. Serum albumin (ALB) was detected as a covariate of clearance (CL): CL (L/h) = 32.5 × (ALB/3.6) 0.965  × (WGHT/70) 3/4 . In population PK-PD modeling, a modified semi-mechanistic myelosuppression model was applied, and characterization of the time course of neutrophil counts was adequate. The covariate selection indicated that α1-acid glycoprotein (AAG) was a predictor of neutropenia. The model-based simulation also showed that ALB and AAG negatively correlated with the development of neutropenia and that the time course of neutrophil counts was predictable. The developed model may facilitate the prediction and care of docetaxel-induced neutropenia.

Kinetic structures of quasi-perpendicular shocks in global particle-in-cell simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peng, Ivy Bo, E-mail: bopeng@kth.se; Markidis, Stefano; Laure, Erwin

2015-09-15

We carried out global Particle-in-Cell simulations of the interaction between the solar wind and a magnetosphere to study the kinetic collisionless physics in super-critical quasi-perpendicular shocks. After an initial simulation transient, a collisionless bow shock forms as a result of the interaction of the solar wind and a planet magnetic dipole. The shock ramp has a thickness of approximately one ion skin depth and is followed by a trailing wave train in the shock downstream. At the downstream edge of the bow shock, whistler waves propagate along the magnetic field lines and the presence of electron cyclotron waves has beenmore » identified. A small part of the solar wind ion population is specularly reflected by the shock while a larger part is deflected and heated by the shock. Solar wind ions and electrons are heated in the perpendicular directions. Ions are accelerated in the perpendicular direction in the trailing wave train region. This work is an initial effort to study the electron and ion kinetic effects developed near the bow shock in a realistic magnetic field configuration.« less

Exploratory modeling and simulation to support development of motesanib in Asian patients with non-small cell lung cancer based on MONET1 study results.

PubMed

Claret, L; Bruno, R; Lu, J-F; Sun, Y-N; Hsu, C-P

2014-04-01

The motesanib phase III MONET1 study failed to show improvement in overall survival (OS) in non-small cell lung cancer, but a subpopulation of Asian patients had a favorable outcome. We performed exploratory modeling and simulations based on MONET1 data to support further development of motesanib in Asian patients. A model-based estimate of time to tumor growth was the best of tested tumor size response metrics in a multivariate OS model (P < 0.00001) to capture treatment effect (hazard ratio, HR) in Asian patients. Significant independent prognostic factors for OS were baseline tumor size (P < 0.0001), smoking history (P < 0.0001), and ethnicity (P < 0.00001). The model successfully predicted OS distributions and HR in the full population and in Asian patients. Simulations indicated that a phase III study in 500 Asian patients would exceed 80% power to confirm superior efficacy of motesanib combination therapy (expected HR: 0.74), suggesting that motesanib combination therapy may benefit Asian patients.

A Stochastic Model of Eye Lens Growth

PubMed Central

Šikić, Hrvoje; Shi, Yanrong; Lubura, Snježana; Bassnett, Steven

2015-01-01

The size and shape of the ocular lens must be controlled with precision if light is to be focused sharply on the retina. The lifelong growth of the lens depends on the production of cells in the anterior epithelium. At the lens equator, epithelial cells differentiate into fiber cells, which are added to the surface of the existing fiber cell mass, increasing its volume and area. We developed a stochastic model relating the rates of cell proliferation and death in various regions of the lens epithelium to deposition of fiber cells and lens growth. Epithelial population dynamics were modeled as a branching process with emigration and immigration between various proliferative zones. Numerical simulations were in agreement with empirical measurements and demonstrated that, operating within the strict confines of lens geometry, a stochastic growth engine can produce the smooth and precise growth necessary for lens function. PMID:25816743

Incorporating pushing in exclusion-process models of cell migration.

PubMed

Yates, Christian A; Parker, Andrew; Baker, Ruth E

2015-05-01

The macroscale movement behavior of a wide range of isolated migrating cells has been well characterized experimentally. Recently, attention has turned to understanding the behavior of cells in crowded environments. In such scenarios it is possible for cells to interact, inducing neighboring cells to move in order to make room for their own movements or progeny. Although the behavior of interacting cells has been modeled extensively through volume-exclusion processes, few models, thus far, have explicitly accounted for the ability of cells to actively displace each other in order to create space for themselves. In this work we consider both on- and off-lattice volume-exclusion position-jump processes in which cells are explicitly allowed to induce movements in their near neighbors in order to create space for themselves to move or proliferate into. We refer to this behavior as pushing. From these simple individual-level representations we derive continuum partial differential equations for the average occupancy of the domain. We find that, for limited amounts of pushing, comparison between the averaged individual-level simulations and the population-level model is nearly as good as in the scenario without pushing. Interestingly, we find that, in the on-lattice case, the diffusion coefficient of the population-level model is increased by pushing, whereas, for the particular off-lattice model that we investigate, the diffusion coefficient is reduced. We conclude, therefore, that it is important to consider carefully the appropriate individual-level model to use when representing complex cell-cell interactions such as pushing.

A Simulation Environment for the Dynamic Evaluation of Disaster Preparedness Policies and Interventions

PubMed Central

Lewis, Bryan; Swarup, Samarth; Bisset, Keith; Eubank, Stephen; Marathe, Madhav; Barrett, Chris

2013-01-01

Disasters affect a society at many levels. Simulation based studies often evaluate the effectiveness of one or two response policies in isolation and are unable to represent impact of the policies to coevolve with others. Similarly, most in-depth analyses are based on a static assessment of the “aftermath” rather than capturing dynamics. We have developed a data-centric simulation environment for applying a systems approach to a dynamic analysis of complex combinations of disaster responses. We analyze an improvised nuclear detonation in Washington DC with this environment. The simulated blast affects the transportation system, communications infrastructure, electrical power system, behaviors and motivations of population, and health status of survivors. The effectiveness of partially restoring wireless communications capacity is analyzed in concert with a range of other disaster response policies. Despite providing a limited increase in cell phone communication, overall health was improved. PMID:23903394

Red blood cell-deformability measurement: review of techniques.

PubMed

Musielak, M

2009-01-01

Cell-deformability characterization involves general measurement of highly complex relationships between cell biology and physical forces to which the cell is subjected. The review takes account of the modern technical solutions simulating the action of the force applied to the red blood cell in macro- and microcirculation. Diffraction ektacytometers and rheoscopes measure the mean deformability value for the total red blood cell population investigated and the deformation distribution index of individual cells, respectively. Deformation assays of a whole single cell are possible by means of optical tweezers. The single cell-measuring setups for micropipette aspiration and atomic force microscopy allow conducting a selective investigation of deformation parameters (e.g., cytoplasm viscosity, viscoelastic membrane properties). The distinction between instrument sensitivity to various RBC-rheological features as well as the influence of temperature on measurement are discussed. The reports quoted confront fascinating possibilities of the techniques with their medical applications since the RBC-deformability has the key position in the etiology of a wide range of conditions.

Contact enhancement of locomotion in spreading cell colonies

NASA Astrophysics Data System (ADS)

D'Alessandro, Joseph; Solon, Alexandre P.; Hayakawa, Yoshinori; Anjard, Christophe; Detcheverry, François; Rieu, Jean-Paul; Rivière, Charlotte

2017-10-01

The dispersal of cells from an initially constrained location is a crucial aspect of many physiological phenomena, ranging from morphogenesis to tumour spreading. In such processes, cell-cell interactions may deeply alter the motion of single cells, and in turn the collective dynamics. While contact phenomena like contact inhibition of locomotion are known to come into play at high densities, here we focus on the little explored case of non-cohesive cells at moderate densities. We fully characterize the spreading of micropatterned colonies of Dictyostelium discoideum cells from the complete set of individual trajectories. From data analysis and simulation of an elementary model, we demonstrate that contact interactions act to speed up the early population spreading by promoting individual cells to a state of higher persistence, which constitutes an as-yet unreported contact enhancement of locomotion. Our findings also suggest that the current modelling paradigm of memoryless active particles may need to be extended to account for the history-dependent internal state of motile cells.

Bioelectronic tongue of taste buds on microelectrode array for salt sensing.

PubMed

Liu, Qingjun; Zhang, Fenni; Zhang, Diming; Hu, Ning; Wang, Hua; Hsia, K Jimmy; Wang, Ping

2013-02-15

Taste has received great attention for its potential applications. In this work, we combine the biological tissue with micro-chips to establish a novel bioelectronic tongue system for salt taste detection. Before experiment, we established a computational model of action potential in salt taste receptor cell, simulating the responsive results to natural salt stimuli of NaCl solution with various concentrations. Then 36-channel microelectrode arrays (MEA) with the diameter of 30 μm were fabricated on the glass substrate, and taste epithelium was stripped from rat and fixed on MEA. When stimulated by the salt stimuli, electrophysiological activities of taste receptor cells in taste buds were measured through a multi-channel recording system. Both simulation and experiment results showed a dose-dependent increase in NaCl-induced potentials of taste receptor cells, which indicated good applications in salt measurements. The multi-channel analysis demonstrated that different groups of MEA channels were activated during stimulations, indicating non-overlapping populations of receptor cells in taste buds involved in salt taste perception. The study provides an effective and reliable biosensor platform to help recognize and distinguish salt taste components. Copyright © 2012 Elsevier B.V. All rights reserved.

The penny pusher: a cellular model of lens growth.

PubMed

Shi, Yanrong; De Maria, Alicia; Lubura, Snježana; Šikić, Hrvoje; Bassnett, Steven

2014-12-16

The mechanisms that regulate the number of cells in the lens and, therefore, its size and shape are unknown. We examined the dynamic relationship between proliferative behavior in the epithelial layer and macroscopic lens growth. The distribution of S-phase cells across the epithelium was visualized by confocal microscopy and cell populations were determined from orthographic projections of the lens surface. The number of S-phase cells in the mouse lens epithelium fell exponentially, to an asymptotic value of approximately 200 cells by 6 months. Mitosis became increasingly restricted to a 300-μm-wide swath of equatorial epithelium, the germinative zone (GZ), within which two peaks in labeling index were detected. Postnatally, the cell population increased to approximately 50,000 cells at 4 weeks of age. Thereafter, the number of cells declined, despite continued growth in lens dimensions. This apparently paradoxical observation was explained by a time-dependent increase in the surface area of cells at all locations. The cell biological measurements were incorporated into a physical model, the Penny Pusher. In this simple model, cells were considered to be of a single type, the proliferative behavior of which depended solely on latitude. Simulations using the Penny Pusher predicted the emergence of cell clones and were in good agreement with data obtained from earlier lineage-tracing studies. The Penny Pusher, a simple stochastic model, offers a useful conceptual framework for the investigation of lens growth mechanisms and provides a plausible alternative to growth models that postulate the existence of lens stem cells. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

Testing a Hypothesis for the Evolution of Sex

NASA Astrophysics Data System (ADS)

Örçal, Bora; Tüzel, Erkan; Sevim, Volkan; Jan, Naeem; Erzan, Ayşe.

An asexual set of primitive bacteria is simulated with a bit-string Penna model with a Fermi function for survival. A recent hypothesis by Jan, Stauffer, and Moseley on the evolution of sex from asexual cells as a strategy for trying to escape the effects of deleterious mutations is checked. This strategy is found to provide a successful scenario for the evolution of a stable macroscopic sexual population.

Effects of aging in catastrophe on the steady state and dynamics of a microtubule population

NASA Astrophysics Data System (ADS)

Jemseena, V.; Gopalakrishnan, Manoj

2015-05-01

Several independent observations have suggested that the catastrophe transition in microtubules is not a first-order process, as is usually assumed. Recent in vitro observations by Gardner et al. [M. K. Gardner et al., Cell 147, 1092 (2011), 10.1016/j.cell.2011.10.037] showed that microtubule catastrophe takes place via multiple steps and the frequency increases with the age of the filament. Here we investigate, via numerical simulations and mathematical calculations, some of the consequences of the age dependence of catastrophe on the dynamics of microtubules as a function of the aging rate, for two different models of aging: exponential growth, but saturating asymptotically, and purely linear growth. The boundary demarcating the steady-state and non-steady-state regimes in the dynamics is derived analytically in both cases. Numerical simulations, supported by analytical calculations in the linear model, show that aging leads to nonexponential length distributions in steady state. More importantly, oscillations ensue in microtubule length and velocity. The regularity of oscillations, as characterized by the negative dip in the autocorrelation function, is reduced by increasing the frequency of rescue events. Our study shows that the age dependence of catastrophe could function as an intrinsic mechanism to generate oscillatory dynamics in a microtubule population, distinct from hitherto identified ones.

Cortical circuitry implementing graphical models.

PubMed

Litvak, Shai; Ullman, Shimon

2009-11-01

In this letter, we develop and simulate a large-scale network of spiking neurons that approximates the inference computations performed by graphical models. Unlike previous related schemes, which used sum and product operations in either the log or linear domains, the current model uses an inference scheme based on the sum and maximization operations in the log domain. Simulations show that using these operations, a large-scale circuit, which combines populations of spiking neurons as basic building blocks, is capable of finding close approximations to the full mathematical computations performed by graphical models within a few hundred milliseconds. The circuit is general in the sense that it can be wired for any graph structure, it supports multistate variables, and it uses standard leaky integrate-and-fire neuronal units. Following previous work, which proposed relations between graphical models and the large-scale cortical anatomy, we focus on the cortical microcircuitry and propose how anatomical and physiological aspects of the local circuitry may map onto elements of the graphical model implementation. We discuss in particular the roles of three major types of inhibitory neurons (small fast-spiking basket cells, large layer 2/3 basket cells, and double-bouquet neurons), subpopulations of strongly interconnected neurons with their unique connectivity patterns in different cortical layers, and the possible role of minicolumns in the realization of the population-based maximum operation.

Physical Forces Shape Group Identity of Swimming Pseudomonas putida Cells.

PubMed

Espeso, David R; Martínez-García, Esteban; de Lorenzo, Víctor; Goñi-Moreno, Ángel

2016-01-01

The often striking macroscopic patterns developed by motile bacterial populations on agar plates are a consequence of the environmental conditions where the cells grow and spread. Parameters such as medium stiffness and nutrient concentration have been reported to alter cell swimming behavior, while mutual interactions among populations shape collective patterns. One commonly observed occurrence is the mutual inhibition of clonal bacteria when moving toward each other, which results in a distinct halt at a finite distance on the agar matrix before having direct contact. The dynamics behind this phenomenon (i.e., intolerance to mix in time and space with otherwise identical others) has been traditionally explained in terms of cell-to-cell competition/cooperation regarding nutrient availability. In this work, the same scenario has been revisited from an alternative perspective: the effect of the physical mechanics that frame the process, in particular the consequences of collisions between moving bacteria and the semi-solid matrix of the swimming medium. To this end, we set up a simple experimental system in which the swimming patterns of Pseudomonas putida were tested with different geometries and agar concentrations. A computational analysis framework that highlights cell-to-medium interactions was developed to fit experimental observations. Simulated outputs suggested that the medium is compressed in the direction of the bacterial front motion. This phenomenon generates what was termed a compression wave that goes through the medium preceding the swimming population and that determines the visible high-level pattern. Taken together, the data suggested that the mechanical effects of the bacteria moving through the medium created a factual barrier that impedes to merge with neighboring cells swimming from a different site. The resulting divide between otherwise clonal bacteria is thus brought about by physical forces-not genetic or metabolic programs.

Can genetically modified Escherichia coli with neutral buoyancy induced by gas vesicles be used as an alternative method to clinorotation for microgravity studies?

PubMed

Benoit, Michael; Klaus, David

2005-01-01

Space flight has been shown to affect various bacterial growth parameters. It is proposed that weightlessness allows the cells to remain evenly distributed, consequently altering the chemical makeup of their surrounding fluid, and hence indirectly affecting their physiological behaviour. In support of this argument, ground-based studies using clinostats to partially simulate the quiescent environment attained in microgravity have generally been successful in producing bacterial growth characteristics that mimic responses reported under actual space conditions. A novel approach for evaluating the effects of reduced cell sedimentation is presented here through use of Escherichia coli cultures genetically modified to be neutrally buoyant. Since clinorotation would not (or would only minimally) affect cell distribution of this already near-colloidal cell system, it was hypothesized that the effects on final population density would be eliminated relative to a static control. Gas-vesicle-producing E. coli cultures were grown under clinostat and static conditions and the culture densities at 60 h were compared. As a control, E. coli that do not produce gas vesicles, but were otherwise identical to the experimental strain, were also grown under clinostat and static conditions. As hypothesized, no significant difference was observed in cell populations at 60 h between the clinorotated and static gas-vesicle-producing E. coli cultures, while the cells that did not produce gas vesicles showed a mean increase in population density of 10.5 % (P = 0.001). These results further suggest that the lack of cumulative cell sedimentation is the dominant effect of space flight on non-stirred, in vitro E. coli cultures.

Heterogeneity and proliferation of invasive cancer subclones in game theory models of the Warburg effect.

PubMed

Archetti, M

2015-04-01

The Warburg effect, a switch from aerobic energy production to anaerobic glycolysis, promotes tumour proliferation and motility by inducing acidification of the tumour microenvironment. Therapies that reduce acidity could impair tumour growth and invasiveness. I analysed the dynamics of cell proliferation and of resistance to therapies that target acidity, in a population of cells, under the Warburg effect. The dynamics of mutant cells with increased glycolysis and motility has been assessed in a multi-player game with collective interactions in the framework of evolutionary game theory. Perturbations of the level of acidity in the microenvironment have been used to simulate the effect of therapies that target glycolysis. The non-linear effects of glycolysis induce frequency-dependent clonal selection leading to coexistence of glycolytic and non-glycolytic cells within a tumour. Mutants with increased motility can invade such a polymorphic population and spread within the tumour. While reducing acidity may produce a sudden reduction in tumour cell proliferation, frequency-dependent selection enables it to adapt to the new conditions and can enable the tumour to restore its original levels of growth and invasiveness. The acidity produced by glycolysis acts as a non-linear public good that leads to coexistence of cells with high and low glycolysis within the tumour. Such a heterogeneous population can easily adapt to changes in acidity. Therapies that target acidity can only be effective in the long term if the cost of glycolysis is high, that is, under non-limiting oxygen concentrations. Their efficacy, therefore, is reduced when combined with therapies that impair angiogenesis. © 2015 The Authors Cell Proliferation Published by John Wiley & Sons Ltd.

Relaxometry model of strong dipolar perturbers for balanced-SSFP: application to quantification of SPIO loaded cells.

PubMed

Lebel, R Marc; Menon, Ravi S; Bowen, Chris V

2006-03-01

Magnetic resonance microscopy using magnetically labeled cells is an emerging discipline offering the potential for non-destructive studies targeting numerous cellular events in medical research. The present work develops a technique to quantify superparamagnetic iron-oxide (SPIO) loaded cells using fully balanced steady state free precession (b-SSFP) imaging. An analytic model based on phase cancellation was derived for a single particle and extended to predict mono-exponential decay versus echo time in the presence of multiple randomly distributed particles. Numerical models verified phase incoherence as the dominant contrast mechanism and evaluated the model using a full range of tissue decay rates, repetition times, and flip angles. Numerical simulations indicated a relaxation rate enhancement (DeltaR(2b)=0.412 gamma . LMD) proportional to LMD, the local magnetic dose (the additional sample magnetization due to the SPIO particles), a quantity related to the concentration of contrast agent. A phantom model of SPIO loaded cells showed excellent agreement with simulations, demonstrated comparable sensitivity to gradient echo DeltaR(*) (2) enhancements, and 14 times the sensitivity of spin echo DeltaR(2) measurements. We believe this model can be used to facilitate the generation of quantitative maps of targeted cell populations. Magn Reson Med, 2006. (c) 2006 Wiley-Liss, Inc.

Starvation-Survival in Haloarchaea.

PubMed

Winters, Yaicha D; Lowenstein, Tim K; Timofeeff, Michael N

2015-11-12

Recent studies claiming to revive ancient microorganisms trapped in fluid inclusions in halite have warranted an investigation of long-term microbial persistence. While starvation-survival is widely reported for bacteria, it is less well known for halophilic archaea-microorganisms likely to be trapped in ancient salt crystals. To better understand microbial survival in fluid inclusions in ancient evaporites, laboratory experiments were designed to simulate growth of halophilic archaea under media-rich conditions, complete nutrient deprivation, and a controlled substrate condition (glycerol-rich) and record their responses. Haloarchaea used for this work included Hbt. salinarum and isolate DV582A-1 (genus Haloterrigena) sub-cultured from 34 kyear Death Valley salt. Hbt. salinarum and DV582A-1 reacted to nutrient limitation with morphological and population changes. Starved populations increased and most cells converted from rods to small cocci within 56 days of nutrient deprivation. The exact timing of starvation adaptations and the physical transformations differed between species, populations of the same species, and cells of the same population. This is the first study to report the timing of starvation strategies for Hbt. salinarum and DV582A-1. The morphological states in these experiments may allow differentiation between cells trapped with adequate nutrients (represented here by early stages in nutrient-rich media) from cells trapped without nutrients (represented here by experimental starvation) in ancient salt. The hypothesis that glycerol, leaked from Dunaliella, provides nutrients for the survival of haloarchaea trapped in fluid inclusions in ancient halite, is also tested. Hbt. salinarum and DV582A-1 were exposed to a mixture of lysed and intact Dunaliella for 56 days. The ability of these organisms to utilize glycerol from Dunaliella cells was assessed by documenting population growth, cell length, and cell morphology. Hbt. salinarum and DV582A-1 experienced size reductions and shape transitions from rods to cocci. In the short-term, these trends more closely resembled the response of these organisms to starvation conditions than to nutrient-rich media. Results from this experiment reproduced the physical state of cells (small cocci) in ancient halite where prokaryotes co-exist with single-celled algae. We conclude that glycerol is not the limiting factor in the survival of haloarchaea for thousands of years in fluid inclusions in halite.

Simulated rRNA/DNA Ratios Show Potential To Misclassify Active Populations as Dormant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steven, Blaire; Hesse, Cedar; Soghigian, John

The use of rRNA/DNA ratios derived from surveys of rRNA sequences in RNA and DNA extracts is an appealing but poorly validated approach to infer the activity status of environmental microbes. To improve the interpretation of rRNA/DNA ratios, we performed simulations to investigate the effects of community structure, rRNA amplification, and sampling depth on the accuracy of rRNA/DNA ratios in classifying bacterial populations as “active” or “dormant.” Community structure was an insignificant factor. In contrast, the extent of rRNA amplification that occurs as cells transition from dormant to growing had a significant effect (P < 0.0001) on classification accuracy, withmore » misclassification errors ranging from 16 to 28%, depending on the rRNA amplification model. The error rate increased to 47% when communities included a mixture of rRNA amplification models, but most of the inflated error was false negatives (i.e., active populations misclassified as dormant). Sampling depth also affected error rates (P < 0.001). Inadequate sampling depth produced various artifacts that are characteristic of rRNA/DNA ratios generated from real communities. These data show important constraints on the use of rRNA/DNA ratios to infer activity status. Whereas classification of populations as active based on rRNA/DNA ratios appears generally valid, classification of populations as dormant is potentially far less accurate.« less

Simulated rRNA/DNA Ratios Show Potential To Misclassify Active Populations as Dormant

DOE PAGES

Steven, Blaire; Hesse, Cedar; Soghigian, John; ...

2017-03-31

The use of rRNA/DNA ratios derived from surveys of rRNA sequences in RNA and DNA extracts is an appealing but poorly validated approach to infer the activity status of environmental microbes. To improve the interpretation of rRNA/DNA ratios, we performed simulations to investigate the effects of community structure, rRNA amplification, and sampling depth on the accuracy of rRNA/DNA ratios in classifying bacterial populations as “active” or “dormant.” Community structure was an insignificant factor. In contrast, the extent of rRNA amplification that occurs as cells transition from dormant to growing had a significant effect (P < 0.0001) on classification accuracy, withmore » misclassification errors ranging from 16 to 28%, depending on the rRNA amplification model. The error rate increased to 47% when communities included a mixture of rRNA amplification models, but most of the inflated error was false negatives (i.e., active populations misclassified as dormant). Sampling depth also affected error rates (P < 0.001). Inadequate sampling depth produced various artifacts that are characteristic of rRNA/DNA ratios generated from real communities. These data show important constraints on the use of rRNA/DNA ratios to infer activity status. Whereas classification of populations as active based on rRNA/DNA ratios appears generally valid, classification of populations as dormant is potentially far less accurate.« less

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.

PubMed

Cun, Yupeng; Yang, Tsun-Po; Achter, Viktor; Lang, Ulrich; Peifer, Martin

2018-06-01

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

[Population characteristics of mucous tissue basocytes in the Mongolian gerbil's jejunum following the 12-day orbital flight onboard space platform "Foton-M3"].

PubMed

Atyakshin, D A; Bykov, E G

2013-01-01

Optical (light) microscopy and histochemical techniques were used for the first-ever studies of the population characteristics of tissue basocytes in the jejunum mucous membrane in three groups of gerbils Meriones unguiculatus: flown over 12 days aboard space platform Foton-M3, subjected to spaceflight factors simulation (SFS) in dedicated system Kontur-L (2) and maintained in standard vivarium conditions (control). Space flight was shown to induce quantitative and qualitative changes in the population of jejunum mucus labrocytes. Reduction of the basocytes population, alterations in age composition and ratio of the morphofunctional cell types in microgravity were indicative of cytoplasmic aggregation intensity, paths of biosynthesis products release into the intersticium, and their tinctorial properties. Also, heparin maturation and liberalization into the extracellular space in support of the jejunum mucus adaptive functions progressed with greater intensity. SFS did not affect size of the basocytes population significantly although it did cause qualitative rearrangements in the population structure.

An exactly solvable, spatial model of mutation accumulation in cancer

NASA Astrophysics Data System (ADS)

Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej

2016-12-01

One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.

A Study of Aberrant Glycosylation in Simulated Microgravity Using Laser Induced AutoFluorescence and Flow Cytometry

NASA Technical Reports Server (NTRS)

Lawless, B. DeSales

1999-01-01

A number of pathologies and cellular dysfunctions including neoplasms have been correlated with autofluorescence. The complications of aging and diabetes have been associated with the accumulation of non-enzymatic glycosylations of tissue macromolecules. These products are known as the Advanced Glycosylated End Products (AGEs). A physical property associated with AGEs is the emission of 570 mn or 630 nm light energy (autofluorescence) following the absorption of 448 mm energy associated with the argon laser. This investigation sought to assess the induction of argon-laser induced autofluorescence in a variety of in vitro culture systems. Different fluorescence intensities distinguished tumor lines from normal cell populations. Laser-stimulated autofluorescence discriminated primary cultures of lymphocytes grown in the presence of excess glucose as opposed to normal glucose concentrations. The effects of deglycosylating agents upon laser-induced autofluorescence were also assessed. The studies included studies of cell cycle analysis using Propidium Iodide stained DNA of cells grown in simulated microgravity using NASA Bioreactor Vessels in media of normal and elevated glucose concentrations.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

PubMed

Ezer, Daphne; Moignard, Victoria; Göttgens, Berthold; Adryan, Boris

2016-08-01

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.

A Novel Microfluidic Cell Co-culture Platform for the Study of the Molecular Mechanisms of Parkinson's Disease and Other Synucleinopathies.

PubMed

Fernandes, João T S; Chutna, Oldriska; Chu, Virginia; Conde, João P; Outeiro, Tiago F

2016-01-01

Although, the precise molecular mechanisms underlying Parkinson's disease (PD) are still elusive, it is now known that spreading of alpha-synuclein (aSyn) pathology and neuroinflammation are important players in disease progression. Here, we developed a novel microfluidic cell-culture platform for studying the communication between two different cell populations, a process of critical importance not only in PD but also in many biological processes. The integration of micro-valves in the device enabled us to control fluid routing, cellular microenvironments, and to simulate paracrine signaling. As proof of concept, two sets of experiments were designed to show how this platform can be used to investigate specific molecular mechanisms associated with PD. In one experiment, naïve H4 neuroglioma cells were co-cultured with cells expressing aSyn tagged with GFP (aSyn-GFP), to study the release and spreading of the protein. In our experimental set up, we induced the release of the contents of aSyn-GFP producing cells to the medium and monitored the protein's diffusion. In another experiment, H4 cells were co-cultured with N9 microglial cells to assess the interplay between two cell lines in response to environmental stimuli. Here, we observed an increase in the levels of reactive oxygen species in H4 cells cultured in the presence of activated N9 cells, confirming the cross talk between different cell populations. In summary, the platform developed in this study affords novel opportunities for the study of the molecular mechanisms involved in PD and other neurodegenerative diseases.

Modeling the population-level effects of hypoxia on a coastal fish: implications of a spatially-explicit individual-based model

NASA Astrophysics Data System (ADS)

Rose, K.; Creekmore, S.; Thomas, P.; Craig, K.; Neilan, R.; Rahman, S.; Wang, L.; Justic, D.

2016-02-01

The northwestern Gulf of Mexico (USA) currently experiences a large hypoxic area ("dead zone") during the summer. The population-level effects of hypoxia on coastal fish are largely unknown. We developed a spatially-explicit, individual-based model to analyze how hypoxia effects on reproduction, growth, and mortality of individual Atlantic croaker could lead to population-level responses. The model follows the hourly growth, mortality, reproduction, and movement of individuals on a 300 x 800 spatial grid of 1 km2 cells for 140 years. Chlorophyll-a concentration and water temperature were specified daily for each grid cell. Dissolved oxygen (DO) was obtained from a 3-D water quality model for four years that differed in their severity of hypoxia. A bioenergetics model was used to represent growth, mortality was assumed stage- and age-dependent, and movement behavior was based on temperature preferences and avoidance of low DO. Hypoxia effects were imposed using exposure-effects sub-models that converted time-varying exposure to DO to reductions in growth and fecundity, and increases in mortality. Using sequences of mild, intermediate, and severe hypoxia years, the model predicted a 20% decrease in population abundance. Additional simulations were performed under the assumption that river-based nutrients loadings that lead to more hypoxia also lead to higher primary production and more food for croaker. Twenty-five percent and 50% nutrient reduction scenarios were simulated by adjusting the cholorphyll-a concentrations used as food proxy for the croaker. We then incrementally increased the DO concentrations to determine how much hypoxia would need to be reduced to offset the lower food production resulting from reduced nutrients. We discuss the generality of our results, the hidden effects of hypoxia on fish, and our overall strategy of combining laboratory and field studies with modeling to produce robust predictions of population responses to stressors under dynamic and multi-stressor conditions.

Computer simulation of a cellular automata model for the immune response in a retrovirus system

NASA Astrophysics Data System (ADS)

Pandey, R. B.

1989-02-01

Immune response in a retrovirus system is modeled by a network of three binary cell elements to take into account some of the main functional features of T4 cells, T8 cells, and viruses. Two different intercell interactions are introduced, one of which leads to three fixed points while the other yields bistable fixed points oscillating between a healthy state and a sick state in a mean field treatment. Evolution of these cells is studied for quenched and annealed random interactions on a simple cubic lattice with a nearest neighbor interaction using inhomogenous cellular automata. Populations of T4 cells and viral cells oscillate together with damping (with constant amplitude) for annealed (quenched) interaction on increasing the value of mixing probability B from zero to a characteristic value B ca ( B cq). For higher B, the average number of T4 cells increases while that of the viral infected cells decreases monotonically on increasing B, suggesting a phase transition at B ca ( B cq).

The Effects of Intrinsic Noise on an Inhomogeneous Lattice of Chemical Oscillators

NASA Astrophysics Data System (ADS)

Giver, Michael; Jabeen, Zahera; Chakraborty, Bulbul

2012-02-01

Intrinsic or demographic noise has been shown to play an important role in the dynamics of a variety of systems including biochemical reactions within cells, predator-prey populations, and oscillatory chemical reaction systems, and is known to give rise to oscillations and pattern formation well outside the parameter range predicted by standard mean-field analysis. Motivated by an experimental model of cells and tissues where the cells are represented by chemical reagents isolated in emulsion droplets, we study the stochastic Brusselator, a simple activator-inhibitor chemical reaction model. Our work extends the results of recent studies on the zero and one dimensional system to the case of a non-uniform one dimensional lattice using a combination of analytical techniques and Monte Carlo simulations.

Determination of Optimal Parameters for Dual-Layer Cathode of Polymer Electrolyte Fuel Cell Using Computational Intelligence-Aided Design

PubMed Central

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs. PMID:25490761

Responses of Escherichia coli, Listeria monocytogenes, and Staphylococcus aureus to Simulated Food Processing Treatments, Determined Using Fluorescence-Activated Cell Sorting and Plate Counting▿

PubMed Central

Kennedy, Deirdre; Cronin, Ultan P.; Wilkinson, Martin G.

2011-01-01

Three common food pathogenic microorganisms were exposed to treatments simulating those used in food processing. Treated cell suspensions were then analyzed for reduction in growth by plate counting. Flow cytometry (FCM) and fluorescence-activated cell sorting (FACS) were carried out on treated cells stained for membrane integrity (Syto 9/propidium iodide) or the presence of membrane potential [DiOC2(3)]. For each microbial species, representative cells from various subpopulations detected by FCM were sorted onto selective and nonselective agar and evaluated for growth and recovery rates. In general, treatments giving rise to the highest reductions in counts also had the greatest effects on cell membrane integrity and membrane potential. Overall, treatments that impacted cell membrane permeability did not necessarily have a comparable effect on membrane potential. In addition, some bacterial species with extensively damaged membranes, as detected by FCM, appeared to be able to replicate and grow after sorting. Growth of sorted cells from various subpopulations was not always reflected in plate counts, and in some cases the staining protocol may have rendered cells unculturable. Optimized FCM protocols generated a greater insight into the extent of the heterogeneous bacterial population responses to food control measures than did plate counts. This study underlined the requirement to use FACS to relate various cytometric profiles generated by various staining protocols with the ability of cells to grow on microbial agar plates. Such information is a prerequisite for more-widespread adoption of FCM as a routine microbiological analytical technique. PMID:21602370

Stochastic hybrid systems for studying biochemical processes.

PubMed

Singh, Abhyudai; Hespanha, João P

2010-11-13

Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently developed techniques that allow fast computations of the statistical moments of the population count, without having to run computationally expensive Monte Carlo simulations of the biochemical reactions. Finally, we review different examples from the literature that illustrate the benefits of using SHSs for modelling biochemical processes.

Proper selection of 1 g controls in simulated microgravity research as illustrated with clinorotated plant cell suspension cultures

NASA Astrophysics Data System (ADS)

Kamal, Khaled Y.; Hemmersbach, Ruth; Medina, F. Javier; Herranz, Raúl

2015-04-01

Understanding the physical and biological effects of the absence of gravity is necessary to conduct operations on space environments. It has been previously shown that the microgravity environment induces the dissociation of cell proliferation from cell growth in young seedling root meristems, but this source material is limited to few cells in each row of meristematic layers. Plant cell cultures, composed by a large and homogeneous population of proliferating cells, are an ideal model to study the effects of altered gravity on cellular mechanisms regulating cell proliferation and associated cell growth. Cell suspension cultures of Arabidopsis thaliana cell line (MM2d) were exposed to 2D-clinorotation in a pipette clinostat for 3.5 or 14 h, respectively, and were then processed either by quick freezing, to be used in flow cytometry, or by chemical fixation, for microscopy techniques. After long-term clinorotation, the proportion of cells in G1 phase was increased and the nucleolus area, as revealed by immunofluorescence staining with anti-nucleolin, was decreased. Despite the compatibility of these results with those obtained in real microgravity on seedling meristems, we provide a technical discussion in the context of clinorotation and proper 1 g controls with respect to suspension cultures. Standard 1 g procedure of sustaining the cell suspension is achieved by continuously shaking. Thus, we compare the mechanical forces acting on cells in clinorotated samples, in a control static sample and in the standard 1 g conditions of suspension cultures in order to define the conditions of a complete and reliable experiment in simulated microgravity with corresponding 1 g controls.

Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling.

PubMed

Ginovart, Marta; Carbó, Rosa; Blanco, Mónica; Portell, Xavier

2017-01-01

Nowadays control of the growth of Saccharomyces to obtain biomass or cellular wall components is crucial for specific industrial applications. The general aim of this contribution is to deal with experimental data obtained from yeast cells and from yeast cultures to attempt the integration of the two levels of information, individual and population, to progress in the control of yeast biotechnological processes by means of the overall analysis of this set of experimental data, and to assist in the improvement of an individual-based model, namely, INDISIM- Saccha . Populations of S. cerevisiae growing in liquid batch culture, in aerobic and microaerophilic conditions, were studied. A set of digital images was taken during the population growth, and a protocol for the treatment and analyses of the images obtained was established. The piecewise linear model of Buchanan was adjusted to the temporal evolutions of the yeast populations to determine the kinetic parameters and changes of growth phases. In parallel, for all the yeast cells analyzed, values of direct morphological parameters, such as area, perimeter, major diameter, minor diameter, and derived ones, such as circularity and elongation, were obtained. Graphical and numerical methods from descriptive statistics were applied to these data to characterize the growth phases and the budding state of the yeast cells in both experimental conditions, and inferential statistical methods were used to compare the diverse groups of data achieved. Oxidative metabolism of yeast in a medium with oxygen available and low initial sugar concentration can be taken into account in order to obtain a greater number of cells or larger cells. Morphological parameters were analyzed statistically to identify which were the most useful for the discrimination of the different states, according to budding and/or growth phase, in aerobic and microaerophilic conditions. The use of the experimental data for subsequent modeling work was then discussed and compared to simulation results generated with INDISIM- Saccha , which allowed us to advance in the development of this yeast model, and illustrated the utility of data at different levels of observation and the needs and logic behind the development of a microbial individual-based model.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

Buckling instability in ordered bacterial colonies

NASA Astrophysics Data System (ADS)

Boyer, Denis; Mather, William; Mondragón-Palomino, Octavio; Orozco-Fuentes, Sirio; Danino, Tal; Hasty, Jeff; Tsimring, Lev S.

2011-04-01

Bacterial colonies often exhibit complex spatio-temporal organization. This collective behavior is affected by a multitude of factors ranging from the properties of individual cells (shape, motility, membrane structure) to chemotaxis and other means of cell-cell communication. One of the important but often overlooked mechanisms of spatio-temporal organization is direct mechanical contact among cells in dense colonies such as biofilms. While in natural habitats all these different mechanisms and factors act in concert, one can use laboratory cell cultures to study certain mechanisms in isolation. Recent work demonstrated that growth and ensuing expansion flow of rod-like bacteria Escherichia coli in confined environments leads to orientation of cells along the flow direction and thus to ordering of cells. However, the cell orientational ordering remained imperfect. In this paper we study one mechanism responsible for the persistence of disorder in growing cell populations. We demonstrate experimentally that a growing colony of nematically ordered cells is prone to the buckling instability. Our theoretical analysis and discrete-element simulations suggest that the nature of this instability is related to the anisotropy of the stress tensor in the ordered cell colony.

Noise facilitates transcriptional control under dynamic inputs.

PubMed

Kellogg, Ryan A; Tay, Savaş

2015-01-29

Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Simulation of Yeast Cooperation in 2D.

PubMed

Wang, M; Huang, Y; Wu, Z

2016-03-01

Evolution of cooperation has been an active research area in evolutionary biology in decades. An important type of cooperation is developed from group selection, when individuals form spatial groups to prevent them from foreign invasions. In this paper, we study the evolution of cooperation in a mixed population of cooperating and cheating yeast strains in 2D with the interactions among the yeast cells restricted to their small neighborhoods. We conduct a computer simulation based on a game theoretic model and show that cooperation is increased when the interactions are spatially restricted, whether the game is of a prisoner's dilemma, snow drifting, or mutual benefit type. We study the evolution of homogeneous groups of cooperators or cheaters and describe the conditions for them to sustain or expand in an opponent population. We show that under certain spatial restrictions, cooperator groups are able to sustain and expand as group sizes become large, while cheater groups fail to expand and keep them from collapse.

Hybrid fluid-particle simulation of whistler-mode waves in a compressed dipole magnetic field: Implications for dayside high-latitude chorus

NASA Astrophysics Data System (ADS)

da Silva, C. L.; Wu, S.; Denton, R. E.; Hudson, M. K.; Millan, R. M.

2017-01-01

In this work we present a methodology for simulating whistler-mode waves self-consistently generated by electron temperature anisotropy in the inner magnetosphere. We present simulation results using a hybrid fluid/particle-in-cell code that treats the hot, anisotropic (i.e., ring current) electron population as particles and the background (i.e., the cold and inertialess) electrons as fluid. Since the hot electrons are only a small fraction of the total population, warm (and isotropic) particle electrons are added to the simulation to increase the fraction of particles with mass, providing a more accurate characterization of the wave dispersion relation. Ions are treated as a fixed background of positive charge density. The plasma transport equations are coupled to Maxwell's equations and solved in a meridional plane (a 2-D simulation with 3-D fields). We use a curvilinear coordinate system that follows the topological curvature of Earth's geomagnetic field lines, based on an analytic expression for a compressed dipole magnetic field. Hence, we are able to simulate whistler wave generation at dawn (pure dipole field lines) and dayside (compressed dipole) by simply adjusting one scalar quantity. We demonstrate how, on the dayside, whistler-mode waves can be locally generated at a range of high latitudes, within pockets of minimum magnetic field, and propagate equatorward. The obtained dayside waves (in a compressed dipole field) have similar amplitude and frequency content to their dawn sector counterparts (in a pure dipole field) but tend to propagate more field aligned.

Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

PubMed

Ponzi, Adam; Wickens, Jeff

2012-01-01

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

Input Dependent Cell Assembly Dynamics in a Model of the Striatal Medium Spiny Neuron Network

PubMed Central

Ponzi, Adam; Wickens, Jeff

2012-01-01

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior. PMID:22438838

Agent-Based Phytoplankton Models of Cellular and Population Processes: Fostering Individual-Based Learning in Undergraduate Research

NASA Astrophysics Data System (ADS)

Berges, J. A.; Raphael, T.; Rafa Todd, C. S.; Bate, T. C.; Hellweger, F. L.

2016-02-01

Engaging undergraduate students in research projects that require expertise in multiple disciplines (e.g. cell biology, population ecology, and mathematical modeling) can be challenging because they have often not developed the expertise that allows them to participate at a satisfying level. Use of agent-based modeling can allow exploration of concepts at more intuitive levels, and encourage experimentation that emphasizes processes over computational skills. Over the past several years, we have involved undergraduate students in projects examining both ecological and cell biological aspects of aquatic microbial biology, using the freely-downloadable, agent-based modeling environment NetLogo (https://ccl.northwestern.edu/netlogo/). In Netlogo, actions of large numbers of individuals can be simulated, leading to complex systems with emergent behavior. The interface features appealing graphics, monitors, and control structures. In one example, a group of sophomores in a BioMathematics program developed an agent-based model of phytoplankton population dynamics in a pond ecosystem, motivated by observed macroscopic changes in cell numbers (due to growth and death), and driven by responses to irradiance, temperature and a limiting nutrient. In a second example, junior and senior undergraduates conducting Independent Studies created a model of the intracellular processes governing stress and cell death for individual phytoplankton cells (based on parameters derived from experiments using single-cell culturing and flow cytometry), and then this model was embedded in the agents in the pond ecosystem model. In our experience, students with a range of mathematical abilities learned to code quickly and could use the software with varying degrees of sophistication, for example, creation of spatially-explicit two and three-dimensional models. Skills developed quickly and transferred readily to other platforms (e.g. Matlab).

Chemotaxis in densely populated tissue determines germinal center anatomy and cell motility: a new paradigm for the development of complex tissues.

PubMed

Hawkins, Jared B; Jones, Mark T; Plassmann, Paul E; Thorley-Lawson, David A

2011-01-01

Germinal centers (GCs) are complex dynamic structures that form within lymph nodes as an essential process in the humoral immune response. They represent a paradigm for studying the regulation of cell movement in the development of complex anatomical structures. We have developed a simulation of a modified cyclic re-entry model of GC dynamics which successfully employs chemotaxis to recapitulate the anatomy of the primary follicle and the development of a mature GC, including correctly structured mantle, dark and light zones. We then show that correct single cell movement dynamics (including persistent random walk and inter-zonal crossing) arise from this simulation as purely emergent properties. The major insight of our study is that chemotaxis can only achieve this when constrained by the known biological properties that cells are incompressible, exist in a densely packed environment, and must therefore compete for space. It is this interplay of chemotaxis and competition for limited space that generates all the complex and biologically accurate behaviors described here. Thus, from a single simple mechanism that is well documented in the biological literature, we can explain both higher level structure and single cell movement behaviors. To our knowledge this is the first GC model that is able to recapitulate both correctly detailed anatomy and single cell movement. This mechanism may have wide application for modeling other biological systems where cells undergo complex patterns of movement to produce defined anatomical structures with sharp tissue boundaries.

Stimulus-Dependent State Transition between Synchronized Oscillation and Randomly Repetitive Burst in a Model Cerebellar Granular Layer

PubMed Central

Tanaka, Shigeru; Nagao, Soichi; Nishino, Tetsuro

2011-01-01

Information processing of the cerebellar granular layer composed of granule and Golgi cells is regarded as an important first step toward the cerebellar computation. Our previous theoretical studies have shown that granule cells can exhibit random alternation between burst and silent modes, which provides a basis of population representation of the passage-of-time (POT) from the onset of external input stimuli. On the other hand, another computational study has reported that granule cells can exhibit synchronized oscillation of activity, as consistent with observed oscillation in local field potential recorded from the granular layer while animals keep still. Here we have a question of whether an identical network model can explain these distinct dynamics. In the present study, we carried out computer simulations based on a spiking network model of the granular layer varying two parameters: the strength of a current injected to granule cells and the concentration of Mg2+ which controls the conductance of NMDA channels assumed on the Golgi cell dendrites. The simulations showed that cells in the granular layer can switch activity states between synchronized oscillation and random burst-silent alternation depending on the two parameters. For higher Mg2+ concentration and a weaker injected current, granule and Golgi cells elicited spikes synchronously (synchronized oscillation state). In contrast, for lower Mg2+ concentration and a stronger injected current, those cells showed the random burst-silent alternation (POT-representing state). It is suggested that NMDA channels on the Golgi cell dendrites play an important role for determining how the granular layer works in response to external input. PMID:21779155

SimulaTE: simulating complex landscapes of transposable elements of populations.

PubMed

Kofler, Robert

2018-04-15

Estimating the abundance of transposable elements (TEs) in populations (or tissues) promises to answer many open research questions. However, progress is hampered by the lack of concordance between different approaches for TE identification and thus potentially unreliable results. To address this problem, we developed SimulaTE a tool that generates TE landscapes for populations using a newly developed domain specific language (DSL). The simple syntax of our DSL allows for easily building even complex TE landscapes that have, for example, nested, truncated and highly diverged TE insertions. Reads may be simulated for the populations using different sequencing technologies (PacBio, Illumina paired-ends) and strategies (sequencing individuals and pooled populations). The comparison between the expected (i.e. simulated) and the observed results will guide researchers in finding the most suitable approach for a particular research question. SimulaTE is implemented in Python and available at https://sourceforge.net/projects/simulates/. Manual https://sourceforge.net/p/simulates/wiki/Home/#manual; Test data and tutorials https://sourceforge.net/p/simulates/wiki/Home/#walkthrough; Validation https://sourceforge.net/p/simulates/wiki/Home/#validation. robert.kofler@vetmeduni.ac.at.

Optimizing critical source control of five priority-regulatory trace elements from industrial wastewater in China: Implications for health management.

PubMed

Wu, Wenjun; Wang, Jinnan; Yu, Yang; Jiang, Hongqiang; Liu, Nianlei; Bi, Jun; Liu, Miaomiao

2018-04-01

Anthropogenic emissions of toxic trace elements (TEs) have caused worldwide concern due to their adverse effects on human health and ecosystems. Based on a stochastic simulation of factors' probability distribution, we established a bottom-up model to estimate the amounts of five priority-regulatory TEs released to aquatic environments from industrial processes in China. Total TE emissions in China in 2010 were estimated at approximately 2.27 t of Hg, 310.09 t of As, 318.17 t of Pb, 79.72 t of Cd, and 1040.32 t of Cr. Raw chemicals, smelting, and mining were the leading sources of TE emissions. There are apparent regional differences in TE pollution. TE emissions are much higher in eastern and central China than in the western provinces and are higher in the south than in the north. This spatial distribution was characterized in detail by allocating the emissions to 10 km × 10 km grid cells. Furthermore, the risk control for the overall emission grid was optimized according to each cell's emission and risk rank. The results show that to control 80% of TE emissions from major sources, the number of top-priority control cells would be between 200 and 400, and less than 10% of the total population would be positively affected. Based on TE risk rankings, decreasing the population weighted risk would increase the number of controlled cells by a factor of 0.3-0.5, but the affected population would increase by a factor of 0.8-1.5. In this case, the adverse effects on people's health would be reduced significantly. Finally, an optimized strategy to control TE emissions is proposed in terms of a cost-benefit trade-off. The estimates in this paper can be used to help establish a regional TE inventory and cyclic simulation, and it can also play supporting roles in minimizing TE health risks and maximizing resilience. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sensitization Prevalence, Antibody Cross-Reactivity and Immunogenic Peptide Profile of Api g 2, the Non-Specific Lipid Transfer Protein 1 of Celery

PubMed Central

Gadermaier, Gabriele; Hauser, Michael; Egger, Matthias; Ferrara, Rosetta; Briza, Peter; Souza Santos, Keity; Zennaro, Danila; Girbl, Tamara; Zuidmeer-Jongejan, Laurian; Mari, Adriano; Ferreira, Fatima

2011-01-01

Background Celery (Apium graveolens) represents a relevant allergen source that can elicit severe reactions in the adult population. To investigate the sensitization prevalence and cross-reactivity of Api g 2 from celery stalks in a Mediterranean population and in a mouse model. Methodology 786 non-randomized subjects from Italy were screened for IgE reactivity to rApi g 2, rArt v 3 (mugwort pollen LTP) and nPru p 3 (peach LTP) using an allergen microarray. Clinical data of 32 selected patients with reactivity to LTP under investigation were evaluated. Specific IgE titers and cross-inhibitions were performed in ELISA and allergen microarray. Balb/c mice were immunized with purified LTPs; IgG titers were determined in ELISA and mediator release was examined using RBL-2H3 cells. Simulated endolysosomal digestion was performed using microsomes obtained from human DCs. Results IgE testing showed a sensitization prevalence of 25.6% to Api g 2, 18.6% to Art v 3, and 28.6% to Pru p 3 and frequent co-sensitization and correlating IgE-reactivity was observed. 10/32 patients suffering from LTP-related allergy reported symptoms upon consumption of celery stalks which mainly presented as OAS. Considerable IgE cross-reactivity was observed between Api g 2, Art v 3, and Pru p 3 with varying inhibition degrees of individual patients' sera. Simulating LTP mono-sensitization in a mouse model showed development of more congruent antibody specificities between Api g 2 and Art v 3. Notably, biologically relevant murine IgE cross-reactivity was restricted to the latter and diverse from Pru p 3 epitopes. Endolysosomal processing of LTP showed generation of similar clusters, which presumably represent T-cell peptides. Conclusions Api g 2 represents a relevant celery stalk allergen in the LTP-sensitized population. The molecule displays common B cell epitopes and endolysosomal peptides that encompass T cell epitopes with pollen and plant-food derived LTP. PMID:21897872

Quasispecies dynamics and the emergence of drug resistance during zidovudine therapy of HIV infection.

PubMed

Frost, S D; McLean, A R

1994-03-01

To investigate the roles of mutation, competition and population dynamics in the emergence of drug resistant mutants during zidovudine therapy. A mathematical model of the population dynamics of the viral quasispecies during zidovudine therapy was investigated. The model was used to simulate changes in the numbers of uninfected and infected cells and the composition of the viral quasispecies in the years following initiation of therapy. Resulting scenarios in asymptomatic and AIDS patients were compared. The model was also used to investigate the efficacy of a treatment regimen involving alternating zidovudine and dideoxyinosine therapy. The behaviour of the model can be divided into three stages. Before therapy, mutation maintains a small pool of resistant mutants, outcompeted to very low levels by sensitive strains. When therapy begins there is a dramatic fall in the total viral load and resistant strains suddenly have the competitive advantage. Thus, it is resistant strains that infect the rising number of uninfected CD4+ cells. During this second stage the rapid effects of population dynamics swamp any effects of mutation between strains. When the populations of infected and uninfected cells approach their treatment equilibrium levels, mutation again becomes important in the slow generation of highly resistant strains. The short-term reduction in viral replication at the initiation of therapy generates a pool of uninfected cells which cause the eventual increase in viral burden. This increase is associated with (but not caused by) a rise in frequency of resistant strains which are at a competitive advantage in the presence of the drug. When therapy is ceased, reversion of resistance is slow as resistant strains are nearly as fit as sensitive strains in the absence of drug.

Modeling of gas-liquid mass transfer in a stirred tank bioreactor agitated by a Rushton turbine or a new pitched blade impeller.

PubMed

Gelves, Ricardo; Dietrich, A; Takors, Ralf

2014-03-01

A combined computational fluid dynamics (CFD) and population balance model (PBM) approach has been applied to simulate hydrodynamics and mass transfer in a 0.18 m(3) gas-liquid stirred bioreactor agitated by (1) a Rushton turbine, and (2) a new pitched blade geometry with rotating cartridges. The operating conditions chosen were motivated by typical settings used for culturing mammalian cells. The effects of turbulence, rotating flow, bubbles breakage and coalescence were simulated using the k-ε, multiple reference frame (MRF), Sliding mesh (SM) and PBM approaches, respectively. Considering the new pitched blade geometry with rotating aeration microspargers, [Formula: see text] mass transfer was estimated to be 34 times higher than the conventional Rushton turbine set-up. Notably, the impeller power consumption was modeled to be about 50 % lower. Independent [Formula: see text] measurements applying the same operational conditions confirmed this finding. Motivated by these simulated and experimental results, the new aeration and stirring device is qualified as a very promising tool especially useful for cell culture applications which are characterized by the challenging problem of achieving relatively high mass transfer conditions while inserting only low stirrer energy.

Electromagnetic particle-in-cell simulations of the solar wind interaction with lunar magnetic anomalies.

PubMed

Deca, J; Divin, A; Lapenta, G; Lembège, B; Markidis, S; Horányi, M

2014-04-18

We present the first three-dimensional fully kinetic and electromagnetic simulations of the solar wind interaction with lunar crustal magnetic anomalies (LMAs). Using the implicit particle-in-cell code iPic3D, we confirm that LMAs may indeed be strong enough to stand off the solar wind from directly impacting the lunar surface forming a mini-magnetosphere, as suggested by spacecraft observations and theory. In contrast to earlier magnetohydrodynamics and hybrid simulations, the fully kinetic nature of iPic3D allows us to investigate the space charge effects and in particular the electron dynamics dominating the near-surface lunar plasma environment. We describe for the first time the interaction of a dipole model centered just below the lunar surface under plasma conditions such that only the electron population is magnetized. The fully kinetic treatment identifies electromagnetic modes that alter the magnetic field at scales determined by the electron physics. Driven by strong pressure anisotropies, the mini-magnetosphere is unstable over time, leading to only temporal shielding of the surface underneath. Future human exploration as well as lunar science in general therefore hinges on a better understanding of LMAs.

Survival probabilities at spherical frontiers.

PubMed

Lavrentovich, Maxim O; Nelson, David R

2015-06-01

Motivated by tumor growth and spatial population genetics, we study the interplay between evolutionary and spatial dynamics at the surfaces of three-dimensional, spherical range expansions. We consider range expansion radii that grow with an arbitrary power-law in time: R(t) = R0(1 + t/t(∗))Θ, where Θ is a growth exponent, R0 is the initial radius, and t(∗) is a characteristic time for the growth, to be affected by the inflating geometry. We vary the parameters t(∗) and Θ to capture a variety of possible growth regimes. Guided by recent results for two-dimensional inflating range expansions, we identify key dimensionless parameters that describe the survival probability of a mutant cell with a small selective advantage arising at the population frontier. Using analytical techniques, we calculate this probability for arbitrary Θ. We compare our results to simulations of linearly inflating expansions (Θ = 1 spherical Fisher-Kolmogorov-Petrovsky-Piscunov waves) and treadmilling populations (Θ = 0, with cells in the interior removed by apoptosis or a similar process). We find that mutations at linearly inflating fronts have survival probabilities enhanced by factors of 100 or more relative to mutations at treadmilling population frontiers. We also discuss the special properties of "marginally inflating" (Θ = 1/2) expansions. Copyright © 2015 Elsevier Inc. All rights reserved.

WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions

PubMed Central

Karr, Jonathan R.; Phillips, Nolan C.; Covert, Markus W.

2014-01-01

Mechanistic ‘whole-cell’ models are needed to develop a complete understanding of cell physiology. However, extracting biological insights from whole-cell models requires running and analyzing large numbers of simulations. We developed WholeCellSimDB, a database for organizing whole-cell simulations. WholeCellSimDB was designed to enable researchers to search simulation metadata to identify simulations for further analysis, and quickly slice and aggregate simulation results data. In addition, WholeCellSimDB enables users to share simulations with the broader research community. The database uses a hybrid relational/hierarchical data format architecture to efficiently store and retrieve both simulation setup metadata and results data. WholeCellSimDB provides a graphical Web-based interface to search, browse, plot and export simulations; a JavaScript Object Notation (JSON) Web service to retrieve data for Web-based visualizations; a command-line interface to deposit simulations; and a Python API to retrieve data for advanced analysis. Overall, we believe WholeCellSimDB will help researchers use whole-cell models to advance basic biological science and bioengineering. Database URL: http://www.wholecellsimdb.org Source code repository URL: http://github.com/CovertLab/WholeCellSimDB PMID:25231498

Intermittent targeted therapies and stochastic evolution in patients affected by chronic myeloid leukemia

NASA Astrophysics Data System (ADS)

Pizzolato, N.; Persano Adorno, D.; Valenti, D.; Spagnolo, B.

2016-05-01

Front line therapy for the treatment of patients affected by chronic myeloid leukemia (CML) is based on the administration of tyrosine kinase inhibitors, namely imatinib or, more recently, axitinib. Although imatinib is highly effective and represents an example of a successful molecular targeted therapy, the appearance of resistance is observed in a proportion of patients, especially those in advanced stages. In this work, we investigate the appearance of resistance in patients affected by CML, by modeling the evolutionary dynamics of cancerous cell populations in a simulated patient treated by an intermittent targeted therapy. We simulate, with the Monte Carlo method, the stochastic evolution of initially healthy cells to leukemic clones, due to genetic mutations and changes in their reproductive behavior. We first present the model and its validation with experimental data by considering a continuous therapy. Then, we investigate how fluctuations in the number of leukemic cells affect patient response to the therapy when the drug is administered with an intermittent time scheduling. Here we show that an intermittent therapy (IT) represents a valid choice in patients with high risk of toxicity, despite an associated delay to the complete restoration of healthy cells. Moreover, a suitably tuned IT can reduce the probability of developing resistance.

Modeling Cancer Cell Growth Dynamics In vitro in Response to Antimitotic Drug Treatment

PubMed Central

Lorz, Alexander; Botesteanu, Dana-Adriana; Levy, Doron

2017-01-01

Investigating the role of intrinsic cell heterogeneity emerging from variations in cell-cycle parameters and apoptosis is a crucial step toward better informing drug administration. Antimitotic agents, widely used in chemotherapy, target exclusively proliferative cells and commonly induce a prolonged mitotic arrest followed by cell death via apoptosis. In this paper, we developed a physiologically motivated mathematical framework for describing cancer cell growth dynamics that incorporates the intrinsic heterogeneity in the time individual cells spend in the cell-cycle and apoptosis process. More precisely, our model comprises two age-structured partial differential equations for the proliferative and apoptotic cell compartments and one ordinary differential equation for the quiescent compartment. To reflect the intrinsic cell heterogeneity that governs the growth dynamics, proliferative and apoptotic cells are structured in “age,” i.e., the amount of time remaining to be spent in each respective compartment. In our model, we considered an antimitotic drug whose effect on the cellular dynamics is to induce mitotic arrest, extending the average cell-cycle length. The prolonged mitotic arrest induced by the drug can trigger apoptosis if the time a cell will spend in the cell cycle is greater than the mitotic arrest threshold. We studied the drug’s effect on the long-term cancer cell growth dynamics using different durations of prolonged mitotic arrest induced by the drug. Our numerical simulations suggest that at confluence and in the absence of the drug, quiescence is the long-term asymptotic behavior emerging from the cancer cell growth dynamics. This pattern is maintained in the presence of small increases in the average cell-cycle length. However, intermediate increases in cell-cycle length markedly decrease the total number of cells and can drive the cancer population to extinction. Intriguingly, a large “switch-on/switch-off” increase in the average cell-cycle length maintains an active cell population in the long term, with oscillating numbers of proliferative cells and a relatively constant quiescent cell number. PMID:28913178

Enhanced quasi-static particle-in-cell simulation of electron cloud instabilities in circular accelerators

NASA Astrophysics Data System (ADS)

Feng, Bing

Electron cloud instabilities have been observed in many circular accelerators around the world and raised concerns of future accelerators and possible upgrades. In this thesis, the electron cloud instabilities are studied with the quasi-static particle-in-cell (PIC) code QuickPIC. Modeling in three-dimensions the long timescale propagation of beam in electron clouds in circular accelerators requires faster and more efficient simulation codes. Thousands of processors are easily available for parallel computations. However, it is not straightforward to increase the effective speed of the simulation by running the same problem size on an increasingly number of processors because there is a limit to domain size in the decomposition of the two-dimensional part of the code. A pipelining algorithm applied on the fully parallelized particle-in-cell code QuickPIC is implemented to overcome this limit. The pipelining algorithm uses multiple groups of processors and optimizes the job allocation on the processors in parallel computing. With this novel algorithm, it is possible to use on the order of 102 processors, and to expand the scale and the speed of the simulation with QuickPIC by a similar factor. In addition to the efficiency improvement with the pipelining algorithm, the fidelity of QuickPIC is enhanced by adding two physics models, the beam space charge effect and the dispersion effect. Simulation of two specific circular machines is performed with the enhanced QuickPIC. First, the proposed upgrade to the Fermilab Main Injector is studied with an eye upon guiding the design of the upgrade and code validation. Moderate emittance growth is observed for the upgrade of increasing the bunch population by 5 times. But the simulation also shows that increasing the beam energy from 8GeV to 20GeV or above can effectively limit the emittance growth. Then the enhanced QuickPIC is used to simulate the electron cloud effect on electron beam in the Cornell Energy Recovery Linac (ERL) due to extremely small emittance and high peak currents anticipated in the machine. A tune shift is discovered from the simulation; however, emittance growth of the electron beam in electron cloud is not observed for ERL parameters.

Simulating Cancer Growth with Multiscale Agent-Based Modeling

PubMed Central

Wang, Zhihui; Butner, Joseph D.; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S.

2014-01-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. PMID:24793698

Division of Labor, Bet Hedging, and the Evolution of Mixed Biofilm Investment Strategies

PubMed Central

McNally, Luke; Ratcliff, William C.

2017-01-01

ABSTRACT Bacterial cells, like many other organisms, face a tradeoff between longevity and fecundity. Planktonic cells are fast growing and fragile, while biofilm cells are often slower growing but stress resistant. Here we ask why bacterial lineages invest simultaneously in both fast- and slow-growing types. We develop a population dynamic model of lineage expansion across a patchy environment and find that mixed investment is favored across a broad range of environmental conditions, even when transmission is entirely via biofilm cells. This mixed strategy is favored because of a division of labor where exponentially dividing planktonic cells can act as an engine for the production of future biofilm cells, which grow more slowly. We use experimental evolution to test our predictions and show that phenotypic heterogeneity is persistent even under selection for purely planktonic or purely biofilm transmission. Furthermore, simulations suggest that maintenance of a biofilm subpopulation serves as a cost-effective hedge against environmental uncertainty, which is also consistent with our experimental findings. PMID:28790201

Discriminating cellular heterogeneity using microwell-based RNA cytometry

PubMed Central

Dimov, Ivan K.; Lu, Rong; Lee, Eric P.; Seita, Jun; Sahoo, Debashis; Park, Seung-min; Weissman, Irving L.; Lee, Luke P.

2014-01-01

Discriminating cellular heterogeneity is important for understanding cellular physiology. However, it is limited by the technical difficulties of single-cell measurements. Here, we develop a two-stage system to determine cellular heterogeneity. In the first stage, we perform multiplex single-cell RNA-cytometry in a microwell array containing over 60,000 reaction chambers. In the second stage, we use the RNA-cytometry data to determine cellular heterogeneity by providing a heterogeneity likelihood score. Moreover, we use Monte-Carlo simulation and RNA-cytometry data to calculate the minimum number of cells required for detecting heterogeneity. We applied this system to characterize the RNA distributions of aging related genes in a highly purified mouse hematopoietic stem cell population. We identified genes that reveal novel heterogeneity of these cells. We also show that changes in expression of genes such as Birc6 during aging can be attributed to the shift of relative portions of cells in the high-expressing subgroup versus low-expressing subgroup. PMID:24667995

Different rates of DNA replication at early versus late S-phase sections: multiscale modeling of stochastic events related to DNA content/EdU (5-ethynyl-2'deoxyuridine) incorporation distributions.

PubMed

Li, Biao; Zhao, Hong; Rybak, Paulina; Dobrucki, Jurek W; Darzynkiewicz, Zbigniew; Kimmel, Marek

2014-09-01

Mathematical modeling allows relating molecular events to single-cell characteristics assessed by multiparameter cytometry. In the present study we labeled newly synthesized DNA in A549 human lung carcinoma cells with 15-120 min pulses of EdU. All DNA was stained with DAPI and cellular fluorescence was measured by laser scanning cytometry. The frequency of cells in the ascending (left) side of the "horseshoe"-shaped EdU/DAPI bivariate distributions reports the rate of DNA replication at the time of entrance to S phase while their frequency in the descending (right) side is a marker of DNA replication rate at the time of transition from S to G2 phase. To understand the connection between molecular-scale events and scatterplot asymmetry, we developed a multiscale stochastic model, which simulates DNA replication and cell cycle progression of individual cells and produces in silico EdU/DAPI scatterplots. For each S-phase cell the time points at which replication origins are fired are modeled by a non-homogeneous Poisson Process (NHPP). Shifted gamma distributions are assumed for durations of cell cycle phases (G1, S and G2 M), Depending on the rate of DNA synthesis being an increasing or decreasing function, simulated EdU/DAPI bivariate graphs show predominance of cells in left (early-S) or right (late-S) side of the horseshoe distribution. Assuming NHPP rate estimated from independent experiments, simulated EdU/DAPI graphs are nearly indistinguishable from those experimentally observed. This finding proves consistency between the S-phase DNA-replication rate based on molecular-scale analyses, and cell population kinetics ascertained from EdU/DAPI scatterplots and demonstrates that DNA replication rate at entrance to S is relatively slow compared with its rather abrupt termination during S to G2 transition. Our approach opens a possibility of similar modeling to study the effect of anticancer drugs on DNA replication/cell cycle progression and also to quantify other kinetic events that can be measured during S-phase. © 2014 International Society for Advancement of Cytometry.

WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions.

PubMed

Karr, Jonathan R; Phillips, Nolan C; Covert, Markus W

2014-01-01

Mechanistic 'whole-cell' models are needed to develop a complete understanding of cell physiology. However, extracting biological insights from whole-cell models requires running and analyzing large numbers of simulations. We developed WholeCellSimDB, a database for organizing whole-cell simulations. WholeCellSimDB was designed to enable researchers to search simulation metadata to identify simulations for further analysis, and quickly slice and aggregate simulation results data. In addition, WholeCellSimDB enables users to share simulations with the broader research community. The database uses a hybrid relational/hierarchical data format architecture to efficiently store and retrieve both simulation setup metadata and results data. WholeCellSimDB provides a graphical Web-based interface to search, browse, plot and export simulations; a JavaScript Object Notation (JSON) Web service to retrieve data for Web-based visualizations; a command-line interface to deposit simulations; and a Python API to retrieve data for advanced analysis. Overall, we believe WholeCellSimDB will help researchers use whole-cell models to advance basic biological science and bioengineering. http://www.wholecellsimdb.org SOURCE CODE REPOSITORY: URL: http://github.com/CovertLab/WholeCellSimDB. © The Author(s) 2014. Published by Oxford University Press.

An individual-based modeling approach to simulate the effects of cellular nutrient competition on Escherichia coli K-12 MG1655 colony behavior and interactions in aerobic structured food systems.

PubMed

Tack, Ignace L M M; Logist, Filip; Noriega Fernández, Estefanía; Van Impe, Jan F M

2015-02-01

Traditional kinetic models in predictive microbiology reliably predict macroscopic dynamics of planktonically-growing cell cultures in homogeneous liquid food systems. However, most food products have a semi-solid structure, where microorganisms grow locally in colonies. Individual colony cells exhibit strongly different and non-normally distributed behavior due to local nutrient competition. As a result, traditional models considering average population behavior in a homogeneous system do not describe colony dynamics in full detail. To incorporate local resource competition and individual cell differences, an individual-based modeling approach has been applied to Escherichia coli K-12 MG1655 colonies, considering the microbial cell as modeling unit. The first contribution of this individual-based model is to describe single colony growth under nutrient-deprived conditions. More specifically, the linear and stationary phase in the evolution of the colony radius, the evolution from a disk-like to branching morphology, and the emergence of a starvation zone in the colony center are simulated and compared to available experimental data. These phenomena occur earlier at more severe nutrient depletion conditions, i.e., at lower nutrient diffusivity and initial nutrient concentration in the medium. Furthermore, intercolony interactions have been simulated. Higher inoculum densities lead to stronger intercolony interactions, such as colony merging and smaller colony sizes, due to nutrient competition. This individual-based model contributes to the elucidation of characteristic experimentally observed colony behavior from mechanistic information about cellular physiology and interactions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computational Transport Modeling of High-Energy Neutrons Found in the Space Environment

NASA Technical Reports Server (NTRS)

Cox, Brad; Theriot, Corey A.; Rohde, Larry H.; Wu, Honglu

2012-01-01

The high charge and high energy (HZE) particle radiation environment in space interacts with spacecraft materials and the human body to create a population of neutrons encompassing a broad kinetic energy spectrum. As an HZE ion penetrates matter, there is an increasing chance of fragmentation as penetration depth increases. When an ion fragments, secondary neutrons are released with velocities up to that of the primary ion, giving some neutrons very long penetration ranges. These secondary neutrons have a high relative biological effectiveness, are difficult to effectively shield, and can cause more biological damage than the primary ions in some scenarios. Ground-based irradiation experiments that simulate the space radiation environment must account for this spectrum of neutrons. Using the Particle and Heavy Ion Transport Code System (PHITS), it is possible to simulate a neutron environment that is characteristic of that found in spaceflight. Considering neutron dosimetry, the focus lies on the broad spectrum of recoil protons that are produced in biological targets. In a biological target, dose at a certain penetration depth is primarily dependent upon recoil proton tracks. The PHITS code can be used to simulate a broad-energy neutron spectrum traversing biological targets, and it account for the recoil particle population. This project focuses on modeling a neutron beamline irradiation scenario for determining dose at increasing depth in water targets. Energy-deposition events and particle fluence can be simulated by establishing cross-sectional scoring routines at different depths in a target. This type of model is useful for correlating theoretical data with actual beamline radiobiology experiments. Other work exposed human fibroblast cells to a high-energy neutron source to study micronuclei induction in cells at increasing depth behind water shielding. Those findings provide supporting data describing dose vs. depth across a water-equivalent medium. This poster presents PHITS data suggesting an increase in dose, up to roughly 10 cm depth, followed by a continual decrease as neutrons come to a stop in the target.

Extinction models for cancer stem cell therapy

PubMed Central

Sehl, Mary; Zhou, Hua; Sinsheimer, Janet S.; Lange, Kenneth L.

2012-01-01

Cells with stem cell-like properties are now viewed as initiating and sustaining many cancers. This suggests that cancer can be cured by driving these cancer stem cells to extinction. The problem with this strategy is that ordinary stem cells are apt to be killed in the process. This paper sets bounds on the killing differential (difference between death rates of cancer stem cells and normal stem cells) that must exist for the survival of an adequate number of normal stem cells. Our main tools are birth–death Markov chains in continuous time. In this framework, we investigate the extinction times of cancer stem cells and normal stem cells. Application of extreme value theory from mathematical statistics yields an accurate asymptotic distribution and corresponding moments for both extinction times. We compare these distributions for the two cell populations as a function of the killing rates. Perhaps a more telling comparison involves the number of normal stem cells NH at the extinction time of the cancer stem cells. Conditioning on the asymptotic time to extinction of the cancer stem cells allows us to calculate the asymptotic mean and variance of NH. The full distribution of NH can be retrieved by the finite Fourier transform and, in some parameter regimes, by an eigenfunction expansion. Finally, we discuss the impact of quiescence (the resting state) on stem cell dynamics. Quiescence can act as a sanctuary for cancer stem cells and imperils the proposed therapy. We approach the complication of quiescence via multitype branching process models and stochastic simulation. Improvements to the τ-leaping method of stochastic simulation make it a versatile tool in this context. We conclude that the proposed therapy must target quiescent cancer stem cells as well as actively dividing cancer stem cells. The current cancer models demonstrate the virtue of attacking the same quantitative questions from a variety of modeling, mathematical, and computational perspectives. PMID:22001354

Extinction models for cancer stem cell therapy.

PubMed

Sehl, Mary; Zhou, Hua; Sinsheimer, Janet S; Lange, Kenneth L

2011-12-01

Cells with stem cell-like properties are now viewed as initiating and sustaining many cancers. This suggests that cancer can be cured by driving these cancer stem cells to extinction. The problem with this strategy is that ordinary stem cells are apt to be killed in the process. This paper sets bounds on the killing differential (difference between death rates of cancer stem cells and normal stem cells) that must exist for the survival of an adequate number of normal stem cells. Our main tools are birth-death Markov chains in continuous time. In this framework, we investigate the extinction times of cancer stem cells and normal stem cells. Application of extreme value theory from mathematical statistics yields an accurate asymptotic distribution and corresponding moments for both extinction times. We compare these distributions for the two cell populations as a function of the killing rates. Perhaps a more telling comparison involves the number of normal stem cells NH at the extinction time of the cancer stem cells. Conditioning on the asymptotic time to extinction of the cancer stem cells allows us to calculate the asymptotic mean and variance of NH. The full distribution of NH can be retrieved by the finite Fourier transform and, in some parameter regimes, by an eigenfunction expansion. Finally, we discuss the impact of quiescence (the resting state) on stem cell dynamics. Quiescence can act as a sanctuary for cancer stem cells and imperils the proposed therapy. We approach the complication of quiescence via multitype branching process models and stochastic simulation. Improvements to the τ-leaping method of stochastic simulation make it a versatile tool in this context. We conclude that the proposed therapy must target quiescent cancer stem cells as well as actively dividing cancer stem cells. The current cancer models demonstrate the virtue of attacking the same quantitative questions from a variety of modeling, mathematical, and computational perspectives. Copyright © 2011 Elsevier Inc. All rights reserved.

Assessment of rockfall susceptibility by integrating statistical and physically-based approaches

NASA Astrophysics Data System (ADS)

Frattini, Paolo; Crosta, Giovanni; Carrara, Alberto; Agliardi, Federico

In Val di Fassa (Dolomites, Eastern Italian Alps) rockfalls constitute the most significant gravity-induced natural disaster that threatens both the inhabitants of the valley, who are few, and the thousands of tourists who populate the area in summer and winter. To assess rockfall susceptibility, we developed an integrated statistical and physically-based approach that aimed to predict both the susceptibility to onset and the probability that rockfalls will attain specific reaches. Through field checks and multi-temporal aerial photo-interpretation, we prepared a detailed inventory of both rockfall source areas and associated scree-slope deposits. Using an innovative technique based on GIS tools and a 3D rockfall simulation code, grid cells pertaining to the rockfall source-area polygons were classified as active or inactive, based on the state of activity of the associated scree-slope deposits. The simulation code allows one to link each source grid cell with scree deposit polygons by calculating the trajectory of each simulated launch of blocks. By means of discriminant analysis, we then identified the mix of environmental variables that best identifies grid cells with low or high susceptibility to rockfalls. Among these variables, structural setting, land use, and morphology were the most important factors that led to the initiation of rockfalls. We developed 3D simulation models of the runout distance, intensity and frequency of rockfalls, whose source grid cells corresponded either to the geomorphologically-defined source polygons ( geomorphological scenario) or to study area grid cells with slope angle greater than an empirically-defined value of 37° ( empirical scenario). For each scenario, we assigned to the source grid cells an either fixed or variable onset susceptibility; the latter was derived from the discriminant model group (active/inactive) membership probabilities. Comparison of these four models indicates that the geomorphological scenario with variable onset susceptibility appears to be the most realistic model. Nevertheless, political and legal issues seem to guide local administrators, who tend to select the more conservative empirically-based scenario as a land-planning tool.

Interplay of differential cell mechanical properties, motility, and proliferation in emergent collective behavior of cell co-cultures

NASA Astrophysics Data System (ADS)

Sutter, Leo; Kolbman, Dan; Wu, Mingming; Ma, Minglin; Das, Moumita

The biophysics of cell co-cultures, i.e. binary systems of cell populations, is of great interest in many biological processes including formation of embryos, and tumor progression. During these processes, different types of cells with different physical properties are mixed with each other, with important consequences for cell-cell interaction, aggregation, and migration. The role of the differences in their physical properties in their collective behavior remains poorly understood. Furthermore, until recently most theoretical studies of collective cell migration have focused on two dimensional systems. Under physiological conditions, however, cells often have to navigate three dimensional and confined micro-environments. We study a confined, three-dimensional binary system of interacting, active, and deformable particles with different physical properties such as deformability, motility, adhesion, and division rates using Langevin Dynamics simulations. Our findings may provide insights into how the differences in and interplay between cell mechanical properties, division, and motility influence emergent collective behavior such as cell aggregation and segregation experimentally observed in co-cultures of breast cancer cells and healthy breast epithelial cells. This work was partially supported by a Cottrell College Science Award.

Solar simulated ultraviolet radiation damages murine neonatal skin and alters Langerhans cell development, but does not induce inflammation.

PubMed

McGee, Heather M; Dharmadasa, Thanuja; Woods, Gregory M

2009-06-01

Development of melanoma has been linked to excessive childhood exposure to sunlight. As neonates have a relatively underdeveloped immune system, it is likely that the immune system reacts differently to the exposure, leading to alterations in this development. This study was designed to assess changes in development of the skin immune system following neonatal irradiation. Ultraviolet radiation exposure led to relative depletion of Langerhans cells, however this was not due to migration or cell death, but rather restriction of Langerhans cells populating the epidermis. During this time, there was evidence of cellular damage, however there was no induction of an inflammatory response. It therefore appears that neonatal exposure to ultraviolet radiation leads to a skew towards a tolerogenic immune response, which may lead to a reduced ability to respond to ultraviolet radiation-induced tumours.

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity.

PubMed

Dalmasso, Giovanni; Marin Zapata, Paula Andrea; Brady, Nathan Ryan; Hamacher-Brady, Anne

2017-01-01

Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity.

MULTISCALE MODELS OF TAXIS-DRIVEN PATTERNING IN BACTERIAL POPULATIONS

PubMed Central

XUE, CHUAN; OTHMER, HANS G.

2009-01-01

Spatially-distributed populations of various types of bacteria often display intricate spatial patterns that are thought to result from the cellular response to gradients of nutrients or other attractants. In the past decade a great deal has been learned about signal transduction, metabolism and movement in E. coli and other bacteria, but translating the individual-level behavior into population-level dynamics is still a challenging problem. However, this is a necessary step because it is computationally impractical to use a strictly cell-based model to understand patterning in growing populations, since the total number of cells may reach 1012 - 1014 in some experiments. In the past phenomenological equations such as the Patlak-Keller-Segel equations have been used in modeling the cell movement that is involved in the formation of such patterns, but the question remains as to how the microscopic behavior can be correctly described by a macroscopic equation. Significant progress has been made for bacterial species that employ a “run-and-tumble” strategy of movement, in that macroscopic equations based on simplified schemes for signal transduction and turning behavior have been derived [14, 15]. Here we extend previous work in a number of directions: (i) we allow for time-dependent signals, which extends the applicability of the equations to natural environments, (ii) we use a more general turning rate function that better describes the biological behavior, and (iii) we incorporate the effect of hydrodynamic forces that arise when cells swim in close proximity to a surface. We also develop a new approach to solving the moment equations derived from the transport equation that does not involve closure assumptions. Numerical examples show that the solution of the lowest-order macroscopic equation agrees well with the solution obtained from a Monte Carlo simulation of cell movement under a variety of temporal protocols for the signal. We also apply the method to derive equations of chemotactic movement that are governed by multiple chemotactic signals. PMID:19784399

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity

PubMed Central

Dalmasso, Giovanni; Marin Zapata, Paula Andrea; Brady, Nathan Ryan; Hamacher-Brady, Anne

2017-01-01

Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity. PMID:28060865

A Petri net model of granulomatous inflammation: implications for IL-10 mediated control of Leishmania donovani infection.

PubMed

Albergante, Luca; Timmis, Jon; Beattie, Lynette; Kaye, Paul M

2013-01-01

Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete "granulomas" within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma.

A Petri Net Model of Granulomatous Inflammation: Implications for IL-10 Mediated Control of Leishmania donovani Infection

PubMed Central

Albergante, Luca; Timmis, Jon; Beattie, Lynette; Kaye, Paul M.

2013-01-01

Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete “granulomas” within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma. PMID:24363630

Dynamical modelling of haematopoiesis: an integrated view over the system in homeostasis and under perturbation.

PubMed

Manesso, Erica; Teles, José; Bryder, David; Peterson, Carsten

2013-03-06

A very high number of different types of blood cells must be generated daily through a process called haematopoiesis in order to meet the physiological requirements of the organism. All blood cells originate from a population of relatively few haematopoietic stem cells residing in the bone marrow, which give rise to specific progenitors through different lineages. Steady-state dynamics are governed by cell division and commitment rates as well as by population sizes, while feedback components guarantee the restoration of steady-state conditions. In this study, all parameters governing these processes were estimated in a computational model to describe the haematopoietic hierarchy in adult mice. The model consisted of ordinary differential equations and included negative feedback regulation. A combination of literature data, a novel divide et impera approach for steady-state calculations and stochastic optimization allowed one to reduce possible configurations of the system. The model was able to recapitulate the fundamental steady-state features of haematopoiesis and simulate the re-establishment of steady-state conditions after haemorrhage and bone marrow transplantation. This computational approach to the haematopoietic system is novel and provides insight into the dynamics and the nature of possible solutions, with potential applications in both fundamental and clinical research.

A dynamic landscape model for fish in the Everglades and its application to restoration

USGS Publications Warehouse

Gaff, H.D.; DeAngelis, D.L.; Gross, L.J.; Salinas, R.; Shorrosh, M.

2000-01-01

A model (ALFISH) for fish functional groups in freshwater marshes of the greater Everglades area of southern Florida has been developed. Its main objective is to assess the spatial pattern of fish densities through time across freshwater marshes. This model has the capability of providing a dynamic measure of the spatially-explicit food resources available to wading birds. ALFISH simulates two functional groups, large and small fish, where the larger ones can prey on the small fish type. Both functional groups are size-structured. The marsh landscape is modeled as 500×500 m spatial cells on a grid across southern Florida. A hydrology model predicts water levels in the spatial cells on 5-day time steps. Fish populations spread across the marsh during flooded conditions and either retreat into refugia (alligator ponds), move to other spatial cells, or die if their cell dries out. ALFISH has been applied to the evaluation of alternative water regulation scenarios under the Central and South Florida Comprehensive Project Review Study. The objective of this Review Study is to compare alternative methods for restoring historical ecological conditions in southern Florida. ALFISH has provided information on which plans are most are likely to increase fish biomass and its availability to wading bird populations.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy.

PubMed

Svoboda, David; Ulman, Vladimir

2017-01-01

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Computer simulation of a cellular automata model for the immune response in a retrovirus system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pandey, R.B.

1989-02-01

Immune response in a retrovirus system is modeled by a network of three binary cell elements to take into account some of the main functional features of T4 cells, T8 cells, and viruses. Two different intercell interactions are introduced, one of which leads to three fixed points while the other yields bistable fixed points oscillating between a healthy state and a sick state in a mean field treatment. Evolution of these cells is studied for quenched and annealed random interactions on a simple cubic lattice with a nearest neighbor interaction using inhomogenous cellular automata. Populations of T4 cells and viralmore » cells oscillate together with damping (with constant amplitude) for annealed (quenched) interaction on increasing the value of mixing probability B from zero to a characteristic value B/sub ca/ (B/sub cq/). For higher B, the average number of T4 cells increases while that of the viral infected cells decreases monotonically on increasing B, suggesting a phase transition at B/sub ca/ (B/sub cq/).« less

The circadian molecular clock regulates adult hippocampal neurogenesis by controlling the timing of cell-cycle entry and exit.

PubMed

Bouchard-Cannon, Pascale; Mendoza-Viveros, Lucia; Yuen, Andrew; Kærn, Mads; Cheng, Hai-Ying M

2013-11-27

The subgranular zone (SGZ) of the adult hippocampus contains a pool of quiescent neural progenitor cells (QNPs) that are capable of entering the cell cycle and producing newborn neurons. The mechanisms that control the timing and extent of adult neurogenesis are not well understood. Here, we show that QNPs of the adult SGZ express molecular-clock components and proliferate in a rhythmic fashion. The clock proteins PERIOD2 and BMAL1 are critical for proper control of neurogenesis. The absence of PERIOD2 abolishes the gating of cell-cycle entrance of QNPs, whereas genetic ablation of bmal1 results in constitutively high levels of proliferation and delayed cell-cycle exit. We use mathematical model simulations to show that these observations may arise from clock-driven expression of a cell-cycle inhibitor that targets the cyclin D/Cdk4-6 complex. Our findings may have broad implications for the circadian clock in timing cell-cycle events of other stem cell populations throughout the body. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Effect of 900 MHz Electromagnetic Radiation on the Induction of ROS in Human Peripheral Blood Mononuclear Cells.

PubMed

Kazemi, E; Mortazavi, S M J; Ali-Ghanbari, A; Sharifzadeh, S; Ranjbaran, R; Mostafavi-Pour, Z; Zal, F; Haghani, M

2015-09-01

Despite numerous studies over a decade, it still remains controversial about the biological effects of RF EMF emitted by mobile phone telephony. Here we investigated the effect of 900 MHz GSM on the induction of oxidative stress and the level of intracellular reactive oxygen species (ROS) in human mononuclear cells, monocytes and lymphocytes as defence system cells. 6 ml Peripheral Blood samples were obtained from 13 healthy volunteers (21-30 year-old). Each sample was devided into 2 groups: one was exposed RF radiation emitted from a mobile phone simulator for 2 hour and the other used as control group which was not exposed to any fields. After that, mononuclear cells were isolated from peripheral blood by density gradient centrifugation in Ficoll-Paque. The intracellular ROS content in monocytes and lymphocytes was measured by the CM-H2DCFDA fluorescence probe using flowcytometry technique. Our results showed significant increase in  ROS production after exposure in population rich in monocytes. This effect was not significant in population rich in lymphocytes in comparison with non exposed cells. The results obtained in this study clearly showed the oxidative stress induction capability of RF electromagnetic field in the portion of PBMCs mostly in monocytes, like the case of exposure to micro organisms, although the advantages or disadvantages of this effect should be evaluated.

Effect of 900 MHz Electromagnetic Radiation on the Induction of ROS in Human Peripheral Blood Mononuclear Cells

PubMed Central

Kazemi, E.; Mortazavi, S. M. J.; Ali-Ghanbari, A.; Sharifzadeh, S.; Ranjbaran, R.; Mostafavi-pour, Z.; Zal, F.; Haghani, M.

2015-01-01

Background Despite numerous studies over a decade, it still remains controversial about the biological effects of RF EMF emitted by mobile phone telephony. Objective Here we investigated the effect of 900 MHz GSM on the induction of oxidative stress and the level of intracellular reactive oxygen species (ROS) in human mononuclear cells, monocytes and lymphocytes as defence system cells. Method 6 ml Peripheral Blood samples were obtained from 13 healthy volunteers (21-30 year-old). Each sample was devided into 2 groups: one was exposed RF radiation emitted from a mobile phone simulator for 2 hour and the other used as control group which was not exposed to any fields. After that, mononuclear cells were isolated from peripheral blood by density gradient centrifugation in Ficoll-Paque. The intracellular ROS content in monocytes and lymphocytes was measured by the CM-H2DCFDA fluorescence probe using flowcytometry technique. Results Our results showed significant increase in  ROS production after exposure in population rich in monocytes. This effect was not significant in population rich in lymphocytes in comparison with non exposed cells. Conclusion The results obtained in this study clearly showed the oxidative stress induction capability of RF electromagnetic field in the portion of PBMCs mostly in monocytes, like the case of exposure to micro organisms, although the advantages or disadvantages of this effect should be evaluated. PMID:26396966

Clinical study and numerical simulation of brain cancer dynamics under radiotherapy

NASA Astrophysics Data System (ADS)

Nawrocki, S.; Zubik-Kowal, B.

2015-05-01

We perform a clinical and numerical study of the progression of brain cancer tumor growth dynamics coupled with the effects of radiotherapy. We obtained clinical data from a sample of brain cancer patients undergoing radiotherapy and compare it to our numerical simulations to a mathematical model of brain tumor cell population growth influenced by radiation treatment. We model how the body biologically receives a physically delivered dose of radiation to the affected tumorous area in the form of a generalized LQ model, modified to account for the conversion process of sublethal lesions into lethal lesions at high radiation doses. We obtain good agreement between our clinical data and our numerical simulations of brain cancer progression given by the mathematical model, which couples tumor growth dynamics and the effect of irradiation. The correlation, spanning a wide dataset, demonstrates the potential of the mathematical model to describe the dynamics of brain tumor growth influenced by radiotherapy.

Adaptability of non-genetic diversity in bacterial chemotaxis

PubMed Central

Frankel, Nicholas W; Pontius, William; Dufour, Yann S; Long, Junjiajia; Hernandez-Nunez, Luis; Emonet, Thierry

2014-01-01

Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability. DOI: http://dx.doi.org/10.7554/eLife.03526.001 PMID:25279698

Advantageous Direct Quantification of Viable Closely Related Probiotics in Petit-Suisse Cheeses under In Vitro Gastrointestinal Conditions by Propidium Monoazide - qPCR

PubMed Central

Villarreal, Martha Lissete Morales; Padilha, Marina; Vieira, Antonio Diogo Silva; Franco, Bernadette Dora Gombossy de Melo; Martinez, Rafael Chacon Ruiz; Saad, Susana Marta Isay

2013-01-01

Species-specific Quantitative Real Time PCR (qPCR) alone and combined with the use of propidium monoazide (PMA) were used along with the plate count method to evaluate the survival of the probiotic strains Lactobacillus acidophilus La-5 and Bifidobacterium animalis subsp. lactis Bb-12, and the bacteriocinogenic and potentially probiotic strain Lactobacillus sakei subsp. sakei 2a in synbiotic (F1) and probiotic (F2) petit-suisse cheeses exposed throughout shelf-life to in vitro simulated gastrointestinal tract conditions. The three strains studied showed a reduction in their viability after the 6 h assay. Bb-12 displayed the highest survival capacity, above 72.6 and 74.6% of the initial populations, respectively, by plate count and PMA-qPCR, maintaining population levels in the range or above 6 log CFU/g. The prebiotic mix of inulin and FOS did not offer any additional protection for the strains against the simulated gastrointestinal environment. The microorganisms' populations were comparable among the three methods at the initial time of the assay, confirming the presence of mainly viable and culturable cells. However, with the intensification of the stress induced throughout the various stages of the in vitro test, the differences among the methods increased. The qPCR was not a reliable enumeration method for the quantification of intact bacterial populations, mixed with large numbers of injured and dead bacteria, as confirmed by the scanning electron microscopy results. Furthermore, bacteria plate counts were much lower (P<0.05) than with the PMA-qPCR method, suggesting the accumulation of stressed or dead microorganisms unable to form colonies. The use of PMA overcame the qPCR inability to differentiate between dead and alive cells. The combination of PMA and species-specific qPCR in this study allowed a quick and unequivocal way of enumeration of viable closely related species incorporated into probiotic and synbiotic petit-suisse cheeses and under stress conditions. PMID:24358142

Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons.

PubMed

Bernardi, Davide; Lindner, Benjamin

2017-06-30

Experimental and theoretical studies suggest that cortical networks are chaotic and coding relies on averages over large populations. However, there is evidence that rats can respond to the short stimulation of a single cortical cell, a theoretically unexplained fact. We study effects of single-cell stimulation on a large recurrent network of integrate-and-fire neurons and propose a simple way to detect the perturbation. Detection rates obtained from simulations and analytical estimates are similar to experimental response rates if the readout is slightly biased towards specific neurons. Near-optimal detection is attained for a broad range of intermediate values of the mean coupling between neurons.

Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons

NASA Astrophysics Data System (ADS)

Bernardi, Davide; Lindner, Benjamin

2017-06-01

Experimental and theoretical studies suggest that cortical networks are chaotic and coding relies on averages over large populations. However, there is evidence that rats can respond to the short stimulation of a single cortical cell, a theoretically unexplained fact. We study effects of single-cell stimulation on a large recurrent network of integrate-and-fire neurons and propose a simple way to detect the perturbation. Detection rates obtained from simulations and analytical estimates are similar to experimental response rates if the readout is slightly biased towards specific neurons. Near-optimal detection is attained for a broad range of intermediate values of the mean coupling between neurons.

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

PubMed

Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

2017-03-18

Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

Ensemble methods for stochastic networks with special reference to the biological clock of Neurospora crassa.

PubMed

Caranica, C; Al-Omari, A; Deng, Z; Griffith, J; Nilsen, R; Mao, L; Arnold, J; Schüttler, H-B

2018-01-01

A major challenge in systems biology is to infer the parameters of regulatory networks that operate in a noisy environment, such as in a single cell. In a stochastic regime it is hard to distinguish noise from the real signal and to infer the noise contribution to the dynamical behavior. When the genetic network displays oscillatory dynamics, it is even harder to infer the parameters that produce the oscillations. To address this issue we introduce a new estimation method built on a combination of stochastic simulations, mass action kinetics and ensemble network simulations in which we match the average periodogram and phase of the model to that of the data. The method is relatively fast (compared to Metropolis-Hastings Monte Carlo Methods), easy to parallelize, applicable to large oscillatory networks and large (~2000 cells) single cell expression data sets, and it quantifies the noise impact on the observed dynamics. Standard errors of estimated rate coefficients are typically two orders of magnitude smaller than the mean from single cell experiments with on the order of ~1000 cells. We also provide a method to assess the goodness of fit of the stochastic network using the Hilbert phase of single cells. An analysis of phase departures from the null model with no communication between cells is consistent with a hypothesis of Stochastic Resonance describing single cell oscillators. Stochastic Resonance provides a physical mechanism whereby intracellular noise plays a positive role in establishing oscillatory behavior, but may require model parameters, such as rate coefficients, that differ substantially from those extracted at the macroscopic level from measurements on populations of millions of communicating, synchronized cells.

Tumour resistance to cisplatin: a modelling approach

NASA Astrophysics Data System (ADS)

Marcu, L.; Bezak, E.; Olver, I.; van Doorn, T.

2005-01-01

Although chemotherapy has revolutionized the treatment of haematological tumours, in many common solid tumours the success has been limited. Some of the reasons for the limitations are: the timing of drug delivery, resistance to the drug, repopulation between cycles of chemotherapy and the lack of complete understanding of the pharmacokinetics and pharmacodynamics of a specific agent. Cisplatin is among the most effective cytotoxic agents used in head and neck cancer treatments. When modelling cisplatin as a single agent, the properties of cisplatin only have to be taken into account, reducing the number of assumptions that are considered in the generalized chemotherapy models. The aim of the present paper is to model the biological effect of cisplatin and to simulate the consequence of cisplatin resistance on tumour control. The 'treated' tumour is a squamous cell carcinoma of the head and neck, previously grown by computer-based Monte Carlo techniques. The model maintained the biological constitution of a tumour through the generation of stem cells, proliferating cells and non-proliferating cells. Cell kinetic parameters (mean cell cycle time, cell loss factor, thymidine labelling index) were also consistent with the literature. A sensitivity study on the contribution of various mechanisms leading to drug resistance is undertaken. To quantify the extent of drug resistance, the cisplatin resistance factor (CRF) is defined as the ratio between the number of surviving cells of the resistant population and the number of surviving cells of the sensitive population, determined after the same treatment time. It is shown that there is a supra-linear dependence of CRF on the percentage of cisplatin-DNA adducts formed, and a sigmoid-like dependence between CRF and the percentage of cells killed in resistant tumours. Drug resistance is shown to be a cumulative process which eventually can overcome tumour regression leading to treatment failure.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Effects of dimensionality on kinetic simulations of laser-ion acceleration in the transparency regime

NASA Astrophysics Data System (ADS)

Stark, D. J.; Yin, L.; Albright, B. J.; Guo, F.

2017-05-01

A particle-in-cell study of laser-ion acceleration mechanisms in the transparency regime illustrates how two-dimensional (2D) S and P simulations (laser polarization in and out of the simulation plane, respectively) capture different physics characterizing these systems, visible in their entirety often in cost-prohibitive three-dimensional (3D) simulations. The electron momentum anisotropy induced in the target by a laser pulse is dramatically different in the two 2D cases, manifested in differences in target expansion timescales, electric field strengths, and density thresholds for the onset of relativistically induced transparency. In particular, 2D-P simulations exhibit dramatically greater electron heating in the simulation plane, whereas 2D-S ones show a much more isotropic energy distribution, similar to 3D. An ion trajectory analysis allows one to isolate the fields responsible for ion acceleration and to characterize the acceleration regimes in time and space. The artificial longitudinal electron heating in 2D-P exaggerates the effectiveness of target-normal sheath acceleration into its dominant acceleration mechanism throughout the laser-plasma interaction, whereas 2D-S and 3D both have sizable populations accelerated preferentially during transparency.

Effects of dimensionality on kinetic simulations of laser-ion acceleration in the transparency regime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stark, David James; Yin, Lin; Albright, Brian James

2017-05-03

A particle-in-cell study of laser-ion acceleration mechanisms in the transparency regime illustrates how two-dimensional (2D) S and P simulations (laser polarization in and out of the simulation plane, respectively) capture different physics characterizing these systems, visible in their entirety in often cost-prohibitive three-dimensional (3D) simulations. The electron momentum anisotropy induced in the target by the laser pulse is dramatically different in the two 2D cases, manifested in differences in target expansion timescales, electric field strengths, and density thresholds for the onset of relativistically induced transparency. In particular, 2D-P simulations exhibit dramatically greater electron heating in the simulation plane, whereas 2D-Smore » ones show a much more isotropic energy distribution, similar to 3D. An ion trajectory analysis allows one to isolate the fields responsible for ion acceleration and to characterize the acceleration regimes in time and space. The artificial longitudinal electron heating in 2D-P exaggerates the effectiveness of target-normal sheath acceleration into its dominant acceleration mechanism throughout the laser-plasma interaction, whereas 2D-S and 3D both have sizable populations accelerated preferentially during transparency.« less

Grid scale drives the scale and long-term stability of place maps

PubMed Central

Mallory, Caitlin S; Hardcastle, Kiah; Bant, Jason S; Giocomo, Lisa M

2018-01-01

Medial entorhinal cortex (MEC) grid cells fire at regular spatial intervals and project to the hippocampus, where place cells are active in spatially restricted locations. One feature of the grid population is the increase in grid spatial scale along the dorsal-ventral MEC axis. However, the difficulty in perturbing grid scale without impacting the properties of other functionally-defined MEC cell types has obscured how grid scale influences hippocampal coding and spatial memory. Here, we use a targeted viral approach to knock out HCN1 channels selectively in MEC, causing grid scale to expand while leaving other MEC spatial and velocity signals intact. Grid scale expansion resulted in place scale expansion in fields located far from environmental boundaries, reduced long-term place field stability and impaired spatial learning. These observations, combined with simulations of a grid-to-place cell model and position decoding of place cells, illuminate how grid scale impacts place coding and spatial memory. PMID:29335607

Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements

PubMed Central

Kapucu, Fikret E.; Välkki, Inkeri; Mikkonen, Jarno E.; Leone, Chiara; Lenk, Kerstin; Tanskanen, Jarno M. A.; Hyttinen, Jari A. K.

2016-01-01

Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the real in vitro MEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis. PMID:27803660

Simulation of Escherichia coli O157:H7 behavior in fresh-cut lettuce under dynamic temperature conditions during distribution from processing to retail.

PubMed

McKellar, Robin C; LeBlanc, Denyse I; Lu, Jianbo; Delaquis, Pascal

2012-03-01

The temperature of packaged lettuce was recorded throughout a retail supply chain in Canada during the various stages of storage and shipping from the processor to retail. Temperatures were monitored in 27 cases of lettuce destined for three stores in three replicate trials conducted during the winter. A dynamic model that predicts the effect of temperature on the growth or die-off of Escherichia coli O157:H7 in packaged fresh-cut lettuce was applied to simulate the behavior of E. coli O157:H7 in the system. Simulations were carried out using distributions to account for variation in the temperature parameter and the die-off coefficient of the dynamic growth/death model. The results indicate that there was a predicted overall mean decline in cell numbers of 0.983 log cfu g⁻¹ and that the extent of cell death was proportional to the total time spent in the cold chain. Slight growth was predicted in a few instances when the dynamic temperature was above the permissive temperature of 5°C. These results suggest that generally there would be little or no growth of E. coli O157:H7 in product maintained at the proper temperature in the chain. Moreover, the predicted decline in cell numbers at refrigeration temperatures suggests that storage at 5°C or below prior to consumption would reduce populations of the pathogen in fresh-cut lettuce.

A Quantitative Comparison of Single-Dye Tracking Analysis Tools Using Monte Carlo Simulations

PubMed Central

McColl, James; Irvine, Kate L.; Davis, Simon J.; Gay, Nicholas J.; Bryant, Clare E.; Klenerman, David

2013-01-01

Single-particle tracking (SPT) is widely used to study processes from membrane receptor organization to the dynamics of RNAs in living cells. While single-dye labeling strategies have the benefit of being minimally invasive, this comes at the expense of data quality; typically a data set of short trajectories is obtained and analyzed by means of the mean square displacements (MSD) or the distribution of the particles’ displacements in a set time interval (jump distance, JD). To evaluate the applicability of both approaches, a quantitative comparison of both methods under typically encountered experimental conditions is necessary. Here we use Monte Carlo simulations to systematically compare the accuracy of diffusion coefficients (D-values) obtained for three cases: one population of diffusing species, two populations with different D-values, and a population switching between two D-values. For the first case we find that the MSD gives more or equally accurate results than the JD analysis (relative errors of D-values <6%). If two diffusing species are present or a particle undergoes a motion change, the JD analysis successfully distinguishes both species (relative error <5%). Finally we apply the JD analysis to investigate the motion of endogenous LPS receptors in live macrophages before and after treatment with methyl-β-cyclodextrin and latrunculin B. PMID:23737978

A quantitative comparison of single-dye tracking analysis tools using Monte Carlo simulations.

PubMed

Weimann, Laura; Ganzinger, Kristina A; McColl, James; Irvine, Kate L; Davis, Simon J; Gay, Nicholas J; Bryant, Clare E; Klenerman, David

2013-01-01

Single-particle tracking (SPT) is widely used to study processes from membrane receptor organization to the dynamics of RNAs in living cells. While single-dye labeling strategies have the benefit of being minimally invasive, this comes at the expense of data quality; typically a data set of short trajectories is obtained and analyzed by means of the mean square displacements (MSD) or the distribution of the particles' displacements in a set time interval (jump distance, JD). To evaluate the applicability of both approaches, a quantitative comparison of both methods under typically encountered experimental conditions is necessary. Here we use Monte Carlo simulations to systematically compare the accuracy of diffusion coefficients (D-values) obtained for three cases: one population of diffusing species, two populations with different D-values, and a population switching between two D-values. For the first case we find that the MSD gives more or equally accurate results than the JD analysis (relative errors of D-values <6%). If two diffusing species are present or a particle undergoes a motion change, the JD analysis successfully distinguishes both species (relative error <5%). Finally we apply the JD analysis to investigate the motion of endogenous LPS receptors in live macrophages before and after treatment with methyl-β-cyclodextrin and latrunculin B.

Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model.

PubMed

Kim, Moses; Christley, Scott; Alverdy, John C; Liu, Donald; An, Gary

2012-02-01

Necrotizing enterocolitis (NEC) is a complex disease involving prematurity, enteral feeding, and bacterial effects. We propose that the underlying initial condition in its pathogenesis is reduced ability of the neonatal gut epithelial cells (NGECs) to clear oxidative stress (OS), and that when such a NGEC population is exposed to enteral feeding, the increased metabolic OS tips the population toward apoptosis, inflammation, bacterial activation, and eventual necrosis. The multi-factorial complexity of NEC requires characterization with computational modeling, and herein, we used an agent-based model (ABM) to instantiate and examine our unifying hypothesis of the pathogenesis of NEC. An ABM of the neonatal gut was created with NGEC computational agents incorporating rules for pathways for OS, p53, tight junctions, Toll-like receptor (TLR)-4, nitric oxide, and nuclear factor-kappa beta (NF-κB). The modeled bacteria activated TLR-4 on contact with NGECs. Simulations included parameter sweeps of OS response, response to feeding, addition of bacteria, and alterations in gut mucus production. The ABM reproduced baseline cellular respiration and clearance of OS. Reduction in OS clearance consistent with clinical NEC led to senescence, apoptosis, or inflammation, with disruption of tight junctions, but rarely to NGEC necrosis. An additional "hit" of bacteria activating TLR-4 potentiated a shift to NGEC necrosis across the entire population. The mucus layer was modeled to limit bacterial-NGEC interactions and reduce this effect, but concomitant apoptosis in the goblet cell population reduced the efficacy of the mucus layer and limited its protective effect in simulated experiments. This finding suggests a means by which increased apoptosis at the cellular population level can lead to a transition to the necrosis outcome. Our ABM incorporates known components of NEC and demonstrates that impaired OS management can lead to apoptosis and inflammation of NGECs, rendering the system susceptible to an additional insult involving regionalized mucus barrier failure and TLR-4 activation, which potentiates the necrosis outcome. This type of integrative dynamic knowledge representation can be a useful adjunct to help guide and contextualize research.

Simulation of the Impact of Climate Variability on Malaria Transmission in the Sahel

NASA Astrophysics Data System (ADS)

Bomblies, A.; Eltahir, E.; Duchemin, J.

2007-12-01

A coupled hydrology and entomology model for simulation of malaria transmission and malaria transmitting mosquito population dynamics is presented. Model development and validation is done using field data and observations collected at Banizoumbou and Zindarou, Niger spanning three wet seasons, from 2005 through 2007. The primary model objective is the accurate determination of climate variability effects on village scale malaria transmission. Malaria transmission dependence on climate variables is highly nonlinear and complex. Temperature and humidity affect mosquito longevity, temperature controls parasite development rates in the mosquito as well as subadult mosquito development rates, and precipitation determines the formation and persistence of adequate breeding pools. Moreover, unsaturated zone hydrology influences overland flow, and climate controlled evapotranspiration rates and root zone uptake therefore also influence breeding pool formation. High resolution distributed hydrologic simulation allows representation of the small-scale ephemeral pools that constitute the primary habitat of Anopheles gambiae mosquitoes, the dominant malaria vectors in the Niger Sahel. Remotely sensed soil type, vegetation type, and microtopography rasters are used to assign the distributed parameter fields for simulation of the land surface hydrologic response to precipitation and runoff generation. Predicted runoff from each cell flows overland and into topographic depressions, with explicit representation of infiltration and evapotranspiration. The model's entomology component interacts with simulated pools. Subadult (aquatic stage) mosquito breeding is simulated in the pools, and water temperature dependent stage advancement rates regulate adult mosquito emergence into the model domain. Once emerged, adult mosquitoes are tracked as independent individual agents that interact with their immediate environment. Attributes relevant to malaria transmission such as gonotrophic state, infected and infectious states, age, and location relative to human population are tracked for each individual. The model operates at a resolution consistent with the characteristic scale of relevant ecological processes. Microhabitat exploitation and spatial structure of the mosquito population surrounding villages is reproduced in this manner. The resulting coupled model predicts not only malaria transmission's response to interannual climate variability, but can also evaluate land use change effects on malaria transmission. The late Professor Andrew Spielman of the Harvard School of Public Health provided medical entomology expertise and was a part of this effort.

In silico characterization of cell-cell interactions using a cellular automata model of cell culture.

PubMed

Kihara, Takanori; Kashitani, Kosuke; Miyake, Jun

2017-07-14

Cell proliferation is a key characteristic of eukaryotic cells. During cell proliferation, cells interact with each other. In this study, we developed a cellular automata model to estimate cell-cell interactions using experimentally obtained images of cultured cells. We used four types of cells; HeLa cells, human osteosarcoma (HOS) cells, rat mesenchymal stem cells (MSCs), and rat smooth muscle A7r5 cells. These cells were cultured and stained daily. The obtained cell images were binarized and clipped into squares containing about 10 4 cells. These cells showed characteristic cell proliferation patterns. The growth curves of these cells were generated from the cell proliferation images and we determined the doubling time of these cells from the growth curves. We developed a simple cellular automata system with an easily accessible graphical user interface. This system has five variable parameters, namely, initial cell number, doubling time, motility, cell-cell adhesion, and cell-cell contact inhibition (of proliferation). Within these parameters, we obtained initial cell numbers and doubling times experimentally. We set the motility at a constant value because the effect of the parameter for our simulation was restricted. Therefore, we simulated cell proliferation behavior with cell-cell adhesion and cell-cell contact inhibition as variables. By comparing growth curves and proliferation cell images, we succeeded in determining the cell-cell interaction properties of each cell. Simulated HeLa and HOS cells exhibited low cell-cell adhesion and weak cell-cell contact inhibition. Simulated MSCs exhibited high cell-cell adhesion and positive cell-cell contact inhibition. Simulated A7r5 cells exhibited low cell-cell adhesion and strong cell-cell contact inhibition. These simulated results correlated with the experimental growth curves and proliferation images. Our simulation approach is an easy method for evaluating the cell-cell interaction properties of cells.

Resynchronization of circadian oscillators and the east-west asymmetry of jet-lag

NASA Astrophysics Data System (ADS)

Lu, Zhixin; Klein-Cardeña, Kevin; Lee, Steven; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward

2016-09-01

Cells in the brain's Suprachiasmatic Nucleus (SCN) are known to regulate circadian rhythms in mammals. We model synchronization of SCN cells using the forced Kuramoto model, which consists of a large population of coupled phase oscillators (modeling individual SCN cells) with heterogeneous intrinsic frequencies and external periodic forcing. Here, the periodic forcing models diurnally varying external inputs such as sunrise, sunset, and alarm clocks. We reduce the dimensionality of the system using the ansatz of Ott and Antonsen and then study the effect of a sudden change of clock phase to simulate cross-time-zone travel. We estimate model parameters from previous biological experiments. By examining the phase space dynamics of the model, we study the mechanism leading to the difference typically experienced in the severity of jet-lag resulting from eastward and westward travel.

Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling

PubMed Central

Ginovart, Marta; Carbó, Rosa; Blanco, Mónica; Portell, Xavier

2018-01-01

Nowadays control of the growth of Saccharomyces to obtain biomass or cellular wall components is crucial for specific industrial applications. The general aim of this contribution is to deal with experimental data obtained from yeast cells and from yeast cultures to attempt the integration of the two levels of information, individual and population, to progress in the control of yeast biotechnological processes by means of the overall analysis of this set of experimental data, and to assist in the improvement of an individual-based model, namely, INDISIM-Saccha. Populations of S. cerevisiae growing in liquid batch culture, in aerobic and microaerophilic conditions, were studied. A set of digital images was taken during the population growth, and a protocol for the treatment and analyses of the images obtained was established. The piecewise linear model of Buchanan was adjusted to the temporal evolutions of the yeast populations to determine the kinetic parameters and changes of growth phases. In parallel, for all the yeast cells analyzed, values of direct morphological parameters, such as area, perimeter, major diameter, minor diameter, and derived ones, such as circularity and elongation, were obtained. Graphical and numerical methods from descriptive statistics were applied to these data to characterize the growth phases and the budding state of the yeast cells in both experimental conditions, and inferential statistical methods were used to compare the diverse groups of data achieved. Oxidative metabolism of yeast in a medium with oxygen available and low initial sugar concentration can be taken into account in order to obtain a greater number of cells or larger cells. Morphological parameters were analyzed statistically to identify which were the most useful for the discrimination of the different states, according to budding and/or growth phase, in aerobic and microaerophilic conditions. The use of the experimental data for subsequent modeling work was then discussed and compared to simulation results generated with INDISIM-Saccha, which allowed us to advance in the development of this yeast model, and illustrated the utility of data at different levels of observation and the needs and logic behind the development of a microbial individual-based model. PMID:29354112

Spectral analysis of pair-correlation bandwidth: application to cell biology images.

PubMed

Binder, Benjamin J; Simpson, Matthew J

2015-02-01

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.

Simulated space environment tests on cadmium sulfide solar cells

NASA Technical Reports Server (NTRS)

Clarke, D. R.; Oman, H.

1971-01-01

Cadmium sulfide (Cu2s - CdS) solar cells were tested under simulated space environmental conditions. Some cells were thermally cycled with illumination from a Xenon-arc solar simulator. A cycle was one hour of illumination followed immediately with one-half hour of darkness. In the light, the cells reached an equilibrium temperature of 60 C (333 K) and in the dark the cell temperature dropped to -120 C (153 K). Other cells were constantly illuminated with a Xenon-arc solar simulator. The equilibrium temperature of these cells was 55 C (328 K). The black vacuum chamber walls were cooled with liquid nitrogen to simulate a space heat sink. Chamber pressure was maintained at 0.000001 torr or less. Almost all of the solar cells tested degraded in power when exposed to a simulated space environment of either thermal cycling or constant illumination. The cells tested the longest were exposed to 10.050 thermal cycles.

A Model for the Epigenetic Switch Linking Inflammation to Cell Transformation: Deterministic and Stochastic Approaches

PubMed Central

Gérard, Claude; Gonze, Didier; Lemaigre, Frédéric; Novák, Béla

2014-01-01

Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-κB, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-κB. In turn, IL6 can promote NF-κB activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation. PMID:24499937

Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor

PubMed Central

2015-01-01

Background Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members. Results Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate. Conclusion In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members. PMID:25708911

Preface of the "Symposium on Mathematical Models and Methods to investigate Heterogeneity in Cell and Cell Population Biology"

NASA Astrophysics Data System (ADS)

Clairambault, Jean

2016-06-01

This session investigates hot topics related to mathematical representations of cell and cell population dynamics in biology and medicine, in particular, but not only, with applications to cancer. Methods in mathematical modelling and analysis, and in statistical inference using single-cell and cell population data, should contribute to focus this session on heterogeneity in cell populations. Among other methods are proposed: a) Intracellular protein dynamics and gene regulatory networks using ordinary/partial/delay differential equations (ODEs, PDEs, DDEs); b) Representation of cell population dynamics using agent-based models (ABMs) and/or PDEs; c) Hybrid models and multiscale models to integrate single-cell dynamics into cell population behaviour; d) Structured cell population dynamics and asymptotic evolution w.r.t. relevant traits; e) Heterogeneity in cancer cell populations: origin, evolution, phylogeny and methods of reconstruction; f) Drug resistance as an evolutionary phenotype: predicting and overcoming it in therapeutics; g) Theoretical therapeutic optimisation of combined drug treatments in cancer cell populations and in populations of other organisms, such as bacteria.

Dynamics of biofilm formation by Listeria monocytogenes on stainless steel under mono-species and mixed-culture simulated fish processing conditions and chemical disinfection challenges.

PubMed

Papaioannou, Eleni; Giaouris, Efstathios D; Berillis, Panagiotis; Boziaris, Ioannis S

2018-02-21

The progressive ability of a six-strains L. monocytogenes cocktail to form biofilm on stainless steel (SS), under fish-processing simulated conditions, was investigated, together with the biocide tolerance of the developed sessile communities. To do this, the pathogenic bacteria were left to form biofilms on SS coupons incubated at 15°C, for up to 240h, in periodically renewable model fish juice substrate, prepared by aquatic extraction of sea bream flesh, under both mono-species and mixed-culture conditions. In the latter case, L. monocytogenes cells were left to produce biofilms together with either a five-strains cocktail of four Pseudomonas species (fragi, savastanoi, putida and fluorescens), or whole fish indigenous microflora. The biofilm populations of L. monocytogenes, Pseudomonas spp., Enterobacteriaceae, H 2 S producing and aerobic plate count (APC) bacteria, both before and after disinfection, were enumerated by selective agar plating, following their removal from surfaces through bead vortexing. Scanning electron microscopy was also applied to monitor biofilm formation dynamics and anti-biofilm biocidal actions. Results revealed the clear dominance of Pseudomonas spp. bacteria in all the mixed-culture sessile communities throughout the whole incubation period, with the in parallel sole presence of L. monocytogenes cells to further increase (ca. 10-fold) their sessile growth. With respect to L. monocytogenes and under mono-species conditions, its maximum biofilm population (ca. 6logCFU/cm 2 ) was reached at 192h of incubation, whereas when solely Pseudomonas spp. cells were also present, its biofilm formation was either slightly hindered or favored, depending on the incubation day. However, when all the fish indigenous microflora was present, biofilm formation by the pathogen was greatly hampered and never exceeded 3logCFU/cm 2 , while under the same conditions, APC biofilm counts had already surpassed 7logCFU/cm 2 by the end of the first 96h of incubation. All here tested disinfection treatments, composed of two common food industry biocides gradually applied for 15 to 30min, were insufficient against L. monocytogenes mono-species biofilm communities, with the resistance of the latter to significantly increase from the 3rd to 7th day of incubation. However, all these treatments resulted in no detectable L. monocytogenes cells upon their application against the mixed-culture sessile communities also containing the fish indigenous microflora, something probably associated with the low attached population level of these pathogenic cells before disinfection (<10 2 CFU/cm 2 ) under such mixed-culture conditions. Taken together, all these results expand our knowledge on both the population dynamics and resistance of L. monocytogenes biofilm cells under conditions resembling those encountered within the seafood industry and should be considered upon designing and applying effective anti-biofilm strategies. Copyright © 2017 Elsevier B.V. All rights reserved.

Monte Carlo simulation models of breeding-population advancement.

Treesearch

J.N. King; G.R. Johnson

1993-01-01

Five generations of population improvement were modeled using Monte Carlo simulations. The model was designed to address questions that are important to the development of an advanced generation breeding population. Specifically we addressed the effects on both gain and effective population size of different mating schemes when creating a recombinant population for...

Effects of dimensionality and laser polarization on kinetic simulations of laser-ion acceleration in the transparency regime

NASA Astrophysics Data System (ADS)

Stark, David; Yin, Lin; Albright, Brian; Guo, Fan

2017-10-01

The often cost-prohibitive nature of three-dimensional (3D) kinetic simulations of laser-plasma interactions has resulted in heavy use of two-dimensional (2D) simulations to extract physics. However, depending on whether the polarization is modeled as 2D-S or 2D-P (laser polarization in and out of the simulation plane, respectively), different results arise. In laser-ion acceleration in the transparency regime, VPIC particle-in-cell simulations show that 2D-S and 2D-P capture different physics that appears in 3D simulations. The electron momentum distribution is virtually two-dimensional in 2D-P, unlike the more isotropic distributions in 2D-S and 3D, leading to greater heating in the simulation plane. As a result, target expansion time scales and density thresholds for the onset of relativistic transparency differ dramatically between 2D-S and 2D-P. The artificial electron heating in 2D-P exaggerates the effectiveness of target-normal sheath acceleration (TNSA) into its dominant acceleration mechanism, whereas 2D-S and 3D both have populations accelerated preferentially during transparency to higher energies than those of TNSA. Funded by the LANL Directed Research and Development Program.

A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

PubMed Central

Eissing, Thomas; Kuepfer, Lars; Becker, Corina; Block, Michael; Coboeken, Katrin; Gaub, Thomas; Goerlitz, Linus; Jaeger, Juergen; Loosen, Roland; Ludewig, Bernd; Meyer, Michaela; Niederalt, Christoph; Sevestre, Michael; Siegmund, Hans-Ulrich; Solodenko, Juri; Thelen, Kirstin; Telle, Ulrich; Weiss, Wolfgang; Wendl, Thomas; Willmann, Stefan; Lippert, Joerg

2011-01-01

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach. PMID:21483730

A dental public health approach based on computational mathematics: Monte Carlo simulation of childhood dental decay.

PubMed

Tennant, Marc; Kruger, Estie

2013-02-01

This study developed a Monte Carlo simulation approach to examining the prevalence and incidence of dental decay using Australian children as a test environment. Monte Carlo simulation has been used for a half a century in particle physics (and elsewhere); put simply, it is the probability for various population-level outcomes seeded randomly to drive the production of individual level data. A total of five runs of the simulation model for all 275,000 12-year-olds in Australia were completed based on 2005-2006 data. Measured on average decayed/missing/filled teeth (DMFT) and DMFT of highest 10% of sample (Sic10) the runs did not differ from each other by more than 2% and the outcome was within 5% of the reported sampled population data. The simulations rested on the population probabilities that are known to be strongly linked to dental decay, namely, socio-economic status and Indigenous heritage. Testing the simulated population found DMFT of all cases where DMFT<>0 was 2.3 (n = 128,609) and DMFT for Indigenous cases only was 1.9 (n = 13,749). In the simulation population the Sic25 was 3.3 (n = 68,750). Monte Carlo simulations were created in particle physics as a computational mathematical approach to unknown individual-level effects by resting a simulation on known population-level probabilities. In this study a Monte Carlo simulation approach to childhood dental decay was built, tested and validated. © 2013 FDI World Dental Federation.

On-lattice agent-based simulation of populations of cells within the open-source Chaste framework.

PubMed

Figueredo, Grazziela P; Joshi, Tanvi V; Osborne, James M; Byrne, Helen M; Owen, Markus R

2013-04-06

Over the years, agent-based models have been developed that combine cell division and reinforced random walks of cells on a regular lattice, reaction-diffusion equations for nutrients and growth factors; and ordinary differential equations for the subcellular networks regulating the cell cycle. When linked to a vascular layer, this multiple scale model framework has been applied to tumour growth and therapy. Here, we report on the creation of an agent-based multi-scale environment amalgamating the characteristics of these models within a Virtual Physiological Human (VPH) Exemplar Project. This project enables reuse, integration, expansion and sharing of the model and relevant data. The agent-based and reaction-diffusion parts of the multi-scale model have been implemented and are available for download as part of the latest public release of Chaste (Cancer, Heart and Soft Tissue Environment; http://www.cs.ox.ac.uk/chaste/), part of the VPH Toolkit (http://toolkit.vph-noe.eu/). The environment functionalities are verified against the original models, in addition to extra validation of all aspects of the code. In this work, we present the details of the implementation of the agent-based environment, including the system description, the conceptual model, the development of the simulation model and the processes of verification and validation of the simulation results. We explore the potential use of the environment by presenting exemplar applications of the 'what if' scenarios that can easily be studied in the environment. These examples relate to tumour growth, cellular competition for resources and tumour responses to hypoxia (low oxygen levels). We conclude our work by summarizing the future steps for the expansion of the current system.

Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling

PubMed Central

Santos, Guido; Lai, Xin; Eberhardt, Martin; Vera, Julio

2018-01-01

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By “multi-level” we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization. PMID:29868515

Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling.

PubMed

Santos, Guido; Lai, Xin; Eberhardt, Martin; Vera, Julio

2018-01-01

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By "multi-level" we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems.

PubMed

Ghaffarizadeh, Ahmadreza; Heiland, Randy; Friedman, Samuel H; Mumenthaler, Shannon M; Macklin, Paul

2018-02-01

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Simulated bat populations erode when exposed to climate change projections for western North America

PubMed Central

Adams, Rick A.

2017-01-01

Recent research has demonstrated that temperature and precipitation conditions correlate with successful reproduction in some insectivorous bat species that live in arid and semiarid regions, and that hot and dry conditions correlate with reduced lactation and reproductive output by females of some species. However, the potential long-term impacts of climate-induced reproductive declines on bat populations in western North America are not well understood. We combined results from long-term field monitoring and experiments in our study area with information on vital rates to develop stochastic age-structured population dynamics models and analyzed how simulated fringed myotis (Myotis thysanodes) populations changed under projected future climate conditions in our study area near Boulder, Colorado (Boulder Models) and throughout western North America (General Models). Each simulation consisted of an initial population of 2,000 females and an approximately stable age distribution at the beginning of the simulation. We allowed each population to be influenced by the mean annual temperature and annual precipitation for our study area and a generalized range-wide model projected through year 2086, for each of four carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, RCP6.0, RCP8.5). Each population simulation was repeated 10,000 times. Of the 8 Boulder Model simulations, 1 increased (+29.10%), 3 stayed approximately stable (+2.45%, +0.05%, -0.03%), and 4 simulations decreased substantially (-44.10%, -44.70%, -44.95%, -78.85%). All General Model simulations for western North America decreased by >90% (-93.75%, -96.70%, -96.70%, -98.75%). These results suggest that a changing climate in western North America has the potential to quickly erode some forest bat populations including species of conservation concern, such as fringed myotis. PMID:28686737

Simulated bat populations erode when exposed to climate change projections for western North America.

PubMed

Hayes, Mark A; Adams, Rick A

2017-01-01

Recent research has demonstrated that temperature and precipitation conditions correlate with successful reproduction in some insectivorous bat species that live in arid and semiarid regions, and that hot and dry conditions correlate with reduced lactation and reproductive output by females of some species. However, the potential long-term impacts of climate-induced reproductive declines on bat populations in western North America are not well understood. We combined results from long-term field monitoring and experiments in our study area with information on vital rates to develop stochastic age-structured population dynamics models and analyzed how simulated fringed myotis (Myotis thysanodes) populations changed under projected future climate conditions in our study area near Boulder, Colorado (Boulder Models) and throughout western North America (General Models). Each simulation consisted of an initial population of 2,000 females and an approximately stable age distribution at the beginning of the simulation. We allowed each population to be influenced by the mean annual temperature and annual precipitation for our study area and a generalized range-wide model projected through year 2086, for each of four carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, RCP6.0, RCP8.5). Each population simulation was repeated 10,000 times. Of the 8 Boulder Model simulations, 1 increased (+29.10%), 3 stayed approximately stable (+2.45%, +0.05%, -0.03%), and 4 simulations decreased substantially (-44.10%, -44.70%, -44.95%, -78.85%). All General Model simulations for western North America decreased by >90% (-93.75%, -96.70%, -96.70%, -98.75%). These results suggest that a changing climate in western North America has the potential to quickly erode some forest bat populations including species of conservation concern, such as fringed myotis.

Chemical Memory Reactions Induced Bursting Dynamics in Gene Expression

PubMed Central

Tian, Tianhai

2013-01-01

Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems. PMID:23349679

Chemical memory reactions induced bursting dynamics in gene expression.

PubMed

Tian, Tianhai

2013-01-01

Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems.

Computationally efficient simulation of electrical activity at cell membranes interacting with self-generated and externally imposed electric fields

NASA Astrophysics Data System (ADS)

Agudelo-Toro, Andres; Neef, Andreas

2013-04-01

Objective. We present a computational method that implements a reduced set of Maxwell's equations to allow simulation of cells under realistic conditions: sub-micron cell morphology, a conductive non-homogeneous space and various ion channel properties and distributions. Approach. While a reduced set of Maxwell's equations can be used to couple membrane currents to extra- and intracellular potentials, this approach is rarely taken, most likely because adequate computational tools are missing. By using these equations, and introducing an implicit solver, numerical stability is attained even with large time steps. The time steps are limited only by the time development of the membrane potentials. Main results. This method allows simulation times of tens of minutes instead of weeks, even for complex problems. The extracellular fields are accurately represented, including secondary fields, which originate at inhomogeneities of the extracellular space and can reach several millivolts. We present a set of instructive examples that show how this method can be used to obtain reference solutions for problems, which might not be accurately captured by the traditional approaches. This includes the simulation of realistic magnitudes of extracellular action potential signals in restricted extracellular space. Significance. The electric activity of neurons creates extracellular potentials. Recent findings show that these endogenous fields act back onto the neurons, contributing to the synchronization of population activity. The influence of endogenous fields is also relevant for understanding therapeutic approaches such as transcranial direct current, transcranial magnetic and deep brain stimulation. The mutual interaction between fields and membrane currents is not captured by today's concepts of cellular electrophysiology, including the commonly used activation function, as those concepts are based on isolated membranes in an infinite, isopotential extracellular space. The presented tool makes simulations with detailed morphology and implicit interactions of currents and fields available to the electrophysiology community.

Building Better Planet Populations for EXOSIMS

NASA Astrophysics Data System (ADS)

Garrett, Daniel; Savransky, Dmitry

2018-01-01

The Exoplanet Open-Source Imaging Mission Simulator (EXOSIMS) software package simulates ensembles of space-based direct imaging surveys to provide a variety of science and engineering yield distributions for proposed mission designs. These mission simulations rely heavily on assumed distributions of planetary population parameters including semi-major axis, planetary radius, eccentricity, albedo, and orbital orientation to provide heuristics for target selection and to simulate planetary systems for detection and characterization. The distributions are encoded in PlanetPopulation modules within EXOSIMS which are selected by the user in the input JSON script when a simulation is run. The earliest written PlanetPopulation modules available in EXOSIMS are based on planet population models where the planetary parameters are considered to be independent from one another. While independent parameters allow for quick computation of heuristics and sampling for simulated planetary systems, results from planet-finding surveys have shown that many parameters (e.g., semi-major axis/orbital period and planetary radius) are not independent. We present new PlanetPopulation modules for EXOSIMS which are built on models based on planet-finding survey results where semi-major axis and planetary radius are not independent and provide methods for sampling their joint distribution. These new modules enhance the ability of EXOSIMS to simulate realistic planetary systems and give more realistic science yield distributions.

Development of an establishment scheme for a DGVM

NASA Astrophysics Data System (ADS)

Song, Xiang; Zeng, Xiaodong; Zhu, Jiawen; Shao, Pu

2016-07-01

Environmental changes are expected to shift the distribution and abundance of vegetation by determining seedling establishment and success. However, most current ecosystem models only focus on the impacts of abiotic factors on biogeophysics (e.g., global distribution, etc.), ignoring their roles in the population dynamics (e.g., seedling establishment rate, mortality rate, etc.) of ecological communities. Such neglect may lead to biases in ecosystem population dynamics (such as changes in population density for woody species in forest ecosystems) and characteristics. In the present study, a new establishment scheme for introducing soil water as a function rather than a threshold was developed and validated, using version 1.0 of the IAP-DGVM as a test bed. The results showed that soil water in the establishment scheme had a remarkable influence on forest transition zones. Compared with the original scheme, the new scheme significantly improved simulations of tree population density, especially in the peripheral areas of forests and transition zones. Consequently, biases in forest fractional coverage were reduced in approximately 78.8% of the global grid cells. The global simulated areas of tree, shrub, grass and bare soil performed better, where the relative biases were reduced from 34.3% to 4.8%, from 27.6% to 13.1%, from 55.2% to 9.2%, and from 37.6% to 3.6%, respectively. Furthermore, the new scheme had more reasonable dependencies of plant functional types (PFTs) on mean annual precipitation, and described the correct dominant PFTs in the tropical rainforest peripheral areas of the Amazon and central Africa.

Enhancing apoptosis in TRAIL-resistant cancer cells using fundamental response rules

PubMed Central

Piras, Vincent; Hayashi, Kentaro; Tomita, Masaru; Selvarajoo, Kumar

2011-01-01

The tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induces apoptosis in malignant cells, while leaving other cells mostly unharmed. However, several carcinomas remain resistant to TRAIL. To investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells, we developed a computational model to analyze the temporal activation profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several (FADD, RIP1, TRAF2 and caspase-8) knock-down conditions. Based on perturbation-response approach utilizing the law of information (signaling flux) conservation, we derived response rules for population-level average cell response. From this approach, i) a FADD-independent pathway to activate p38 and JNK, ii) a crosstalk between RIP1 and p38, and iii) a crosstalk between p62 and JNK are predicted. Notably, subsequent simulations suggest that targeting a novel molecule at p62/sequestosome-1 junction will optimize apoptosis through signaling flux redistribution. This study offers a valuable prospective to sensitive TRAIL-based therapy. PMID:22355661

A Computational Study of Phenotype Switching in Bacillus Subtilis Biofilm

NASA Astrophysics Data System (ADS)

Smith, Howard; Wang, Xiaoling; Jiang, Yi

Bacillus Subtilis (B. Subtilis), is known to differentiate into three main phenotypes during biofilm growth. Novel techniques to track the spatial and temporal evolution of the three main phenotypes exhibited by B. Subtilis have been developed. However, the techniques do not explain the environmental causes of the phenotype switching and how this leads to the spatiotemporal organization of the biofilm. We hypothesize that cells switch their phenotype according to nutrients and autoinducer levels. We test the hypothesis using a hybrid agent-based and continuous model. The bacteria in our model are individual cells that can (i) grow and divide by the intake of nutrients, (ii) produce and secrete EPS, (iii) form spores and (iv) produce an auto inducer. Using a threshold for nutrient and thresholds for autoinducers, we were able to reproduce the experimental spatiotemporal dynamics. From our simulations we observed that in order to reproduce experimental results, two different autoinducers were necessary. The results also suggest that low-EPS producing biofilms generally obtained higher cell populations. Furthermore, most of the cells that become spore forming cells arise from matrix producing cells.

On Information Metrics for Spatial Coding.

PubMed

Souza, Bryan C; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano B L

2018-04-01

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Capturing CD4 cells using a functionalized circular microfluidic device and glutaraldehyde as biolinker for tuberculosis detection and diagnosis

NASA Astrophysics Data System (ADS)

Shih, Yeu-Farn; Huang, Nien-Tsu; Lee, Chih-Kung

2015-03-01

It is estimated that about one-third of the world's population has already been infected by tuberculosis. Mycobacterium tuberculosis, in general, can result in an active case of tuberculosis in approximately 5%-10% of those who suffer from latent tuberculosis and the chance of becoming ill is the highest within one of year of getting the disease. Although a newly developed methods called interferon gamma release assay (IGRA) can monitor CD4 cells secreted cytokine to diagnose tuberculosis (TB) condition. However, it is difficult to count total numbers of cytokine secreted CD4 cells, which make the diagnosis less accurate. Therefore, we develop a functionalized polydimethylsiloxane (PDMS) device using glutaraldehyde to capture CD4 cells. To enhance the capture efficiency, we use COMSOL simulation to optimize the arrangement of PDMS micro pillars to make cells uniformly distributed in the device. Our preliminary data showed the microfluidic configuration in a circular shape with HCP patterned micro pillars turned 30 degrees offers the highest cell capture rate.

Transcriptional dynamics with time-dependent reaction rates

NASA Astrophysics Data System (ADS)

Nandi, Shubhendu; Ghosh, Anandamohan

2015-02-01

Transcription is the first step in the process of gene regulation that controls cell response to varying environmental conditions. Transcription is a stochastic process, involving synthesis and degradation of mRNAs, that can be modeled as a birth-death process. We consider a generic stochastic model, where the fluctuating environment is encoded in the time-dependent reaction rates. We obtain an exact analytical expression for the mRNA probability distribution and are able to analyze the response for arbitrary time-dependent protocols. Our analytical results and stochastic simulations confirm that the transcriptional machinery primarily act as a low-pass filter. We also show that depending on the system parameters, the mRNA levels in a cell population can show synchronous/asynchronous fluctuations and can deviate from Poisson statistics.

Multidrug resistance-associated protein 4 (MRP4) controls ganciclovir intracellular accumulation and contributes to ganciclovir-induced neutropenia in renal transplant patients.

PubMed

Billat, Pierre-André; Ossman, Tahani; Saint-Marcoux, Franck; Essig, Marie; Rerolle, Jean-Philippe; Kamar, Nassim; Rostaing, Lionel; Kaminski, Hannah; Fabre, Gabin; Otyepka, Michal; Woillard, Jean-Baptiste; Marquet, Pierre; Trouillas, Patrick; Picard, Nicolas

2016-09-01

Ganciclovir (GCV) is the cornerstone of cytomegalovirus prevention and treatment in transplant patients. It is associated with problematic adverse hematological effects in this population of immunosuppressed patients, which may lead to dose reduction thus favoring resistance. GCV crosses the membranes of cells, is activated by phosphorylation, and then stops the replication of viral DNA. Its intracellular accumulation might favor host DNA polymerase inhibition, hence toxicity. Following this hypothesis, we investigated the association between a selected panel of membrane transporter polymorphisms and the evolution of neutrophil counts in n=174 renal transplant recipients. An independent population of n=96 renal transplants served as a replication and experiments using HEK293T-transfected cells were performed to validate the clinical findings. In both cohorts, we found a variant in ABCC4 (rs11568658) associated with decreased neutrophil counts following valganciclovir (GCV prodrug) administration (exploratory cohort: β±SD=-0.68±0.28, p=0.029; replication cohort: β±SD=-0.84±0.29, p=0.0078). MRP4-expressing cells showed decreased GCV accumulation as compared to negative control cells (transfected with an empty vector) (-61%; p<0.0001). The efflux process was almost abolished in cells expressing MRP4 rs11568658 variant protein. Molecular dynamic simulations of GCV membrane crossing showed a preferred location of the drug just beneath the polar head group region, which supports its interaction with efflux transporters. Copyright © 2016 Elsevier Ltd. All rights reserved.

A model system for pathogen detection using a two-component bacteriophage/bioluminescent signal amplification assay

NASA Astrophysics Data System (ADS)

Bright, Nathan G.; Carroll, Richard J.; Applegate, Bruce M.

2004-03-01

Microbial contamination has become a mounting concern the last decade due to an increased emphasis of minimally processed food products specifically produce, and the recognition of foodborne pathogens such as Campylobacter jejuni, Escherichia coli O157:H7, and Listeria monocytogenes. This research investigates a detection approach utilizing bacteriophage pathogen specificity coupled with a bacterial bioluminescent bioreporter utilizing the quorum sensing molecule from Vibrio fischeri, N-(3-oxohexanoyl)-homoserine lactone (3-oxo-C6-HSL). The 3-oxo-C6-HSL molecules diffuse out of the target cell after infection and induce bioluminescence from a population of 3-oxo-C6-HSL bioreporters (ROLux). E. coli phage M13, a well-characterized bacteriophage, offers a model system testing the use of bacteriophage for pathogen detection through cell-to-cell communication via a LuxR/3-oxo-C6-HSL system. Simulated temperate phage assays tested functionality of the ROLux reporter and production of 3-oxo-C6-HSL by various test strains. These assays showed detection limits of 102cfu after 24 hours in a varietry of detection formats. Assays incorporating the bacteriophage M13-luxI with the ROLux reporter and a known population of target cells were subsequently developed and have shown consistent detection limits of 105cfu target organisms. Measurable light response from high concentrations of target cells was almost immediate, suggesting an enrichment step to further improve detection limits and reduce assay time.

Simulating cancer growth with multiscale agent-based modeling.

PubMed

Wang, Zhihui; Butner, Joseph D; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S

2015-02-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural dynamics of motion processing and speed discrimination.

PubMed

Chey, J; Grossberg, S; Mingolla, E

1998-09-01

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-turned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1-->MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.

An epidemiological modelling study to estimate the composition of HIV-positive populations including migrants from endemic settings.

PubMed

Nakagawa, Fumiyo

2017-01-28

Migrants account for a significant number of people living with HIV in Europe, and it is important to fully consider this population in national estimates. Using a novel approach with the UK as an example, we present key public health measures of the HIV epidemic, taking into account both in-country infections and infections likely to have been acquired abroad. Mathematical model calibrated to extensive data sources. An individual-based stochastic simulation model is used to calibrate to routinely collected surveillance data in the UK. Data on number of new HIV diagnoses, number of deaths, CD4 cell count at diagnosis, as well as time of arrival into the UK for migrants and the annual number of people receiving care were used. An estimated 106 400 (90% plausibility range: 88 700-124 600) people were living with HIV in the UK in 2013. Twenty-three percent of these people, 24 600 (15 000-36 200) were estimated to be undiagnosed; this number has remained stable over the last decade. An estimated 32% of the total undiagnosed population had CD4 cell count less than 350 cells/μl in 2013. Twenty-five and 23% of black African men and women heterosexuals living with HIV were undiagnosed respectively. We have shown a working example to characterize the HIV population in a European context which incorporates migrants from countries with generalized epidemics. Despite all aspects of HIV care being free and widely available to anyone in need in the UK, there is still a substantial number of people who are not yet diagnosed and thus not in care.

Concurrent Isolation of 3 Distinct Cardiac Stem Cell Populations From a Single Human Heart Biopsy.

PubMed

Monsanto, Megan M; White, Kevin S; Kim, Taeyong; Wang, Bingyan J; Fisher, Kristina; Ilves, Kelli; Khalafalla, Farid G; Casillas, Alexandria; Broughton, Kathleen; Mohsin, Sadia; Dembitsky, Walter P; Sussman, Mark A

2017-07-07

The relative actions and synergism between distinct myocardial-derived stem cell populations remain obscure. Ongoing debates on optimal cell population(s) for treatment of heart failure prompted implementation of a protocol for isolation of multiple stem cell populations from a single myocardial tissue sample to develop new insights for achieving myocardial regeneration. Establish a robust cardiac stem cell isolation and culture protocol to consistently generate 3 distinct stem cell populations from a single human heart biopsy. Isolation of 3 endogenous cardiac stem cell populations was performed from human heart samples routinely discarded during implantation of a left ventricular assist device. Tissue explants were mechanically minced into 1 mm 3 pieces to minimize time exposure to collagenase digestion and preserve cell viability. Centrifugation removes large cardiomyocytes and tissue debris producing a single cell suspension that is sorted using magnetic-activated cell sorting technology. Initial sorting is based on tyrosine-protein kinase Kit (c-Kit) expression that enriches for 2 c-Kit + cell populations yielding a mixture of cardiac progenitor cells and endothelial progenitor cells. Flowthrough c-Kit - mesenchymal stem cells are positively selected by surface expression of markers CD90 and CD105. After 1 week of culture, the c-Kit + population is further enriched by selection for a CD133 + endothelial progenitor cell population. Persistence of respective cell surface markers in vitro is confirmed both by flow cytometry and immunocytochemistry. Three distinct cardiac cell populations with individualized phenotypic properties consistent with cardiac progenitor cells, endothelial progenitor cells, and mesenchymal stem cells can be successfully concurrently isolated and expanded from a single tissue sample derived from human heart failure patients. © 2017 American Heart Association, Inc.

Reconstructing a Large-Scale Population for Social Simulation

NASA Astrophysics Data System (ADS)

Fan, Zongchen; Meng, Rongqing; Ge, Yuanzheng; Qiu, Xiaogang

The advent of social simulation has provided an opportunity to research on social systems. More and more researchers tend to describe the components of social systems in a more detailed level. Any simulation needs the support of population data to initialize and implement the simulation systems. However, it's impossible to get the data which provide full information about individuals and households. We propose a two-step method to reconstruct a large-scale population for a Chinese city according to Chinese culture. Firstly, a baseline population is generated through gathering individuals into households one by one; secondly, social relationships such as friendship are assigned to the baseline population. Through a case study, a population of 3,112,559 individuals gathered in 1,133,835 households is reconstructed for Urumqi city, and the results show that the generated data can respect the real data quite well. The generated data can be applied to support modeling of some social phenomenon.

Modeling Early Galaxies Using Radiation Hydrodynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

This simulation uses a flux-limited diffusion solver to explore the radiation hydrodynamics of early galaxies, in particular, the ionizing radiation created by Population III stars. At the time of this rendering, the simulation has evolved to a redshift of 3.5. The simulation volume is 11.2 comoving megaparsecs, and has a uniform grid of 10243 cells, with over 1 billion dark matter and star particles. This animation shows a combined view of the baryon density, dark matter density, radiation energy and emissivity from this simulation. The multi-variate rendering is particularly useful because is shows both the baryonic matter ("normal") and darkmore » matter, and the pressure and temperature variables are properties of only the baryonic matter. Visible in the gas density are "bubbles", or shells, created by the radiation feedback from young stars. Seeing the bubbles from feedback provides confirmation of the physics model implemented. Features such as these are difficult to identify algorithmically, but easily found when viewing the visualization. Simulation was performed on Kraken at the National Institute for Computational Sciences. Visualization was produced using resources of the Argonne Leadership Computing Facility at Argonne National Laboratory.« less

The Emergence of Predators in Early Life: There was No Garden of Eden

PubMed Central

de Nooijer, Silvester; Holland, Barbara R.; Penny, David

2009-01-01

Background Eukaryote cells are suggested to arise somewhere between 0.85∼2.7 billion years ago. However, in the present world of unicellular organisms, cells that derive their food and metabolic energy from larger cells engulfing smaller cells (phagocytosis) are almost exclusively eukaryotic. Combining these propositions, that eukaryotes were the first phagocytotic predators and that they arose only 0.85∼2.7 billion years ago, leads to an unexpected prediction of a long period (∼1–3 billion years) with no phagocytotes – a veritable Garden of Eden. Methodology We test whether such a long period is reasonable by simulating a population of very simple unicellular organisms - given only basic physical, biological and ecological principles. Under a wide range of initial conditions, cellular specialization occurs early in evolution; we find a range of cell types from small specialized primary producers to larger opportunistic or specialized predators. Conclusions Both strategies, specialized smaller cells and phagocytotic larger cells are apparently fundamental biological strategies that are expected to arise early in cellular evolution. Such early predators could have been ‘prokaryotes’, but if the earliest cells on the eukaryote lineage were predators then this explains most of their characteristic features. PMID:19492046

Generating Inviscid and Viscous Fluid-Flow Simulations over an Aircraft Surface Using a Fluid-Flow Mesh

NASA Technical Reports Server (NTRS)

Rodriguez, David L. (Inventor); Sturdza, Peter (Inventor)

2013-01-01

Fluid-flow simulation over a computer-generated aircraft surface is generated using inviscid and viscous simulations. A fluid-flow mesh of fluid cells is obtained. At least one inviscid fluid property for the fluid cells is determined using an inviscid fluid simulation that does not simulate fluid viscous effects. A set of intersecting fluid cells that intersects the aircraft surface are identified. One surface mesh polygon of the surface mesh is identified for each intersecting fluid cell. A boundary-layer prediction point for each identified surface mesh polygon is determined. At least one boundary-layer fluid property for each boundary-layer prediction point is determined using the at least one inviscid fluid property of the corresponding intersecting fluid cell and a boundary-layer simulation that simulates fluid viscous effects. At least one updated fluid property for at least one fluid cell is determined using the at least one boundary-layer fluid property and the inviscid fluid simulation.

A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation.

PubMed

Purvis, Jeremy E; Chatterjee, Manash S; Brass, Lawrence F; Diamond, Scott L

2008-11-15

To quantify how various molecular mechanisms are integrated to maintain platelet homeostasis and allow responsiveness to adenosine diphosphate (ADP), we developed a computational model of the human platelet. Existing kinetic information for 77 reactions, 132 fixed kinetic rate constants, and 70 species was combined with electrochemical calculations, measurements of platelet ultrastructure, novel experimental results, and published single-cell data. The model accurately predicted: (1) steady-state resting concentrations for intracellular calcium, inositol 1,4,5-trisphosphate, diacylglycerol, phosphatidic acid, phosphatidylinositol, phosphatidylinositol phosphate, and phosphatidylinositol 4,5-bisphosphate; (2) transient increases in intracellular calcium, inositol 1,4,5-trisphosphate, and G(q)-GTP in response to ADP; and (3) the volume of the platelet dense tubular system. A more stringent test of the model involved stochastic simulation of individual platelets, which display an asynchronous calcium spiking behavior in response to ADP. Simulations accurately reproduced the broad frequency distribution of measured spiking events and demonstrated that asynchronous spiking was a consequence of stochastic fluctuations resulting from the small volume of the platelet. The model also provided insights into possible mechanisms of negative-feedback signaling, the relative potency of platelet agonists, and cell-to-cell variation across platelet populations. This integrative approach to platelet biology offers a novel and complementary strategy to traditional reductionist methods.

SU-E-T-667: Radiosensitization Due to Gold Nanoparticles: A Monte Carlo Cellular Dosimetry Investigation of An Expansive Parameter Space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martinov, M; Thomson, R

2015-06-15

Purpose: To investigate dose enhancement to cellular compartments following gold nanoparticle (GNP) uptake in tissue, varying cell and tissue morphology, intra and extracellular GNP distribution, and source energy using Monte Carlo (MC) simulations. Methods: Models of single and multiple cells are developed for normal and cancerous tissues; cells (outer radii 5–10 µm) are modeled as concentric spheres comprising the nucleus (radii 2.5–7.5 µm) and cytoplasm. GNP distributions modeled include homogeneous distributions throughout the cytoplasm, variable numbers of GNP-containing endosomes within the cytoplasm, or distributed in a spherical shell about the nucleus. Gold concentrations range from 1 to 30 mg/g. Dosemore » to nucleus and to cytoplasm for simulations including GNPs are compared to simulations without GNPs to compute Nuclear and Cytoplasm Dose Enhancement Factors (NDEF, CDEF). Photon source energies are between 20 keV and 1.25 MeV. Results: DEFs are highly sensitive to GNP intracellular distribution; for a 2.5 µm radius nucleus irradiated by a 30 keV source, NDEF varies from 1.2 for a single endosome containing all GNPs to 8.2 for GNPs distributed about the nucleus (7 mg/g). DEFs vary with cell dimensions and source energy: NDEFs vary from 2.5 (90 keV) to 8.2 (30 keV) for a 2.5 µm radius nucleus and from 1.1 (90 keV) to 1.3 (30 keV) for a 7.5 µm radius nucleus, both with GNPs in a spherical shell about the nucleus (7 mg/g). NDEF and CDEF are generally different within a single cell. For multicell models, the presence of gold within intervening tissues between source and target perturbs the fluence reaching cellular targets, resulting in DEF inhomogeneities within a population of irradiated cells. Conclusion: DEFs vary by an order of magnitude for different cell models, GNP distributions, and source energies, demonstrating the importance of detailed modelling for advancing GNP development for radiotherapy. Funding provided by the Natural Sciences and Engineering Council of Canada (NSERC), and the Canada Research Chairs Program (CRC)« less

Linear time algorithms to construct populations fitting multiple constraint distributions at genomic scales.

PubMed

Siragusa, Enrico; Haiminen, Niina; Utro, Filippo; Parida, Laxmi

2017-10-09

Computer simulations can be used to study population genetic methods, models and parameters, as well as to predict potential outcomes. For example, in plant populations, predicting the outcome of breeding operations can be studied using simulations. In-silico construction of populations with pre-specified characteristics is an important task in breeding optimization and other population genetic studies. We present two linear time Simulation using Best-fit Algorithms (SimBA) for two classes of problems where each co-fits two distributions: SimBA-LD fits linkage disequilibrium and minimum allele frequency distributions, while SimBA-hap fits founder-haplotype and polyploid allele dosage distributions. An incremental gap-filling version of previously introduced SimBA-LD is here demonstrated to accurately fit the target distributions, allowing efficient large scale simulations. SimBA-hap accuracy and efficiency is demonstrated by simulating tetraploid populations with varying numbers of founder haplotypes, we evaluate both a linear time greedy algoritm and an optimal solution based on mixed-integer programming. SimBA is available on http://researcher.watson.ibm.com/project/5669.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

PubMed

Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

Simultaneous multi-species tracking in live cells with quantum dot conjugates.

PubMed

Clausen, Mathias P; Arnspang, Eva C; Ballou, Byron; Bear, James E; Lagerholm, B Christoffer

2014-01-01

Quantum dots are available in a range of spectrally separated emission colors and with a range of water-stabilizing surface coatings that offers great flexibility for enabling bio-specificity. In this study, we have taken advantage of this flexibility to demonstrate that it is possible to perform a simultaneous investigation of the lateral dynamics in the plasma membrane of i) the transmembrane epidermal growth factor receptor, ii) the glucosylphospatidylinositol-anchored protein CD59, and iii) ganglioside GM1-cholera toxin subunit B clusters in a single cell. We show that a large number of the trajectories are longer than 50 steps, which we by simulations show to be sufficient for robust single trajectory analysis. This analysis shows that the populations of the diffusion coefficients are heterogeneously distributed for all three species, but differ between the different species. We further show that the heterogeneity is decreased upon treating the cells with methyl-β-cyclodextrin.

Global stability and tumor clearance conditions for a cancer chemotherapy system

NASA Astrophysics Data System (ADS)

Valle, Paul A.; Starkov, Konstantin E.; Coria, Luis N.

2016-11-01

In this paper we study the global dynamics of a cancer chemotherapy system presented by de Pillis et al. (2007). This mathematical model describes the interaction between tumor cells, effector-immune cells, circulating lymphocytes and chemotherapy treatment. By applying the localization method of compact invariant sets, we find lower and upper bounds for these three cells populations. Further, we define a bounded domain in R+,04 where all compact invariant sets of the system are located and provide conditions under which this domain is positively invariant. We apply LaSalle's invariance principle and one result concerning two-dimensional competitive systems in order to derive sufficient conditions for tumor clearance and global asymptotic stability of the tumor-free equilibrium point. These conditions are computed by using bounds of the localization domain and they are given in terms of the chemotherapy treatment. Finally, we perform numerical simulations in order to illustrate our results.

Human Prostate Side Population Cells Demonstrate Stem Cell Properties in Recombination with Urogenital Sinus Mesenchyme

PubMed Central

Foster, Barbara A.; Gangavarapu, Kalyan J.; Mathew, Grinu; Azabdaftari, Gissou; Morrison, Carl D.; Miller, Austin; Huss, Wendy J.

2013-01-01

Stem cell enrichment provides a tool to examine prostate stem cells obtained from benign and malignant tissue. Functional assays can enrich stem cells based on common stem cell phenotypes, such as high ATP binding cassette (ABC) transporter mediated efflux of Hoechst substrates (side population assay). This functional assay is based upon mechanisms that protect cells from environmental insult thus contributing to the survival and protection of the stem cell population. We have isolated and analyzed cells digested from twelve clinical prostate specimens based on the side population assay. Prostate stem cell properties of the isolated cells were tested by serial recombination with rat urogenital mesenchyme. Recombinants with side population cells demonstrate an increase in the frequency of human ductal growth and the number of glands per recombinant when compared to recombinants with non-side population cells. Isolated cells were capable of prostatic growth for up to three generations in the recombination assay with as little as 125 sorted prostate cells. The ability to reproducibly use cells isolated by fluorescence activated cell sorting from human prostate tissue is an essential step to a better understanding of human prostate stem cell biology. ABC transporter G2 (ABCG2) was expressed in recombinants from side population cells indicating the side population cells have self-renewal properties. Epithelial cell differentiation of recombinants was determined by immunohistochemical analysis for expression of the basal, luminal, and neuroendocrine markers, p63, androgen receptor, prostate specific antigen, and chromogranin A, respectively. Thus, the ABCG2 expressing side population demonstrates multipotency and self-renewal properties indicating stem cells are within this population. PMID:23383057

A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy

NASA Astrophysics Data System (ADS)

Portz, Travis; Kuang, Yang; Nagy, John D.

2012-03-01

Prostate cancer is commonly treated by a form of hormone therapy called androgen suppression. This form of treatment, while successful at reducing the cancer cell population, adversely affects quality of life and typically leads to a recurrence of the cancer in an androgen-independent form. Intermittent androgen suppression aims to alleviate some of these adverse affects by cycling the patient on and off treatment. Clinical studies have suggested that intermittent therapy is capable of maintaining androgen dependence over multiple treatment cycles while increasing quality of life during off-treatment periods. This paper presents a mathematical model of prostate cancer to study the dynamics of androgen suppression therapy and the production of prostate-specific antigen (PSA), a clinical marker for prostate cancer. Preliminary models were based on the assumption of an androgen-independent (AI) cell population with constant net growth rate. These models gave poor accuracy when fitting clinical data during simulation. The final model presented hypothesizes an AI population with increased sensitivity to low levels of androgen. It also hypothesizes that PSA production is heavily dependent on androgen. The high level of accuracy in fitting clinical data with this model appears to confirm these hypotheses, which are also consistent with biological evidence.

Making sense of snapshot data: ergodic principle for clonal cell populations

PubMed Central

2017-01-01

Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations. PMID:29187636

Making sense of snapshot data: ergodic principle for clonal cell populations.

PubMed

Thomas, Philipp

2017-11-01

Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations. © 2017 The Author(s).

A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy

NASA Astrophysics Data System (ADS)

Liu, Xiangdong; Li, Qingze; Pan, Jianxin

2018-06-01

Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.

On the global dynamics of a chronic myelogenous leukemia model

NASA Astrophysics Data System (ADS)

Krishchenko, Alexander P.; Starkov, Konstantin E.

2016-04-01

In this paper we analyze some features of global dynamics of a three-dimensional chronic myelogenous leukemia (CML) model with the help of the stability analysis and the localization method of compact invariant sets. The behavior of CML model is defined by concentrations of three cellpopulations circulating in the blood: naive T cells, effector T cells specific to CML and CML cancer cells. We prove that the dynamics of the CML system around the tumor-free equilibrium point is unstable. Further, we compute ultimate upper bounds for all three cell populations and provide the existence conditions of the positively invariant polytope. One ultimate lower bound is obtained as well. Moreover, we describe the iterative localization procedure for refining localization bounds; this procedure is based on cyclic using of localizing functions. Applying this procedure we obtain conditions under which the internal tumor equilibrium point is globally asymptotically stable. Our theoretical analyses are supplied by results of the numerical simulation.

Comment on ``Correlated noise in a logistic growth model''

NASA Astrophysics Data System (ADS)

Behera, Anita; O'Rourke, S. Francesca C.

2008-01-01

We argue that the results published by Ai [Phys. Rev. E 67, 022903 (2003)] on “correlated noise in logistic growth” are not correct. Their conclusion that, for larger values of the correlation parameter λ , the cell population is peaked at x=0 , which denotes a high extinction rate, is also incorrect. We find the reverse behavior to their results, that increasing λ promotes the stable growth of tumor cells. In particular, their results for the steady-state probability, as a function of cell number, at different correlation strengths, presented in Figs. 1 and 2 of their paper show different behavior than one would expect from the simple mathematical expression for the steady-state probability. Additionally, their interpretation that at small values of cell number the steady-state probability increases as the correlation parameter is increased is also questionable. Another striking feature in their Figs. 1 and 3 is that, for the same values of the parameters λ and α , their simulation produces two different curves, both qualitatively and quantitatively.

Efficacy of simulated cefditoren versus amoxicillin-clavulanate free concentrations in countering intrastrain ftsI gene diffusion in Haemophilus influenzae.

PubMed

González, Natalia; Aguilar, Lorenzo; Sevillano, David; Giménez, Maria-Jose; Alou, Luis; Cafini, Fabio; Torrico, Martha; López, Ana-Maria; Coronel, Pilar; Prieto, Jose

2011-06-01

This study explores the effects of cefditoren (CDN) versus amoxicillin-clavulanic acid (AMC) on the evolution (within a single strain) of total and recombined populations derived from intrastrain ftsI gene diffusion in β-lactamase-positive (BL⁺) and β-lactamase-negative (BL⁻) Haemophilus influenzae. DNA from β-lactamase-negative, ampicillin-resistant (BLNAR) isolates (DNA(BLNAR)) and from β-lactamase-positive, amoxicillin-clavulanate-resistant (BLPACR) (DNA(BLPACR)) isolates was extracted and added to a 10⁷-CFU/ml suspension of one BL⁺ strain (CDN MIC, 0.007 μg/ml; AMC MIC, 1 μg/ml) or one BL⁻ strain (CDN MIC, 0.015 μg/ml; AMC MIC, 0.5 μg/ml) in Haemophilus Test Medium (HTM). The mixture was incubated for 3 h and was then inoculated into a two-compartment computerized device simulating free concentrations of CDN (400 mg twice a day [b.i.d.]) or AMC (875 and 125 mg three times a day [t.i.d.]) in serum over 24 h. Controls were antibiotic-free simulations. Colony counts were performed; the total population and the recombined population were differentiated; and postsimulation MICs were determined. At time zero, the recombined population was 0.00095% of the total population. In controls, the BL⁻ and BL⁺ total populations and the BL⁻ recombined population increased (from ≈3 log₁₀ to 4.5 to 5 log₁₀), while the BL⁺ recombined population was maintained in simulations with DNA(BLPACR) and was decreased by ≈2 log₁₀ with DNA(BLNAR). CDN was bactericidal (percentage of the dosing interval for which experimental antibiotic concentrations exceeded the MIC [ft>MIC], >88%), and no recombined populations were detected from 4 h on. AMC was bactericidal against BL⁻ strains (ft>MIC, 74.0%) in DNA(BLNAR) and DNA(BLPACR) simulations, with a small final recombined population (MIC, 4 μg/ml; ft>MIC, 30.7%) in DNA(BLPACR) simulations. When AMC was used against the BL⁺ strain (in DNA(BLNAR) or DNA(BLPACR) simulations), the bacterial load was reduced ≈2 log₁₀ (ft>MIC, 44.3%), but 6.3% and 32% of the total population corresponded to a recombined population (MIC, 16 μg/ml; ft>MIC, 0%) in DNA(BLNAR) and DNA(BLPACR) simulations, respectively. AMC, but not CDN, unmasked BL⁺ recombined populations obtained by transformation. ft>MIC values higher than those classically considered for bacteriological response are needed to counter intrastrain ftsI gene diffusion by covering recombined populations.

Simulated microgravity, Mars gravity, and 2g hypergravity affect cell cycle regulation, ribosome biogenesis, and epigenetics in Arabidopsis cell cultures.

PubMed

Kamal, Khaled Y; Herranz, Raúl; van Loon, Jack J W A; Medina, F Javier

2018-04-23

Gravity is the only component of Earth environment that remained constant throughout the entire process of biological evolution. However, it is still unclear how gravity affects plant growth and development. In this study, an in vitro cell culture of Arabidopsis thaliana was exposed to different altered gravity conditions, namely simulated reduced gravity (simulated microgravity, simulated Mars gravity) and hypergravity (2g), to study changes in cell proliferation, cell growth, and epigenetics. The effects after 3, 14, and 24-hours of exposure were evaluated. The most relevant alterations were found in the 24-hour treatment, being more significant for simulated reduced gravity than hypergravity. Cell proliferation and growth were uncoupled under simulated reduced gravity, similarly, as found in meristematic cells from seedlings grown in real or simulated microgravity. The distribution of cell cycle phases was changed, as well as the levels and gene transcription of the tested cell cycle regulators. Ribosome biogenesis was decreased, according to levels and gene transcription of nucleolar proteins and the number of inactive nucleoli. Furthermore, we found alterations in the epigenetic modifications of chromatin. These results show that altered gravity effects include a serious disturbance of cell proliferation and growth, which are cellular functions essential for normal plant development.

Functional modeling of the human auditory brainstem response to broadband stimulationa)

PubMed Central

Verhulst, Sarah; Bharadwaj, Hari M.; Mehraei, Golbarg; Shera, Christopher A.; Shinn-Cunningham, Barbara G.

2015-01-01

Population responses such as the auditory brainstem response (ABR) are commonly used for hearing screening, but the relationship between single-unit physiology and scalp-recorded population responses are not well understood. Computational models that integrate physiologically realistic models of single-unit auditory-nerve (AN), cochlear nucleus (CN) and inferior colliculus (IC) cells with models of broadband peripheral excitation can be used to simulate ABRs and thereby link detailed knowledge of animal physiology to human applications. Existing functional ABR models fail to capture the empirically observed 1.2–2 ms ABR wave-V latency-vs-intensity decrease that is thought to arise from level-dependent changes in cochlear excitation and firing synchrony across different tonotopic sections. This paper proposes an approach where level-dependent cochlear excitation patterns, which reflect human cochlear filter tuning parameters, drive AN fibers to yield realistic level-dependent properties of the ABR wave-V. The number of free model parameters is minimal, producing a model in which various sources of hearing-impairment can easily be simulated on an individualized and frequency-dependent basis. The model fits latency-vs-intensity functions observed in human ABRs and otoacoustic emissions while maintaining rate-level and threshold characteristics of single-unit AN fibers. The simulations help to reveal which tonotopic regions dominate ABR waveform peaks at different stimulus intensities. PMID:26428802

Population pharmacokinetics and pharmacodynamics of hydroxyurea in sickle cell anemia patients, a basis for optimizing the dosing regimen

PubMed Central

2011-01-01

Background Hydroxyurea (HU) is the first approved pharmacological treatment of sickle cell anemia (SCA). The objectives of this study were to develop population pharmacokinetic(PK)-pharmacodynamic(PD) models for HU in order to characterize the exposure-efficacy relationships and their variability, compare two dosing regimens by simulations and develop some recommendations for monitoring the treatment. Methods The models were built using population modelling software NONMEM VII based on data from two clinical studies of SCA adult patients receiving 500-2000 mg of HU once daily. Fetal hemoglobin percentage (HbF%) and mean corpuscular volume (MCV) were used as biomarkers for response. A sequential modelling approach was applied. Models were evaluated using simulation-based techniques. Comparisons of two dosing regimens were performed by simulating 10000 patients in each arm during 12 months. Results The PK profiles were described by a bicompartmental model. The median (and interindividual coefficient of variation (CV)) of clearance was 11.6 L/h (30%), the central volume was 45.3 L (35%). PK steady-state was reached in about 35 days. For a given dosing regimen, HU exposure varied approximately fivefold among patients. The dynamics of HbF% and MCV were described by turnover models with inhibition of elimination of response. In the studied range of drug exposures, the effect of HU on HbF% was at its maximum (median Imax was 0.57, CV was 27%); the effect on MCV was close to its maximum, with median value of 0.14 and CV of 49%. Simulations showed that 95% of the steady-state levels of HbF% and MCV need 26 months and 3 months to be reached, respectively. The CV of the steady-state value of HbF% was about 7 times larger than that of MCV. Simulations with two different dosing regimens showed that continuous dosing led to a stronger HbF% increase in some patients. Conclusions The high variability of response to HU was related in part to pharmacokinetics and to pharmacodynamics. The steady-state value of MCV at month 3 is not predictive of the HbF% value at month 26. Hence, HbF% level may be a better biomarker for monitoring HU treatment. Continuous dosing might be more advantageous in terms of HbF% for patients who have a strong response to HU. Trial Registration The clinical studies whose data are analysed and reported in this work were not required to be registered in France at their time. Both studies were approved by local ethics committees (of Mondor Hospital and of Kremlin-Bicetre Hospital) and written informed consent was obtained from each patient. PMID:21619673

Superconductivity in human body; myth or necessity.

PubMed

Alexiou, Athanasios; Rekkas, John

2015-01-01

During the last years there is an increasing trend on the study of mitochondrial populations mainly in neural cells, due to their association with neurological disorders like Alzheimer's disease, Parkinson's disease, Autism, and CMT2A. Several studies concerning modeling of mitochondrial protein pathways, simulation of mitochondrial dynamics, biomarkers associated with Reactive Oxygen Species and many other related topics are already published. In this study we establish the idea of natural superconductivity in mitochondrial level as a necessary theoretical framework for the normal production of ATP and the avoidance of adverse reactions in Central Neural System.

Description of the meteoroid detection experiment flown on the Pioneer 10 and 11 Jupiter flyby missions

NASA Technical Reports Server (NTRS)

Oneal, R. L. (Compiler)

1974-01-01

The meteoroid detection experiment has the objective of measuring the population of 10 to the minus 9th power and 10 to the minus 8th power grams mass particles in interplanetary space with emphasis on making these measurements in the Asteroid Belt. The instrument design, which uses the pressurized-cell-penetration detection technique, and the tests involved in obtaining a flight-qualified instrument are described. The successful demonstration of flight-quality penetration detectors to function properly under long-term simulated space environments is also described.

Application of evolutionary games to modeling carcinogenesis.

PubMed

Swierniak, Andrzej; Krzeslak, Michal

2013-06-01

We review a quite large volume of literature concerning mathematical modelling of processes related to carcinogenesis and the growth of cancer cell populations based on the theory of evolutionary games. This review, although partly idiosyncratic, covers such major areas of cancer-related phenomena as production of cytotoxins, avoidance of apoptosis, production of growth factors, motility and invasion, and intra- and extracellular signaling. We discuss the results of other authors and append to them some additional results of our own simulations dealing with the possible dynamics and/or spatial distribution of the processes discussed.

Plant toxins and trophic cascades alter fire regime and succession on a boral forest landscape

USGS Publications Warehouse

Feng, Zhilan; Alfaro-Murillo, Jorge A.; DeAngelis, Donald L.; Schmidt, Jennifer; Barga, Matthew; Zheng, Yiqiang; Ahmad Tamrin, Muhammad Hanis B.; Olson, Mark; Glaser, Tim; Kielland, Knut; Chapin, F. Stuart; Bryant, John

2012-01-01

Two models were integrated in order to study the effect of plant toxicity and a trophic cascade on forest succession and fire patterns across a boreal landscape in central Alaska. One of the models, ALFRESCO, is a cellular automata model that stochastically simulates transitions from spruce dominated 1 km2 spatial cells to deciduous woody vegetation based on stochastic fires, and from deciduous woody vegetation to spruce based on age of the cell with some stochastic variation. The other model, the ‘toxin-dependent functional response’ model (TDFRM) simulates woody vegetation types with different levels of toxicity, an herbivore browser (moose) that can forage selectively on these types, and a carnivore (wolf) that preys on the herbivore. Here we replace the simple succession rules in each ALFRESCO cell by plant–herbivore–carnivore dynamics from TDFRM. The central hypothesis tested in the integrated model is that the herbivore, by feeding selectively on low-toxicity deciduous woody vegetation, speeds succession towards high-toxicity evergreens, like spruce. Wolves, by keeping moose populations down, can help slow the succession. Our results confirmed this hypothesis for the model calibrated to the Tanana floodplain of Alaska. We used the model to estimate the effects of different levels of wolf control. Simulations indicated that management reductions in wolf densities could reduce the mean time to transition from deciduous to spruce by more than 15 years, thereby increasing landscape flammability. The integrated model can be useful in estimating ecosystem impacts of wolf control and moose harvesting in central Alaska.

Survival of akinetes (resting-state cells of cyanobacteria) in low earth orbit and simulated extraterrestrial conditions.

PubMed

Olsson-Francis, Karen; de la Torre, Rosa; Towner, Martin C; Cockell, Charles S

2009-12-01

Cyanobacteria are photosynthetic organisms that have been considered for space applications, such as oxygen production in bioregenerative life support systems, and can be used as a model organism for understanding microbial survival in space. Akinetes are resting-state cells of cyanobacteria that are produced by certain genera of heterocystous cyanobacteria to survive extreme environmental conditions. Although they are similar in nature to endospores, there have been no investigations into the survival of akinetes in extraterrestrial environments. The aim of this work was to examine the survival of akinetes from Anabaena cylindrica in simulated extraterrestrial conditions and in Low Earth Orbit (LEO). Akinetes were dried onto limestone rocks and sent into LEO for 10 days on the ESA Biopan VI. In ground-based experiments, the rocks were exposed to periods of desiccation, vacuum (0.7×10(-3) kPa), temperature extremes (-80 to 80°C), Mars conditions (-27°C, 0.8 kPa, CO(2)) and UV radiation (325-400 nm). A proportion of the akinete population was able to survive a period of 10 days in LEO and 28 days in Mars simulated conditions, when the rocks were not subjected to UV radiation. Furthermore, the akinetes were able to survive 28 days of exposure to desiccation and low temperature with high viability remaining. Yet long periods of vacuum and high temperature were lethal to the akinetes. This work shows that akinetes are extreme-tolerating states of cyanobacteria that have a practical use in space applications and yield new insight into the survival of microbial resting-state cells in space conditions.

Probabilistic representation of gene regulatory networks.

PubMed

Mao, Linyong; Resat, Haluk

2004-09-22

Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. The simulation software is available upon request. Supplementary material will be made available on the OUP server.

Modeling and Simulation of III-Nitride-Based Solar Cells using NextnanoRTM

NASA Astrophysics Data System (ADS)

Refaei, Malak

Nextnano3 software is a well-known package for simulating semiconductor band-structures at the nanoscale and predicting the general electronic structure. In this work, it is further demonstrated as a viable tool for the simulation of III-nitride solar cells. In order to prove this feasibility, the generally accepted solar cell simulation package, PC1D, was chosen for comparison. To critique the results from both PC1D and Nextnano3, the fundamental drift-diffusion equations were used to calculate the performance of a simple p-n homojunction solar cell device analytically. Silicon was picked as the material for this comparison between the outputs of the two simulators as well as the results of the drift-diffusion equations because it is a well-known material in both software tools. After substantiating the capabilities of Nextnano3 for the simulation solar cells, an InGaN single-junction solar cell was simulated. The effects of various indium compositions and device structures on the performance of this InGaN p-n homojunction solar cell was then investigated using Nextnano 3 as a simulation tool. For single-junction devices with varying bandgap, an In0.6Ga0.4N device with a bandgap of 1.44 eV was found to be the optimum. The results of this research demonstrate that the Nextnano3 software can be used to usefully simulate solar cells in general, and III-nitride solar cells specifically, for future study of nanoscale structured devices.

Zooplankton interactions with toxic phytoplankton: Some implications for food web studies and algal defence strategies of feeding selectivity behaviour, toxin dilution and phytoplankton population diversity

NASA Astrophysics Data System (ADS)

Barreiro, A.; Guisande, C.; Maneiro, I.; Vergara, A. R.; Riveiro, I.; Iglesias, P.

2007-11-01

This study focuses on the interactions between toxic phytoplankton and zooplankton grazers. The experimental conditions used are an attempt to simulate situations that have, so far, received little attention. We presume the phytoplankton community to be a set of species where a population of a toxic species is intrinsically diverse by the presence of coexisting strains with different toxic properties. The other species in the community may not always be high-quality food for herbivorous zooplankton. Zooplankton populations may have developed adaptive responses to sympatric toxic phytoplankton species. Zooplankton grazers may perform a specific feeding behaviour and its consequences on fitness will depend on the species ingested, the effect of toxins, and the presence of mechanisms of toxin dilution and compensatory feeding. Our target species are a strain of the dinoflagellate Alexandrium minutum and a sympatric population of the copepod Acartia clausi. Mixed diets were used with two kinds of A. minutum cells: non-toxic and toxic. The flagellate Rhodomonas baltica and the non-toxic dinoflagellate Alexandrium tamarense were added as accompanying species. The effect of each alga was studied in separate diets. The toxic A. minutum cells were shown to have negative effects on egg production, hatching success and total reproductive output, while, in terms of its effect on fitness, the non-toxic A. minutum was the best quality food offered. R. baltica and A. tamarense were in intermediate positions. In the mixed diets, copepods showed a strong preference for toxic A. minutum cells and a weaker one for A. tamarense cells, while non-toxic A. minutum was slightly negatively selected and R. baltica strongly negatively selected. Although the level of toxins accumulated by copepods was very similar, in both the diet with only toxic A. minutum cells and in the mixed diet, the negative effects on fitness in the mixed diet could be offset by toxin dilution mechanisms. The implications of these findings are the fact that mesozooplankton may not play an important role in phytoplankton blooms development. Phytoplankton endotoxin production does not seem to be an evolutionary stable strategy as a defence against some herbivores.

Spatial structure and nutrients promote invasion of IncP-1 plasmids in bacterial populations

PubMed Central

Fox, Randal E; Zhong, Xue; Krone, Stephen M; Top, Eva M

2008-01-01

In spite of the importance of plasmids in bacterial adaptation, we have a poor understanding of their dynamics. It is not known if or how plasmids persist in and spread through (invade) a bacterial population when there is no selection for plasmid-encoded traits. Moreover, the differences in dynamics between spatially structured and mixed populations are poorly understood. Through a joint experimental/theoretical approach, we tested the hypothesis that self-transmissible IncP-1 plasmids can invade a bacterial population in the absence of selection when initially very rare, but only in spatially structured habitats and when nutrients are regularly replenished. Using protocols that differed in the degree of spatial structure and nutrient levels, the invasiveness of plasmid pB10 in Escherichia coli was monitored during at least 15 days, with an initial fraction of plasmid-bearing (p+) cells as low as 10−7. To further explore the mechanisms underlying plasmid dynamics, we developed a spatially explicit mathematical model. When cells were grown on filters and transferred to fresh medium daily, the p+ fraction increased to 13%, whereas almost complete invasion occurred when the population structure was disturbed daily. The plasmid was unable to invade in liquid. When carbon source levels were lower or not replenished, plasmid invasion was hampered. Simulations of the mathematical model closely matched the experimental results and produced estimates of the effects of alternative experimental parameters. This allowed us to isolate the likely mechanisms most responsible for the observations. In conclusion, spatial structure and nutrient availability can be key determinants in the invasiveness of plasmids. PMID:18528415

Fidelity of the ensemble code for visual motion in primate retina.

PubMed

Frechette, E S; Sher, A; Grivich, M I; Petrusca, D; Litke, A M; Chichilnisky, E J

2005-07-01

Sensory experience typically depends on the ensemble activity of hundreds or thousands of neurons, but little is known about how populations of neurons faithfully encode behaviorally important sensory information. We examined how precisely speed of movement is encoded in the population activity of magnocellular-projecting parasol retinal ganglion cells (RGCs) in macaque monkey retina. Multi-electrode recordings were used to measure the activity of approximately 100 parasol RGCs simultaneously in isolated retinas stimulated with moving bars. To examine how faithfully the retina signals motion, stimulus speed was estimated directly from recorded RGC responses using an optimized algorithm that resembles models of motion sensing in the brain. RGC population activity encoded speed with a precision of approximately 1%. The elementary motion signal was conveyed in approximately 10 ms, comparable to the interspike interval. Temporal structure in spike trains provided more precise speed estimates than time-varying firing rates. Correlated activity between RGCs had little effect on speed estimates. The spatial dispersion of RGC receptive fields along the axis of motion influenced speed estimates more strongly than along the orthogonal direction, as predicted by a simple model based on RGC response time variability and optimal pooling. on and off cells encoded speed with similar and statistically independent variability. Simulation of downstream speed estimation using populations of speed-tuned units showed that peak (winner take all) readout provided more precise speed estimates than centroid (vector average) readout. These findings reveal how faithfully the retinal population code conveys information about stimulus speed and the consequences for motion sensing in the brain.

Sensitivity analyses for simulating pesticide impacts on honey bee colonies

EPA Science Inventory

We employ Monte Carlo simulation and sensitivity analysis techniques to describe the population dynamics of pesticide exposure to a honey bee colony using the VarroaPop + Pesticide model. Simulations are performed of hive population trajectories with and without pesti...

Development of a novel cell sorting method that samples population diversity in flow cytometry.

PubMed

Osborne, Geoffrey W; Andersen, Stacey B; Battye, Francis L

2015-11-01

Flow cytometry based electrostatic cell sorting is an important tool in the separation of cell populations. Existing instruments can sort single cells into multi-well collection plates, and keep track of cell of origin and sorted well location. However currently single sorted cell results reflect the population distribution and fail to capture the population diversity. Software was designed that implements a novel sorting approach, "Slice and Dice Sorting," that links a graphical representation of a multi-well plate to logic that ensures that single cells are sampled and sorted from all areas defined by the sort region/s. Therefore the diversity of the total population is captured, and the more frequently occurring or rarer cell types are all sampled. The sorting approach was tested computationally, and using functional cell based assays. Computationally we demonstrate that conventional single cell sorting can sample as little as 50% of the population diversity dependant on the population distribution, and that Slice and Dice sorting samples much more of the variety present within a cell population. We then show by sorting single cells into wells using the Slice and Dice sorting method that there are cells sorted using this method that would be either rarely sorted, or not sorted at all using conventional single cell sorting approaches. The present study demonstrates a novel single cell sorting method that samples much more of the population diversity than current methods. It has implications in clonal selection, stem cell sorting, single cell sequencing and any areas where population heterogeneity is of importance. © 2015 International Society for Advancement of Cytometry.

Effects of space flight, clinorotation, and centrifugation on the substrate utilization efficiency of E. coli

NASA Technical Reports Server (NTRS)

Brown, Robert B.; Klaus, D.; Todd, P.

2002-01-01

Cultures of Escherichia coli grown in space reached a 25% higher average final cell population than those in comparably matched ground controls (p<0.05). However, both groups consumed the same quantity of glucose, which suggests that space flight not only stimulated bacterial growth as has been previously reported, but also resulted in a 25% more efficient utilization of the available nutrients. Supporting experiments performed in "simulated weightlessness" under clinorotation produced similar trends of increased growth and efficiency, but to a lesser extent in absolute values. These experiments resulted in increases of 12% and 9% in average final cell population (p<0.05), while the efficiency of substrate utilization improved by 6% and 9% relative to static controls (p=0.12 and p<0.05, respectively). In contrast, hypergravity, produced by centrifugation, predictably resulted in the opposite effect--a decrease of 33% to 40% in final cell numbers with corresponding 29% to 40% lower net growth efficiencies (p<0.01). Collectively, these findings support the hypothesis that the increased bacterial growth observed in weightlessness is a result of reduced extracellular mass transport that occurs in the absence of sedimentation and buoyancy-driven convection, which consequently also improves substrate utilization efficiency in suspended cultures.

A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis.

PubMed

Uriu, Koichiro; Bhavna, Rajasekaran; Oates, Andrew C; Morelli, Luis G

2017-08-15

In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a 'segmentation clock', in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease. © 2017. Published by The Company of Biologists Ltd.

A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis

PubMed Central

Bhavna, Rajasekaran; Oates, Andrew C.; Morelli, Luis G.

2017-01-01

ABSTRACT In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a ‘segmentation clock’, in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease. PMID:28652318

Computation Molecular Kinetics Model of HZE Induced Cell Cycle Arrest

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Ren, Lei

2004-01-01

Cell culture models play an important role in understanding the biological effectiveness of space radiation. High energy and charge (HZE) ions produce prolonged cell cycle arrests at the G1/S and G2/M transition points in the cell cycle. A detailed description of these phenomena is needed to integrate knowledge of the expression of DNA damage in surviving cells, including the determination of relative effectiveness factors between different types of radiation that produce differential types of DNA damage and arrest durations. We have developed a hierarchical kinetics model that tracks the distribution of cells in various cell phase compartments (early G1, late G1, S, G2, and M), however with transition rates that are controlled by rate-limiting steps in the kinetics of cyclin-cdk's interactions with their families of transcription factors and inhibitor molecules. The coupling of damaged DNA molecules to the downstream cyclin-cdk inhibitors is achieved through a description of the DNA-PK and ATM signaling pathways. For HZE irradiations we describe preliminary results, which introduce simulation of the stochastic nature of the number of direct particle traversals per cell in the modulation of cyclin-cdk and cell cycle population kinetics. Comparison of the model to data for fibroblast cells irradiated photons or HZE ions are described.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability.

PubMed

Barik, Debashis; Ball, David A; Peccoud, Jean; Tyson, John J

2016-12-01

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

PubMed

Rizzetto, Simone; Eltahla, Auda A; Lin, Peijie; Bull, Rowena; Lloyd, Andrew R; Ho, Joshua W K; Venturi, Vanessa; Luciani, Fabio

2017-10-06

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCRαβ reconstruction was achieved for 6 datasets (81% - 100%) with at least 0.25 millions (PE) reads of length >50 bp, while it failed for datasets with <30 bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability

PubMed Central

Ball, David A.

2016-01-01

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally. PMID:27935947

Energized Oxygen : Speiser Current Sheet Bifurcation

NASA Astrophysics Data System (ADS)

George, D. E.; Jahn, J. M.

2017-12-01

A single population of energized Oxygen (O+) is shown to produce a cross-tail bifurcated current sheet in 2.5D PIC simulations of the magnetotail without the influence of magnetic reconnection. Treatment of oxygen in simulations of space plasmas, specifically a magnetotail current sheet, has been limited to thermal energies despite observations of and mechanisms which explain energized ions. We performed simulations of a homogeneous oxygen background, that has been energized in a physically appropriate manner, to study the behavior of current sheets and magnetic reconnection, specifically their bifurcation. This work uses a 2.5D explicit Particle-In-a-Cell (PIC) code to investigate the dynamics of energized heavy ions as they stream Dawn-to-Dusk in the magnetotail current sheet. We present a simulation study dealing with the response of a current sheet system to energized oxygen ions. We establish a, well known and studied, 2-species GEM Challenge Harris current sheet as a starting point. This system is known to eventually evolve and produce magnetic reconnection upon thinning of the current sheet. We added a uniform distribution of thermal O+ to the background. This 3-species system is also known to eventually evolve and produce magnetic reconnection. We add one additional variable to the system by providing an initial duskward velocity to energize the O+. We also traced individual particle motion within the PIC simulation. Three main results are shown. First, energized dawn- dusk streaming ions are clearly seen to exhibit sustained Speiser motion. Second, a single population of heavy ions clearly produces a stable bifurcated current sheet. Third, magnetic reconnection is not required to produce the bifurcated current sheet. Finally a bifurcated current sheet is compatible with the Harris current sheet model. This work is the first step in a series of investigations aimed at studying the effects of energized heavy ions on magnetic reconnection. This work differs significantly from previous investigations involving heavy ions in that they are energized as opposed to being simply thermal. This is a variation based firmly on published in-situ measurements. It also differs in that a complete population is used as opposed to simply test particles in a magnetic field model.

Errors in short circuit measurements due to spectral mismatch between sunlight and solar simulators

NASA Technical Reports Server (NTRS)

Curtis, H. B.

1976-01-01

Errors in short circuit current measurement were calculated for a variety of spectral mismatch conditions. The differences in spectral irradiance between terrestrial sunlight and three types of solar simulator were studied, as well as the differences in spectral response between three types of reference solar cells and various test cells. The simulators considered were a short arc xenon lamp AMO sunlight simulator, an ordinary quartz halogen lamp, and an ELH-type quartz halogen lamp. Three types of solar cells studied were a silicon cell, a cadmium sulfide cell and a gallium arsenide cell.

Design and Performance of a Triple Source Air Mass Zero Solar Simulator

NASA Technical Reports Server (NTRS)

Jenkins, Phillip; Scheiman, David; Snyder, David

2005-01-01

Simulating the sun in a laboratory for the purpose of measuring solar cells has long been a challenge for engineers and scientists. Multi-junction cells demand higher fidelity of a solar simulator than do single junction cells, due to a need for close spectral matching as well as AM0 intensity. A GaInP/GaAs/Ge solar cell for example, requires spectral matching in three distinct spectral bands (figure 1). A commercial single source high-pressure xenon arc solar simulator such as the Spectrolab X-25 at NASA Glenn Research Center, can match the top two junctions of a GaInP/GaAs/Ge cell to within 1.3% mismatch, with the GaAs cell receiving slightly more current than required. The Ge bottom cell however, is mismatched +8.8%. Multi source simulators are designed to match the current for all junctions but typically have small illuminated areas, less uniformity and less beam collimation compared to an X-25 simulator. It was our intent when designing a multi source simulator to preserve as many aspects of the X-25 while adding multi-source capability.

Depletion of pro-inflammatory CD161(+) double negative (CD3(+)CD4(-)CD8(-)) T cells in AIDS patients is ameliorated by expansion of the γδ T cell population.

PubMed

Singleterry, Will L; Henderson, Harold; Cruse, Julius M

2012-02-01

In this present investigation, flow cytometry was utilized to evaluate 13 healthy controls and 31 HIV-1 infected patients who had advanced to the AIDS stage of infection (CD4 count below 200 cells/mm(3)), for the expression of CD161 on CD3(+) double negative (DN) (CD3(+)CD4(-)CD8(-)) T cells, CD4(+) T cells, CD8(+) T cells and γδ T cells. The observed depletion of CD161(+) T cells from peripheral circulation was due primarily to the loss of CD4(+)CD161(+) T cells; as these cells represented 8.67±0.74% of the total healthy control peripheral T cell population, while the CD4(+)CD161(+) T cells of the AIDS group represented only 3.35±0.41% (p=<0.0001) of the total peripheral T cell population. We have also shown here that the DN T cell population was more than doubled in the AIDS group, with the DN T cell population expanding from 3.29±0.45% of the healthy control peripheral T cell population to 8.64±1.16% (p=0.0001) of the AIDS group peripheral T cell population. By evaluating the expression of CD161 on the surface of the DN T cells we showed that within the healthy control group, 47.4±4.99% of the DN T cells were positive for the expression of CD161, while only 26.4±3.54% (p=0.002) of the AIDS group's DN T cells expressed CD161. Despite CD161 expression being halved on the DN T cells of the AIDS group, when we compared the total peripheral T cell percentage of CD161(+) DN T cells between the healthy control group and the AIDS group, there was no statistical difference. Even though only 26.4% DN T cells within the AIDS group were positive for CD161(+), the overall DN T cell population had expanded to such an extent that there was no statistical difference between the groups with regard to CD161(+) DN T cells as a percentage of the total peripheral T cell population. Furthermore, we showed that within the DN T cell population, there was an approximate 2:1 ratio of γδ to αβ T cells, and this ratio was maintained in both the healthy control group and the AIDS group. While evaluating γδ T cells we also discovered that CD8(+) γδ T cells were expanded from 0.62±.09% of the healthy control peripheral T cell population to 5.01±.88% (p=<0.0001) of the peripheral T cell population of the AIDS group; and that this population of CD8(+) γδ T cells underwent the same reduction in percentage of cells expressing CD161(+), further demonstrated that the phenomenon of CD161(+) percentage reduction and compensatory increase in total cell population was affecting the entire circulating γδ T cell population. Copyright © 2011 Elsevier Inc. All rights reserved.

Application of a computer simulation model to migrating white-fronted geese in the Klamath Basin

USGS Publications Warehouse

Frederick, R.B.; Clark, William R.; Takekawa, John Y.; McCullough, Dale R.; Barrett, R.H.

1992-01-01

The Pacific greater white-fronted goose (Anser albifrons) population has declined precipitously over the past 20 years. Loss of wetland habitat in California wintering areas has had a significant effect on the population, so recovery of the population may depend on innovative management of the few remaining wetlands. A computer simulation model, REFMOD, was applied to greater white-fronted geese in the Klamath Basin, northern California, to investigate the importance of food availability and hunting disturbance to migrating and wintering populations. Time spent flying and feeding was simulated during fall and early winter, and the resulting energy expenditure was compared with energy consumed to calculate an overall energy balance. This energy balance and the ease with which waterfowl acquired needed food affected emigration rate, and thus, the waterfowl population level was directly tied to availability and distribution of food. The model validly described distances moved by geese from their Tule Lake Refuge roosting site (core) to feeding sites within the surrounding Klamath Basin arena, and exhibited a capability to simulate observed time spent feeding. Based on 25 stochastic simulations, greater white-fronted goose population dynamics were validly simulated over the fall and early-winter (P>0.8). When food was removed from the Tule Lake Refuge, simulated geese had to fly farther (P<0.0001) to find food, hastening emigration and resulting in a decline (P<0.05) in use of the Klamath Basin by geese. Although barley is normally abundant in the basin and is extensively used by geese, simulated elimination of barley in the arena did not cause a reduction in goose numbers (P>0.05). The elimination did cause an increase in the distance traveled to feed (P<0.05), but the availability of other foods in the basin (e.g., potatoes) was evidently sufficient to support the population. The elimination of hunting in the Klamath Basin, and the related decrease in disturbance of feeding birds, had little effect (P>0.05) on the distance traveled to feed or on goose numbers. A 10-fold increase in disturbance hastened emigration and reduced population levels (P<0.0001) during the season by about 30%; a 100-fold increase in disturbance reduced population levels (P<0.0001) by 85%. When goose immigration was increased to simulate an average peak population of approximately 500 000 geese, population levels remained high throughout the fall, indicating the Klamath Basin can sustain a population much larger than currently exists. This suggests food availability and disturbance levels in the Klamath Basin are not responsible for observed population declines during the last 2 decades. REFMOD can easily be used to evaluate the effects of other scenarios related to hunting regimes and food distribution and availability.

Upregulation of erythropoietin receptor in UT-7/EPO cells inhibits simulated microgravity-induced cell apoptosis

NASA Astrophysics Data System (ADS)

Zou, Li-xue; Cui, Shao-yan; Zhong, Jian; Yi, Zong-chun; Sun, Yan; Fan, Yu-bo; Zhuang, Feng-yuan

2011-07-01

Hematopoietic progenitor cell proliferation can be altered in either spaceflight or under simulated microgravity experiments on the ground, however, the underlying mechanism remains unknown. Our previous study showed that exposure of the human erythropoietin (EPO)-dependent leukemia cell line UT-7/EPO to conditions of simulated microgravity significantly inhibited the cellular proliferation rate and induced cell apoptosis. We postulated that the downregulation of the erythropoietin receptor (EPOR) expression in UT-7/EPO cells under simulated microgravity may be a possible reason for microgravity triggered apoptosis. In this paper, a human EPOR gene was transferred into UT-7/EPO cells and the resulting expression of EPOR on the surface of UT-7/EPO cells increased approximately 61% ( p < 0.05) as selected by the antibiotic G418. It was also shown through cytometry assays and morphological observations that microgravity-induced apoptosis markedly decreased in these UT-7/EPO-EPOR cells. Thus, we concluded that upregulation of EPOR in UT-7/EPO cells could inhibit the simulated microgravity-induced cell apoptosis in this EPO dependent cell line.

Food for Thought: A Population Simulation Kit. Revised.

ERIC Educational Resources Information Center

Fletcher, Carol C.

Designed to foster an understanding of some of the relationships among population growth and distribution of people, food, and land area, this simulation kit deals with the following concepts: (1) the finite nature of land and resources, (2) the size and rate of growth of population, (3) the unequal distribution of population throughout the world,…

Survival of Listeria monocytogenes strain H7762 and resistance to simulated gastric fluid following exposure to frankfurter exudate.

PubMed

Wonderling, Laura D; Bayles, Darrell O

2004-06-01

Listeria monocytogenes strain H7762, a frankfurter isolate, was tested to determine whether it was able to survive at 4 degrees C in frankfurter pack fluid (exudate) and to determine whether food exposure affects its acid sensitivity. Cultures were sampled and tested for acid sensitivity by challenge with simulated gastric fluid (SGF). SGF challenges performed immediately after inoculation revealed that between 20 and 26% of the cells survived the full 30 min of SGF challenge regardless of whether the cells were inoculated into brain heart infusion broth (BHI) or exudate. After 2 days of incubation, cells exposed to both exudate and BHI had significantly decreased SGF resistance; however, the cells exposed to exudate were significantly more SGF resistant than cells exposed to BHI (after 15 min of SGF treatment, 33% of the exudate-exposed cells survived and 12% of the BHI-exposed cells survived). L. monocytogenes exposed to exudate had greater SGF resistance at all challenge times compared with BHI-exposed cells from day 2 through day 4. From days 8 to 15, exudate-exposed cells continued to have greater SGF resistance than BHI-exposed cells up to 10 min of SGF challenge but were as sensitive as the BHI-exposed cells at 20 to 30 min of challenge. By day 25, cells exposed to exudate were significantly more sensitive to SGF challenge than BHI-exposed cells. The survivor data generated from SGF challenges were modeled by a nonlinear regression analysis to calculate the underlying distribution of SGF resistance found in the challenged populations. These analyses indicated that L. monocytogenes exposed to exudate at 4 degrees C had a broader distribution of resistance to SGF compared with cells exposed to BHI at 4 degrees C. In addition, the mean time of death during SGF treatment was greater after exposure to exudate, indicating that cells exposed to exudate were more resistant to killing by SGF These data suggest that exposure to frankfurter exudate might render L. monocytogenes more able to survive the stomach environment during the initial stages of infection.

The E-MOSAICS project: simulating the formation and co-evolution of galaxies and their star cluster populations

NASA Astrophysics Data System (ADS)

Pfeffer, Joel; Kruijssen, J. M. Diederik; Crain, Robert A.; Bastian, Nate

2018-04-01

We introduce the MOdelling Star cluster population Assembly In Cosmological Simulations within EAGLE (E-MOSAICS) project. E-MOSAICS incorporates models describing the formation, evolution, and disruption of star clusters into the EAGLE galaxy formation simulations, enabling the examination of the co-evolution of star clusters and their host galaxies in a fully cosmological context. A fraction of the star formation rate of dense gas is assumed to yield a cluster population; this fraction and the population's initial properties are governed by the physical properties of the natal gas. The subsequent evolution and disruption of the entire cluster population are followed accounting for two-body relaxation, stellar evolution, and gravitational shocks induced by the local tidal field. This introductory paper presents a detailed description of the model and initial results from a suite of 10 simulations of ˜L⋆ galaxies with disc-like morphologies at z = 0. The simulations broadly reproduce key observed characteristics of young star clusters and globular clusters (GCs), without invoking separate formation mechanisms for each population. The simulated GCs are the surviving population of massive clusters formed at early epochs (z ≳ 1-2), when the characteristic pressures and surface densities of star-forming gas were significantly higher than observed in local galaxies. We examine the influence of the star formation and assembly histories of galaxies on their cluster populations, finding that (at similar present-day mass) earlier-forming galaxies foster a more massive and disruption-resilient cluster population, while galaxies with late mergers are capable of forming massive clusters even at late cosmic epochs. We find that the phenomenological treatment of interstellar gas in EAGLE precludes the accurate modelling of cluster disruption in low-density environments, but infer that simulations incorporating an explicitly modelled cold interstellar gas phase will overcome this shortcoming.

Simulating free-roaming cat population management options in open demographic environments.

PubMed

Miller, Philip S; Boone, John D; Briggs, Joyce R; Lawler, Dennis F; Levy, Julie K; Nutter, Felicia B; Slater, Margaret; Zawistowski, Stephen

2014-01-01

Large populations of free-roaming cats (FRCs) generate ongoing concerns for welfare of both individual animals and populations, for human public health, for viability of native wildlife populations, and for local ecological damage. Managing FRC populations is a complex task, without universal agreement on best practices. Previous analyses that use simulation modeling tools to evaluate alternative management methods have focused on relative efficacy of removal (or trap-return, TR), typically involving euthanasia, and sterilization (or trap-neuter-return, TNR) in demographically isolated populations. We used a stochastic demographic simulation approach to evaluate removal, permanent sterilization, and two postulated methods of temporary contraception for FRC population management. Our models include demographic connectivity to neighboring untreated cat populations through natural dispersal in a metapopulation context across urban and rural landscapes, and also feature abandonment of owned animals. Within population type, a given implementation rate of the TR strategy results in the most rapid rate of population decline and (when populations are isolated) the highest probability of population elimination, followed in order of decreasing efficacy by equivalent rates of implementation of TNR and temporary contraception. Even low levels of demographic connectivity significantly reduce the effectiveness of any management intervention, and continued abandonment is similarly problematic. This is the first demographic simulation analysis to consider the use of temporary contraception and account for the realities of FRC dispersal and owned cat abandonment.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth.

PubMed

de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-12-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

NASA Astrophysics Data System (ADS)

de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-12-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth.

The role of cell size in density gradient electrophoretic separation of mouse leukemia cells according to position in the cell cycle

NASA Technical Reports Server (NTRS)

Plank, L. D.; Kunze, M. E.; Todd, P. W.

1985-01-01

Cultured mouse leukemia cells line L5178Y were subjected to upward electrophoresis in a density gradient and the slower migrating cell populations were enriched in G2 cells. It is indicated that this cell line does not change electrophoretic mobility through the cell cycle. The possibility that increased sedimentation downward on the part of the larger G2 cells caused this separation was explored. Two different cell populations were investigated. The log phase population was found to migrate upward faster than the G2 population, and a similar difference between their velocities and calculated on the basis of a 1 um diameter difference between the two cell populations. The G2 and G1 enriched populations were isolated by Ficoll density gradient sedimentation. The bottom fraction was enriched in G2 cells and the top fraction was enriched with G1 cells, especially when compared with starting materials. The electrophoretic mobilities of these two cell populations did not differ significantly from one another. Cell diameter dependent migration curves were calculated and were found to be different. Families of migration curves that differ when cell size is considered as a parameter are predicted.

An overview of the utility of population simulation software in molecular ecology.

PubMed

Hoban, Sean

2014-05-01

Stochastic simulation software that simultaneously model genetic, population and environmental processes can inform many topics in molecular ecology. These include forecasting species and community response to environmental change, inferring dispersal ecology, revealing cryptic mating, quantifying past population dynamics, assessing in situ management options and monitoring neutral and adaptive biodiversity change. Advances in population demographic-genetic simulation software, especially with respect to individual life history, landscapes and genetic processes, are transforming and expanding the ways that molecular data can be used. The aim of this review is to explain the roles that such software can play in molecular ecology studies (whether as a principal component or a supporting function) so that researchers can decide whether, when and precisely how simulations can be incorporated into their work. First, I use seven case studies to demonstrate how simulations are employed, their specific advantage/necessity and what alternative or complementary (nonsimulation) approaches are available. I also explain how simulations can be integrated with existing spatial, environmental, historical and genetic data sets. I next describe simulation features that may be of interest to molecular ecologists, such as spatial and behavioural considerations and species' interactions, to provide guidance on how particular simulation capabilities can serve particular needs. Lastly, I discuss the prospect of simulation software in emerging challenges (climate change, biodiversity monitoring, population exploitation) and opportunities (genomics, ancient DNA), in order to emphasize that the scope of simulation-based work is expanding. I also suggest practical considerations, priorities and elements of best practice. This should accelerate the uptake of simulation approaches and firmly embed them as a versatile tool in the molecular ecologist's toolbox. © 2014 John Wiley & Sons Ltd.

Protein PSMD8 may mediate microgravity-induced cell cycle arrest

NASA Astrophysics Data System (ADS)

Hang, Xiaoming; Sun, Yeqing; Xu, Dan; Wu, Di; Chen, Xiaoning

Microgravity environment of space can induce a serial of changes in cells, such as morphology alterations, cytoskeleton disorder and cell cycle disturbance. Our previous study of simulated-microgravity on zebrafish (Danio rerio) embryos demonstrated 26s proteasome non-ATPase regulatory subunit 8 (PSMD8) might be a microgravity sensitive gene. However, functional study on PSMD8 is very limited and it has not been cloned in zebrafish till now. In this study, we tried to clone PSMD8 gene in zebrafish, quantify its protein expression level in zebrafish embryos after simulated microgravity and identify its possible function in cell cycle regulation. A rotary cell culture system (RCCS) designed by national aeronautics and apace administration (NASA) of America was used to simulate microgravity. The full-length of psmd8 gene in zebrafish was cloned. Preliminary analysis on its sequence and phylogenetic tree construction were carried out subsequently. Quantitative analysis by western blot showed that PSMD8 protein expression levels were significantly increased 1.18 and 1.22 times after 24-48hpf and 24-72hpf simulated microgravity, respectively. Moreover, a significant delay on zebrafish embryo development was found in simulated-microgravity exposed group. Inhibition of PSMD8 protein in zebrafish embryonic cell lines ZF4 could block cell cycle in G1 phase, which indicated that PSMD8 may play a role in cell cycle regulation. Interestingly, simulated-microgravity could also block ZF4 cell in G1 phase. Whether it is PSMD8 mediated cell cycle regulation result in the zebrafish embryo development delay after simulated microgravity exposure still needs further study. Key Words: PSMD8; Simulated-microgravity; Cell cycle; ZF4 cell line

Sensitivity analyses for simulating pesticide impacts on honey bee colonies

USDA-ARS?s Scientific Manuscript database

We employ Monte Carlo simulation and sensitivity analysis techniques to describe the population dynamics of pesticide exposure to a honey bee colony using the VarroaPop+Pesticide model. Simulations are performed of hive population trajectories with and without pesticide exposure to determine the eff...

Computer Simulation of the Population Growth (Schizosaccharomyces Pombe) Experiment.

ERIC Educational Resources Information Center

Daley, Michael; Hillier, Douglas

1981-01-01

Describes a computer program (available from authors) developed to simulate "Growth of a Population (Yeast) Experiment." Students actively revise the counting techniques with realistically simulated haemocytometer or eye-piece grid and are reminded of the necessary dilution technique. Program can be modified to introduce such variables…

Tracking molecular dynamics without tracking: image correlation of photo-activation microscopy

NASA Astrophysics Data System (ADS)

Pandžić, Elvis; Rossy, Jérémie; Gaus, Katharina

2015-03-01

Measuring protein dynamics in the plasma membrane can provide insights into the mechanisms of receptor signaling and other cellular functions. To quantify protein dynamics on the single molecule level over the entire cell surface, sophisticated approaches such as single particle tracking (SPT), photo-activation localization microscopy (PALM) and fluctuation-based analysis have been developed. However, analyzing molecular dynamics of fluorescent particles with intermittent excitation and low signal-to-noise ratio present at high densities has remained a challenge. We overcame this problem by applying spatio-temporal image correlation spectroscopy (STICS) analysis to photo-activated (PA) microscopy time series. In order to determine under which imaging conditions this approach is valid, we simulated PA images of diffusing particles in a homogeneous environment and varied photo-activation, reversible blinking and irreversible photo-bleaching rates. Further, we simulated data with high particle densities that populated mobile objects (such as adhesions and vesicles) that often interfere with STICS and fluctuation-based analysis. We demonstrated in experimental measurements that the diffusion coefficient of the epidermal growth factor receptor (EGFR) fused to PAGFP in live COS-7 cells can be determined in the plasma membrane and revealed differences in the time-dependent diffusion maps between wild-type and mutant Lck in activated T cells. In summary, we have developed a new analysis approach for live cell photo-activation microscopy data based on image correlation spectroscopy to quantify the spatio-temporal dynamics of single proteins.

Tracking molecular dynamics without tracking: image correlation of photo-activation microscopy.

PubMed

Pandžić, Elvis; Rossy, Jérémie; Gaus, Katharina

2015-03-09

Measuring protein dynamics in the plasma membrane can provide insights into the mechanisms of receptor signaling and other cellular functions. To quantify protein dynamics on the single molecule level over the entire cell surface, sophisticated approaches such as single particle tracking (SPT), photo-activation localization microscopy (PALM) and fluctuation-based analysis have been developed. However, analyzing molecular dynamics of fluorescent particles with intermittent excitation and low signal-to-noise ratio present at high densities has remained a challenge. We overcame this problem by applying spatio-temporal image correlation spectroscopy (STICS) analysis to photo-activated (PA) microscopy time series. In order to determine under which imaging conditions this approach is valid, we simulated PA images of diffusing particles in a homogeneous environment and varied photo-activation, reversible blinking and irreversible photo-bleaching rates. Further, we simulated data with high particle densities that populated mobile objects (such as adhesions and vesicles) that often interfere with STICS and fluctuation-based analysis. We demonstrated in experimental measurements that the diffusion coefficient of the epidermal growth factor receptor (EGFR) fused to PAGFP in live COS-7 cells can be determined in the plasma membrane and revealed differences in the time-dependent diffusion maps between wild-type and mutant Lck in activated T cells. In summary, we have developed a new analysis approach for live cell photo-activation microscopy data based on image correlation spectroscopy to quantify the spatio-temporal dynamics of single proteins.

Generating Inviscid and Viscous Fluid Flow Simulations over a Surface Using a Quasi-simultaneous Technique

NASA Technical Reports Server (NTRS)

Sturdza, Peter (Inventor); Martins-Rivas, Herve (Inventor); Suzuki, Yoshifumi (Inventor)

2014-01-01

A fluid-flow simulation over a computer-generated surface is generated using a quasi-simultaneous technique. The simulation includes a fluid-flow mesh of inviscid and boundary-layer fluid cells. An initial fluid property for an inviscid fluid cell is determined using an inviscid fluid simulation that does not simulate fluid viscous effects. An initial boundary-layer fluid property a boundary-layer fluid cell is determined using the initial fluid property and a viscous fluid simulation that simulates fluid viscous effects. An updated boundary-layer fluid property is determined for the boundary-layer fluid cell using the initial fluid property, initial boundary-layer fluid property, and an interaction law. The interaction law approximates the inviscid fluid simulation using a matrix of aerodynamic influence coefficients computed using a two-dimensional surface panel technique and a fluid-property vector. An updated fluid property is determined for the inviscid fluid cell using the updated boundary-layer fluid property.

40 CFR 86.163-00 - Spot check correlation procedures for vehicles tested using a simulation of the environmental...

Code of Federal Regulations, 2010 CFR

2010-07-01

... vehicles tested using a simulation of the environmental test cell for air conditioning emission testing. 86... tested using a simulation of the environmental test cell for air conditioning emission testing. This section is applicable for vehicles which are tested using a simulation of the environmental test cell...

40 CFR 86.163-00 - Spot check correlation procedures for vehicles tested using a simulation of the environmental...

Code of Federal Regulations, 2013 CFR

2013-07-01

... vehicles tested using a simulation of the environmental test cell for air conditioning emission testing. 86... tested using a simulation of the environmental test cell for air conditioning emission testing. This section is applicable for vehicles which are tested using a simulation of the environmental test cell...

40 CFR 86.163-00 - Spot check correlation procedures for vehicles tested using a simulation of the environmental...

Code of Federal Regulations, 2012 CFR

2012-07-01

... vehicles tested using a simulation of the environmental test cell for air conditioning emission testing. 86... tested using a simulation of the environmental test cell for air conditioning emission testing. This section is applicable for vehicles which are tested using a simulation of the environmental test cell...

40 CFR 86.163-00 - Spot check correlation procedures for vehicles tested using a simulation of the environmental...

Code of Federal Regulations, 2011 CFR

2011-07-01

... vehicles tested using a simulation of the environmental test cell for air conditioning emission testing. 86... tested using a simulation of the environmental test cell for air conditioning emission testing. This section is applicable for vehicles which are tested using a simulation of the environmental test cell...

40 CFR 86.163-00 - Spot check correlation procedures for vehicles tested using a simulation of the environmental...

Code of Federal Regulations, 2014 CFR

2014-07-01

... vehicles tested using a simulation of the environmental test cell for air conditioning emission testing. 86... tested using a simulation of the environmental test cell for air conditioning emission testing. This section is applicable for vehicles which are tested using a simulation of the environmental test cell...

Preosteoblast production 55 hours after a 12.5-day spaceflight on Cosmos 1887

NASA Technical Reports Server (NTRS)

Garetto, L. P.; Gonsalves, M. R.; Morey, E. R.; Durnova, G.; Roberts, W. E.; Morey-Holton, E. (Principal Investigator)

1990-01-01

The influence of 12.5 days of spaceflight and a 55 h stressful recovery period (at 1 g) on fibroblastlike osteoblast precursor cells was assessed in the periodontal ligament (PDL) of rats that were 91 days old at launch. Nuclear morphometry was used as a marker for precursor cell differentiation in 3 microns sections cut in the midsagittal plane from the maxillary first molar. According to nuclear volume, cells were classified as preosteoblasts (C + D cells, greater than or equal to 120 microns 3) and less differentiated progenitor cells (A + A' cells, 40-79 microns 3). Compared with synchronous controls (simulated flight conditions), the 55 h postflight recovery period at 1 g resulted in a 40% decrease in the A + A' cell population, a 42% increase in the C + D cells, and a 39% increase in the number of PDL fibroblastlike cells near the bone surface. These results are consistent with a postflight osteogenic response in PDL. This recovery response occurred despite physiological stress in the flight animals that resulted in a highly significant (P less than or equal to 0.001) increase in adrenal weight. The data suggest that after spaceflight there is a strong and rapid recovery mechanism for osteoblast differentiation that is not suppressed by physiological stress.

Investigation and simulation of dissolution with concurrent degradation under healthy and hypoalbuminaemic simulated parenteral conditions- case example Amphotericin B.

PubMed

Díaz de León-Ortega, Ricardo; D'Arcy, Deirdre M; Bolhuis, A; Fotaki, N

2018-06-01

Guidance on dissolution testing for parenteral formulations is limited and not often related in vivo performance. Critically ill patients represent a target cohort, frequently hypoalbuminaemic, to whom certain parenteral formulations are administered. Amphotericin B (AmB) is a poorly soluble, highly protein-bound drug, available as lipid-based formulations and used in critical illness. The aim of this study was to develop media representing hypoalbuminaemic and healthy plasma, and to understand and simulate the dissolution profile of AmB in biorelevant media. Dissolution media were prepared with bovine serum albumin (BSA) in Krebs-Ringer buffer, and tested in a flow through cell apparatus and a bottle/stirrer setup. Drug activity was tested against Candida albicans. BSA concentration was positively associated with solubility, degradation rate and maximum amount dissolved and negatively associated with dissolution rate constant and antifungal activity. In the bottle/stirrer setup, a biexponential model successfully described simultaneous dissolution and degradation and increased in agitation reduced the discriminatory ability of the test. The hydrodynamics provided by the flow-through cell apparatus was not adequate to dissolve the drug. Establishing discriminating test methods with albumin present in the dissolution media, representing the target population, supports future development of biorelevant and clinically relevant tests for parenteral formulations. Copyright © 2018 Elsevier B.V. All rights reserved.

Stochastic Effects in Computational Biology of Space Radiation Cancer Risk

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Pluth, Janis; Harper, Jane; O'Neill, Peter

2007-01-01

Estimating risk from space radiation poses important questions on the radiobiology of protons and heavy ions. We are considering systems biology models to study radiation induced repair foci (RIRF) at low doses, in which less than one-track on average transverses the cell, and the subsequent DNA damage processing and signal transduction events. Computational approaches for describing protein regulatory networks coupled to DNA and oxidative damage sites include systems of differential equations, stochastic equations, and Monte-Carlo simulations. We review recent developments in the mathematical description of protein regulatory networks and possible approaches to radiation effects simulation. These include robustness, which states that regulatory networks maintain their functions against external and internal perturbations due to compensating properties of redundancy and molecular feedback controls, and modularity, which leads to general theorems for considering molecules that interact through a regulatory mechanism without exchange of matter leading to a block diagonal reduction of the connecting pathways. Identifying rate-limiting steps, robustness, and modularity in pathways perturbed by radiation damage are shown to be valid techniques for reducing large molecular systems to realistic computer simulations. Other techniques studied are the use of steady-state analysis, and the introduction of composite molecules or rate-constants to represent small collections of reactants. Applications of these techniques to describe spatial and temporal distributions of RIRF and cell populations following low dose irradiation are described.

Effects of Grid Resolution on Modeled Air Pollutant Concentrations Due to Emissions from Large Point Sources: Case Study during KORUS-AQ 2016 Campaign

NASA Astrophysics Data System (ADS)

Ju, H.; Bae, C.; Kim, B. U.; Kim, H. C.; Kim, S.

2017-12-01

Large point sources in the Chungnam area received a nation-wide attention in South Korea because the area is located southwest of the Seoul Metropolitan Area whose population is over 22 million and the summertime prevalent winds in the area is northeastward. Therefore, emissions from the large point sources in the Chungnam area were one of the major observation targets during the KORUS-AQ 2016 including aircraft measurements. In general, horizontal grid resolutions of eulerian photochemical models have profound effects on estimated air pollutant concentrations. It is due to the formulation of grid models; that is, emissions in a grid cell will be assumed to be mixed well under planetary boundary layers regardless of grid cell sizes. In this study, we performed series of simulations with the Comprehensive Air Quality Model with eXetension (CAMx). For 9-km and 3-km simulations, we used meteorological fields obtained from the Weather Research and Forecast model while utilizing the "Flexi-nesting" option in the CAMx for the 1-km simulation. In "Flexi-nesting" mode, CAMx interpolates or assigns model inputs from the immediate parent grid. We compared modeled concentrations with ground observation data as well as aircraft measurements to quantify variations of model bias and error depending on horizontal grid resolutions.

Method for rapid isolation of sensitive mutants

DOEpatents

Freyer, James P.

1997-01-01

Sensitive mammalian cell mutants are rapidly isolated using flow cytometry. A first population of clonal spheroids is established to contain both normal and mutant cells. The population may be naturally occurring or may arise from mutagenized cells. The first population is then flow sorted by size to obtain a second population of clonal spheroids of a first uniform size. The second population is then exposed to a DNA-damaging agent that is being investigated. The exposed second population is placed in a growth medium to form a third population of clonal spheroids comprising spheroids of increased size from the mammalian cells that are resistant to the DNA-damaging agent and spheroids of substantially the first uniform size formed from the mammalian cells that are sensitive to the DNA-damaging agent. The third population is not flow sorted to differentiate the spheroids formed from resistant mammalian cells from spheroids formed from sensitive mammalian cells. The spheroids formed from sensitive mammalian cells are now treated to recover viable sensitive cells from which a sensitive cell line can be cloned.

Method for rapid isolation of sensitive mutants

DOEpatents

Freyer, J.P.

1997-07-29

Sensitive mammalian cell mutants are rapidly isolated using flow cytometry. A first population of clonal spheroids is established to contain both normal and mutant cells. The population may be naturally occurring or may arise from mutagenized cells. The first population is then flow sorted by size to obtain a second population of clonal spheroids of a first uniform size. The second population is then exposed to a DNA-damaging agent that is being investigated. The exposed second population is placed in a growth medium to form a third population of clonal spheroids comprising spheroids of increased size from the mammalian cells that are resistant to the DNA-damaging agent and spheroids of substantially the first uniform size formed from the mammalian cells that are sensitive to the DNA-damaging agent. The third population is not flow sorted to differentiate the spheroids formed from resistant mammalian cells from spheroids formed from sensitive mammalian cells. The spheroids formed from sensitive mammalian cells are now treated to recover viable sensitive cells from which a sensitive cell line can be cloned. 15 figs.

On The Development of Biophysical Models for Space Radiation Risk Assessment

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Dicello, J. F.

1999-01-01

Experimental techniques in molecular biology are being applied to study biological risks from space radiation. The use of molecular assays presents a challenge to biophysical models which in the past have relied on descriptions of energy deposition and phenomenological treatments of repair. We describe a biochemical kinetics model of cell cycle control and DNA damage response proteins in order to model cellular responses to radiation exposures. Using models of cyclin-cdk, pRB, E2F's, p53, and GI inhibitors we show that simulations of cell cycle populations and GI arrest can be described by our biochemical approach. We consider radiation damaged DNA as a substrate for signal transduction processes and consider a dose and dose-rate reduction effectiveness factor (DDREF) for protein expression.

Modelling a stochastic HIV model with logistic target cell growth and nonlinear immune response function

NASA Astrophysics Data System (ADS)

Wang, Yan; Jiang, Daqing; Alsaedi, Ahmed; Hayat, Tasawar

2018-07-01

A stochastic HIV viral model with both logistic target cell growth and nonlinear immune response function is formulated to investigate the effect of white noise on each population. The existence of the global solution is verified. By employing a novel combination of Lyapunov functions, we obtain the existence of the unique stationary distribution for small white noises. We also derive the extinction of the virus for large white noises. Numerical simulations are performed to highlight the effect of white noises on model dynamic behaviour under the realistic parameters. It is found that the small intensities of white noises can keep the irregular blips of HIV virus and CTL immune response, while the larger ones force the virus infection and immune response to lose efficacy.

A quantitative test of population genetics using spatiogenetic patterns in bacterial colonies.

PubMed

Korolev, Kirill S; Xavier, João B; Nelson, David R; Foster, Kevin R

2011-10-01

It is widely accepted that population-genetics theory is the cornerstone of evolutionary analyses. Empirical tests of the theory, however, are challenging because of the complex relationships between space, dispersal, and evolution. Critically, we lack quantitative validation of the spatial models of population genetics. Here we combine analytics, on- and off-lattice simulations, and experiments with bacteria to perform quantitative tests of the theory. We study two bacterial species, the gut microbe Escherichia coli and the opportunistic pathogen Pseudomonas aeruginosa, and show that spatiogenetic patterns in colony biofilms of both species are accurately described by an extension of the one-dimensional stepping-stone model. We use one empirical measure, genetic diversity at the colony periphery, to parameterize our models and show that we can then accurately predict another key variable: the degree of short-range cell migration along an edge. Moreover, the model allows us to estimate other key parameters, including effective population size (density) at the expansion frontier. While our experimental system is a simplification of natural microbial community, we argue that it constitutes proof of principle that the spatial models of population genetics can quantitatively capture organismal evolution.

Microbial survival in deep space environment.

NASA Technical Reports Server (NTRS)

Silverman, G. J.

1971-01-01

Review of the knowledge available on the extent to which microorganisms (mainly microbial spores, vegetative cells, and fungi) are capable of surviving the environment of deep space, based on recent simulation experiments of deep space. A description of the experimental procedures used is followed by a discussion of deep space ecology, the behavior of microorganisms in ultrahigh vacuum, and factors influencing microbial survival. It is concluded that, so far, simulation experiments have proved far less lethal to microorganisms than to other forms of life. There are, however, wide gaps in the knowledge available, and no accurate predictions can as yet be made on the degree of lethality that might be incurred by a microbial population on a given mission. Therefore, sterilization of spacecraft surfaces is deemed necessary if induced panspermia (i.e., interplanetary life propagation) is to be avoided.

Spatial genetic structure in continuous and fragmented populations of Pinus pinaster Aiton.

PubMed

De-Lucas, A I; González-Martínez, S C; Vendramin, G G; Hidalgo, E; Heuertz, M

2009-11-01

Habitat fragmentation, i.e., the reduction of populations into small isolated remnants, is expected to increase spatial genetic structure (SGS) in plant populations through nonrandom mating, lower population densities and potential aggregation of reproductive individuals. We investigated the effects of population size reduction and genetic isolation on SGS in maritime pine (Pinus pinaster Aiton) using a combined experimental and simulation approach. Maritime pine is a wind-pollinated conifer which has a scattered distribution in the Iberian Peninsula as a result of forest fires and habitat fragmentation. Five highly polymorphic nuclear microsatellites were genotyped in a total of 394 individuals from two population pairs from the Iberian Peninsula, formed by one continuous and one fragmented population each. In agreement with predictions, SGS was significant and stronger in fragments (Sp = 0.020 and Sp = 0.026) than in continuous populations, where significant SGS was detected for one population only (Sp = 0.010). Simulations suggested that under fat-tailed dispersal, small population size is a stronger determinant of SGS than genetic isolation, while under normal dispersal, genetic isolation has a stronger effect. SGS was always stronger in real populations than in simulations, except if unrealistically narrow dispersal and/or high variance of reproductive success were modelled (even when accounting for potential overestimation of SGS in real populations as a result of short-distance sampling). This suggests that factors such as nonrandom mating or selection not considered in the simulations were additionally operating on SGS in Iberian maritime pine populations.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems

PubMed Central

Ghaffarizadeh, Ahmadreza; Mumenthaler, Shannon M.

2018-01-01

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net. PMID:29474446

Using a Population-Ecology Simulation in College Courses.

ERIC Educational Resources Information Center

Hinze, Kenneth E.

1984-01-01

Describes instructional use of a microcomputer version of the WORLD2 global population-ecology simulation. Reactions of students and instructors are discussed and a WORLD2 simulation assignment is appended. The BASIC version used by the author runs on Apple II, DOS 3.3, with 80 column board. (MBR)

SimBA: simulation algorithm to fit extant-population distributions.

PubMed

Parida, Laxmi; Haiminen, Niina

2015-03-14

Simulation of populations with specified characteristics such as allele frequencies, linkage disequilibrium etc., is an integral component of many studies, including in-silico breeding optimization. Since the accuracy and sensitivity of population simulation is critical to the quality of the output of the applications that use them, accurate algorithms are required to provide a strong foundation to the methods in these studies. In this paper we present SimBA (Simulation using Best-fit Algorithm) a non-generative approach, based on a combination of stochastic techniques and discrete methods. We optimize a hill climbing algorithm and extend the framework to include multiple subpopulation structures. Additionally, we show that SimBA is very sensitive to the input specifications, i.e., very similar but distinct input characteristics result in distinct outputs with high fidelity to the specified distributions. This property of the simulation is not explicitly modeled or studied by previous methods. We show that SimBA outperforms the existing population simulation methods, both in terms of accuracy as well as time-efficiency. Not only does it construct populations that meet the input specifications more stringently than other published methods, SimBA is also easy to use. It does not require explicit parameter adaptations or calibrations. Also, it can work with input specified as distributions, without an exemplar matrix or population as required by some methods. SimBA is available at http://researcher.ibm.com/project/5669 .

The commitment of human cells to senescence.

PubMed

Holliday, Robin

2014-01-01

Fifty years ago, it was demonstrated by Leonard Hayflick that human diploid fibroblasts grown in culture have a finite lifespan. Since that time, innumerable experiments have been published to discover the mechanism(s) that are responsible for this 'Hayflick limit' to continuous growth. Much new information has been gained, but there are certain features of this experimental system which have not been fully understood. One is the fact that different populations of the foetal lung strains WI-38 and MRC-5 have a range in division potential of at least a millionfold. The commitment theory of cellular aging, published more than 30 years ago, is able to explain this, but it has been consistently ignored. The theory predicts that bottlenecks, which are transient reductions in population size, can significantly reduce lifespan, or increase variability of lifespans. Computer simulations specify the effects of bottlenecks on longevity, and these were confirmed in two series of experiments. Commitment to senescence may be the loss of telomerase, which leads to the erosion of telomeres and the inability to grow indefinitely. Many experiments have been done with skin fibroblasts from human donors of different age, and it was originally thought that in vitro lifespan was inversely correlated with donor age. In these experiments, a single skin biopsy produces a population of cells that are grown to senescence. However, there is no reason to believe that skin fibroblasts are less variable in their in vitro lifespan than foetal lung strains, in which case the data points with skin cells are so variable that they may completely obscure any inverse correlation between culture lifespans and donor age.

Simulator spectral characterization using balloon calibrated solar cells with narrow band pass filters

NASA Technical Reports Server (NTRS)

Goodelle, G. S.; Brooks, G. R.; Seaman, C. H.

1981-01-01

The development and implementation of an instrument for spectral measurement of solar simulators for testing solar cell characteristics is reported. The device was constructed for detecting changes in solar simulator behavior and for comparing simulator spectral irradiance to solar AM0 output. It consists of a standard solar cell equipped with a band pass filter narrow enough so that, when flown on a balloon to sufficient altitude along with sufficient numbers of cells, each equipped with filters of different bandpass ratings, the entire spectral response of the standard cell can be determined. Measured short circuit currents from the balloon flights thus produce cell devices which, when exposed to solar simulator light, have a current which does or does not respond as observed under actual AM0 conditions. Improvements of the filtered cells in terms of finer bandpass filter tuning and measurement of temperature coefficients are indicated.

A New Improved and Extended Version of the Multicell Bacterial Simulator gro.

PubMed

Gutiérrez, Martín; Gregorio-Godoy, Paula; Pérez Del Pulgar, Guillermo; Muñoz, Luis E; Sáez, Sandra; Rodríguez-Patón, Alfonso

2017-08-18

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 10 5 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

Novel, Moon and Mars, partial gravity simulation paradigms and their effects on the balance between cell growth and cell proliferation during early plant development.

PubMed

Manzano, Aránzazu; Herranz, Raúl; den Toom, Leonardus A; Te Slaa, Sjoerd; Borst, Guus; Visser, Martijn; Medina, F Javier; van Loon, Jack J W A

2018-01-01

Clinostats and Random Positioning Machine (RPM) are used to simulate microgravity, but, for space exploration, we need to know the response of living systems to fractional levels of gravity (partial gravity) as they exist on Moon and Mars. We have developed and compared two different paradigms to simulate partial gravity using the RPM, one by implementing a centrifuge on the RPM (RPM HW ), the other by applying specific software protocols to driving the RPM motors (RPM SW ). The effects of the simulated partial gravity were tested in plant root meristematic cells, a system with known response to real and simulated microgravity. Seeds of Arabidopsis thaliana were germinated under simulated Moon (0.17  g ) and Mars (0.38  g ) gravity. In parallel, seeds germinated under simulated microgravity (RPM), or at 1  g control conditions. Fixed root meristematic cells from 4-day grown seedlings were analyzed for cell proliferation rate and rate of ribosome biogenesis using morphometrical methods and molecular markers of the regulation of cell cycle and nucleolar activity. Cell proliferation appeared increased and cell growth was depleted under Moon gravity, compared with the 1  g control. The effects were even higher at the Moon level than at simulated microgravity, indicating that meristematic competence (balance between cell growth and proliferation) is also affected at this gravity level. However, the results at the simulated Mars level were close to the 1  g static control. This suggests that the threshold for sensing and responding to gravity alteration in the root would be at a level intermediate between Moon and Mars gravity. Both partial g simulation strategies seem valid and show similar results at Moon g -levels, but further research is needed, in spaceflight and simulation facilities, especially around and beyond Mars g levels to better understand more precisely the differences and constrains in the use of these facilities for the space biology community.

Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

NASA Astrophysics Data System (ADS)

Nadler, Ethan O.; Mao, Yao-Yuan; Wechsler, Risa H.; Garrison-Kimmel, Shea; Wetzel, Andrew

2018-06-01

We identify subhalos in dark matter–only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)–mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an 85% out-of-bag classification score. We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.

A fortran program for Monte Carlo simulation of oil-field discovery sequences

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.

1993-01-01

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

Immunoinformatics Approach in Designing Epitope-based Vaccine Against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b).

PubMed

Zahroh, Hilyatuz; Ma'rup, Ahmad; Tambunan, Usman Sumo Friend; Parikesit, Arli Aditya

2016-01-01

Meningitis infection is one of the major threats during Hajj season in Mecca. Meningitis vaccines are available, but their uses are limited in some countries due to religious reasons. Furthermore, they only give protection to certain serogroups, not to all types of meningitis-inducing bacteria. Recently, research on epitope-based vaccines has been developed intensively. Such vaccines have potential advantages over conventional vaccines in that they are safer to use and well responded to the antibody. In this study, we developed epitope-based vaccine candidates against various meningitis-inducing bacteria, including Streptococcus pneumoniae , Neisseria meningitidis , and Haemophilus influenzae type b. The epitopes were selected from their protein of polysaccharide capsule. B-cell epitopes were predicted by using BCPred, while T-cell epitope for major histocompatibility complex (MHC) class I was predicted using PAProC, TAPPred, and Immune Epitope Database. Immune Epitope Database was also used to predict T-cell epitope for MHC class II. Population coverage and molecular docking simulation were predicted against previously generated epitope vaccine candidates. The best candidates for MHC class I- and class II-restricted T-cell epitopes were MQYGDKTTF, MKEQNTLEI, ECTEGEPDY, DLSIVVPIY, YPMAMMWRNASNRAI, TLQMTLLGIVPNLNK, ETSLHHIPGISNYFI, and SLLYILEKNAEMEFD, which showed 80% population coverage. The complexes of class I T-cell epitopes-HLA-C*03:03 and class II T-cell epitopes-HLA-DRB1*11:01 showed better affinity than standards as evaluated from their Δ G binding value and the binding interaction between epitopes and HLA molecules. These peptide constructs may further be undergone in vitro and in vivo testings for the development of targeted vaccine against meningitis infection.

Neuronal variability in orbitofrontal cortex during economic decisions.

PubMed

Conen, Katherine E; Padoa-Schioppa, Camillo

2015-09-01

Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells. Copyright © 2015 the American Physiological Society.

Dielectrophoretic capture of low abundance cell population using thick electrodes.

PubMed

Marchalot, Julien; Chateaux, Jean-François; Faivre, Magalie; Mertani, Hichem C; Ferrigno, Rosaria; Deman, Anne-Laure

2015-09-01

Enrichment of rare cell populations such as Circulating Tumor Cells (CTCs) is a critical step before performing analysis. This paper presents a polymeric microfluidic device with integrated thick Carbon-PolyDimethylSiloxane composite (C-PDMS) electrodes designed to carry out dielectrophoretic (DEP) trapping of low abundance biological cells. Such conductive composite material presents advantages over metallic structures. Indeed, as it combines properties of both the matrix and doping particles, C-PDMS allows the easy and fast integration of conductive microstructures using a soft-lithography approach while preserving O2 plasma bonding properties of PDMS substrate and avoiding a cumbersome alignment procedure. Here, we first performed numerical simulations to demonstrate the advantage of such thick C-PDMS electrodes over a coplanar electrode configuration. It is well established that dielectrophoretic force ([Formula: see text]) decreases quickly as the distance from the electrode surface increases resulting in coplanar configuration to a low trapping efficiency at high flow rate. Here, we showed quantitatively that by using electrodes as thick as a microchannel height, it is possible to extend the DEP force influence in the whole volume of the channel compared to coplanar electrode configuration and maintaining high trapping efficiency while increasing the throughput. This model was then used to numerically optimize a thick C-PDMS electrode configuration in terms of trapping efficiency. Then, optimized microfluidic configurations were fabricated and tested at various flow rates for the trapping of MDA-MB-231 breast cancer cell line. We reached trapping efficiencies of 97% at 20 μl/h and 78.7% at 80 μl/h, for 100 μm thick electrodes. Finally, we applied our device to the separation and localized trapping of CTCs (MDA-MB-231) from a red blood cells sample (concentration ratio of 1:10).

The effect of different solar simulators on the measurement of short-circuit current temperature coefficients

NASA Technical Reports Server (NTRS)

Curtis, H. B.; Hart, R. E., Jr.

1982-01-01

Gallium arsenide solar cells are considered for several high temperature missions in space. Both near-Sun and concentrator missions could involve cell temperatures on the order of 200 C. Performance measurements of cells at elevated temperatures are usually made using simulated sunlight and a matched reference cell. Due to the change in bandgap with increasing temperature at portions of the spectrum where considerable simulated irradiance is present, there are significant differences in measured short circuit current at elevated temperatures among different simulators. To illustrate this, both experimental and theoretical data are presented for gallium arsenide cells.

SEIR model simulation for Hepatitis B

NASA Astrophysics Data System (ADS)

Side, Syafruddin; Irwan, Mulbar, Usman; Sanusi, Wahidah

2017-09-01

Mathematical modelling and simulation for Hepatitis B discuss in this paper. Population devided by four variables, namely: Susceptible, Exposed, Infected and Recovered (SEIR). Several factors affect the population in this model is vaccination, immigration and emigration that occurred in the population. SEIR Model obtained Ordinary Differential Equation (ODE) non-linear System 4-D which then reduces to 3-D. SEIR model simulation undertaken to predict the number of Hepatitis B cases. The results of the simulation indicates the number of Hepatitis B cases will increase and then decrease for several months. The result of simulation using the number of case in Makassar also found the basic reproduction number less than one, that means, Makassar city is not an endemic area of Hepatitis B.

The simulation of CZTS solar cell for performance improvement

NASA Astrophysics Data System (ADS)

Kumar, Atul; Thakur, Ajay D.

2018-05-01

A Copper-Zinc-Tin-Sulphide (CZTS) based solar cell of Mo/CZTS/CdS/ZnO is simulated using SCAPS. Quantum efficiency and IV curve of the simulated output of CZTS solar cell is mapped with highest efficiency reported in literature for CZTS solar cell. A modification in back contact thus shottky barrier, spike type band alignment at the CZTS-n type layer junction and higher electron mobility (owing to alkali doping in CZT)S are implement in simulation of CZTS solar cell. An improvement in the solar cell efficiency compared to the standard cell configuration of Mo/CZTS/CdS/ZnO is found. CZTS is plagued with low Voc and low FF which can be increased by optimization as suggested in paper.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

PubMed

Jonke, Zeno; Legenstein, Robert; Habenschuss, Stefan; Maass, Wolfgang

2017-08-30

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code. SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns. Copyright © 2017 the authors 0270-6474/17/378511-13$15.00/0.

Survivability of Psychrobacter cryohalolentis K5 Under Simulated Martian Surface Conditions

NASA Technical Reports Server (NTRS)

Smith, David J.; Schuerger, Andrew C.; Davidson, Mark M.; Pacala, Stephen W.; Bakermans, Corien; Onstott, Tullis

2008-01-01

Spacecraft launched to Mars can retain viable terrestrial microorganisms on board that may survive the interplanetary transit. Such biota might compromise the search for life beyond Earth if capable of propagating on Mars. The current study explored the survivability of Psychrobacter cryohalolentis K5, a psychrotolerant microorganism obtained from a Siberian permafrost cryopeg, under simulated martian surface conditions of high ultraviolet irradiation, high desiccation, low temperature, and low atmospheric pressure. First, a desiccation experiment compared the survival of P. cryohalolentis cells embedded, or not embedded, within a medium/salt matrix (MSM) maintained at 25 degrees C for 24 hr within a laminar flow hood. Results indicate that the presence of the MSM enhanced survival of the bacterial cells by 1 to 3 orders of magnitude. Second, tests were conducted in a Mars Simulation Chamber to determine the UV tolerance of the microorganism. No viable vegetative cells of P. cryohalolentis were detected after 8 hr of exposure to Mars-normal conditions of 4.55 W/m(2) UVC irradiation (200-280 nm), -12.5 degrees C, 7.1 mbar, and a Mars gas mix composed of CO2 (95.3%), N2 (2.7%), Ar (1.6%), O2 (0.2%), and H(2)O (0.03%). Third, an experiment was conducted within the Mars chamber in which total atmospheric opacities were simulated at tau = 0.1 (dust-free CO2 atmosphere at 7.1 mbar), 0.5 (normal clear sky with 0.4 = dust opacity and 0.1 = CO2-only opacity), and 3.5 (global dust storm) to determine the survivability of P. cryohalolentis to partially shielded UVC radiation. The survivability of the bacterium increased with the level of UVC attenuation, though population levels still declined several orders of magnitude compared to UVC-absent controls over an 8 hr exposure period.

Survivability of Psychrobacter cryohalolentis K5 Under Simulated Martian Surface Conditions

NASA Astrophysics Data System (ADS)

Smith, David J.; Schuerger, Andrew C.; Davidson, Mark M.; Pacala, Stephen W.; Bakermans, Corien; Onstott, Tullis C.

2009-03-01

Spacecraft launched to Mars can retain viable terrestrial microorganisms on board that may survive the interplanetary transit. Such biota might compromise the search for life beyond Earth if capable of propagating on Mars. The current study explored the survivability of Psychrobacter cryohalolentis K5, a psychrotolerant microorganism obtained from a Siberian permafrost cryopeg, under simulated martian surface conditions of high ultraviolet irradiation, high desiccation, low temperature, and low atmospheric pressure. First, a desiccation experiment compared the survival of P. cryohalolentis cells embedded, or not embedded, within a medium/salt matrix (MSM) maintained at 25°C for 24 h within a laminar flow hood. Results indicate that the presence of the MSM enhanced survival of the bacterial cells by 1 to 3 orders of magnitude. Second, tests were conducted in a Mars Simulation Chamber to determine the UV tolerance of the microorganism. No viable vegetative cells of P. cryohalolentis were detected after 8 h of exposure to Mars-normal conditions of 4.55 W/m2 UVC irradiation (200-280 nm), -12.5°C, 7.1 mbar, and a Mars gas mix composed of CO2 (95.3%), N2 (2.7%), Ar (1.6%), O2 (0.2%), and H2O (0.03%). Third, an experiment was conducted within the Mars chamber in which total atmospheric opacities were simulated at τ = 0.1 (dust-free CO2 atmosphere at 7.1 mbar), 0.5 (normal clear sky with 0.4 = dust opacity and 0.1 = CO2-only opacity), and 3.5 (global dust storm) to determine the survivability of P. cryohalolentis to partially shielded UVC radiation. The survivability of the bacterium increased with the level of UVC attenuation, though population levels still declined several orders of magnitude compared to UVC-absent controls over an 8 h exposure period.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

PubMed Central

Gerstner, Wulfram

2017-01-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

Single cell versus large population analysis: cell variability in elemental intracellular concentration and distribution.

PubMed

Malucelli, Emil; Procopio, Alessandra; Fratini, Michela; Gianoncelli, Alessandra; Notargiacomo, Andrea; Merolle, Lucia; Sargenti, Azzurra; Castiglioni, Sara; Cappadone, Concettina; Farruggia, Giovanna; Lombardo, Marco; Lagomarsino, Stefano; Maier, Jeanette A; Iotti, Stefano

2018-01-01

The quantification of elemental concentration in cells is usually performed by analytical assays on large populations missing peculiar but important rare cells. The present article aims at comparing the elemental quantification in single cells and cell population in three different cell types using a new approach for single cells elemental analysis performed at sub-micrometer scale combining X-ray fluorescence microscopy and atomic force microscopy. The attention is focused on the light element Mg, exploiting the opportunity to compare the single cell quantification to the cell population analysis carried out by a highly Mg-selective fluorescent chemosensor. The results show that the single cell analysis reveals the same Mg differences found in large population of the different cell strains studied. However, in one of the cell strains, single cell analysis reveals two cells with an exceptionally high intracellular Mg content compared with the other cells of the same strain. The single cell analysis allows mapping Mg and other light elements in whole cells at sub-micrometer scale. A detailed intensity correlation analysis on the two cells with the highest Mg content reveals that Mg subcellular localization correlates with oxygen in a different fashion with respect the other sister cells of the same strain. Graphical abstract Single cells or large population analysis this is the question!

Development of a reconstructed cornea from collagen-chondroitin sulfate foams and human cell cultures.

PubMed

Vrana, N Engin; Builles, Nicolas; Justin, Virginie; Bednarz, Jurgen; Pellegrini, Graziella; Ferrari, Barbara; Damour, Odile; Hulmes, David J S; Hasirci, Vasif

2008-12-01

To develop an artificial cornea, the ability to coculture the different cell types present in the cornea is essential. Here the goal was to develop a full-thickness artificial cornea using an optimized collagen-chondroitin sulfate foam, with a thickness close to that of human cornea, by coculturing human corneal epithelial and stromal cells and transfected human endothelial cells. Corneal extracellular matrix was simulated by a porous collagen/glycosaminoglycan-based scaffold seeded with stromal keratocytes and then, successively, epithelial and endothelial cells. Scaffolds were characterized for bulk porosity and pore size distribution. The performance of the three-dimensional construct was studied by histology, immunofluorescence, and immunohistochemistry. The scaffold had 85% porosity and an average pore size of 62.1 microm. Keratocytes populated the scaffold and produced a newly synthesized extracellular matrix as characterized by immunohistochemistry. Even though the keratocytes lost their CD34 phenotype marker, the absence of smooth muscle actin fibers showed that these cells had not differentiated into myofibroblasts. The epithelial cells formed a stratified epithelium and began basement membrane deposition. An endothelial cell monolayer beneath the foam was also apparent. These results demonstrate that collagen-chondroitin sulfate scaffolds are good substrates for artificial cornea construction with good resilience, long-term culture capability, and handling properties.

Coherent ongoing subthreshold state of a cortical neural network regulated by slow- and fast-spiking interneurons.

PubMed

Hoshino, Osamu

2006-12-01

Although details of cortical interneurons in anatomy and physiology have been well understood, little is known about how they contribute to ongoing spontaneous neuronal activity that could have a great impact on subsequent neuronal information processing. Simulating a cortical neural network model of an early sensory area, we investigated whether and how two distinct types of inhibitory interneurons, or fast-spiking interneurons with narrow axonal arbors and slow-spiking interneurons with wide axonal arbors, have a spatiotemporal influence on the ongoing activity of principal cells and subsequent cognitive information processing. In the model, dynamic cell assemblies, or population activation of principal cells, expressed information about specific sensory features. Within cell assemblies, fast-spiking interneurons give a feedback inhibitory effect on principal cells. Between cell assemblies, slow-spiking interneurons give a lateral inhibitory effect on principal cells. Here, we show that these interneurons keep the network at a subthreshold level for action potential generation under the ongoing state, by which the reaction time of principal cells to sensory stimulation could be accelerated. We suggest that the best timing of inhibition mediated by fast-spiking interneurons and slow-spiking interneurons allows the network to remain near threshold for rapid responses to input.

Bridging the Timescales of Single-Cell and Population Dynamics

NASA Astrophysics Data System (ADS)

Jafarpour, Farshid; Wright, Charles S.; Gudjonson, Herman; Riebling, Jedidiah; Dawson, Emma; Lo, Klevin; Fiebig, Aretha; Crosson, Sean; Dinner, Aaron R.; Iyer-Biswas, Srividya

2018-04-01

How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to the replication-competent (stalked) stage of the Caulobacter crescentus life cycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For C. crescentus cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time and, thus, yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell-age distribution, and the quiescence timescale.

Simulated population responses of common carp to commercial exploitation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weber, Michael J.; Hennen, Matthew J.; Brown, Michael L.

2011-12-01

Common carp Cyprinus carpio is a widespread invasive species that can become highly abundant and impose deleterious ecosystem effects. Thus, aquatic resource managers are interested in controlling common carp populations. Control of invasive common carp populations is difficult, due in part to the inherent uncertainty of how populations respond to exploitation. To understand how common carp populations respond to exploitation, we evaluated common carp population dynamics (recruitment, growth, and mortality) in three natural lakes in eastern South Dakota. Common carp exhibited similar population dynamics across these three systems that were characterized by consistent recruitment (ages 3 to 15 years present),more » fast growth (K = 0.37 to 0.59), and low mortality (A = 1 to 7%). We then modeled the effects of commercial exploitation on size structure, abundance, and egg production to determine its utility as a management tool to control populations. All three populations responded similarly to exploitation simulations with a 575-mm length restriction, representing commercial gear selectivity. Simulated common carp size structure modestly declined (9 to 37%) in all simulations. Abundance of common carp declined dramatically (28 to 56%) at low levels of exploitation (0 to 20%) but exploitation >40% had little additive effect and populations were only reduced by 49 to 79% despite high exploitation (>90%). Maximum lifetime egg production was reduced from 77 to 89% at a moderate level of exploitation (40%), indicating the potential for recruitment overfishing. Exploitation further reduced common carp size structure, abundance, and egg production when simulations were not size selective. Our results provide insights to how common carp populations may respond to exploitation. Although commercial exploitation may be able to partially control populations, an integrated removal approach that removes all sizes of common carp has a greater chance of controlling population abundance and reducing perturbations induced by this invasive species.« less

Simulated effects of recruitment variability, exploitation, and reduced habitat area on the muskellunge population in Shoepack Lake, Voyageurs National Park, Minnesota

USGS Publications Warehouse

Frohnauer, N.K.; Pierce, C.L.; Kallemeyn, L.W.

2007-01-01

The genetically unique population of muskellunge Esox masquinongy inhabiting Shoepack Lake in Voyageurs National Park, Minnesota, is potentially at risk for loss of genetic variability and long-term viability. Shoepack Lake has been subject to dramatic surface area changes from the construction of an outlet dam by beavers Castor canadensis and its subsequent failure. We simulated the long-term dynamics of this population in response to recruitment variation, increased exploitation, and reduced habitat area. We then estimated the effective population size of the simulated population and evaluated potential threats to long-term viability, based on which we recommend management actions to help preserve the long-term viability of the population. Simulations based on the population size and habitat area at the beginning of a companion study resulted in an effective population size that was generally above the threshold level for risk of loss of genetic variability, except when fishing mortality was increased. Simulations based on the reduced habitat area after the beaver dam failure and our assumption of a proportional reduction in population size resulted in an effective population size that was generally below the threshold level for risk of loss of genetic variability. Our results identified two potential threats to the long-term viability of the Shoepack Lake muskellunge population, reduction in habitat area and exploitation. Increased exploitation can be prevented through traditional fishery management approaches such as the adoption of no-kill, barbless hook, and limited entry regulations. Maintenance of the greatest possible habitat area and prevention of future habitat area reductions will require maintenance of the outlet dam built by beavers. Our study should enhance the long-term viability of the Shoepack Lake muskellunge population and illustrates a useful approach for other unique populations. ?? Copyright by the American Fisheries Society 2007.

Populational equilibrium through exosome-mediated Wnt signaling in tumor progression of diffuse large B-cell lymphoma.

PubMed

Koch, Raphael; Demant, Martin; Aung, Thiha; Diering, Nina; Cicholas, Anna; Chapuy, Bjoern; Wenzel, Dirk; Lahmann, Marlen; Güntsch, Annemarie; Kiecke, Christina; Becker, Sabrina; Hupfeld, Timo; Venkataramani, Vivek; Ziepert, Marita; Opitz, Lennart; Klapper, Wolfram; Trümper, Lorenz; Wulf, Gerald G

2014-04-03

Tumors are composed of phenotypically heterogeneous cell populations. The nongenomic mechanisms underlying transitions and interactions between cell populations are largely unknown. Here, we show that diffuse large B-cell lymphomas possess a self-organized infrastructure comprising side population (SP) and non-SP cells, where transitions between clonogenic states are modulated by exosome-mediated Wnt signaling. DNA methylation modulated SP-non-SP transitions and was correlated with the reciprocal expressions of Wnt signaling pathway agonist Wnt3a in SP cells and the antagonist secreted frizzled-related protein 4 in non-SP cells. Lymphoma SP cells exhibited autonomous clonogenicity and exported Wnt3a via exosomes to neighboring cells, thus modulating population equilibrium in the tumor.

Model of Exploratory Search for Mating Partners by Fission Yeast

NASA Astrophysics Data System (ADS)

Hurwitz, Daniel; Bendezu, Felipe; Martin, Sophie; Vavylonis, Dimitrios

2014-03-01

During conditions of nitrogen starvation, the model eukaryote S. pombe (fission yeast) undergoes sexual sporulation. Because fission yeast are non-motile, contact between opposite mating types during spore formation is accomplished by polarizing growth, via the Rho GTP-ase Cdc42, in each mating type towards the selected mate, a process known as shmooing. Recent findings showed that cells pick one of their neighboring compatible mates by randomizing the position of the Cdc42 complex about the cell membrane, such that the complex is stabilized near areas of high concentration of the opposite mating type pheromone. We developed Monte Carlo simulations to model partner finding in populations of mating cells and in small cell clusters. We assume that pheromones are secreted at the site of Cdc42 accumulation and that the Cdc42 dwell time increases in response to increasing pheromone concentration. We measured the number of cells that succeed in successful reciprocal pairing, the number of cells that were unable to find a partner, and the number of cells that picked a partner already engaged with another cell. For optimal cell pairing, we find the pheromone concentration decay length is around 1 micron, of order the cell size. We show that non-linear response of Cdc42 dwell time to pheromone concentration improves the number of successful pairs for a given spatial cell distribution. We discuss how these results compare to non-exploratory pairing mechanisms.

Sensing and enumerating rare circulating cells with diffuse light

NASA Astrophysics Data System (ADS)

Zettergren, Eric; Vickers, Dwayne; Niedre, Mark

2011-02-01

Detection and quantification of circulating cells in live animals is a challenging and important problem in many areas of biomedical research. Current methods involve extraction of blood samples and counting of cells ex-vivo. Since only small blood volumes are analyzed at specific time points, monitoring of changes in cell populations over time is difficult and rare cells often escape detection. The goal of this research is to develop a method for enumerating very rare circulating cells in the bloodstream non-invasively. This would have many applications in biomedical research, including monitoring of cancer metastasis and tracking of hematopoietic stem cells. In this work we describe the optical configuration of our instrument which allows fluorescence detection of single cells in diffusive media at the mesoscopic scale. Our instrument design consists of two continuous wave laser diode sources and an 8-channel fiber coupled multi-anode photon counting PMT. Fluorescence detector fibers were arranged circularly around the target in a miniaturized ring configuration. Cell-simulating fluorescent microspheres and fluorescently-labeled cells were passed through a limb mimicking phantom with similar optical properties and background fluorescence as a limb of a mouse. Our data shows that we are able to successfully detect and count these with high quantitative accuracy. Future work includes characterization of our instrument using fluorescently labeled cells in-vivo. If successful, this technique would allow several orders of magnitude in vivo detection sensitivity improvement versus current approaches.

Effects of radial compression on a novel simulated intervertebral disc-like assembly using bone marrow-derived mesenchymal stem cell cell-sheets for annulus fibrosus regeneration.

PubMed

See, Eugene Yong-Shun; Toh, Siew Lok; Goh, James Cho-Hong

2011-10-01

The aim of this study was to develop a tissue engineering approach in regenerating the annulus fibrosus (AF) as part of an overall strategy to produce a tissue-engineered intervertebral disc (IVD) replacement. To determine whether a rehabilitative simulation regime on bone marrow–derived mesenchymal stem cell cell-sheet is able to aid the regeneration of the AF. No previous study has used bone marrow–derived mesenchymal stem cell cell-sheets simulated by a rehabilitative regime to regenerate the AF. The approach was to use bone marrow–derived stem cells to form cell-sheets and incorporating them onto silk scaffolds to simulate the native lamellae of the AF. The in vitro experimental model used to study the efficacy of such a system was made up of the tissue engineering AF construct wrapped around a silicone disc to form a simulated IVD-like assembly. The assembly was cultured within a custom-designed bioreactor that provided a compressive mechanical stimulation onto the silicone disc. The silicone nucleus pulposus would bulge radially and compress the simulated AF to mimic the physiological conditions. The simulated IVD-like assembly was compressed using a rehabilitative regime that lasted for 4 weeks at 0.25 Hz, for 15 minutes each day. With the rehabilitative regime, the cell-sheets remained viable but showed a decrease in cell numbers and viability. Gene expression analysis showed significant upregulation of IVD-related genes and there was an increased ratio of collagen type II to collagen type I found within the extracellular matrix. The results suggested that a rehabilitative regime caused extensive remodeling to take place within the simulated IVD-like assembly, producing extracellular matrix similar to that found in the inner AF.

Bioactive polymers for cardiac tissue engineering

NASA Astrophysics Data System (ADS)

Wall, Samuel Thomas

2007-05-01

Prevalent in the US and worldwide, acute myocardial infarctions (AMI) can cause ischemic injuries to the heart that persist and lead to progressive degradation of the organ. Tissue engineering techniques exploiting biomaterials present a hopeful means of treating these injuries, either by mechanically stabilizing the injured ventricle, or by fostering cell growth to replace myocytes lost to damage. This thesis describes the development and testing of a synthetic extracellular matrix for cardiac tissue engineering applications. The first stage of this process was using an advanced finite element model of an injured ovine left ventricle to evaluate the potential benefits of injecting synthetic materials into the heart. These simulations indicated that addition of small amounts non-contractile material (on the order of 1--5% total wall volume) to infarct border zone regions reduced pathological systolic fiber stress to levels near those found in normal remote regions. Simulations also determined that direct addition to the infarct itself caused increases in ventricle ejection fraction while the underlying performance of the pump, ascertained by the Starling relation, was not improved. From these theoretical results, biomaterials were developed specifically for injection into the injured myocardium, and were characterized and tested for their mechanical properties and ability to sustain the proliferation of a stem cell population suitable for transplantation. Thermoresponsive synthetic copolymer hydrogels consisting of N-isopropylacrylamide and acrylic acid, p(NIPAAm-co-AAc), crosslinked with protease degradable amino acid sequences and modified with integrin binding ligands were synthesized, characterized in vitro, and used for myocardial implantation. These injectable materials could maintain a population of bone marrow derived mesenchymal stem cells in both two dimensional and three dimensional culture, and when tested in vivo in a murine infarct model they stabilized injured ventricles, reducing functional loss over 6 weeks, and promoted the survival of transplanted stem cells. In addition, modifications to the hydrogel to impart novel bioactivity through a developed tethered form of the protein sonic hedgehog were synthesized and characterized. This tethered form increased protein potency, induced angiogenesis, and could be incorporated into the hydrogel material for future implantation studies in the injured ventricle.

Coupling Cellular Automata Land Use Change with Distributed Hydrologic Models

NASA Astrophysics Data System (ADS)

Shu, L.; Duffy, C.

2017-12-01

There has been extensive research on LUC modeling with broad applications to simulating urban growth and changing demographic patterns across multiple scales. The importance of land conversion is a critical issue in watershed scale studies and is generally not treated in most watershed modeling approaches. In this study we apply spatially explicit hydrologic and landuse change models and the Conestoga Watershed in Lancaster County, Pennsylvania. The Penn State Integrated Hydrologic Model (PIHM) partitions the water balance in space and time over the urban catchment, the coupled Cellular Automata Land Use Change model (CALUC) dynamically simulates the evolution of land use classes based on physical measures associated with population change and land use demand factors. The CALUC model is based on iteratively applying discrete rules to each individual spatial cell. The essence the CA modeling involves calculation of the Transition Potential (TP) for conversion of a grid cell from one land use class to another. This potential includes five factors: random perturbation, suitability, accessibility, neighborhood effect, inertia effects and zonal factors. In spite of simplicity, this CALUC model has been shown to be very effective for simulating LUC leading to the emergence of complex spatial patterns. The components of TP are derived from present land use data for landuse reanalysis and for realistic future land use scenarios. For the CALUC we use early-settlement (circa 1790) initial land class values and final or present-day (2010) land classes to calibrate the model. CALUC- PIHM dynamically simulates the hydrologic response of conversion from pre-settlement to present landuse. The simulations highlight the capability and value of dynamic coupling of catchment hydrology with land use change over long time periods. Analysis of the simulation uses various metrics such as the distributed water balance, flow duration curves, etc. to show how deforestation, urbanization and agricultural land development interact for the period 1790- present.

Finite Element Analysis of Osteocytes Mechanosensitivity Under Simulated Microgravity

NASA Astrophysics Data System (ADS)

Yang, Xiao; Sun, Lian-Wen; Du, Cheng-Fei; Wu, Xin-Tong; Fan, Yu-Bo

2018-04-01

It was found that the mechanosensitivity of osteocytes could be altered under simulated microgravity. However, how the mechanical stimuli as the biomechanical origins cause the bioresponse in osteocytes under microgravity is unclear yet. Computational studies may help us to explore the mechanical deformation changes of osteocytes under microgravity. Here in this paper, we intend to use the computational simulation to investigate the mechanical behavior of osteocytes under simulated microgravity. In order to obtain the shape information of osteocytes, the biological experiment was conducted under simulated microgravity prior to the numerical simulation The cells were rotated by a clinostat for 6 hours or 5 days and fixed, the cytoskeleton and the nucleus were immunofluorescence stained and scanned, and the cell shape and the fluorescent intensity were measured from fluorescent images to get the dimension information of osteocytes The 3D finite element (FE) cell models were then established based on the scanned image stacks. Several components such as the actin cortex, the cytoplasm, the nucleus, the cytoskeleton of F-actin and microtubules were considered in the model. The cell models in both 6 hours and 5 days groups were then imposed by three magnitudes (0.5, 10 and 15 Pa) of simulating fluid shear stress, with cell total displacement and the internal discrete components deformation calculated. The results showed that under the simulated microgravity: (1) the nuclear area and height statistically significantly increased, which made the ratio of membrane-cortex height to nucleus height statistically significantly decreased; (2) the fluid shear stress-induced maximum displacements and average displacements in the whole cell decreased, with the deformation decreasing amplitude was largest when exposed to 1.5Pa of fluid shear stress; (3) the fluid shear stress-induced deformation of cell membrane-cortex and cytoskeleton decreased, while the fluid shear stress-induced deformation of nucleus increased. The results suggested the mechanical behavior of whole osteocyte cell body was suppressed by simulated microgravity, and this decrement was enlarged with either the increasing amplitude of fluid shear stress or the duration of simulated microgravity. What's more, the mechanical behavior of membrane-cortex and cytoskeleton was suppressed by the simulated microgravity, which indicated the mechanotransduction process in the cell body may be further inhibited. On the contrary, the cell nucleus deformation increased under simulated microgravity, which may be related to either the decreased amount of cytoskeleton or the increased volume occupied proportion of nucleus in whole cell under the simulated microgravity. The numerical results supported our previous biological experiments, and showed particularly affected cellular components under the simulated microgravity. The computational study here may help us to better understand the mechanism of mechanosensitivity changes in osteocytes under simulated microgravity, and further to explore the mechanism of the bone loss in space flight.

Two-Dimensional Self-Consistent Radio Frequency Plasma Simulations Relevant to the Gaseous Electronics Conference RF Reference Cell

PubMed Central

Lymberopoulos, Dimitris P.; Economou, Demetre J.

1995-01-01

Over the past few years multidimensional self-consistent plasma simulations including complex chemistry have been developed which are promising tools for furthering our understanding of reactive gas plasmas and for reactor design and optimization. These simulations must be benchmarked against experimental data obtained in well-characterized systems such as the Gaseous Electronics Conference (GEC) reference cell. Two-dimensional simulations relevant to the GEC Cell are reviewed in this paper with emphasis on fluid simulations. Important features observed experimentally, such as off-axis maxima in the charge density and hot spots of metastable species density near the electrode edges in capacitively-coupled GEC cells, have been captured by these simulations. PMID:29151756

Time-course study of different innate immune mediators produced by UV-irradiated skin: comparative effects of short and daily versus a single harmful UV exposure.

PubMed

Cela, Eliana M; Friedrich, Adrian; Paz, Mariela L; Vanzulli, Silvia I; Leoni, Juliana; González Maglio, Daniel H

2015-05-01

The modulatory effects of solar UV radiation on the immune system have been widely studied. As the skin is the main target of UV radiation, our purpose was to compare the impact on skin innate immunity of two contrasting ways to be exposed to sunlight. Hairless mice were UV irradiated with a single high UV dose simulating a harmful exposure, or with repetitive low UV doses simulating short occasional daily exposures. Skin samples were taken at different times after UV irradiation to evaluate skin histology, inflammatory cell recruitment, epidermal T-cell population and the mitochondrial function of epidermal cells. The transcriptional profiles of pro-inflammatory cytokines, chemokines, antimicrobial peptides and Toll-like receptors were evaluated by RT-PCR and ELISA in tissue homogenates. Finally, a lymphangiography was performed to assess modification in the lymphatic vessel system. A single high UV dose produces a deep inflammatory state characterized by the production of pro-inflammatory cytokines and chemokines that, in turn, induces the recruitment of neutrophils and macrophages into the irradiated area. On the other hand, repetitive low UV doses drive the skin to a photo-induced alert state in which there is no sign of inflammation, but the epithelium undergoes changes in thickness, the lymphatic circulation increases, and the transcription of antimicrobial peptides is induced. © 2014 John Wiley & Sons Ltd.

PIC simulations of wave-particle interactions with an initial electron velocity distribution from a kinetic ring current model

DOE PAGES

Yu, Yiqun; Delzanno, Gian Luca; Jordanova, Vania Koleva; ...

2017-07-15

Whistler wave-particle interactions play an important role in the Earth inner magnetospheric dynamics and have been the subject of numerous investigations. By running a global kinetic ring current model (RAM-SCB) in a storm event occurred on Oct 23–24 2002, we obtain the ring current electron distribution at a selected location at MLT of 9 and L of 6 where the electron distribution is composed of a warm population in the form of a partial ring in the velocity space (with energy around 15 keV) in addition to a cool population with a Maxwellian-like distribution. The warm population is likely frommore » the injected plasma sheet electrons during substorm injections that supply fresh source to the inner magnetosphere. These electron distributions are then used as input in an implicit particle-in-cell code (iPIC3D) to study whistler-wave generation and the subsequent wave-particle interactions. Here, we find that whistler waves are excited and propagate in the quasi-parallel direction along the background magnetic field. Several different wave modes are instantaneously generated with different growth rates and frequencies. The wave mode at the maximum growth rate has a frequency around 0.62ω ce, which corresponds to a parallel resonant energy of 2.5 keV. Linear theory analysis of wave growth is in excellent agreement with the simulation results. These waves grow initially due to the injected warm electrons and are later damped due to cyclotron absorption by electrons whose energy is close to the resonant energy and can effectively attenuate waves. The warm electron population overall experiences net energy loss and anisotropy drop while moving along the diffusion surfaces towards regions of lower phase space density, while the cool electron population undergoes heating when the waves grow, suggesting the cross-population interactions.« less

PIC simulations of wave-particle interactions with an initial electron velocity distribution from a kinetic ring current model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Yiqun; Delzanno, Gian Luca; Jordanova, Vania Koleva

Whistler wave-particle interactions play an important role in the Earth inner magnetospheric dynamics and have been the subject of numerous investigations. By running a global kinetic ring current model (RAM-SCB) in a storm event occurred on Oct 23–24 2002, we obtain the ring current electron distribution at a selected location at MLT of 9 and L of 6 where the electron distribution is composed of a warm population in the form of a partial ring in the velocity space (with energy around 15 keV) in addition to a cool population with a Maxwellian-like distribution. The warm population is likely frommore » the injected plasma sheet electrons during substorm injections that supply fresh source to the inner magnetosphere. These electron distributions are then used as input in an implicit particle-in-cell code (iPIC3D) to study whistler-wave generation and the subsequent wave-particle interactions. Here, we find that whistler waves are excited and propagate in the quasi-parallel direction along the background magnetic field. Several different wave modes are instantaneously generated with different growth rates and frequencies. The wave mode at the maximum growth rate has a frequency around 0.62ω ce, which corresponds to a parallel resonant energy of 2.5 keV. Linear theory analysis of wave growth is in excellent agreement with the simulation results. These waves grow initially due to the injected warm electrons and are later damped due to cyclotron absorption by electrons whose energy is close to the resonant energy and can effectively attenuate waves. The warm electron population overall experiences net energy loss and anisotropy drop while moving along the diffusion surfaces towards regions of lower phase space density, while the cool electron population undergoes heating when the waves grow, suggesting the cross-population interactions.« less

Simulation of Population Processes with a Programmable Pocket Calculator.

ERIC Educational Resources Information Center

Kidd, N. A. C.

1979-01-01

Presents a set of simulation models for use in teaching population dynamics. These models are designed specifically for use with a programmable pocket calculator, and can be used to demonstrate growth of populations with discrete or overlapping generations and also to explore effects of density-dependent and -independent mortality. (Author/CS)

Linking population viability, habitat suitability, and landscape simulation models for conservation planning

Treesearch

Michael A. Larson; Frank R., III Thompson; Joshua J. Millspaugh; William D. Dijak; Stephen R. Shifley

2004-01-01

Methods for habitat modeling based on landscape simulations and population viability modeling based on habitat quality are well developed, but no published study of which we are aware has effectively joined them in a single, comprehensive analysis. We demonstrate the application of a population viability model for ovenbirds (Seiurus aurocapillus)...

The Impact of Distraction on the Driving Performance of Adolescents with and without Attention Deficit Hyperactivity Disorder

PubMed Central

Narad, Megan; Garner, Annie A.; Brassell, Anne A.; Saxby, Dyani; Antonini, Tanya N.; O'Brien, Kathleen M.; Tamm, Leanne; Matthews, Gerald; Epstein, Jeffery N.

2013-01-01

Importance This study extends the literature regarding Attention-Deficit/Hyperactivity Disorder (ADHD) related driving impairments to a newly-licensed, adolescent population. Objective To investigate the combined risks of adolescence, ADHD, and distracted driving (cell phone conversation and text messaging) on driving performance. Design Adolescents with and without ADHD engaged in a simulated drive under three conditions (no distraction, cell phone conversation, texting). During each condition, one unexpected event (e.g., car suddenly merging into driver's lane) was introduced. Setting Driving simulator. Participants Adolescents aged 16–17 with ADHD (n=28) and controls (n=33). Interventions/Main Exposures Cell phone conversation, texting, and no distraction while driving. Outcome Measures Self-report of driving history; Average speed, standard deviation of speed, standard deviation of lateral position, braking reaction time during driving simulation. Results Adolescents with ADHD reported fewer months of driving experience and a higher proportion of driving violations than controls. After controlling for months of driving history, adolescents with ADHD demonstrated more variability in speed and lane position than controls. There were no group differences for braking reaction time. Further, texting negatively impacted the driving performance of all participants as evidenced by increased variability in speed and lane position. Conclusions This study, one of the first to investigate distracted driving in adolescents with ADHD, adds to a growing body of literature documenting that individuals with ADHD are at increased risk for negative driving outcomes. Furthermore, texting significantly impairs the driving performance of all adolescents and increases existing driving-related impairment in adolescents with ADHD, highlighting the need for education and enforcement of regulations against texting for this age group. PMID:23939758

Small median tumor diameter at cure threshold (<20 mm) among aggressive non-small cell lung cancers in male smokers predicts both chest X-ray and CT screening outcomes in a novel simulation framework.

PubMed

Goldwasser, Deborah L; Kimmel, Marek

2013-01-01

The effectiveness of population-wide lung cancer screening strategies depends on the underlying natural course of lung cancer. We evaluate the expected stage distribution in the Mayo CT screening study under an existing simulation model of non-small cell lung cancer (NSCLC) progression calibrated to the Mayo lung project (MLP). Within a likelihood framework, we evaluate whether the probability of 5-year NSCLC survival conditional on tumor diameter at detection depends significantly on screening detection modality, namely chest X-ray and computed tomography. We describe a novel simulation framework in which tumor progression depends on cellular proliferation and mutation within a stem cell compartment of the tumor. We fit this model to randomized trial data from the MLP and produce estimates of the median radiologic size at the cure threshold. We examine the goodness of model fit with respect to radiologic tumor size and 5-year NSCLC survival among incident cancers in both the MLP and Mayo CT studies. An existing model of NSCLC progression under-predicts the number of advanced-stage incident NSCLCs among males in the Mayo CT study (p-value = 0.004). The probability of 5-year NSCLC survival conditional on tumor diameter depends significantly on detection modality (p-value = 0.0312). In our new model, selected solution sets having a median tumor diameter of 16.2-22.1 mm at cure threshold among aggressive NSCLCs predict both MLP and Mayo CT outcomes. We conclude that the median lung tumor diameter at cure threshold among aggressive NSCLCs in male smokers may be small (<20 mm). Copyright © 2012 UICC.

Compression simulations of plant tissue in 3D using a mass-spring system approach and discrete element method.

PubMed

Pieczywek, Piotr M; Zdunek, Artur

2017-10-18

A hybrid model based on a mass-spring system methodology coupled with the discrete element method (DEM) was implemented to simulate the deformation of cellular structures in 3D. Models of individual cells were constructed using the particles which cover the surfaces of cell walls and are interconnected in a triangle mesh network by viscoelastic springs. The spatial arrangement of the cells required to construct a virtual tissue was obtained using Poisson-disc sampling and Voronoi tessellation in 3D space. Three structural features were included in the model: viscoelastic material of cell walls, linearly elastic interior of the cells (simulating compressible liquid) and a gas phase in the intercellular spaces. The response of the models to an external load was demonstrated during quasi-static compression simulations. The sensitivity of the model was investigated at fixed compression parameters with variable tissue porosity, cell size and cell wall properties, such as thickness and Young's modulus, and a stiffness of the cell interior that simulated turgor pressure. The extent of the agreement between the simulation results and other models published is discussed. The model demonstrated the significant influence of tissue structure on micromechanical properties and allowed for the interpretation of the compression test results with respect to changes occurring in the structure of the virtual tissue. During compression virtual structures composed of smaller cells produced higher reaction forces and therefore they were stiffer than structures with large cells. The increase in the number of intercellular spaces (porosity) resulted in a decrease in reaction forces. The numerical model was capable of simulating the quasi-static compression experiment and reproducing the strain stiffening observed in experiment. Stress accumulation at the edges of the cell walls where three cells meet suggests that cell-to-cell debonding and crack propagation through the contact edge of neighboring cells is one of the most prevalent ways for tissue to rupture.

Mosquito population dynamics from cellular automata-based simulation

NASA Astrophysics Data System (ADS)

Syafarina, Inna; Sadikin, Rifki; Nuraini, Nuning

2016-02-01

In this paper we present an innovative model for simulating mosquito-vector population dynamics. The simulation consist of two stages: demography and dispersal dynamics. For demography simulation, we follow the existing model for modeling a mosquito life cycles. Moreover, we use cellular automata-based model for simulating dispersal of the vector. In simulation, each individual vector is able to move to other grid based on a random walk. Our model is also capable to represent immunity factor for each grid. We simulate the model to evaluate its correctness. Based on the simulations, we can conclude that our model is correct. However, our model need to be improved to find a realistic parameters to match real data.

Characterization of human skeletal stem and bone cell populations using dielectrophoresis.

PubMed

Ismail, A; Hughes, M P; Mulhall, H J; Oreffo, R O C; Labeed, F H

2015-02-01

Dielectrophoresis (DEP) is a non-invasive cell analysis method that uses differences in electrical properties between particles and surrounding medium to determine a unique set of cellular properties that can be used as a basis for cell separation. Cell-based therapies using skeletal stem cells are currently one of the most promising areas for treating a variety of skeletal and muscular disorders. However, identifying and sorting these cells remains a challenge in the absence of unique skeletal stem cell markers. DEP provides an ideal method for identifying subsets of cells without the need for markers by using their dielectric properties. This study used a 3D dielectrophoretic well chip device to determine the dielectric characteristics of two osteosarcoma cell lines (MG-63 and SAOS-2) and an immunoselected enriched skeletal stem cell fraction (STRO-1 positive cell) of human bone marrow. Skeletal cells were exposed to a series of different frequencies to induce dielectrophoretic cell movement, and a model was developed to generate the membrane and cytoplasmic properties of the cell populations. Differences were observed in the dielectric properties of MG-63, SAOS-2 and STRO-1 enriched skeletal populations, which could potentially be used to sort cells in mixed populations. This study provide evidence of the ability to characterize different human skeletal stem and mature cell populations, and acts as a proof-of-concept that dielectrophoresis can be exploited to detect, isolate and separate skeletal cell populations from heterogeneous bone marrow cell populations. Copyright © 2012 John Wiley & Sons, Ltd.

A phase code for memory could arise from circuit mechanisms in entorhinal cortex

PubMed Central

Hasselmo, Michael E.; Brandon, Mark P.; Yoshida, Motoharu; Giocomo, Lisa M.; Heys, James G.; Fransen, Erik; Newman, Ehren L.; Zilli, Eric A.

2009-01-01

Neurophysiological data reveals intrinsic cellular properties that suggest how entorhinal cortical neurons could code memory by the phase of their firing. Potential cellular mechanisms for this phase coding in models of entorhinal function are reviewed. This mechanism for phase coding provides a substrate for modeling the responses of entorhinal grid cells, as well as the replay of neural spiking activity during waking and sleep. Efforts to implement these abstract models in more detailed biophysical compartmental simulations raise specific issues that could be addressed in larger scale population models incorporating mechanisms of inhibition. PMID:19656654

Investigation of the Carbon Arc Source as an AM0 Solar Simulator for Use in Characterizing Multi-Junction Solar Cells

NASA Technical Reports Server (NTRS)

Xu, Jianzeng; Woodyward, James R.

2005-01-01

The operation of multi-junction solar cells used for production of space power is critically dependent on the spectral irradiance of the illuminating light source. Unlike single-junction cells where the spectral irradiance of the simulator and computational techniques may be used to optimized cell designs, optimization of multi-junction solar cell designs requires a solar simulator with a spectral irradiance that closely matches AM0.

SEIR model simulation for Hepatitis B

NASA Astrophysics Data System (ADS)

Side, Syafruddin; Irwan, Mulbar, Usman; Sanusi, Wahidah

2017-09-01

Mathematical modelling and simulation for Hepatitis B discuss in this paper. Population devided by four variables, namely: Susceptible, Exposed, Infected and Recovered (SEIR). Several factors affect the population in this model is vaccination, immigration and emigration that occurred in the population. SEIR Model obtained Ordinary Differential Equation (ODE) non-linear System 4-D which then reduces to 3-D. SEIR model simulation undertaken to predict the number of Hepatitis B cases. The results of the simulation indicates the number of Hepatitis B cases will increase and then decrease for several months. The result of simulation using the number of case in Makassar also found the basic reproduction number less than one, that means, Makassar city is not an endemic area of Hepatitis B. With approval from the proceedings editor article 020185 titled, "SEIR model simulation for Hepatitis B," is retracted from the public record, as it is a duplication of article 020198 published in the same volume.

Isolation, Culture, and Differentiation of Bone Marrow Stromal Cells and Osteoclast Progenitors from Mice.

PubMed

Maridas, David E; Rendina-Ruedy, Elizabeth; Le, Phuong T; Rosen, Clifford J

2018-01-06

Bone marrow stromal cells (BMSCs) constitute a cell population routinely used as a representation of mesenchymal stem cells in vitro. They reside within the bone marrow cavity alongside hematopoietic stem cells (HSCs), which can give rise to red blood cells, immune progenitors, and osteoclasts. Thus, extractions of cell populations from the bone marrow results in a very heterogeneous mix of various cell populations, which can present challenges in experimental design and confound data interpretation. Several isolation and culture techniques have been developed in laboratories in order to obtain more or less homogeneous populations of BMSCs and HSCs invitro. Here, we present two methods for isolation of BMSCs and HSCs from mouse long bones: one method that yields a mixed population of BMSCs and HSCs and one method that attempts to separate the two cell populations based on adherence. Both methods provide cells suitable for osteogenic and adipogenic differentiation experiments as well as functional assays.

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations.

PubMed

Gómez-Villafuertes, Rosa; Paniagua-Herranz, Lucía; Gascon, Sergio; de Agustín-Durán, David; Ferreras, María de la O; Gil-Redondo, Juan Carlos; Queipo, María José; Menendez-Mendez, Aida; Pérez-Sen, Ráquel; Delicado, Esmerilda G; Gualix, Javier; Costa, Marcos R; Schroeder, Timm; Miras-Portugal, María Teresa; Ortega, Felipe

2017-12-16

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

The finite state projection approach to analyze dynamics of heterogeneous populations

NASA Astrophysics Data System (ADS)

Johnson, Rob; Munsky, Brian

2017-06-01

Population modeling aims to capture and predict the dynamics of cell populations in constant or fluctuating environments. At the elementary level, population growth proceeds through sequential divisions of individual cells. Due to stochastic effects, populations of cells are inherently heterogeneous in phenotype, and some phenotypic variables have an effect on division or survival rates, as can be seen in partial drug resistance. Therefore, when modeling population dynamics where the control of growth and division is phenotype dependent, the corresponding model must take account of the underlying cellular heterogeneity. The finite state projection (FSP) approach has often been used to analyze the statistics of independent cells. Here, we extend the FSP analysis to explore the coupling of cell dynamics and biomolecule dynamics within a population. This extension allows a general framework with which to model the state occupations of a heterogeneous, isogenic population of dividing and expiring cells. The method is demonstrated with a simple model of cell-cycle progression, which we use to explore possible dynamics of drug resistance phenotypes in dividing cells. We use this method to show how stochastic single-cell behaviors affect population level efficacy of drug treatments, and we illustrate how slight modifications to treatment regimens may have dramatic effects on drug efficacy.

Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nadler, Ethan O.; Mao, Yao -Yuan; Wechsler, Risa H.

Here, we identify subhalos in dark matter–only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)–mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an 85% out-of-bag classification score.more » We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.« less

Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

DOE PAGES

Nadler, Ethan O.; Mao, Yao -Yuan; Wechsler, Risa H.; ...

2018-06-01

Here, we identify subhalos in dark matter–only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)–mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an 85% out-of-bag classification score.more » We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.« less

cellGPU: Massively parallel simulations of dynamic vertex models

NASA Astrophysics Data System (ADS)

Sussman, Daniel M.

2017-10-01

Vertex models represent confluent tissue by polygonal or polyhedral tilings of space, with the individual cells interacting via force laws that depend on both the geometry of the cells and the topology of the tessellation. This dependence on the connectivity of the cellular network introduces several complications to performing molecular-dynamics-like simulations of vertex models, and in particular makes parallelizing the simulations difficult. cellGPU addresses this difficulty and lays the foundation for massively parallelized, GPU-based simulations of these models. This article discusses its implementation for a pair of two-dimensional models, and compares the typical performance that can be expected between running cellGPU entirely on the CPU versus its performance when running on a range of commercial and server-grade graphics cards. By implementing the calculation of topological changes and forces on cells in a highly parallelizable fashion, cellGPU enables researchers to simulate time- and length-scales previously inaccessible via existing single-threaded CPU implementations. Program Files doi:http://dx.doi.org/10.17632/6j2cj29t3r.1 Licensing provisions: MIT Programming language: CUDA/C++ Nature of problem: Simulations of off-lattice "vertex models" of cells, in which the interaction forces depend on both the geometry and the topology of the cellular aggregate. Solution method: Highly parallelized GPU-accelerated dynamical simulations in which the force calculations and the topological features can be handled on either the CPU or GPU. Additional comments: The code is hosted at https://gitlab.com/dmsussman/cellGPU, with documentation additionally maintained at http://dmsussman.gitlab.io/cellGPUdocumentation

PROPAGATOR: a synchronous stochastic wildfire propagation model with distributed computation engine

NASA Astrophysics Data System (ADS)

D´Andrea, M.; Fiorucci, P.; Biondi, G.; Negro, D.

2012-04-01

PROPAGATOR is a stochastic model of forest fire spread, useful as a rapid method for fire risk assessment. The model is based on a 2D stochastic cellular automaton. The domain of simulation is discretized using a square regular grid with cell size of 20x20 meters. The model uses high-resolution information such as elevation and type of vegetation on the ground. Input parameters are wind direction, speed and the ignition point of fire. The simulation of fire propagation is done via a stochastic mechanism of propagation between a burning cell and a non-burning cell belonging to its neighbourhood, i.e. the 8 adjacent cells in the rectangular grid. The fire spreads from one cell to its neighbours with a certain base probability, defined using vegetation types of two adjacent cells, and modified by taking into account the slope between them, wind direction and speed. The simulation is synchronous, and takes into account the time needed by the burning fire to cross each cell. Vegetation cover, slope, wind speed and direction affect the fire-propagation speed from cell to cell. The model simulates several mutually independent realizations of the same stochastic fire propagation process. Each of them provides a map of the area burned at each simulation time step. Propagator simulates self-extinction of the fire, and the propagation process continues until at least one cell of the domain is burning in each realization. The output of the model is a series of maps representing the probability of each cell of the domain to be affected by the fire at each time-step: these probabilities are obtained by evaluating the relative frequency of ignition of each cell with respect to the complete set of simulations. Propagator is available as a module in the OWIS (Opera Web Interfaces) system. The model simulation runs on a dedicated server and it is remote controlled from the client program, NAZCA. Ignition points of the simulation can be selected directly in a high-resolution, three-dimensional graphical representation of the Italian territory within NAZCA. The other simulation parameters, namely wind speed and direction, number of simulations, computing grid size and temporal resolution, can be selected from within the program interface. The output of the simulation is showed in real-time during the simulation, and are also available off-line and on the DEWETRA system, a Web GIS-based system for environmental risk assessment, developed according to OGC-INSPIRE standards. The model execution is very fast, providing a full prevision for the scenario in few minutes, and can be useful for real-time active fire management and suppression.

Gene expression distribution deconvolution in single-cell RNA sequencing.

PubMed

Wang, Jingshu; Huang, Mo; Torre, Eduardo; Dueck, Hannah; Shaffer, Sydney; Murray, John; Raj, Arjun; Li, Mingyao; Zhang, Nancy R

2018-06-26

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND's noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Copyright © 2018 the Author(s). Published by PNAS.

A simulation study of hardwood rootstock populations in young loblolly pine plantations

Treesearch

David R. Weise; Glenn R. Glover

1988-01-01

A computer program to simulate spatial distribution of hardwood rootstock populations is presented. Nineteen 3 to 6 yearold loblolly pine (Pinus taeda L.) plantations in Alabama and Georgia were measured to provide information for the simulator. Spatial pattern, expressed as Pielou's nonrandomness index (PNI), ranged from 0.47 to 2.45. Scatterplots illustrated no...

Characterization of rabbit limbal epithelial side population cells using RNA sequencing and single-cell qRT-PCR.

PubMed

Kameishi, Sumako; Umemoto, Terumasa; Matsuzaki, Yu; Fujita, Masako; Okano, Teruo; Kato, Takashi; Yamato, Masayuki

2016-05-06

Corneal epithelial stem cells reside in the limbus, a transitional zone between the cornea and conjunctiva, and are essential for maintaining homeostasis in the corneal epithelium. Although our previous studies demonstrated that rabbit limbal epithelial side population (SP) cells exhibit stem cell-like phenotypes with Hoechst 33342 staining, the different characteristics and/or populations of these cells remain unclear. Therefore, in this study, we determined the gene expression profiles of limbal epithelial SP cells by RNA sequencing using not only present public databases but also contigs that were created by de novo transcriptome assembly as references for mapping. Our transcriptome data indicated that limbal epithelial SP cells exhibited a stem cell-like phenotype compared with non-SP cells. Importantly, gene ontology analysis following RNA sequencing demonstrated that limbal epithelial SP cells exhibited significantly enhanced expression of mesenchymal/endothelial cell markers rather than epithelial cell markers. Furthermore, single-cell quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) demonstrated that the limbal epithelial SP population consisted of at least two immature cell populations with endothelial- or mesenchymal-like phenotypes. Therefore, our present results may propose the presence of a novel population of corneal epithelial stem cells distinct from conventional epithelial stem cells. Copyright © 2015 Elsevier Inc. All rights reserved.

SU-E-T-398: Evaluation of Radiobiological Parameters Using Serial Tumor Imaging During Radiotherapy as An Inverse Ill-Posed Problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chvetsov, A; Sandison, G; Schwartz, J

Purpose: Combination of serial tumor imaging with radiobiological modeling can provide more accurate information on the nature of treatment response and what underlies resistance. The purpose of this article is to improve the algorithms related to imaging-based radiobilogical modeling of tumor response. Methods: Serial imaging of tumor response to radiation therapy represents a sum of tumor cell sensitivity, tumor growth rates, and the rate of cell loss which are not separated explicitly. Accurate treatment response assessment would require separation of these radiobiological determinants of treatment response because they define tumor control probability. We show that the problem of reconstruction ofmore » radiobiological parameters from serial imaging data can be considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind because it is governed by a sum of several exponential processes. Therefore, the parameter reconstruction can be solved using regularization methods. Results: To study the reconstruction problem, we used a set of serial CT imaging data for the head and neck cancer and a two-level cell population model of tumor response which separates the entire tumor cell population in two subpopulations of viable and lethally damage cells. The reconstruction was done using a least squared objective function and a simulated annealing algorithm. Using in vitro data for radiobiological parameters as reference data, we shown that the reconstructed values of cell surviving fractions and potential doubling time exhibit non-physical fluctuations if no stabilization algorithms are applied. The variational regularization allowed us to obtain statistical distribution for cell surviving fractions and cell number doubling times comparable to in vitro data. Conclusion: Our results indicate that using variational regularization can increase the number of free parameters in the model and open the way to development of more advanced algorithms which take into account tumor heterogeneity, for example, related to hypoxia.« less

Peripheral blood mononuclear cells analysis in microfluidic flow by coherent imaging tools

NASA Astrophysics Data System (ADS)

Dannhauser, David; Rossi, Domenico; Memmolo, Pasquale; Causa, Filippo; Finizio, Andrea; Ferraro, Pietro; Netti, Paolo A.

2017-06-01

Cell of human blood stream are divided into two groups: Red Blood Cells (RBC) and White Blood Cells (WBC). RBC have a peculiar biconcave disk shape and they are responsible for the delivering of O2 and CO2 through the body. WBC are a more widespread class of cell ensuring immunity against pathogens. They can be divided in two main classes: granulocyte cells and A-granulocyte cells. Neutrophils, basophils and eosinophils belong to the granulocyte cell class, while lymphocytes and monocytes belong to A-granulocyte. Both in RBC and WBC, the intrinsic physical properties of a cell are indicators of cell condition and, furthermore, of the overall human body state. Thus, the accurate comprehension of the physiological structure of WBCs is fundamental to recognize diseases. Here we show the possibility to simple and straightforwardly characterize the physical properties of individual RBC and mononuclear WBC in a microfluidic context, using a wide angle light scattering apparatus and a corresponding theoretical simulation of Optical Signature (OS). A non-Newtonian polymer alignment solution for cell is used to ensure an individual cell alignment in the microfluidic flow, thus permitting a precise investigation. Additionally, Quantitative Phase Imaging (QPI) holographic measurements are performed to estimate cell morphometric features, such as their refractive index. We analyzed more than 200 WBCs and 100 RBCs of three different probands. Results showed distinct cell populations according to their measured dimensions and shape, which can be associated to the presence of RBC, lymphocytes and monocytes.

Differences in Cell Division Rates Drive the Evolution of Terminal Differentiation in Microbes

PubMed Central

Matias Rodrigues, João F.; Rankin, Daniel J.; Rossetti, Valentina; Wagner, Andreas; Bagheri, Homayoun C.

2012-01-01

Multicellular differentiated organisms are composed of cells that begin by developing from a single pluripotent germ cell. In many organisms, a proportion of cells differentiate into specialized somatic cells. Whether these cells lose their pluripotency or are able to reverse their differentiated state has important consequences. Reversibly differentiated cells can potentially regenerate parts of an organism and allow reproduction through fragmentation. In many organisms, however, somatic differentiation is terminal, thereby restricting the developmental paths to reproduction. The reason why terminal differentiation is a common developmental strategy remains unexplored. To understand the conditions that affect the evolution of terminal versus reversible differentiation, we developed a computational model inspired by differentiating cyanobacteria. We simulated the evolution of a population of two cell types –nitrogen fixing or photosynthetic– that exchange resources. The traits that control differentiation rates between cell types are allowed to evolve in the model. Although the topology of cell interactions and differentiation costs play a role in the evolution of terminal and reversible differentiation, the most important factor is the difference in division rates between cell types. Faster dividing cells always evolve to become the germ line. Our results explain why most multicellular differentiated cyanobacteria have terminally differentiated cells, while some have reversibly differentiated cells. We further observed that symbioses involving two cooperating lineages can evolve under conditions where aggregate size, connectivity, and differentiation costs are high. This may explain why plants engage in symbiotic interactions with diazotrophic bacteria. PMID:22511858

A novel approach for extracting viscoelastic parameters of living cells through combination of inverse finite element simulation and Atomic Force Microscopy.

PubMed

Wei, Fanan; Yang, Haitao; Liu, Lianqing; Li, Guangyong

2017-03-01

Dynamic mechanical behaviour of living cells has been described by viscoelasticity. However, quantitation of the viscoelastic parameters for living cells is far from sophisticated. In this paper, combining inverse finite element (FE) simulation with Atomic Force Microscope characterization, we attempt to develop a new method to evaluate and acquire trustworthy viscoelastic index of living cells. First, influence of the experiment parameters on stress relaxation process is assessed using FE simulation. As suggested by the simulations, cell height has negligible impact on shape of the force-time curve, i.e. the characteristic relaxation time; and the effect originates from substrate can be totally eliminated when stiff substrate (Young's modulus larger than 3 GPa) is used. Then, so as to develop an effective optimization strategy for the inverse FE simulation, the parameters sensitivity evaluation is performed for Young's modulus, Poisson's ratio, and characteristic relaxation time. With the experiment data obtained through typical stress relaxation measurement, viscoelastic parameters are extracted through the inverse FE simulation by comparing the simulation results and experimental measurements. Finally, reliability of the acquired mechanical parameters is verified with different load experiments performed on the same cell.

Response of Human Prostate Cancer Cells to Mitoxantrone Treatment in Simulated Microgravity Environment

NASA Astrophysics Data System (ADS)

Zhang, Ye; Wu, Honglu

2012-07-01

RESPONSE OF HUMAN PROSTATE CANCER CELLS TO MITOXANTRONE TREATMENT IN SIMULATED MICROGRAVITY ENVIRONMENT Ye Zhang1,2, Christopher Edwards3, and Honglu Wu1 1 NASA-Johnson Space Center, Houston, TX 2 Wyle Integrated Science and Engineering Group, Houston, TX 3 Oregon State University, Corvallis, OR This study explores the changes in growth of human prostate cancer cells (LNCaP) and their response to the treatment of an antineoplastic agent, mitoxantrone, under the simulated microgravity condition. In comparison to static 1g, microgravity and simulated microgravity have been shown to alter global gene expression patterns and protein levels in various cultured cell models or animals. However, very little is known about the effect of altered gravity on the responses of cells to the treatment of drugs, especially chemotherapy drugs. To test the hypothesis that zero gravity would result in altered regulations of cells in response to antineoplastic agents, we cultured LNCaP cells in either a High Aspect Ratio Vessel (HARV) bioreactor at the rotating condition to model microgravity in space or in the static condition as control, and treated the cells with mitoxantrone. Cell growth, as well as expressions of oxidative stress related genes, were analyzed after the drug treatment. Compared to static 1g controls, the cells cultured in the simulated microgravity environment did not present significant differences in cell viability, growth rate, or cell cycle distribution. However, after mitoxantrone treatment, a significant proportion of bioreactor cultured cells became apoptotic or was arrested in G2. Several oxidative stress related genes also showed a higher expression level post mitoxantrone treatment. Our results indicate that simulated microgravity may alter the response of LNCaP cells to mitoxantrone treatment. Understanding the mechanisms by which cells respond to drugs differently in an altered gravity environment will be useful for the improvement of cancer treatment on the ground. This study explores the changes in growth of human prostate cancer cells (LNCaP) and their response to the treatment of an antineoplastic agent, mitoxantrone, under the simulated microgravity condition. In comparison to static 1g, microgravity and simulated microgravity have been shown to alter global gene expression patterns and protein levels in various cultured cell models or animals. However, very little is known about the effect of altered gravity on the responses of cells to the treatment of drugs, especially chemotherapy drugs. To test the hypothesis that zero gravity would result in altered regulations of cells in response to antineoplastic agents, we cultured LNCaP cells in either a High Aspect Ratio Vessel (HARV) bioreactor at the rotating condition to model microgravity in space or in the static condition as control, and treated the cells with mitoxantrone. Cell growth, as well as expressions of oxidative stress related genes, were analyzed after the drug treatment. Compared to static 1g controls, the cells cultured in the simulated microgravity environment did not present significant differences in cell viability, growth rate, or cell cycle distribution. However, after mitoxantrone treatment, a significant proportion of bioreactor cultured cells became apoptotic or was arrested in G2. Several oxidative stress related genes also showed a higher expression level post mitoxantrone treatment. Our results indicate that simulated microgravity may alter the response of LNCaP cells to mitoxantrone treatment. Understanding the mechanisms by which cells respond to drugs differently in an altered gravity environment will be useful for the improvement of cancer treatment on the ground.

Noise-induced bistability in the quasi-neutral coexistence of viral RNAs under different replication modes.

PubMed

Sardanyés, Josep; Arderiu, Andreu; Elena, Santiago F; Alarcón, Tomás

2018-05-01

Evolutionary and dynamical investigations into real viral populations indicate that RNA replication can range between the two extremes represented by so-called 'stamping machine replication' (SMR) and 'geometric replication' (GR). The impact of asymmetries in replication for single-stranded (+) sense RNA viruses has been mainly studied with deterministic models. However, viral replication should be better described by including stochasticity, as the cell infection process is typically initiated with a very small number of RNA macromolecules, and thus largely influenced by intrinsic noise. Under appropriate conditions, deterministic theoretical descriptions of viral RNA replication predict a quasi-neutral coexistence scenario, with a line of fixed points involving different strands' equilibrium ratios depending on the initial conditions. Recent research into the quasi-neutral coexistence in two competing populations reveals that stochastic fluctuations fundamentally alter the mean-field scenario, and one of the two species outcompetes the other. In this article, we study this phenomenon for viral RNA replication modes by means of stochastic simulations and a diffusion approximation. Our results reveal that noise has a strong impact on the amplification of viral RNAs, also causing the emergence of noise-induced bistability. We provide analytical criteria for the dominance of (+) sense strands depending on the initial populations on the line of equilibria, which are in agreement with direct stochastic simulation results. The biological implications of this noise-driven mechanism are discussed within the framework of the evolutionary dynamics of RNA viruses with different modes of replication. © 2018 The Author(s).

Emergence of cytotoxic resistance in cancer cell populations: Single-cell mechanisms and population-level consequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lorenzi, Tommaso; Chisholm, Rebecca H.; Lorz, Alexander

We formulate an individual-based model and a population model of phenotypic evolution, under cytotoxic drugs, in a cancer cell population structured by the expression levels of survival-potential and proliferation-potential. We apply these models to a recently studied experimental system. Our results suggest that mechanisms based on fundamental laws of biology can reversibly push an actively-proliferating, and drug-sensitive, cell population to transition into a weakly-proliferative and drug-tolerant state, which will eventually facilitate the emergence of more potent, proliferating and drug-tolerant cells.

A Preclinical Population Pharmacokinetic Model for Anti-CD20/CD3 T-Cell-Dependent Bispecific Antibodies.

PubMed

Ferl, Gregory Z; Reyes, Arthur; Sun, Liping L; Cheu, Melissa; Oldendorp, Amy; Ramanujan, Saroja; Stefanich, Eric G

2018-05-01

CD20 is a cell-surface receptor expressed by healthy and neoplastic B cells and is a well-established target for biologics used to treat B-cell malignancies. Pharmacokinetic (PK) and pharmacodynamic (PD) data for the anti-CD20/CD3 T-cell-dependent bispecific antibody BTCT4465A were collected in transgenic mouse and nonhuman primate (NHP) studies. Pronounced nonlinearity in drug elimination was observed in the murine studies, and time-varying, nonlinear PK was observed in NHPs, where three empirical drug elimination terms were identified using a mixed-effects modeling approach: i) a constant nonsaturable linear clearance term (7 mL/day/kg); ii) a rapidly decaying time-varying, linear clearance term (t ½  = 1.6 h); and iii) a slowly decaying time-varying, nonlinear clearance term (t ½  = 4.8 days). The two time-varying drug elimination terms approximately track with time scales of B-cell depletion and T-cell migration/expansion within the central blood compartment. The mixed-effects NHP model was scaled to human and prospective clinical simulations were generated. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Optimizing homeostatic cell renewal in hierarchical tissues

PubMed Central

Fider, Nicole A.

2018-01-01

In order to maintain homeostasis, mature cells removed from the top compartment of hierarchical tissues have to be replenished by means of differentiation and self-renewal events happening in the more primitive compartments. As each cell division is associated with a risk of mutation, cell division patterns have to be optimized, in order to minimize or delay the risk of malignancy generation. Here we study this optimization problem, focusing on the role of division tree length, that is, the number of layers of cells activated in response to the loss of terminally differentiated cells, which is related to the balance between differentiation and self-renewal events in the compartments. Using both analytical methods and stochastic simulations in a metapopulation-style model, we find that shorter division trees are advantageous if the objective is to minimize the total number of one-hit mutants in the cell population. Longer division trees on the other hand minimize the accumulation of two-hit mutants, which is a more likely evolutionary goal given the key role played by tumor suppressor genes in cancer initiation. While division tree length is the most important property determining mutant accumulation, we also find that increasing the size of primitive compartments helps to delay two-hit mutant generation. PMID:29447149

On the evolution of primitive genetic codes.

PubMed

Weberndorfer, Günter; Hofacker, Ivo L; Stadler, Peter F

2003-10-01

The primordial genetic code probably has been a drastically simplified ancestor of the canonical code that is used by contemporary cells. In order to understand how the present-day code came about we first need to explain how the language of the building plan can change without destroying the encoded information. In this work we introduce a minimal organism model that is based on biophysically reasonable descriptions of RNA and protein, namely secondary structure folding and knowledge based potentials. The evolution of a population of such organism under competition for a common resource is simulated explicitly at the level of individual replication events. Starting with very simple codes, and hence greatly reduced amino acid alphabets, we observe a diversification of the codes in most simulation runs. The driving force behind this effect is the possibility to produce fitter proteins when the repertoire of amino acids is enlarged.

Ultimate dynamics of the Kirschner-Panetta model: Tumor eradication and related problems

NASA Astrophysics Data System (ADS)

Starkov, Konstantin E.; Krishchenko, Alexander P.

2017-10-01

In this paper we consider the ultimate dynamics of the Kirschner-Panetta model which was created for studying the immune response to tumors under special types of immunotherapy. New ultimate upper bounds for compact invariant sets of this model are given, as well as sufficient conditions for the existence of a positively invariant polytope. We establish three types of conditions for the nonexistence of compact invariant sets in the domain of the tumor-cell population. Our main results are two types of conditions for global tumor elimination depending on the ratio between the proliferation rate of the immune cells and their mortality rate. These conditions are described in terms of simple algebraic inequalities imposed on model parameters and treatment parameters. Our theoretical studies of ultimate dynamics are complemented by numerical simulation results.