cell model based: Topics by Science.gov

Sample records for cell model based

An efficient current-based logic cell model for crosstalk delay analysis

NASA Astrophysics Data System (ADS)

Nazarian, Shahin; Das, Debasish

2013-04-01

Logic cell modelling is an important component in the analysis and design of CMOS integrated circuits, mostly due to nonlinear behaviour of CMOS cells with respect to the voltage signal at their input and output pins. A current-based model for CMOS logic cells is presented, which can be used for effective crosstalk noise and delta delay analysis in CMOS VLSI circuits. Existing current source models are expensive and need a new set of Spice-based characterisation, which is not compatible with typical EDA tools. In this article we present Imodel, a simple nonlinear logic cell model that can be derived from the typical cell libraries such as NLDM, with accuracy much higher than NLDM-based cell delay models. In fact, our experiments show an average error of 3% compared to Spice. This level of accuracy comes with a maximum runtime penalty of 19% compared to NLDM-based cell delay models on medium-sized industrial designs.
Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

PubMed Central

Hwang, Minki; Garbey, Marc; Berceli, Scott A.; Tran-Son-Tay, Roger

2011-01-01

Emergent behaviors of multi-cellular biological systems (MCBS) result from the behaviors of each individual cells and their interactions with other cells and with the environment. Modeling MCBS requires incorporating these complex interactions among the individual cells and the environment. Modeling approaches for MCBS can be grouped into two categories: continuum models and cell-based models. Continuum models usually take the form of partial differential equations, and the model equations provide insight into the relationship among the components in the system. Cell-based models simulate each individual cell behavior and interactions among them enabling the observation of the emergent system behavior. This review focuses on the cell-based models of MCBS, and especially, the technical aspect of the rule-based simulation method for MCBS is reviewed. How to implement the cell behaviors and the interactions with other cells and with the environment into the computational domain is discussed. The cell behaviors reviewed in this paper are division, migration, apoptosis/necrosis, and differentiation. The environmental factors such as extracellular matrix, chemicals, microvasculature, and forces are also discussed. Application examples of these cell behaviors and interactions are presented. PMID:21369345
Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion

PubMed Central

Andasari, Vivi; Roper, Ryan T.; Swat, Maciej H.; Chaplain, Mark A. J.

2012-01-01

In this paper we present a multiscale, individual-based simulation environment that integrates CompuCell3D for lattice-based modelling on the cellular level and Bionetsolver for intracellular modelling. CompuCell3D or CC3D provides an implementation of the lattice-based Cellular Potts Model or CPM (also known as the Glazier-Graner-Hogeweg or GGH model) and a Monte Carlo method based on the metropolis algorithm for system evolution. The integration of CC3D for cellular systems with Bionetsolver for subcellular systems enables us to develop a multiscale mathematical model and to study the evolution of cell behaviour due to the dynamics inside of the cells, capturing aspects of cell behaviour and interaction that is not possible using continuum approaches. We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and -catenin. In this model, which we refer to as the centre-based model, an alternative individual-based modelling technique was used, namely, a lattice-free approach. In many respects, the GGH or CPM methodology and the approach of the centre-based model have the same overall goal, that is to mimic behaviours and interactions of biological cells. Although the mathematical foundations and computational implementations of the two approaches are very different, the results of the presented simulations are compatible with each other, suggesting that by using individual-based approaches we can formulate a natural way of describing complex multi-cell, multiscale models. The ability to easily reproduce results of one modelling approach using an alternative approach is also essential from a model cross-validation standpoint and also helps to identify any modelling artefacts specific to a given computational approach. PMID:22461894
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.
Applying the cell-based coagulation model in the management of critical bleeding.

PubMed

Ho, K M; Pavey, W

2017-03-01

The cell-based coagulation model was proposed 15 years ago, yet has not been applied commonly in the management of critical bleeding. Nevertheless, this alternative model may better explain the physiological basis of current coagulation management during critical bleeding. In this article we describe the limitations of the traditional coagulation protein cascade and standard coagulation tests, and explain the potential advantages of applying the cell-based model in current coagulation management strategies. The cell-based coagulation model builds on the traditional coagulation model and explains many recent clinical observations and research findings related to critical bleeding unexplained by the traditional model, including the encouraging results of using empirical 1:1:1 fresh frozen plasma:platelets:red blood cells transfusion strategy, and the use of viscoelastic and platelet function tests in patients with critical bleeding. From a practical perspective, applying the cell-based coagulation model also explains why new direct oral anticoagulants are effective systemic anticoagulants even without affecting activated partial thromboplastin time or the International Normalized Ratio in a dose-related fashion. The cell-based coagulation model represents the most cohesive scientific framework on which we can understand and manage coagulation during critical bleeding.
AlgiMatrix™-Based 3D Cell Culture System as an In Vitro Tumor Model: An Important Tool in Cancer Research.

PubMed

Godugu, Chandraiah; Singh, Mandip

2016-01-01

Routinely used two-dimensional cell culture-based models often fail while translating the observations into in vivo models. This setback is more common in cancer research, due to several reasons. The extracellular matrix and cell-to-cell interactions are not present in two-dimensional (2D) cell culture models. Diffusion of drug molecules into cancer cells is hindered by barriers of extracellular components in in vivo conditions, these barriers are absent in 2D cell culture models. To better mimic or simulate the in vivo conditions present in tumors, the current study used the alginate based three-dimensional cell culture (AlgiMatrix™) model, which resembles close to the in vivo tumor models. The current study explains the detailed protocols involved in AlgiMatrix™ based in vitro non-small-cell lung cancer (NSCLC) models. The suitability of this model was studied by evaluating, cytotoxicity, apoptosis, and penetration of nanoparticles into the in vitro tumor spheroids. This study also demonstrated the effect of EphA2 receptor targeted docetaxel-loaded nanoparticles on MDA-MB-468 TNBC cell lines. The methods section is subdivided into three subsections such as (1) preparation of AlgiMatrix™-based 3D in vitro tumor models and cytotoxicity assays, (2) free drug and nanoparticle uptake into spheroid studies, and (3) western blot, IHC, and RT-PCR studies.
A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants.

PubMed

Drake, Andrew W; Klakamp, Scott L

2007-01-10

A new 4-parameter nonlinear equation based on the standard multiple independent binding site model (MIBS) is presented for fitting cell-based ligand titration data in order to calculate the ligand/cell receptor equilibrium dissociation constant and the number of receptors/cell. The most commonly used linear (Scatchard Plot) or nonlinear 2-parameter model (a single binding site model found in commercial programs like Prism(R)) used for analysis of ligand/receptor binding data assumes only the K(D) influences the shape of the titration curve. We demonstrate using simulated data sets that, depending upon the cell surface receptor expression level, the number of cells titrated, and the magnitude of the K(D) being measured, this assumption of always being under K(D)-controlled conditions can be erroneous and can lead to unreliable estimates for the binding parameters. We also compare and contrast the fitting of simulated data sets to the commonly used cell-based binding equation versus our more rigorous 4-parameter nonlinear MIBS model. It is shown through these simulations that the new 4-parameter MIBS model, when used for cell-based titrations under optimal conditions, yields highly accurate estimates of all binding parameters and hence should be the preferred model to fit cell-based experimental nonlinear titration data.
Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.
Effect of Grain Boundaries on the Performance of Thin-Film-Based Polycrystalline Silicon Solar Cells: A Numerical Modeling

NASA Astrophysics Data System (ADS)

Chhetri, Nikita; Chatterjee, Somenath

2018-01-01

Solar cells/photovoltaic, a renewable energy source, is appraised to be the most effective alternative to the conventional electrical energy generator. A cost-effective alternative of crystalline wafer-based solar cell is thin-film polycrystalline-based solar cell. This paper reports the numerical analysis of dependency of the solar cell parameters (i.e., efficiency, fill factor, open-circuit voltage and short-circuit current density) on grain size for thin-film-based polycrystalline silicon (Si) solar cells. A minority carrier lifetime model is proposed to do a correlation between the grains, grain boundaries and lifetime for thin-film-based polycrystalline Si solar cells in MATLAB environment. As observed, the increment in the grain size diameter results in increase in minority carrier lifetime in polycrystalline Si thin film. A non-equivalent series resistance double-diode model is used to find the dark as well as light (AM1.5) current-voltage (I-V) characteristics for thin-film-based polycrystalline Si solar cells. To optimize the effectiveness of the proposed model, a successive approximation method is used and the corresponding fitting parameters are obtained. The model is validated with the experimentally obtained results reported elsewhere. The experimentally reported solar cell parameters can be found using the proposed model described here.
From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

PubMed

Paini, Alicia; Sala Benito, Jose Vicente; Bessems, Jos; Worth, Andrew P

2017-12-01

Physiologically based kinetic (PBK) models and the virtual cell based assay can be linked to form so called physiologically based dynamic (PBD) models. This study illustrates the development and application of a PBK model for prediction of estragole-induced DNA adduct formation and hepatotoxicity in humans. To address the hepatotoxicity, HepaRG cells were used as a surrogate for liver cells, with cell viability being used as the in vitro toxicological endpoint. Information on DNA adduct formation was taken from the literature. Since estragole induced cell damage is not directly caused by the parent compound, but by a reactive metabolite, information on the metabolic pathway was incorporated into the model. In addition, a user-friendly tool was developed by implementing the PBK/D model into a KNIME workflow. This workflow can be used to perform in vitro to in vivo extrapolation and forward as backward dosimetry in support of chemical risk assessment. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Multiscale Modeling of Angiogenesis and Predictive Capacity

NASA Astrophysics Data System (ADS)

Pillay, Samara; Byrne, Helen; Maini, Philip

Tumors induce the growth of new blood vessels from existing vasculature through angiogenesis. Using an agent-based approach, we model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death-like processes. We use the transition probabilities associated with the discrete model and a discrete conservation equation for cell occupancy to determine collective cell behavior, in terms of partial differential equations (PDEs). We derive three PDE models incorporating single, multi-species and no volume exclusion. By fitting the parameters in our PDE models and other well-established continuum models to agent-based simulations during a specific time period, and then comparing the outputs from the PDE models and agent-based model at later times, we aim to determine how well the PDE models predict the future behavior of the agent-based model. We also determine whether predictions differ across PDE models and the significance of those differences. This may impact drug development strategies based on PDE models.
A hybrid agent-based approach for modeling microbiological systems.

PubMed

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.
Logic-Based Models for the Analysis of Cell Signaling Networks†

PubMed Central

2010-01-01

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. PMID:20225868
Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.
A hybrid computational model to explore the topological characteristics of epithelial tissues.

PubMed

González-Valverde, Ismael; García-Aznar, José Manuel

2017-11-01

Epithelial tissues show a particular topology where cells resemble a polygon-like shape, but some biological processes can alter this tissue topology. During cell proliferation, mitotic cell dilation deforms the tissue and modifies the tissue topology. Additionally, cells are reorganized in the epithelial layer and these rearrangements also alter the polygon distribution. We present here a computer-based hybrid framework focused on the simulation of epithelial layer dynamics that combines discrete and continuum numerical models. In this framework, we consider topological and mechanical aspects of the epithelial tissue. Individual cells in the tissue are simulated by an off-lattice agent-based model, which keeps the information of each cell. In addition, we model the cell-cell interaction forces and the cell cycle. Otherwise, we simulate the passive mechanical behaviour of the cell monolayer using a material that approximates the mechanical properties of the cell. This continuum approach is solved by the finite element method, which uses a dynamic mesh generated by the triangulation of cell polygons. Forces generated by cell-cell interaction in the agent-based model are also applied on the finite element mesh. Cell movement in the agent-based model is driven by the displacements obtained from the deformed finite element mesh of the continuum mechanical approach. We successfully compare the results of our simulations with some experiments about the topology of proliferating epithelial tissues in Drosophila. Our framework is able to model the emergent behaviour of the cell monolayer that is due to local cell-cell interactions, which have a direct influence on the dynamics of the epithelial tissue. Copyright © 2017 John Wiley & Sons, Ltd.
Comparing ESC and iPSC-Based Models for Human Genetic Disorders.

PubMed

Halevy, Tomer; Urbach, Achia

2014-10-24

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.
A polynomial based model for cell fate prediction in human diseases.

PubMed

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.
Spatial Pattern of Cell Damage in Tissue from Heavy Ions

NASA Technical Reports Server (NTRS)

Ponomarev, Artem L.; Huff, Janice L.; Cucinotta, Francis A.

2007-01-01

A new Monte Carlo algorithm was developed that can model passage of heavy ions in a tissue, and their action on the cellular matrix for 2- or 3-dimensional cases. The build-up of secondaries such as projectile fragments, target fragments, other light fragments, and delta-rays was simulated. Cells were modeled as a cell culture monolayer in one example, where the data were taken directly from microscopy (2-d cell matrix). A simple model of tissue was given as abstract spheres with close approximation to real cell geometries (3-d cell matrix), as well as a realistic model of tissue was proposed based on microscopy images. Image segmentation was used to identify cells in an irradiated cell culture monolayer, or slices of tissue. The cells were then inserted into the model box pixel by pixel. In the case of cell monolayers (2-d), the image size may exceed the modeled box size. Such image was is moved with respect to the box in order to sample as many cells as possible. In the case of the simple tissue (3-d), the tissue box is modeled with periodic boundary conditions, which extrapolate the technique to macroscopic volumes of tissue. For real tissue, specific spatial patterns for cell apoptosis and necrosis are expected. The cell patterns were modeled based on action cross sections for apoptosis and necrosis estimated based on BNL data, and other experimental data.
Agent-Based Deterministic Modeling of the Bone Marrow Homeostasis.

PubMed

Kurhekar, Manish; Deshpande, Umesh

2016-01-01

Modeling of stem cells not only describes but also predicts how a stem cell's environment can control its fate. The first stem cell populations discovered were hematopoietic stem cells (HSCs). In this paper, we present a deterministic model of bone marrow (that hosts HSCs) that is consistent with several of the qualitative biological observations. This model incorporates stem cell death (apoptosis) after a certain number of cell divisions and also demonstrates that a single HSC can potentially populate the entire bone marrow. It also demonstrates that there is a production of sufficient number of differentiated cells (RBCs, WBCs, etc.). We prove that our model of bone marrow is biologically consistent and it overcomes the biological feasibility limitations of previously reported models. The major contribution of our model is the flexibility it allows in choosing model parameters which permits several different simulations to be carried out in silico without affecting the homeostatic properties of the model. We have also performed agent-based simulation of the model of bone marrow system proposed in this paper. We have also included parameter details and the results obtained from the simulation. The program of the agent-based simulation of the proposed model is made available on a publicly accessible website.
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis.

PubMed

Wang, Zi; Wang, Dali; Li, Chengcheng; Xu, Yichi; Li, Husheng; Bao, Zhirong

2018-04-25

Cell movement in the early phase of C. elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C. elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging. Source code is available at https://github.com/zwang84/drl4cellmovement. dwang7@utk.edu, baoz@mskcc.org. Supplementary data are available at Bioinformatics online.

Induced Pluripotent Stem Cell Models to Enable In Vitro Models for Screening in the Central Nervous System.

PubMed

Hunsberger, Joshua G; Efthymiou, Anastasia G; Malik, Nasir; Behl, Mamta; Mead, Ivy L; Zeng, Xianmin; Simeonov, Anton; Rao, Mahendra

2015-08-15

There is great need to develop more predictive drug discovery tools to identify new therapies to treat diseases of the central nervous system (CNS). Current nonpluripotent stem cell-based models often utilize non-CNS immortalized cell lines and do not enable the development of personalized models of disease. In this review, we discuss why in vitro models are necessary for translational research and outline the unique advantages of induced pluripotent stem cell (iPSC)-based models over those of current systems. We suggest that iPSC-based models can be patient specific and isogenic lines can be differentiated into many neural cell types for detailed comparisons. iPSC-derived cells can be combined to form small organoids, or large panels of lines can be developed that enable new forms of analysis. iPSC and embryonic stem cell-derived cells can be readily engineered to develop reporters for lineage studies or mechanism of action experiments further extending the utility of iPSC-based systems. We conclude by describing novel technologies that include strategies for the development of diversity panels, novel genomic engineering tools, new three-dimensional organoid systems, and modified high-content screens that may bring toxicology into the 21st century. The strategic integration of these technologies with the advantages of iPSC-derived cell technology, we believe, will be a paradigm shift for toxicology and drug discovery efforts.
Emergence of tissue mechanics from cellular processes: shaping a fly wing

NASA Astrophysics Data System (ADS)

Merkel, Matthias; Etournay, Raphael; Popovic, Marko; Nandi, Amitabha; Brandl, Holger; Salbreux, Guillaume; Eaton, Suzanne; Jülicher, Frank

Nowadays, biologistsare able to image biological tissueswith up to 10,000 cells in vivowhere the behavior of each individual cell can be followed in detail.However, how precisely large-scale tissue deformation and stresses emerge from cellular behavior remains elusive. Here, we study this question in the developing wing of the fruit fly. To this end, we first establish a geometrical framework that exactly decomposes tissue deformation into contributions by different kinds of cellular processes. These processes comprise cell shape changes, cell neighbor exchanges, cell divisions, and cell extrusions. As the key idea, we introduce a tiling of the cellular network into triangles. This approach also reveals that tissue deformation can also be created by correlated cellular motion. Based on quantifications using these concepts, we developed a novel continuum mechanical model for the fly wing. In particular, our model includes active anisotropic stresses and a delay in the response of cell rearrangements to material stresses. A different approach to study the emergence of tissue mechanics from cellular behavior are cell-based models. We characterize the properties of a cell-based model for 3D tissues that is a hybrid between single particle models and the so-called vertex models.
From single cells to tissue architecture-a bottom-up approach to modelling the spatio-temporal organisation of complex multi-cellular systems.

PubMed

Galle, J; Hoffmann, M; Aust, G

2009-01-01

Collective phenomena in multi-cellular assemblies can be approached on different levels of complexity. Here, we discuss a number of mathematical models which consider the dynamics of each individual cell, so-called agent-based or individual-based models (IBMs). As a special feature, these models allow to account for intracellular decision processes which are triggered by biomechanical cell-cell or cell-matrix interactions. We discuss their impact on the growth and homeostasis of multi-cellular systems as simulated by lattice-free models. Our results demonstrate that cell polarisation subsequent to cell-cell contact formation can be a source of stability in epithelial monolayers. Stroma contact-dependent regulation of tumour cell proliferation and migration is shown to result in invasion dynamics in accordance with the migrating cancer stem cell hypothesis. However, we demonstrate that different regulation mechanisms can equally well comply with present experimental results. Thus, we suggest a panel of experimental studies for the in-depth validation of the model assumptions.
Optimal Charging of Nickel-Hydrogen Batteries for Life Extension

NASA Technical Reports Server (NTRS)

Hartley, Tom T.; Lorenzo, Carl F.

2002-01-01

We are exploring the possibility of extending the cycle life of battery systems by using a charging profile that minimizes cell damage. Only nickel-hydrogen cells are discussed at this time, but applications to lithium-ion cells are being considered. The process first requires the development of a fractional calculus based nonlinear dynamic model of the specific cells being used. The parameters of this model are determined from the cell transient responses. To extend cell cycle life, an instantaneous damage rate model is developed. The model is based on cycle life data and is highly dependent on cell voltage. Once both the cell dynamic model and the instantaneous damage rate model have been determined, the charging profile for a specific cell is determined by numerical optimization. Results concerning the percentage life extension for different charging strategies are presented. The overall procedure is readily adaptable to real-time implementations where the charging profile can maintain its minimum damage nature as the specific cell ages.
Agent-Based Computational Modeling to Examine How Individual Cell Morphology Affects Dosimetry

EPA Science Inventory

Cell-based models utilizing high-content screening (HCS) data have applications for predictive toxicology. Evaluating concentration-dependent effects on cell fate and state response is a fundamental utilization of HCS data.Although HCS assays may capture quantitative readouts at ...
Modeling potential Emerald Ash Borer spread through GIS/cell-based/gravity models with data bolstered by web-based inputs

Treesearch

Louis R. Iverson; Anantha M. Prasad; Davis Sydnor; Jonathan Bossenbroek; Mark W. Schwartz; Mark W. Schwartz

2006-01-01

We model the susceptibility and potential spread of the organism across the eastern United States and especially through Michigan and Ohio using Forest Inventory and Analysis (FIA) data. We are also developing a cell-based model for the potential spread of the organism. We have developed a web-based tool for public agencies and private individuals to enter the...
A cell-based assay for aggregation inhibitors as therapeutics of polyglutamine-repeat disease and validation in Drosophila

NASA Astrophysics Data System (ADS)

Apostol, Barbara L.; Kazantsev, Alexsey; Raffioni, Simona; Illes, Katalin; Pallos, Judit; Bodai, Laszlo; Slepko, Natalia; Bear, James E.; Gertler, Frank B.; Hersch, Steven; Housman, David E.; Marsh, J. Lawrence; Michels Thompson, Leslie

2003-05-01

The formation of polyglutamine-containing aggregates and inclusions are hallmarks of pathogenesis in Huntington's disease that can be recapitulated in model systems. Although the contribution of inclusions to pathogenesis is unclear, cell-based assays can be used to screen for chemical compounds that affect aggregation and may provide therapeutic benefit. We have developed inducible PC12 cell-culture models to screen for loss of visible aggregates. To test the validity of this approach, compounds that inhibit aggregation in the PC12 cell-based screen were tested in a Drosophila model of polyglutamine-repeat disease. The disruption of aggregation in PC12 cells strongly correlates with suppression of neuronal degeneration in Drosophila. Thus, the engineered PC12 cells coupled with the Drosophila model provide a rapid and effective method to screen and validate compounds.
A theory of the n-i-p silicon solar cell

NASA Technical Reports Server (NTRS)

Goradia, C.; Weinberg, I.; Baraona, C.

1981-01-01

A computer model has been developed, based on an analytical theory of the high base resistivity BSF n(+)(pi)p(+) or p(+)(nu)n(+) silicon solar cell. The model makes very few assumptions and accounts for nonuniform optical generation, generation and recombination in the junction space charge region, and bandgap narrowing in the heavily doped regions. The paper presents calculated results based on this model and compares them to available experimental data. Also discussed is radiation damage in high base resistivity n(+)(pi)p(+) space solar cells.
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.
Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

NASA Astrophysics Data System (ADS)

Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.
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.
Comparison of Multiscale Method of Cells-Based Models for Predicting Elastic Properties of Filament Wound C/C-SiC

NASA Technical Reports Server (NTRS)

Pineda, Evan J.; Fassin, Marek; Bednarcyk, Brett A.; Reese, Stefanie; Simon, Jaan-Willem

2017-01-01

Three different multiscale models, based on the method of cells (generalized and high fidelity) micromechanics models were developed and used to predict the elastic properties of C/C-SiC composites. In particular, the following multiscale modeling strategies were employed: Concurrent multiscale modeling of all phases using the generalized method of cells, synergistic (two-way coupling in space) multiscale modeling with the generalized method of cells, and hierarchical (one-way coupling in space) multiscale modeling with the high fidelity generalized method of cells. The three models are validated against data from a hierarchical multiscale finite element model in the literature for a repeating unit cell of C/C-SiC. Furthermore, the multiscale models are used in conjunction with classical lamination theory to predict the stiffness of C/C-SiC plates manufactured via a wet filament winding and liquid silicon infiltration process recently developed by the German Aerospace Institute.
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
Comparison of a Rat Primary Cell-Based Blood-Brain Barrier Model With Epithelial and Brain Endothelial Cell Lines: Gene Expression and Drug Transport.

PubMed

Veszelka, Szilvia; Tóth, András; Walter, Fruzsina R; Tóth, Andrea E; Gróf, Ilona; Mészáros, Mária; Bocsik, Alexandra; Hellinger, Éva; Vastag, Monika; Rákhely, Gábor; Deli, Mária A

2018-01-01

Cell culture-based blood-brain barrier (BBB) models are useful tools for screening of CNS drug candidates. Cell sources for BBB models include primary brain endothelial cells or immortalized brain endothelial cell lines. Despite their well-known differences, epithelial cell lines are also used as surrogate models for testing neuropharmaceuticals. The aim of the present study was to compare the expression of selected BBB related genes including tight junction proteins, solute carriers (SLC), ABC transporters, metabolic enzymes and to describe the paracellular properties of nine different culture models. To establish a primary BBB model rat brain capillary endothelial cells were co-cultured with rat pericytes and astrocytes (EPA). As other BBB and surrogate models four brain endothelial cells lines, rat GP8 and RBE4 cells, and human hCMEC/D3 cells with or without lithium treatment (D3 and D3L), and four epithelial cell lines, native human intestinal Caco-2 and high P-glycoprotein expressing vinblastine-selected VB-Caco-2 cells, native MDCK and MDR1 transfected MDCK canine kidney cells were used. To test transporter functionality, the permeability of 12 molecules, glucopyranose, valproate, baclofen, gabapentin, probenecid, salicylate, rosuvastatin, pravastatin, atorvastatin, tacrine, donepezil, was also measured in the EPA and epithelial models. Among the junctional protein genes, the expression level of occludin was high in all models except the GP8 and RBE4 cells, and each model expressed a unique claudin pattern. Major BBB efflux (P-glycoprotein or ABCB1) and influx transporters (GLUT-1, LAT-1) were present in all models at mRNA levels. The transcript of BCRP (ABCG2) was not expressed in MDCK, GP8 and RBE4 cells. The absence of gene expression of important BBB efflux and influx transporters BCRP, MRP6, -9, MCT6, -8, PHT2, OATPs in one or both types of epithelial models suggests that Caco-2 or MDCK models are not suitable to test drug candidates which are substrates of these transporters. Brain endothelial cell lines GP8, RBE4, D3 and D3L did not form a restrictive paracellular barrier necessary for screening small molecular weight pharmacons. Therefore, among the tested culture models, the primary cell-based EPA model is suitable for the functional analysis of the BBB.
Agent-Based Computational Modeling of Cell Culture: Understanding Dosimetry In Vitro as Part of In Vitro to In Vivo Extrapolation

EPA Science Inventory

Quantitative characterization of cellular dose in vitro is needed for alignment of doses in vitro and in vivo. We used the agent-based software, CompuCell3D (CC3D), to provide a stochastic description of cell growth in culture. The model was configured so that isolated cells assu...
Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.
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).
Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

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

Smith, Kandler A; Schimpe, Michael; von Kuepach, Markus Edler

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storagemore » and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.« less

Assessment of Safety and Functional Efficacy of Stem Cell-Based Therapeutic Approaches Using Retinal Degenerative Animal Models

PubMed Central

Lin, Tai-Chi; Zhu, Danhong; Hinton, David R.; Clegg, Dennis O.; Humayun, Mark S.

2017-01-01

Dysfunction and death of retinal pigment epithelium (RPE) and or photoreceptors can lead to irreversible vision loss. The eye represents an ideal microenvironment for stem cell-based therapy. It is considered an “immune privileged” site, and the number of cells needed for therapy is relatively low for the area of focused vision (macula). Further, surgical placement of stem cell-derived grafts (RPE, retinal progenitors, and photoreceptor precursors) into the vitreous cavity or subretinal space has been well established. For preclinical tests, assessments of stem cell-derived graft survival and functionality are conducted in animal models by various noninvasive approaches and imaging modalities. In vivo experiments conducted in animal models based on replacing photoreceptors and/or RPE cells have shown survival and functionality of the transplanted cells, rescue of the host retina, and improvement of visual function. Based on the positive results obtained from these animal experiments, human clinical trials are being initiated. Despite such progress in stem cell research, ethical, regulatory, safety, and technical difficulties still remain a challenge for the transformation of this technique into a standard clinical approach. In this review, the current status of preclinical safety and efficacy studies for retinal cell replacement therapies conducted in animal models will be discussed. PMID:28928775
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.
Microfluidic devices for modeling cell-cell and particle-cell interactions in the microvasculature

PubMed Central

Prabhakarpandian, Balabhaskar; Shen, Ming-Che; Pant, Kapil; Kiani, Mohammad F.

2011-01-01

Cell-fluid and cell-cell interactions are critical components of many physiological and pathological conditions in the microvasculature. Similarly, particle-cell interactions play an important role in targeted delivery of therapeutics to tissue. Development of in vitro fluidic devices to mimic these microcirculatory processes has been a critical step forward in our understanding of the inflammatory process, development of nano-particulate drug carriers, and developing realistic in vitro models of the microvasculature and its surrounding tissue. However, widely used parallel plate flow based devices and assays have a number of important limitations for studying the physiological conditions in vivo. In addition, these devices are resource hungry and time consuming for performing various assays. Recently developed, more realistic, microfluidic based devices have been able to overcome many of these limitations. In this review, an overview of the fluidic devices and their use in studying the effects of shear forces on cell-cell and cell-particle interactions is presented. In addition, use of mathematical models and Computational Fluid Dynamics (CFD) based models for interpreting the complex flow patterns in the microvasculature are highlighted. Finally, the potential of 3D microfluidic devices and imaging for better representing in vivo conditions under which cell-cell and cell-particle interactions take place are discussed. PMID:21763328
Induced Pluripotent Stem Cells for Disease Modeling and Evaluation of Therapeutics for Niemann-Pick Disease Type A.

PubMed

Long, Yan; Xu, Miao; Li, Rong; Dai, Sheng; Beers, Jeanette; Chen, Guokai; Soheilian, Ferri; Baxa, Ulrich; Wang, Mengqiao; Marugan, Juan J; Muro, Silvia; Li, Zhiyuan; Brady, Roscoe; Zheng, Wei

2016-12-01

: Niemann-Pick disease type A (NPA) is a lysosomal storage disease caused by mutations in the SMPD1 gene that encodes acid sphingomyelinase (ASM). Deficiency in ASM function results in lysosomal accumulation of sphingomyelin and neurodegeneration. Currently, there is no effective treatment for NPA. To accelerate drug discovery for treatment of NPA, we generated induced pluripotent stem cells from two patient dermal fibroblast lines and differentiated them into neural stem cells. The NPA neural stem cells exhibit a disease phenotype of lysosomal sphingomyelin accumulation and enlarged lysosomes. By using this disease model, we also evaluated three compounds that reportedly reduced lysosomal lipid accumulation in Niemann-Pick disease type C as well as enzyme replacement therapy with ASM. We found that α-tocopherol, δ-tocopherol, hydroxypropyl-β-cyclodextrin, and ASM reduced sphingomyelin accumulation and enlarged lysosomes in NPA neural stem cells. Therefore, the NPA neural stem cells possess the characteristic NPA disease phenotype that can be ameliorated by tocopherols, cyclodextrin, and ASM. Our results demonstrate the efficacies of cyclodextrin and tocopherols in the NPA cell-based model. Our data also indicate that the NPA neural stem cells can be used as a new cell-based disease model for further study of disease pathophysiology and for high-throughput screening to identify new lead compounds for drug development. Currently, there is no effective treatment for Niemann-Pick disease type A (NPA). To accelerate drug discovery for treatment of NPA, NPA-induced pluripotent stem cells were generated from patient dermal fibroblasts and differentiated into neural stem cells. By using the differentiated NPA neuronal cells as a cell-based disease model system, α-tocopherol, δ-tocopherol, and hydroxypropyl-β-cyclodextrin significantly reduced sphingomyelin accumulation in these NPA neuronal cells. Therefore, this cell-based NPA model can be used for further study of disease pathophysiology and for high-throughput screening of compound libraries to identify lead compounds for drug development. ©AlphaMed Press.
Rule-based modeling with Virtual Cell

PubMed Central

Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

2016-01-01

Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444
Identification of Putative Cardiovascular System Developmental Toxicants using a Classification Model based on Signaling Pathway-Adverse Outcome Pathways

EPA Science Inventory

An important challenge for an integrative approach to developmental systems toxicology is associating putative molecular initiating events (MIEs), cell signaling pathways, cell function and modeled fetal exposure kinetics. We have developed a chemical classification model based o...
Modelling of internal architecture of kinesin nanomotor as a machine language.

PubMed

Khataee, H R; Ibrahim, M Y

2012-09-01

Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.
Agent-Based Computational Modeling of Cell Culture ...

EPA Pesticide Factsheets

Quantitative characterization of cellular dose in vitro is needed for alignment of doses in vitro and in vivo. We used the agent-based software, CompuCell3D (CC3D), to provide a stochastic description of cell growth in culture. The model was configured so that isolated cells assumed a “fried egg shape” but became increasingly cuboidal with increasing confluency. The surface area presented by each cell to the overlying medium varies from cell-to-cell and is a determinant of diffusional flux of toxicant from the medium into the cell. Thus, dose varies among cells for a given concentration of toxicant in the medium. Computer code describing diffusion of H2O2 from medium into each cell and clearance of H2O2 was calibrated against H2O2 time-course data (25, 50, or 75 uM H2O2 for 60 min) obtained with the Amplex Red assay for the medium and the H2O2-sensitive fluorescent reporter, HyPer, for cytosol. Cellular H2O2 concentrations peaked at about 5 min and were near baseline by 10 min. The model predicted a skewed distribution of surface areas, with between cell variation usually 2 fold or less. Predicted variability in cellular dose was in rough agreement with the variation in the HyPer data. These results are preliminary, as the model was not calibrated to the morphology of a specific cell type. Future work will involve morphology model calibration against human bronchial epithelial (BEAS-2B) cells. Our results show, however, the potential of agent-based modeling
A Comprehensive Fluid Dynamic-Diffusion Model of Blood Microcirculation with Focus on Sickle Cell Disease

NASA Astrophysics Data System (ADS)

Le Floch, Francois; Harris, Wesley L.

2009-11-01

A novel methodology has been developed to address sickle cell disease, based on highly descriptive mathematical models for blood flow in the capillaries. Our investigations focus on the coupling between oxygen delivery and red blood cell dynamics, which is crucial to understanding sickle cell crises and is unique to this blood disease. The main part of our work is an extensive study of blood dynamics through simulations of red cells deforming within the capillary vessels, and relies on the use of a large mathematical system of equations describing oxygen transfer, blood plasma dynamics and red cell membrane mechanics. This model is expected to lead to the development of new research strategies for sickle cell disease. Our simulation model could be used not only to assess current researched remedies, but also to spur innovative research initiatives, based on our study of the physical properties coupled in sickle cell disease.
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.
Modelling the Immune Response to Cancer: An Individual-Based Approach Accounting for the Difference in Movement Between Inactive and Activated T Cells.

PubMed

Macfarlane, Fiona R; Lorenzi, Tommaso; Chaplain, Mark A J

2018-06-01

A growing body of experimental evidence indicates that immune cells move in an unrestricted search pattern if they are in the pre-activated state, whilst they tend to stay within a more restricted area upon activation induced by the presence of tumour antigens. This change in movement is not often considered in the existing mathematical models of the interactions between immune cells and cancer cells. With the aim to fill such a gap in the existing literature, in this work we present a spatially structured individual-based model of tumour-immune competition that takes explicitly into account the difference in movement between inactive and activated immune cells. In our model, a Lévy walk is used to capture the movement of inactive immune cells, whereas Brownian motion is used to describe the movement of antigen-activated immune cells. The effects of activation of immune cells, the proliferation of cancer cells and the immune destruction of cancer cells are also modelled. We illustrate the ability of our model to reproduce qualitatively the spatial trajectories of immune cells observed in experimental data of single-cell tracking. Computational simulations of our model further clarify the conditions for the onset of a successful immune action against cancer cells and may suggest possible targets to improve the efficacy of cancer immunotherapy. Overall, our theoretical work highlights the importance of taking into account spatial interactions when modelling the immune response to cancer cells.
"A Cellular Encounter": Constructing the Cell as a Whole System Using Illustrative Models

ERIC Educational Resources Information Center

Cohen, Joel I.

2014-01-01

A standard part of biology curricula is a project-based assessment of cell structure and function. However, these are often individual assignments that promote little problem-solving or group learning and avoid the subject of organelle chemical interactions. I evaluate a model-based cell project designed to foster group and individual guided…
Multiscale Modeling of Cell Interaction in Angiogenesis: From the Micro- to Macro-scale

NASA Astrophysics Data System (ADS)

Pillay, Samara; Maini, Philip; Byrne, Helen

Solid tumors require a supply of nutrients to grow in size. To this end, tumors induce the growth of new blood vessels from existing vasculature through the process of angiogenesis. In this work, we use a discrete agent-based approach to model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death processes. We use the transition probabilities associated with the discrete models to determine collective cell behavior, in terms of partial differential equations, using a Markov chain and master equation framework. We find that the cell-level dynamics gives rise to a migrating cell front in the form of a traveling wave on the macro-scale. The behavior of this front depends on the cell interactions that are included and the extent to which volume exclusion is taken into account in the discrete micro-scale model. We also find that well-established continuum models of angiogenesis cannot distinguish between certain types of cell behavior on the micro-scale. This may impact drug development strategies based on these models.
Protein-based human iPS cells efficiently generate functional dopamine neurons and can treat a rat model of Parkinson disease.

PubMed

Rhee, Yong-Hee; Ko, Ji-Yun; Chang, Mi-Yoon; Yi, Sang-Hoon; Kim, Dohoon; Kim, Chun-Hyung; Shim, Jae-Won; Jo, A-Young; Kim, Byung-Woo; Lee, Hyunsu; Lee, Suk-Ho; Suh, Wonhee; Park, Chang-Hwan; Koh, Hyun-Chul; Lee, Yong-Sung; Lanza, Robert; Kim, Kwang-Soo; Lee, Sang-Hun

2011-06-01

Parkinson disease (PD) involves the selective loss of midbrain dopamine (mDA) neurons and is a possible target disease for stem cell-based therapy. Human induced pluripotent stem cells (hiPSCs) are a potentially unlimited source of patient-specific cells for transplantation. However, it is critical to evaluate the safety of hiPSCs generated by different reprogramming methods. Here, we compared multiple hiPSC lines derived by virus- and protein-based reprogramming to human ES cells (hESCs). Neuronal precursor cells (NPCs) and dopamine (DA) neurons delivered from lentivirus-based hiPSCs exhibited residual expression of exogenous reprogramming genes, but those cells derived from retrovirus- and protein-based hiPSCs did not. Furthermore, NPCs derived from virus-based hiPSCs exhibited early senescence and apoptotic cell death during passaging, which was preceded by abrupt induction of p53. In contrast, NPCs derived from hESCs and protein-based hiPSCs were highly expandable without senescence. DA neurons derived from protein-based hiPSCs exhibited gene expression, physiological, and electrophysiological properties similar to those of mDA neurons. Transplantation of these cells into rats with striatal lesions, a model of PD, significantly rescued motor deficits. These data support the clinical potential of protein-based hiPSCs for personalized cell therapy of PD.
Advances in Reprogramming-Based Study of Neurologic Disorders

PubMed Central

Baldwin, Kristin K.

2015-01-01

The technology to convert adult human non-neural cells into neural lineages, through induced pluripotent stem cells (iPSCs), somatic cell nuclear transfer, and direct lineage reprogramming or transdifferentiation has progressed tremendously in recent years. Reprogramming-based approaches aimed at manipulating cellular identity have enormous potential for disease modeling, high-throughput drug screening, cell therapy, and personalized medicine. Human iPSC (hiPSC)-based cellular disease models have provided proof of principle evidence of the validity of this system. However, several challenges remain before patient-specific neurons produced by reprogramming can provide reliable insights into disease mechanisms or be efficiently applied to drug discovery and transplantation therapy. This review will first discuss limitations of currently available reprogramming-based methods in faithfully and reproducibly recapitulating disease pathology. Specifically, we will address issues such as culture heterogeneity, interline and inter-individual variability, and limitations of two-dimensional differentiation paradigms. Second, we will assess recent progress and the future prospects of reprogramming-based neurologic disease modeling. This includes three-dimensional disease modeling, advances in reprogramming technology, prescreening of hiPSCs and creating isogenic disease models using gene editing. PMID:25749371
Relating cell shape and mechanical stress in a spatially disordered epithelium using a vertex-based model

PubMed Central

Nestor-Bergmann, Alexander; Goddard, Georgina; Woolner, Sarah; Jensen, Oliver E

2018-01-01

Abstract Using a popular vertex-based model to describe a spatially disordered planar epithelial monolayer, we examine the relationship between cell shape and mechanical stress at the cell and tissue level. Deriving expressions for stress tensors starting from an energetic formulation of the model, we show that the principal axes of stress for an individual cell align with the principal axes of shape, and we determine the bulk effective tissue pressure when the monolayer is isotropic at the tissue level. Using simulations for a monolayer that is not under peripheral stress, we fit parameters of the model to experimental data for Xenopus embryonic tissue. The model predicts that mechanical interactions can generate mesoscopic patterns within the monolayer that exhibit long-range correlations in cell shape. The model also suggests that the orientation of mechanical and geometric cues for processes such as cell division are likely to be strongly correlated in real epithelia. Some limitations of the model in capturing geometric features of Xenopus epithelial cells are highlighted. PMID:28992197
Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

PubMed Central

Chudasama, Vaishali L.; Ovacik, Meric A.; Abernethy, Darrell R.

2015-01-01

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens. PMID:26163548
Mechanical characterization of disordered and anisotropic cellular monolayers

NASA Astrophysics Data System (ADS)

Nestor-Bergmann, Alexander; Johns, Emma; Woolner, Sarah; Jensen, Oliver E.

2018-05-01

We consider a cellular monolayer, described using a vertex-based model, for which cells form a spatially disordered array of convex polygons that tile the plane. Equilibrium cell configurations are assumed to minimize a global energy defined in terms of cell areas and perimeters; energy is dissipated via dynamic area and length changes, as well as cell neighbor exchanges. The model captures our observations of an epithelium from a Xenopus embryo showing that uniaxial stretching induces spatial ordering, with cells under net tension (compression) tending to align with (against) the direction of stretch, but with the stress remaining heterogeneous at the single-cell level. We use the vertex model to derive the linearized relation between tissue-level stress, strain, and strain rate about a deformed base state, which can be used to characterize the tissue's anisotropic mechanical properties; expressions for viscoelastic tissue moduli are given as direct sums over cells. When the base state is isotropic, the model predicts that tissue properties can be tuned to a regime with high elastic shear resistance but low resistance to area changes, or vice versa.
FY09 Final Report for LDRD Project: Understanding Viral Quasispecies Evolution through Computation and Experiment

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

Zhou, C

2009-11-12

In FY09 they will (1) complete the implementation, verification, calibration, and sensitivity and scalability analysis of the in-cell virus replication model; (2) complete the design of the cell culture (cell-to-cell infection) model; (3) continue the research, design, and development of their bioinformatics tools: the Web-based structure-alignment-based sequence variability tool and the functional annotation of the genome database; (4) collaborate with the University of California at San Francisco on areas of common interest; and (5) submit journal articles that describe the in-cell model with simulations and the bioinformatics approaches to evaluation of genome variability and fitness.
Simulation of Cell Patterning Triggered by Cell Death and Differential Adhesion in Drosophila Wing.

PubMed

Nagai, Tatsuzo; Honda, Hisao; Takemura, Masahiko

2018-02-27

The Drosophila wing exhibits a well-ordered cell pattern, especially along the posterior margin, where hair cells are arranged in a zigzag pattern in the lateral view. Based on an experimental result observed during metamorphosis of Drosophila, we considered that a pattern of initial cells autonomously develops to the zigzag pattern through cell differentiation, intercellular communication, and cell death (apoptosis) and performed computer simulations of a cell-based model of vertex dynamics for tissues. The model describes the epithelial tissue as a monolayer cell sheet of polyhedral cells. Their vertices move according to equations of motion, minimizing the sum total of the interfacial and elastic energies of cells. The interfacial energy densities between cells are introduced consistently with an ideal zigzag cell pattern, extracted from the experimental result. The apoptosis of cells is modeled by gradually reducing their equilibrium volume to zero and by assuming that the hair cells prohibit neighboring cells from undergoing apoptosis. Based on experimental observations, we also assumed wing elongation along the proximal-distal axis. Starting with an initial cell pattern similar to the micrograph experimentally obtained just before apoptosis, we carried out the simulations according to the model mentioned above and successfully reproduced the ideal zigzag cell pattern. This elucidates a physical mechanism of patterning triggered by cell apoptosis theoretically and exemplifies, to our knowledge, a new framework to study apoptosis-induced patterning. We conclude that the zigzag cell pattern is formed by an autonomous communicative process among the participant cells. Copyright © 2018 Biophysical Society. All rights reserved.

Improvements to Fidelity, Generation and Implementation of Physics-Based Lithium-Ion Reduced-Order Models

NASA Astrophysics Data System (ADS)

Rodriguez Marco, Albert

Battery management systems (BMS) require computationally simple but highly accurate models of the battery cells they are monitoring and controlling. Historically, empirical equivalent-circuit models have been used, but increasingly researchers are focusing their attention on physics-based models due to their greater predictive capabilities. These models are of high intrinsic computational complexity and so must undergo some kind of order-reduction process to make their use by a BMS feasible: we favor methods based on a transfer-function approach of battery cell dynamics. In prior works, transfer functions have been found from full-order PDE models via two simplifying assumptions: (1) a linearization assumption--which is a fundamental necessity in order to make transfer functions--and (2) an assumption made out of expedience that decouples the electrolyte-potential and electrolyte-concentration PDEs in order to render an approach to solve for the transfer functions from the PDEs. This dissertation improves the fidelity of physics-based models by eliminating the need for the second assumption and, by linearizing nonlinear dynamics around different constant currents. Electrochemical transfer functions are infinite-order and cannot be expressed as a ratio of polynomials in the Laplace variable s. Thus, for practical use, these systems need to be approximated using reduced-order models that capture the most significant dynamics. This dissertation improves the generation of physics-based reduced-order models by introducing different realization algorithms, which produce a low-order model from the infinite-order electrochemical transfer functions. Physics-based reduced-order models are linear and describe cell dynamics if operated near the setpoint at which they have been generated. Hence, multiple physics-based reduced-order models need to be generated at different setpoints (i.e., state-of-charge, temperature and C-rate) in order to extend the cell operating range. This dissertation improves the implementation of physics-based reduced-order models by introducing different blending approaches that combine the pre-computed models generated (offline) at different setpoints in order to produce good electrochemical estimates (online) along the cell state-of-charge, temperature and C-rate range.
Defining new criteria for selection of cell-based intestinal models using publicly available databases

PubMed Central

2012-01-01

Background The criteria for choosing relevant cell lines among a vast panel of available intestinal-derived lines exhibiting a wide range of functional properties are still ill-defined. The objective of this study was, therefore, to establish objective criteria for choosing relevant cell lines to assess their appropriateness as tumor models as well as for drug absorption studies. Results We made use of publicly available expression signatures and cell based functional assays to delineate differences between various intestinal colon carcinoma cell lines and normal intestinal epithelium. We have compared a panel of intestinal cell lines with patient-derived normal and tumor epithelium and classified them according to traits relating to oncogenic pathway activity, epithelial-mesenchymal transition (EMT) and stemness, migratory properties, proliferative activity, transporter expression profiles and chemosensitivity. For example, SW480 represent an EMT-high, migratory phenotype and scored highest in terms of signatures associated to worse overall survival and higher risk of recurrence based on patient derived databases. On the other hand, differentiated HT29 and T84 cells showed gene expression patterns closest to tumor bulk derived cells. Regarding drug absorption, we confirmed that differentiated Caco-2 cells are the model of choice for active uptake studies in the small intestine. Regarding chemosensitivity we were unable to confirm a recently proposed association of chemo-resistance with EMT traits. However, a novel signature was identified through mining of NCI60 GI50 values that allowed to rank the panel of intestinal cell lines according to their drug responsiveness to commonly used chemotherapeutics. Conclusions This study presents a straightforward strategy to exploit publicly available gene expression data to guide the choice of cell-based models. While this approach does not overcome the major limitations of such models, introducing a rank order of selected features may allow selecting model cell lines that are more adapted and pertinent to the addressed biological question. PMID:22726358
Using state variables to model the response of tumour cells to radiation and heat: a novel multi-hit-repair approach.

PubMed

Scheidegger, Stephan; Fuchs, Hans U; Zaugg, Kathrin; Bodis, Stephan; Füchslin, Rudolf M

2013-01-01

In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.
Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

PubMed Central

Wynn, Michelle L.; Kulesa, Paul M.; Schnell, Santiago

2012-01-01

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact. PMID:22219399
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.
Cytoskeletal Mechanics

NASA Astrophysics Data System (ADS)

Mofrad, Mohammad R. K.; Kamm, Roger D.

2011-08-01

1. Introduction and the biological basis for cell mechanics Mohammad R. K. Mofrad and Roger Kamm; 2. Experimental measurements of intracellular mechanics Paul Janmey and Christoph Schmidt; 3. The cytoskeleton as a soft glassy material Jeffrey Fredberg and Ben Fabry; 4. Continuum elastic or viscoelastic models for the cell Mohammad R. K. Mofrad, Helene Karcher and Roger Kamm; 5. Multiphasic models of cell mechanics Farshid Guuilak, Mansoor A. Haider, Lori A. Setton, Tod A. Laursen and Frank P. T. Baaijens; 6. Models of cytoskeletal mechanics based on tensegrity Dimitrije Stamenovic; 7. Cells, gels and mechanics Gerald H. Pollack; 8. Polymer-based models of cytoskeletal networks F. C. MacKintosh; 9. Cell dynamics and the actin cytoskeleton James L. McGrath and C. Forbes Dewey, Jr; 10. Active cellular motion: continuum theories and models Marc Herant and Micah Dembo; 11. Summary Mohammad R. K. Mofrad and Roger Kamm.
Stem Cells for Cartilage Repair: Preclinical Studies and Insights in Translational Animal Models and Outcome Measures.

PubMed

Lo Monaco, Melissa; Merckx, Greet; Ratajczak, Jessica; Gervois, Pascal; Hilkens, Petra; Clegg, Peter; Bronckaers, Annelies; Vandeweerd, Jean-Michel; Lambrichts, Ivo

2018-01-01

Due to the restricted intrinsic capacity of resident chondrocytes to regenerate the lost cartilage postinjury, stem cell-based therapies have been proposed as a novel therapeutic approach for cartilage repair. Moreover, stem cell-based therapies using mesenchymal stem cells (MSCs) or induced pluripotent stem cells (iPSCs) have been used successfully in preclinical and clinical settings. Despite these promising reports, the exact mechanisms underlying stem cell-mediated cartilage repair remain uncertain. Stem cells can contribute to cartilage repair via chondrogenic differentiation, via immunomodulation, or by the production of paracrine factors and extracellular vesicles. But before novel cell-based therapies for cartilage repair can be introduced into the clinic, rigorous testing in preclinical animal models is required. Preclinical models used in regenerative cartilage studies include murine, lapine, caprine, ovine, porcine, canine, and equine models, each associated with its specific advantages and limitations. This review presents a summary of recent in vitro data and from in vivo preclinical studies justifying the use of MSCs and iPSCs in cartilage tissue engineering. Moreover, the advantages and disadvantages of utilizing small and large animals will be discussed, while also describing suitable outcome measures for evaluating cartilage repair.
Stem Cells for Cartilage Repair: Preclinical Studies and Insights in Translational Animal Models and Outcome Measures

PubMed Central

Ratajczak, Jessica; Gervois, Pascal; Clegg, Peter; Bronckaers, Annelies; Vandeweerd, Jean-Michel; Lambrichts, Ivo

2018-01-01

Due to the restricted intrinsic capacity of resident chondrocytes to regenerate the lost cartilage postinjury, stem cell-based therapies have been proposed as a novel therapeutic approach for cartilage repair. Moreover, stem cell-based therapies using mesenchymal stem cells (MSCs) or induced pluripotent stem cells (iPSCs) have been used successfully in preclinical and clinical settings. Despite these promising reports, the exact mechanisms underlying stem cell-mediated cartilage repair remain uncertain. Stem cells can contribute to cartilage repair via chondrogenic differentiation, via immunomodulation, or by the production of paracrine factors and extracellular vesicles. But before novel cell-based therapies for cartilage repair can be introduced into the clinic, rigorous testing in preclinical animal models is required. Preclinical models used in regenerative cartilage studies include murine, lapine, caprine, ovine, porcine, canine, and equine models, each associated with its specific advantages and limitations. This review presents a summary of recent in vitro data and from in vivo preclinical studies justifying the use of MSCs and iPSCs in cartilage tissue engineering. Moreover, the advantages and disadvantages of utilizing small and large animals will be discussed, while also describing suitable outcome measures for evaluating cartilage repair. PMID:29535784
Physical models of collective cell motility: from cell to tissue

NASA Astrophysics Data System (ADS)

Camley, B. A.; Rappel, W.-J.

2017-03-01

In this article, we review physics-based models of collective cell motility. We discuss a range of techniques at different scales, ranging from models that represent cells as simple self-propelled particles to phase field models that can represent a cell’s shape and dynamics in great detail. We also extensively review the ways in which cells within a tissue choose their direction, the statistics of cell motion, and some simple examples of how cell-cell signaling can interact with collective cell motility. This review also covers in more detail selected recent works on collective cell motion of small numbers of cells on micropatterns, in wound healing, and the chemotaxis of clusters of cells.
Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

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

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less
Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

DOE PAGES

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.; ...

2018-01-12

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less
Integrated control strategy for autonomous decentralized conveyance systems based on distributed MEMS arrays

NASA Astrophysics Data System (ADS)

Zhou, Lingfei; Chapuis, Yves-Andre; Blonde, Jean-Philippe; Bervillier, Herve; Fukuta, Yamato; Fujita, Hiroyuki

2004-07-01

In this paper, the authors proposed to study a model and a control strategy of a two-dimensional conveyance system based on the principles of the Autonomous Decentralized Microsystems (ADM). The microconveyance system is based on distributed cooperative MEMS actuators which can produce a force field onto the surface of the device to grip and move a micro-object. The modeling approach proposed here is based on a simple model of a microconveyance system which is represented by a 5 x 5 matrix of cells. Each cell is consisted of a microactuator, a microsensor, and a microprocessor to provide actuation, autonomy and decentralized intelligence to the cell. Thus, each cell is able to identify a micro-object crossing on it and to decide by oneself the appropriate control strategy to convey the micro-object to its destination target. The control strategy could be established through five simple decision rules that the cell itself has to respect at each calculate cycle time. Simulation and FPGA implementation results are given in the end of the paper in order to validate model and control approach of the microconveyance system.
Agent-based modeling: case study in cleavage furrow models.

PubMed

Mogilner, Alex; Manhart, Angelika

2016-11-07

The number of studies in cell biology in which quantitative models accompany experiments has been growing steadily. Roughly, mathematical and computational techniques of these models can be classified as "differential equation based" (DE) or "agent based" (AB). Recently AB models have started to outnumber DE models, but understanding of AB philosophy and methodology is much less widespread than familiarity with DE techniques. Here we use the history of modeling a fundamental biological problem-positioning of the cleavage furrow in dividing cells-to explain how and why DE and AB models are used. We discuss differences, advantages, and shortcomings of these two approaches. © 2016 Mogilner and Manhart. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
Theory of the high base resistivity n(+)pp(+) silicon solar cell and its application to radiation damage effects

NASA Technical Reports Server (NTRS)

Goradia, C.; Weinberg, I.

1985-01-01

Particulate radiation in space is a principal source of silicon solar cell degradation, and an investigation of cell radiation damage at higher base resistivities appears to have implication toward increasing solar cell and, therefore, useful satellite lifetimes in the space environment. However, contrary to expectations, it has been found that for cells with resistivities of 84 and 1250 ohm cm, the radiation resistance decreases as cell base resistivity increases. An analytical solar-cell computer model was developed with the objective to determine the reasons for this unexpected behavior. The present paper has the aim to describe the analytical model and its use in interpreting the behavior, under irradiation, of high-resistivity solar cells. Attention is given to boundary conditions at the space-charge region edges, cell currents, cell voltages, the generation of the theoretical I-V characteristic, experimental results, and computer calculations.
DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

PubMed

Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Emerging In Vitro Liver Technologies for Drug Metabolism and Inter-Organ Interactions

PubMed Central

Bale, Shyam Sundhar; Moore, Laura

2016-01-01

In vitro liver models provide essential information for evaluating drug metabolism, metabolite formation, and hepatotoxicity. Interfacing liver models with other organ models could provide insights into the desirable as well as unintended systemic side effects of therapeutic agents and their metabolites. Such information is invaluable for drug screening processes particularly in the context of secondary organ toxicity. While interfacing of liver models with other organ models has been achieved, platforms that effectively provide human-relevant precise information are needed. In this concise review, we discuss the current state-of-the-art of liver-based multiorgan cell culture platforms primarily from a drug and metabolite perspective, and highlight the importance of media-to-cell ratio in interfacing liver models with other organ models. In addition, we briefly discuss issues related to development of optimal liver models that include recent advances in hepatic cell lines, stem cells, and challenges associated with primary hepatocyte-based liver models. Liver-based multiorgan models that achieve physiologically relevant coupling of different organ models can have a broad impact in evaluating drug efficacy and toxicity, as well as mechanistic investigation of human-relevant disease conditions. PMID:27049038
A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management

NASA Astrophysics Data System (ADS)

Cordoba-Arenas, Andrea; Onori, Simona; Rizzoni, Giorgio

2015-04-01

A crucial step towards the large-scale introduction of plug-in hybrid electric vehicles (PHEVs) in the market is to reduce the cost of its battery systems. Currently, battery cycle- and calendar-life represents one of the greatest uncertainties in the total life-cycle cost of battery systems. The field of battery aging modeling and prognosis has seen progress with respect to model-based and data-driven approaches to describe the aging of battery cells. However, in real world applications cells are interconnected and aging propagates. The propagation of aging from one cell to others exhibits itself in a reduced battery system life. This paper proposes a control-oriented battery pack model that describes the propagation of aging and its effect on the life span of battery systems. The modeling approach is such that it is able to predict pack aging, thermal, and electrical dynamics under actual PHEV operation, and includes consideration of random variability of the cells, electrical topology and thermal management. The modeling approach is based on the interaction between dynamic system models of the electrical and thermal dynamics, and dynamic models of cell aging. The system-level state-of-health (SOH) is assessed based on knowledge of individual cells SOH, pack electrical topology and voltage equalization approach.
Single-cell-based computer simulation of the oxygen-dependent tumour response to irradiation

NASA Astrophysics Data System (ADS)

Harting, Christine; Peschke, Peter; Borkenstein, Klaus; Karger, Christian P.

2007-08-01

Optimization of treatment plans in radiotherapy requires the knowledge of tumour control probability (TCP) and normal tissue complication probability (NTCP). Mathematical models may help to obtain quantitative estimates of TCP and NTCP. A single-cell-based computer simulation model is presented, which simulates tumour growth and radiation response on the basis of the response of the constituting cells. The model contains oxic, hypoxic and necrotic tumour cells as well as capillary cells which are considered as sources of a radial oxygen profile. Survival of tumour cells is calculated by the linear quadratic model including the modified response due to the local oxygen concentration. The model additionally includes cell proliferation, hypoxia-induced angiogenesis, apoptosis and resorption of inactivated tumour cells. By selecting different degrees of angiogenesis, the model allows the simulation of oxic as well as hypoxic tumours having distinctly different oxygen distributions. The simulation model showed that poorly oxygenated tumours exhibit an increased radiation tolerance. Inter-tumoural variation of radiosensitivity flattens the dose response curve. This effect is enhanced by proliferation between fractions. Intra-tumoural radiosensitivity variation does not play a significant role. The model may contribute to the mechanistic understanding of the influence of biological tumour parameters on TCP. It can in principle be validated in radiation experiments with experimental tumours.
Probing eukaryotic cell mechanics via mesoscopic simulations

NASA Astrophysics Data System (ADS)

Pivkin, Igor V.; Lykov, Kirill; Nematbakhsh, Yasaman; Shang, Menglin; Lim, Chwee Teck

2017-11-01

We developed a new mesoscopic particle based eukaryotic cell model which takes into account cell membrane, cytoskeleton and nucleus. The breast epithelial cells were used in our studies. To estimate the viscoelastic properties of cells and to calibrate the computational model, we performed micropipette aspiration experiments. The model was then validated using data from microfluidic experiments. Using the validated model, we probed contributions of sub-cellular components to whole cell mechanics in micropipette aspiration and microfluidics experiments. We believe that the new model will allow to study in silico numerous problems in the context of cell biomechanics in flows in complex domains, such as capillary networks and microfluidic devices.
Predicting electroporation of cells in an inhomogeneous electric field based on mathematical modeling and experimental CHO-cell permeabilization to propidium iodide determination.

PubMed

Dermol, Janja; Miklavčič, Damijan

2014-12-01

High voltage electric pulses cause electroporation of the cell membrane. Consequently, flow of the molecules across the membrane increases. In our study we investigated possibility to predict the percentage of the electroporated cells in an inhomogeneous electric field on the basis of the experimental results obtained when cells were exposed to a homogeneous electric field. We compared and evaluated different mathematical models previously suggested by other authors for interpolation of the results (symmetric sigmoid, asymmetric sigmoid, hyperbolic tangent and Gompertz curve). We investigated the density of the cells and observed that it has the most significant effect on the electroporation of the cells while all four of the mathematical models yielded similar results. We were able to predict electroporation of cells exposed to an inhomogeneous electric field based on mathematical modeling and using mathematical formulations of electroporation probability obtained experimentally using exposure to the homogeneous field of the same density of cells. Models describing cell electroporation probability can be useful for development and presentation of treatment planning for electrochemotherapy and non-thermal irreversible electroporation. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic metabolic modeling for a MAB bioprocess.

PubMed

Gao, Jianying; Gorenflo, Volker M; Scharer, Jeno M; Budman, Hector M

2007-01-01

Production of monoclonal antibodies (MAb) for diagnostic or therapeutic applications has become an important task in the pharmaceutical industry. The efficiency of high-density reactor systems can be potentially increased by model-based design and control strategies. Therefore, a reliable kinetic model for cell metabolism is required. A systematic procedure based on metabolic modeling is used to model nutrient uptake and key product formation in a MAb bioprocess during both the growth and post-growth phases. The approach combines the key advantages of stoichiometric and kinetic models into a complete metabolic network while integrating the regulation and control of cellular activity. This modeling procedure can be easily applied to any cell line during both the cell growth and post-growth phases. Quadratic programming (QP) has been identified as a suitable method to solve the underdetermined constrained problem related to model parameter identification. The approach is illustrated for the case of murine hybridoma cells cultivated in stirred spinners.
Study on activity measurement of Nostoc flagelliforme cells based on color identification

NASA Astrophysics Data System (ADS)

Wang, Yizhong; Su, Jianyu; Liu, Tiegen; Kong, Fanzhi; Jia, Shiru

2008-12-01

In order to measure the activities of Nostoc flagelliforme cells, a new method based on color identification was proposed in this paper. N. flagelliforme cells were colored with fluoreseein diaeetate. Then, an image of colored N. flagelliforme cells was taken, and changed from RGB model to HIS model. Its histogram of hue H was calculated, which was used as the input of a designed BP network. The output of the BP network was the description of measured activity of N. flagelliforme cells. After training, the activity of N. flagelliforme cells was identified by the BP network according to the histogram of H of their colored image. Experiments were conducted with satisfied results to show the feasibility and usefulness of activity measurement of N. flagelliforme cells based on color identification.
Relation Between the Cell Volume and the Cell Cycle Dynamics in Mammalian cell

NASA Astrophysics Data System (ADS)

Magno, A. C. G.; Oliveira, I. L.; Hauck, J. V. S.

2016-08-01

The main goal of this work is to add and analyze an equation that represents the volume in a dynamical model of the mammalian cell cycle proposed by Gérard and Goldbeter (2011) [1]. The cell division occurs when the cyclinB/Cdkl complex is totally degraded (Tyson and Novak, 2011)[2] and it reaches a minimum value. At this point, the cell is divided into two newborn daughter cells and each one will contain the half of the cytoplasmic content of the mother cell. The equations of our base model are only valid if the cell volume, where the reactions occur, is constant. Whether the cell volume is not constant, that is, the rate of change of its volume with respect to time is explicitly taken into account in the mathematical model, then the equations of the original model are no longer valid. Therefore, every equations were modified from the mass conservation principle for considering a volume that changes with time. Through this approach, the cell volume affects all model variables. Two different dynamic simulation methods were accomplished: deterministic and stochastic. In the stochastic simulation, the volume affects every model's parameters which have molar unit, whereas in the deterministic one, it is incorporated into the differential equations. In deterministic simulation, the biochemical species may be in concentration units, while in stochastic simulation such species must be converted to number of molecules which are directly proportional to the cell volume. In an effort to understand the influence of the new equation a stability analysis was performed. This elucidates how the growth factor impacts the stability of the model's limit cycles. In conclusion, a more precise model, in comparison to the base model, was created for the cell cycle as it now takes into consideration the cell volume variation
Freezing Transition Studies Through Constrained Cell Model Simulation

NASA Astrophysics Data System (ADS)

Nayhouse, Michael; Kwon, Joseph Sang-Il; Heng, Vincent R.; Amlani, Ankur M.; Orkoulas, G.

2014-10-01

In the present work, a simulation method based on cell models is used to deduce the fluid-solid transition of a system of particles that interact via a pair potential, , which is of the form with . The simulations are implemented under constant-pressure conditions on a generalized version of the constrained cell model. The constrained cell model is constructed by dividing the volume into Wigner-Seitz cells and confining each particle in a single cell. This model is a special case of a more general cell model which is formed by introducing an additional field variable that controls the number of particles per cell and, thus, the relative stability of the solid against the fluid phase. High field values force configurations with one particle per cell and thus favor the solid phase. Fluid-solid coexistence on the isotherm that corresponds to a reduced temperature of 2 is determined from constant-pressure simulations of the generalized cell model using tempering and histogram reweighting techniques. The entire fluid-solid phase boundary is determined through a thermodynamic integration technique based on histogram reweighting, using the previous coexistence point as a reference point. The vapor-liquid phase diagram is obtained from constant-pressure simulations of the unconstrained system using tempering and histogram reweighting. The phase diagram of the system is found to contain a stable critical point and a triple point. The phase diagram of the corresponding constrained cell model is also found to contain both a stable critical point and a triple point.
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.
Computational Modeling of Tissue Self-Assembly

NASA Astrophysics Data System (ADS)

Neagu, Adrian; Kosztin, Ioan; Jakab, Karoly; Barz, Bogdan; Neagu, Monica; Jamison, Richard; Forgacs, Gabor

As a theoretical framework for understanding the self-assembly of living cells into tissues, Steinberg proposed the differential adhesion hypothesis (DAH) according to which a specific cell type possesses a specific adhesion apparatus that combined with cell motility leads to cell assemblies of various cell types in the lowest adhesive energy state. Experimental and theoretical efforts of four decades turned the DAH into a fundamental principle of developmental biology that has been validated both in vitro and in vivo. Based on computational models of cell sorting, we have developed a DAH-based lattice model for tissues in interaction with their environment and simulated biological self-assembly using the Monte Carlo method. The present brief review highlights results on specific morphogenetic processes with relevance to tissue engineering applications. Our own work is presented on the background of several decades of theoretical efforts aimed to model morphogenesis in living tissues. Simulations of systems involving about 105 cells have been performed on high-end personal computers with CPU times of the order of days. Studied processes include cell sorting, cell sheet formation, and the development of endothelialized tubes from rings made of spheroids of two randomly intermixed cell types, when the medium in the interior of the tube was different from the external one. We conclude by noting that computer simulations based on mathematical models of living tissues yield useful guidelines for laboratory work and can catalyze the emergence of innovative technologies in tissue engineering.
CellML metadata standards, associated tools and repositories

PubMed Central

Beard, Daniel A.; Britten, Randall; Cooling, Mike T.; Garny, Alan; Halstead, Matt D.B.; Hunter, Peter J.; Lawson, James; Lloyd, Catherine M.; Marsh, Justin; Miller, Andrew; Nickerson, David P.; Nielsen, Poul M.F.; Nomura, Taishin; Subramanium, Shankar; Wimalaratne, Sarala M.; Yu, Tommy

2009-01-01

The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website. PMID:19380315
Electrolytic hydrogen production: An analysis and review

NASA Technical Reports Server (NTRS)

Evangelista, J.; Phillips, B.; Gordon, L.

1975-01-01

The thermodynamics of water electrolysis cells is presented, followed by a review of current and future technology of commercial cells. The irreversibilities involved are analyzed and the resulting equations assembled into a computer simulation model of electrolysis cell efficiency. The model is tested by comparing predictions based on the model to actual commercial cell performance, and a parametric investigation of operating conditions is performed. Finally, the simulation model is applied to a study of electrolysis cell dynamics through consideration of an ideal pulsed electrolyzer.
Micro-scale blood particulate dynamics using a non-uniform rational B-spline-based isogeometric analysis.

PubMed

Chivukula, V; Mousel, J; Lu, J; Vigmostad, S

2014-12-01

The current research presents a novel method in which blood particulates - biconcave red blood cells (RBCs) and spherical cells are modeled using isogeometric analysis, specifically Non-Uniform Rational B-Splines (NURBS) in 3-D. The use of NURBS ensures that even with a coarse representation, the geometry of the blood particulates maintains an accurate description when subjected to large deformations. The fundamental advantage of this method is the coupling of the geometrical description and the stress analysis of the cell membrane into a single, unified framework. Details on the modeling approach, implementation of boundary conditions and the membrane mechanics analysis using isogeometric modeling are presented, along with validation cases for spherical and biconcave cells. Using NURBS - based isogeometric analysis, the behavior of individual cells in fluid flow is presented and analyzed in different flow regimes using as few as 176 elements for a spherical cell and 220 elements for a biconcave RBC. This work provides a framework for modeling a large number of 3-D deformable biological cells, each with its own geometric description and membrane properties. To the best knowledge of the authors, this is the first application of the NURBS - based isogeometric analysis to model and simulate blood particulates in flow in 3D. Copyright © 2014 John Wiley & Sons, Ltd.
WE-H-BRA-08: A Monte Carlo Cell Nucleus Model for Assessing Cell Survival Probability Based On Particle Track Structure Analysis

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

Lee, B; Georgia Institute of Technology, Atlanta, GA; Wang, C

Purpose: To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus. Methods: A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean-free-path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin-dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4-DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities.more » These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death. Results: Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high-LET radiation than low-LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L-Q behavior of sparsely ionizing radiation. Conclusion: Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell survival curves for high-LET radiation.« less
Modeling, simulation and optimization of a no-chamber solid oxide fuel cell operated with a flat-flame burner

NASA Astrophysics Data System (ADS)

Vogler, Marcel; Horiuchi, Michio; Bessler, Wolfgang G.

A detailed computational model of a direct-flame solid oxide fuel cell (DFFC) is presented. The DFFC is based on a fuel-rich methane-air flame stabilized on a flat-flame burner and coupled to a solid oxide fuel cell (SOFC). The model consists of an elementary kinetic description of the premixed methane-air flame, a stagnation-point flow description of the coupled heat and mass transport within the gas phase, an elementary kinetic description of the electrochemistry, as well as heat, mass and charge transport within the SOFC. Simulated current-voltage characteristics show excellent agreement with experimental data published earlier (Kronemayer et al., 2007 [10]). The model-based analysis of loss processes reveals that ohmic resistance in the current collection wires dominates polarization losses, while electronic loss currents in the mixed conducting electrolyte have only little influence on the polarized cell. The model was used to propose an optimized cell design. Based on this analysis, power densities of above 200 mW cm -2 can be expected.
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.
A simple and accurate rule-based modeling framework for simulation of autocrine/paracrine stimulation of glioblastoma cell motility and proliferation by L1CAM in 2-D culture.

PubMed

Caccavale, Justin; Fiumara, David; Stapf, Michael; Sweitzer, Liedeke; Anderson, Hannah J; Gorky, Jonathan; Dhurjati, Prasad; Galileo, Deni S

2017-12-11

Glioblastoma multiforme (GBM) is a devastating brain cancer for which there is no known cure. Its malignancy is due to rapid cell division along with high motility and invasiveness of cells into the brain tissue. Simple 2-dimensional laboratory assays (e.g., a scratch assay) commonly are used to measure the effects of various experimental perturbations, such as treatment with chemical inhibitors. Several mathematical models have been developed to aid the understanding of the motile behavior and proliferation of GBM cells. However, many are mathematically complicated, look at multiple interdependent phenomena, and/or use modeling software not freely available to the research community. These attributes make the adoption of models and simulations of even simple 2-dimensional cell behavior an uncommon practice by cancer cell biologists. Herein, we developed an accurate, yet simple, rule-based modeling framework to describe the in vitro behavior of GBM cells that are stimulated by the L1CAM protein using freely available NetLogo software. In our model L1CAM is released by cells to act through two cell surface receptors and a point of signaling convergence to increase cell motility and proliferation. A simple graphical interface is provided so that changes can be made easily to several parameters controlling cell behavior, and behavior of the cells is viewed both pictorially and with dedicated graphs. We fully describe the hierarchical rule-based modeling framework, show simulation results under several settings, describe the accuracy compared to experimental data, and discuss the potential usefulness for predicting future experimental outcomes and for use as a teaching tool for cell biology students. It is concluded that this simple modeling framework and its simulations accurately reflect much of the GBM cell motility behavior observed experimentally in vitro in the laboratory. Our framework can be modified easily to suit the needs of investigators interested in other similar intrinsic or extrinsic stimuli that influence cancer or other cell behavior. This modeling framework of a commonly used experimental motility assay (scratch assay) should be useful to both researchers of cell motility and students in a cell biology teaching laboratory.
Multiscale Modeling of Virus Entry via Receptor-Mediated Endocytosis

NASA Astrophysics Data System (ADS)

Liu, Jin

2012-11-01

Virus infections are ubiquitous and remain major threats to human health worldwide. Viruses are intracellular parasites and must enter host cells to initiate infection. Receptor-mediated endocytosis is the most common entry pathway taken by viruses, the whole process is highly complex and dictated by various events, such as virus motions, membrane deformations, receptor diffusion and ligand-receptor reactions, occurring at multiple length and time scales. We develop a multiscale model for virus entry through receptor-mediated endocytosis. The binding of virus to cell surface is based on a mesoscale three dimensional stochastic adhesion model, the internalization (endocytosis) of virus and cellular membrane deformation is based on the discretization of Helfrich Hamiltonian in a curvilinear space using Monte Carlo method. The multiscale model is based on the combination of these two models. We will implement this model to study the herpes simplex virus entry into B78 cells and compare the model predictions with experimental measurements.
Three-dimensional Model of Tissue and Heavy Ions Effects

NASA Technical Reports Server (NTRS)

Ponomarev, Artem L.; Sundaresan, Alamelu; Huff, Janice L.; Cucinotta, Francis A.

2007-01-01

A three-dimensional tissue model was incorporated into a new Monte Carlo algorithm that simulates passage of heavy ions in a tissue box . The tissue box was given as a realistic model of tissue based on confocal microscopy images. The action of heavy ions on the cellular matrix for 2- or 3-dimensional cases was simulated. Cells were modeled as a cell culture monolayer in one example, where the data were taken directly from microscopy (2-d cell matrix), and as a multi-layer obtained from confocal microscopy (3-d case). Image segmentation was used to identify cells with precise areas/volumes in an irradiated cell culture monolayer, and slices of tissue with many cell layers. The cells were then inserted into the model box of the simulated physical space pixel by pixel. In the case of modeled tissues (3-d), the tissue box had periodic boundary conditions imposed, which extrapolates the technique to macroscopic volumes of tissue. For the real tissue (3-d), specific spatial patterns for cell apoptosis and necrosis are expected. The cell patterns were modeled based on action cross sections for apoptosis and necrosis estimated from current experimental data. A spatial correlation function indicating a higher spatial concentration of damaged cells from heavy ions relative to the low-LET radiation cell damage pattern is presented. The spatial correlation effects among necrotic cells can help studying microlesions in organs, and probable effects of directionality of heavy ion radiation on epithelium and endothelium.
Dynamical crossover in a stochastic model of cell fate decision

NASA Astrophysics Data System (ADS)

Yamaguchi, Hiroki; Kawaguchi, Kyogo; Sagawa, Takahiro

2017-07-01

We study the asymptotic behaviors of stochastic cell fate decision between proliferation and differentiation. We propose a model of a self-replicating Langevin system, where cells choose their fate (i.e., proliferation or differentiation) depending on local cell density. Based on this model, we propose a scenario for multicellular organisms to maintain the density of cells (i.e., homeostasis) through finite-ranged cell-cell interactions. Furthermore, we numerically show that the distribution of the number of descendant cells changes over time, thus unifying the previously proposed two models regarding homeostasis: the critical birth death process and the voter model. Our results provide a general platform for the study of stochastic cell fate decision in terms of nonequilibrium statistical mechanics.
Differentiation and Characterization of Dopaminergic Neurons From Baboon Induced Pluripotent Stem Cells.

PubMed

Grow, Douglas A; Simmons, DeNard V; Gomez, Jorge A; Wanat, Matthew J; McCarrey, John R; Paladini, Carlos A; Navara, Christopher S

2016-09-01

: The progressive death of dopamine producing neurons in the substantia nigra pars compacta is the principal cause of symptoms of Parkinson's disease (PD). Stem cells have potential therapeutic use in replacing these cells and restoring function. To facilitate development of this approach, we sought to establish a preclinical model based on a large nonhuman primate for testing the efficacy and safety of stem cell-based transplantation. To this end, we differentiated baboon fibroblast-derived induced pluripotent stem cells (biPSCs) into dopaminergic neurons with the application of specific morphogens and growth factors. We confirmed that biPSC-derived dopaminergic neurons resemble those found in the human midbrain based on cell type-specific expression of dopamine markers TH and GIRK2. Using the reverse transcriptase quantitative polymerase chain reaction, we also showed that biPSC-derived dopaminergic neurons express PAX6, FOXA2, LMX1A, NURR1, and TH genes characteristic of this cell type in vivo. We used perforated patch-clamp electrophysiology to demonstrate that biPSC-derived dopaminergic neurons fired spontaneous rhythmic action potentials and high-frequency action potentials with spike frequency adaption upon injection of depolarizing current. Finally, we showed that biPSC-derived neurons released catecholamines in response to electrical stimulation. These results demonstrate the utility of the baboon model for testing and optimizing the efficacy and safety of stem cell-based therapeutic approaches for the treatment of PD. Functional dopamine neurons were produced from baboon induced pluripotent stem cells, and their properties were compared to baboon midbrain cells in vivo. The baboon has advantages as a clinically relevant model in which to optimize the efficacy and safety of stem cell-based therapies for neurodegenerative diseases, such as Parkinson's disease. Baboons possess crucial neuroanatomical and immunological similarities to humans, and baboon pluripotent stem cells can be differentiated into functional neurons that mimic those in the human brain, thus laying the foundation for the utility of the baboon model for evaluating stem cell therapies. ©AlphaMed Press.
Case Study: Organotypic human in vitro models of embryonic ...

EPA Pesticide Factsheets

Morphogenetic fusion of tissues is a common event in embryonic development and disruption of fusion is associated with birth defects of the eye, heart, neural tube, phallus, palate, and other organ systems. Embryonic tissue fusion requires precise regulation of cell-cell and cell-matrix interactions that drive proliferation, differentiation, and morphogenesis. Chemical low-dose exposures can disrupt morphogenesis across space and time by interfering with key embryonic fusion events. The Morphogenetic Fusion Task uses computer and in vitro models to elucidate consequences of developmental exposures. The Morphogenetic Fusion Task integrates multiple approaches to model responses to chemicals that leaad to birth defects, including integrative mining on ToxCast DB, ToxRefDB, and chemical structures, advanced computer agent-based models, and human cell-based cultures that model disruption of cellular and molecular behaviors including mechanisms predicted from integrative data mining and agent-based models. The purpose of the poster is to indicate progress on the CSS 17.02 Virtual Tissue Models Morphogenesis Task 1 products for the Board of Scientific Counselors meeting on Nov 16-17.
A lineage CLOUD for neoblasts.

PubMed

Tran, Thao Anh; Gentile, Luca

2018-05-10

In planarians, pluripotency can be studied in vivo in the adult animal, making these animals a unique model system where pluripotency-based regeneration (PBR)-and its therapeutic potential-can be investigated. This review focuses on recent findings to build a cloud model of fate restriction likelihood for planarian stem and progenitor cells. Recently, a computational approach based on functional and molecular profiling at the single cell level was proposed for human hematopoietic stem cells. Based on data generated both in vivo and ex vivo, we hypothesized that planarian stem cells could acquire multiple direction lineage biases, following a "badlands" landscape. Instead of a discrete tree-like hierarchy, where the potency of stem/progenitor cells reduces stepwise, we propose a Continuum of LOw-primed UnDifferentiated Planarian Stem/Progenitor Cells (CLOUD-PSPCs). Every subclass of neoblast/progenitor cells is a cloud of likelihood, as the single cell transcriptomics data indicate. The CLOUD-HSPCs concept was substantiated by in vitro data from cell culture; therefore, to confirm the CLOUD-PSPCs model, the planarian community needs to develop new tools, like live cell tracking. Future studies will allow a deeper understanding of PBR in planarian, and the possible implications for regenerative therapies in human. Copyright © 2018 Elsevier Ltd. All rights reserved.
Characterization of biomechanical properties of cells through dielectrophoresis-based cell stretching and actin cytoskeleton modeling.

PubMed

Bai, Guohua; Li, Ying; Chu, Henry K; Wang, Kaiqun; Tan, Qiulin; Xiong, Jijun; Sun, Dong

2017-04-04

Cytoskeleton is a highly dynamic network that helps to maintain the rigidity of a cell, and the mechanical properties of a cell are closely related to many cellular functions. This paper presents a new method to probe and characterize cell mechanical properties through dielectrophoresis (DEP)-based cell stretching manipulation and actin cytoskeleton modeling. Leukemia NB4 cells were used as cell line, and changes in their biological properties were examined after chemotherapy treatment with doxorubicin (DOX). DEP-integrated microfluidic chip was utilized as a low-cost and efficient tool to study the deformability of cells. DEP forces used in cell stretching were first evaluated through computer simulation, and the results were compared with modeling equations and with the results of optical stretching (OT) experiments. Structural parameters were then extracted by fitting the experimental data into the actin cytoskeleton model, and the underlying mechanical properties of the cells were subsequently characterized. The DEP forces generated under different voltage inputs were calculated and the results from different approaches demonstrate good approximations to the force estimation. Both DEP and OT stretching experiments confirmed that DOX-treated NB4 cells were stiffer than the untreated cells. The structural parameters extracted from the model and the confocal images indicated significant change in actin network after DOX treatment. The proposed DEP method combined with actin cytoskeleton modeling is a simple engineering tool to characterize the mechanical properties of cells.

Models of collective cell spreading with variable cell aspect ratio: a motivation for degenerate diffusion models.

PubMed

Simpson, Matthew J; Baker, Ruth E; McCue, Scott W

2011-02-01

Continuum diffusion models are often used to represent the collective motion of cell populations. Most previous studies have simply used linear diffusion to represent collective cell spreading, while others found that degenerate nonlinear diffusion provides a better match to experimental cell density profiles. In the cell modeling literature there is no guidance available with regard to which approach is more appropriate for representing the spreading of cell populations. Furthermore, there is no knowledge of particular experimental measurements that can be made to distinguish between situations where these two models are appropriate. Here we provide a link between individual-based and continuum models using a multiscale approach in which we analyze the collective motion of a population of interacting agents in a generalized lattice-based exclusion process. For round agents that occupy a single lattice site, we find that the relevant continuum description of the system is a linear diffusion equation, whereas for elongated rod-shaped agents that occupy L adjacent lattice sites we find that the relevant continuum description is connected to the porous media equation (PME). The exponent in the nonlinear diffusivity function is related to the aspect ratio of the agents. Our work provides a physical connection between modeling collective cell spreading and the use of either the linear diffusion equation or the PME to represent cell density profiles. Results suggest that when using continuum models to represent cell population spreading, we should take care to account for variations in the cell aspect ratio because different aspect ratios lead to different continuum models.
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
Stability and bifurcation in a model for the dynamics of stem-like cells in leukemia under treatment

NASA Astrophysics Data System (ADS)

Rǎdulescu, I. R.; Cândea, D.; Halanay, A.

2012-11-01

A mathematical model for the dynamics of leukemic cells during treatment is introduced. Delay differential equations are used to model cells' evolution and are based on the Mackey-Glass approach, incorporating Goldie-Coldman law. Since resistance is propagated by cells that have the capacity of self-renewal, a population of stem-like cells is studied. Equilibrium points are calculated and their stability properties are investigated.
Mathematical model of macrophage-facilitated breast cancer cells invasion.

PubMed

Knútsdóttir, Hildur; Pálsson, Eirikur; Edelstein-Keshet, Leah

2014-09-21

Mortality from breast cancer stems from its tendency to invade into surrounding tissues and organs. Experiments have shown that this metastatic process is facilitated by macrophages in a short-ranged chemical signalling loop. Macrophages secrete epidermal growth factor, EGF, and respond to the colony stimulating factor 1, CSF-1. Tumor cells secrete CSF-1 and respond to EGF. In this way, the cells coordinate aggregation and cooperative migration. Here we investigate this process in a model for in vitro interactions using two distinct but related mathematical approaches. In the first, we analyze and simulate a set of partial differential equations to determine conditions for aggregation. In the second, we use a cell-based discrete 3D simulation to follow the fates and motion of individual cells during aggregation. Linear stability analysis of the PDE model reveals that decreasing the chemical secretion, chemotaxis coefficients or density of cells or increasing the chemical degradation in the model could eliminate the spontaneous aggregation of cells. Simulations with the discrete model show that the ratio between tumor cells and macrophages in aggregates increases when the EGF secretion parameter is increased. The results also show how CSF-1/CSF-1R autocrine signalling in tumor cells affects the ratio between the two cell types. Comparing the continuum results with simulations of a discrete cell-based model, we find good qualitative agreement. Copyright © 2014 Elsevier Ltd. All rights reserved.
Chitosan-based nanocoatings for hypothermic storage of living cells.

PubMed

Bulwan, Maria; Antosiak-Iwańska, Magdalena; Godlewska, Ewa; Granicka, Ludomira; Zapotoczny, Szczepan; Nowakowska, Maria

2013-11-01

The formation of ultrathin chitosan-based nanocoating on HL-60 model cells and their protective function in hypothermic storage are presented. HL-60 cells are encapsulated in ultrathin shells by adsorbing cationic and anionic chitosan derivatives in a stepwise, layer-by-layer, procedure carried out in an aqueous medium under mild conditions. The chitosan-based films are also deposited on model lipid bilayer and the interactions are studied using ellipsometry and atomic force microscopy. The cells covered with the chitosan-based films and stored at 4 °C for 24 h express viability comparable to that of the control sample incubated at 37 °C, while the unprotected cells stored under the same conditions do not show viability. It is shown that the chitosan-based shell protects HL-60 cells against damaging effect of hypothermic storage. Such nanocoatings provide protection, mechanical stability, and support the cell membrane, while ensuring penetration of small molecules such as nutrients/gases what is essential for cell viability. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Establishment of a translational endothelial cell model using directed differentiation of induced pluripotent stem cells from Cynomolgus monkey.

PubMed

Thoma, Eva C; Heckel, Tobias; Keller, David; Giroud, Nicolas; Leonard, Brian; Christensen, Klaus; Roth, Adrian; Bertinetti-Lapatki, Cristina; Graf, Martin; Patsch, Christoph

2016-10-25

Due to their broad differentiation potential, pluripotent stem cells (PSCs) offer a promising approach for generating relevant cellular models for various applications. While human PSC-based cellular models are already advanced, similar systems for non-human primates (NHPs) are still lacking. However, as NHPs are the most appropriate animals for evaluating the safety of many novel pharmaceuticals, the availability of in vitro systems would be extremely useful to bridge the gap between cellular and animal models. Here, we present a NHP in vitro endothelial cell system using induced pluripotent stem cells (IPSCs) from Cynomolgus monkey (Macaca fascicularis). Based on an adapted protocol for human IPSCs, we directly differentiated macaque IPSCs into endothelial cells under chemically defined conditions. The resulting endothelial cells can be enriched using immuno-magnetic cell sorting and display endothelial marker expression and function. RNA sequencing revealed that the differentiation process closely resembled vasculogenesis. Moreover, we showed that endothelial cells derived from macaque and human IPSCs are highly similar with respect to gene expression patterns and key endothelial functions, such as inflammatory responses. These data demonstrate the power of IPSC differentiation technology to generate defined cell types for use as translational in vitro models to compare cell type-specific responses across species.
Three-dimensional prostate tumor model based on a hyaluronic acid-alginate hydrogel for evaluation of anti-cancer drug efficacy.

PubMed

Tang, Yadong; Huang, Boxin; Dong, Yuqin; Wang, Wenlong; Zheng, Xi; Zhou, Wei; Zhang, Kun; Du, Zhiyun

2017-10-01

In vitro cell-based assays are widely applied to evaluate anti-cancer drug efficacy. However, the conventional approaches are mostly based on two-dimensional (2D) culture systems, making it difficult to recapitulate the in vivo tumor scenario because of spatial limitations. Here, we develop an in vitro three-dimensional (3D) prostate tumor model based on a hyaluronic acid (HA)-alginate hybrid hydrogel to bridge the gap between in vitro and in vivo anticancer drug evaluations. In situ encapsulation of PCa cells was achieved by mixing HA and alginate aqueous solutions in the presence of cells and then crosslinking with calcium ions. Unlike in 2D culture, cells were found to aggregate into spheroids in a 3D matrix. The expression of epithelial to mesenchyme transition (EMT) biomarkers was found to be largely enhanced, indicating an increased invasion and metastasis potential in the hydrogel matrix. A significant up-regulation of proangiogenic growth factors (IL-8, VEGF) and matrix metalloproteinases (MMPs) was observed in 3D-cultured PCa cells. The results of anti-cancer drug evaluation suggested a higher drug tolerance within the 3D tumor model compared to conventional 2D-cultured cells. Finally, we found that the drug effect within the in vitro 3D cancer model based on HA-alginate matrix exhibited better predictability for in vivo drug efficacy.
Evaluation of automated cell disruptor methods for oomycetous and ascomycetous model organisms

USDA-ARS?s Scientific Manuscript database

Two automated cell disruptor-based methods for RNA extraction; disruption of thawed cells submerged in TRIzol Reagent (method QP), and direct disruption of frozen cells on dry ice (method CP), were optimized for a model oomycete, Phytophthora capsici, and compared with grinding in a mortar and pestl...
The Danger Model Approach to the Pathogenesis of the Rheumatic Diseases

PubMed Central

Pacheco-Tena, César; González-Chávez, Susana Aideé

2015-01-01

The danger model was proposed by Polly Matzinger as complement to the traditional self-non-self- (SNS-) model to explain the immunoreactivity. The danger model proposes a central role of the tissular cells' discomfort as an element to prime the immune response processes in opposition to the traditional SNS-model where foreignness is a prerequisite. However recent insights in the proteomics of diverse tissular cells have revealed that under stressful conditions they have a significant potential to initiate, coordinate, and perpetuate autoimmune processes, in many cases, ruling over the adaptive immune response cells; this ruling potential can also be confirmed by observations in several genetically manipulated animal models. Here, we review the pathogenesis of rheumatic diseases such as systemic lupus erythematous, rheumatoid arthritis, spondyloarthritis including ankylosing spondylitis, psoriasis, and Crohn's disease and provide realistic approaches based on the logic of the danger model. We assume that tissular dysfunction is a prerequisite for chronic autoimmunity and propose two genetically conferred hypothetical roles for the tissular cells causing the disease: (A) the Impaired cell and (B) the paranoid cell. Both roles are not mutually exclusive. Some examples in human disease and in animal models are provided based on current evidence. PMID:25973436
Tracking of cell nuclei for assessment of in vitro uptake kinetics in ultrasound-mediated drug delivery using fibered confocal fluorescence microscopy.

PubMed

Derieppe, Marc; de Senneville, Baudouin Denis; Kuijf, Hugo; Moonen, Chrit; Bos, Clemens

2014-10-01

Previously, we demonstrated the feasibility to monitor ultrasound-mediated uptake of a cell-impermeable model drug in real time with fibered confocal fluorescence microscopy. Here, we present a complete post-processing methodology, which corrects for cell displacements, to improve the accuracy of pharmacokinetic parameter estimation. Nucleus detection was performed based on the radial symmetry transform algorithm. Cell tracking used an iterative closest point approach. Pharmacokinetic parameters were calculated by fitting a two-compartment model to the time-intensity curves of individual cells. Cells were tracked successfully, improving time-intensity curve accuracy and pharmacokinetic parameter estimation. With tracking, 93 % of the 370 nuclei showed a fluorescence signal variation that was well-described by a two-compartment model. In addition, parameter distributions were narrower, thus increasing precision. Dedicated image analysis was implemented and enabled studying ultrasound-mediated model drug uptake kinetics in hundreds of cells per experiment, using fiber-based confocal fluorescence microscopy.
Low-high junction theory applied to solar cells

NASA Technical Reports Server (NTRS)

Godlewski, M. P.; Baraona, C. R.; Brandhorst, H. W., Jr.

1974-01-01

Recent use of alloying techniques for rear contact formation has yielded a new kind of silicon solar cell, the back surface field (BSF) cell, with abnormally high open-circuit voltage and improved radiation resistance. Several analytical models for open-circuit voltage based on the reverse saturation current are formulated to explain these observations. The zero surface recombination velocity (SRV) case of the conventional cell model, the drift field model, and the low-high junction (LHJ) model can predict the experimental trends. The LHJ model applies the theory of the low-high junction and is considered to reflect a more realistic view of cell fabrication. This model can predict the experimental trends observed for BSF cells.
Computational approaches to substrate-based cell motility

DOE PAGES

Ziebert, Falko; Aranson, Igor S.

2016-07-15

Substrate-based crawling motility of eukaryotic cells is essential for many biological functions, both in developing and mature organisms. Motility dysfunctions are involved in several life-threatening pathologies such as cancer and metastasis. Motile cells are also a natural realization of active, self-propelled ‘particles’, a popular research topic in nonequilibrium physics. Finally, from the materials perspective, assemblies of motile cells and evolving tissues constitute a class of adaptive self-healing materials that respond to the topography, elasticity, and surface chemistry of the environment and react to external stimuli. Although a comprehensive understanding of substrate-based cell motility remains elusive, progress has been achieved recentlymore » in its modeling on the whole cell level. Furthermore we survey the most recent advances in computational approaches to cell movement and demonstrate how these models improve our understanding of complex self-organized systems such as living cells.« less
Mathematical model of water transport in Bacon and alkaline matrix-type hydrogen-oxygen fuel cells

NASA Technical Reports Server (NTRS)

Prokopius, P. R.; Easter, R. W.

1972-01-01

Based on general mass continuity and diffusive transport equations, a mathematical model was developed that simulates the transport of water in Bacon and alkaline-matrix fuel cells. The derived model was validated by using it to analytically reproduce various Bacon and matrix-cell experimental water transport transients.
Ethics and Policy Issues for Stem Cell Research and Pulmonary Medicine

PubMed Central

Lowenthal, Justin

2015-01-01

Stem cell research and related initiatives in regenerative medicine, cell-based therapy, and tissue engineering have generated considerable scientific and public interest. Researchers are applying stem cell technologies to chest medicine in a variety of ways: using stem cells as models for drug discovery, testing stem cell-based therapies for conditions as diverse as COPD and cystic fibrosis, and producing functional lung and tracheal tissue for physiologic modeling and potential transplantation. Although significant scientific obstacles remain, it is likely that stem cell-based regenerative medicine will have a significant clinical impact in chest medicine. However, stem cell research has also generated substantial controversy, posing a variety of ethical and regulatory challenges for research and clinical practice. Some of the most prominent ethical questions related to the use of stem cell technologies in chest medicine include (1) implications for donors, (2) scientific prerequisites for clinical testing and use, (3) stem cell tourism, (4) innovation and clinical use of emerging stem cell-based interventions, (5) responsible translation of stem cell-based therapies to clinical use, and (6) appropriate and equitable access to emerging therapies. Having a sense of these issues should help to put emerging scientific advances into appropriate context and to ensure the responsible clinical translation of promising therapeutics. PMID:25732448
Ethics and policy issues for stem cell research and pulmonary medicine.

PubMed

Lowenthal, Justin; Sugarman, Jeremy

2015-03-01

Stem cell research and related initiatives in regenerative medicine, cell-based therapy, and tissue engineering have generated considerable scientific and public interest. Researchers are applying stem cell technologies to chest medicine in a variety of ways: using stem cells as models for drug discovery, testing stem cell-based therapies for conditions as diverse as COPD and cystic fibrosis, and producing functional lung and tracheal tissue for physiologic modeling and potential transplantation. Although significant scientific obstacles remain, it is likely that stem cell-based regenerative medicine will have a significant clinical impact in chest medicine. However, stem cell research has also generated substantial controversy, posing a variety of ethical and regulatory challenges for research and clinical practice. Some of the most prominent ethical questions related to the use of stem cell technologies in chest medicine include (1) implications for donors, (2) scientific prerequisites for clinical testing and use, (3) stem cell tourism, (4) innovation and clinical use of emerging stem cell-based interventions, (5) responsible translation of stem cell-based therapies to clinical use, and (6) appropriate and equitable access to emerging therapies. Having a sense of these issues should help to put emerging scientific advances into appropriate context and to ensure the responsible clinical translation of promising therapeutics.
Modeling mechanical inhomogeneities in small populations of proliferating monolayers and spheroids.

PubMed

Lejeune, Emma; Linder, Christian

2018-06-01

Understanding the mechanical behavior of multicellular monolayers and spheroids is fundamental to tissue culture, organism development, and the early stages of tumor growth. Proliferating cells in monolayers and spheroids experience mechanical forces as they grow and divide and local inhomogeneities in the mechanical microenvironment can cause individual cells within the multicellular system to grow and divide at different rates. This differential growth, combined with cell division and reorganization, leads to residual stress. Multiple different modeling approaches have been taken to understand and predict the residual stresses that arise in growing multicellular systems, particularly tumor spheroids. Here, we show that by using a mechanically robust agent-based model constructed with the peridynamic framework, we gain a better understanding of residual stresses in multicellular systems as they grow from a single cell. In particular, we focus on small populations of cells (1-100 s) where population behavior is highly stochastic and prior investigation has been limited. We compare the average strain energy density of cells in monolayers and spheroids using different growth and division rules and find that, on average, cells in spheroids have a higher strain energy density than cells in monolayers. We also find that cells in the interior of a growing spheroid are, on average, in compression. Finally, we demonstrate the importance of accounting for stochastic fluctuations in the mechanical environment, particularly when the cellular response to mechanical cues is nonlinear. The results presented here serve as a starting point for both further investigation with agent-based models, and for the incorporation of major findings from agent-based models into continuum scale models when explicit representation of individual cells is not computationally feasible.
The Effect of Primary Cancer Cell Culture Models on the Results of Drug Chemosensitivity Assays: The Application of Perfusion Microbioreactor System as Cell Culture Vessel

PubMed Central

Chen, Yi-Dao; Huang, Shiang-Fu; Wang, Hung-Ming

2015-01-01

To precisely and faithfully perform cell-based drug chemosensitivity assays, a well-defined and biologically relevant culture condition is required. For the former, a perfusion microbioreactor system capable of providing a stable culture condition was adopted. For the latter, however, little is known about the impact of culture models on the physiology and chemosensitivity assay results of primary oral cavity cancer cells. To address the issues, experiments were performed. Results showed that minor environmental pH change could significantly affect the metabolic activity of cells, demonstrating the importance of stable culture condition for such assays. Moreover, the culture models could also significantly influence the metabolic activity and proliferation of cells. Furthermore, the choice of culture models might lead to different outcomes of chemosensitivity assays. Compared with the similar test based on tumor-level assays, the spheroid model could overestimate the drug resistance of cells to cisplatin, whereas the 2D and 3D culture models might overestimate the chemosensitivity of cells to such anticancer drug. In this study, the 3D culture models with same cell density as that in tumor samples showed comparable chemosensitivity assay results as the tumor-level assays. Overall, this study has provided some fundamental information for establishing a precise and faithful drug chemosensitivity assay. PMID:25654105
Systematic reconstruction of TRANSPATH data into Cell System Markup Language

PubMed Central

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-01-01

Background Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. Results We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. Conclusion By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions. PMID:18570683
Systematic reconstruction of TRANSPATH data into cell system markup language.

PubMed

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-06-23

Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.
Modeling and Control of a Delayed Hepatitis B Virus Model with Incubation Period and Combination Treatment.

PubMed

Sun, Deshun; Liu, Fei

2018-06-01

In this paper, a hepatitis B virus (HBV) model with an incubation period and delayed state and control variables is firstly proposed. Furthermore, the combination treatment is adopted to have a longer-lasting effect than mono-therapy. The equilibrium points and basic reproduction number are calculated, and then the local stability is analyzed on this model. We then present optimal control strategies based on the Pontryagin's minimum principle with an objective function not only to reduce the levels of exposed cells, infected cells and free viruses nearly to zero at the end of therapy, but also to minimize the drug side-effect and the cost of treatment. What's more, we develop a numerical simulation algorithm for solving our HBV model based on the combination of forward and backward difference approximations. The state dynamics of uninfected cells, exposed cells, infected cells, free viruses, CTL and ALT are simulated with or without optimal control, which show that HBV is reduced nearly to zero based on the time-varying optimal control strategies whereas the disease would break out without control. At last, by the simulations, we prove that strategy A is the best among the three kinds of strategies we adopt and further comparisons have been done between model (1) and model (2).

Simple rules for a "simple" nervous system? Molecular and biomathematical approaches to enteric nervous system formation and malformation.

PubMed

Newgreen, Donald F; Dufour, Sylvie; Howard, Marthe J; Landman, Kerry A

2013-10-01

We review morphogenesis of the enteric nervous system from migratory neural crest cells, and defects of this process such as Hirschsprung disease, centering on cell motility and assembly, and cell adhesion and extracellular matrix molecules, along with cell proliferation and growth factors. We then review continuum and agent-based (cellular automata) models with rules of cell movement and logistical proliferation. Both movement and proliferation at the individual cell level are modeled with stochastic components from which stereotyped outcomes emerge at the population level. These models reproduced the wave-like colonization of the intestine by enteric neural crest cells, and several new properties emerged, such as colonization by frontal expansion, which were later confirmed biologically. These models predict a surprising level of clonal heterogeneity both in terms of number and distribution of daughter cells. Biologically, migrating cells form stable chains made up of unstable cells, but this is not seen in the initial model. We outline additional rules for cell differentiation into neurons, axon extension, cell-axon and cell-cell adhesions, chemotaxis and repulsion which can reproduce chain migration. After the migration stage, the cells re-arrange as a network of ganglia. Changes in cell adhesion molecules parallel this, and we describe additional rules based on Steinberg's Differential Adhesion Hypothesis, reflecting changing levels of adhesion in neural crest cells and neurons. This was able to reproduce enteric ganglionation in a model. Mouse mutants with disturbances of enteric nervous system morphogenesis are discussed, and these suggest future refinement of the models. The modeling suggests a relatively simple set of cell behavioral rules could account for complex patterns of morphogenesis. The model has allowed the proposal that Hirschsprung disease is mostly an enteric neural crest cell proliferation defect, not a defect of cell migration. In addition, the model suggests an explanations for zonal and skip segment variants of Hirschsprung disease, and also gives a novel stochastic explanation for the observed discordancy of Hirschsprung disease in identical twins. © 2013 Elsevier Inc. All rights reserved.
Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity

PubMed Central

Chen, Can; Wang, Ting; Wu, Fengbo; Huang, Wei; He, Gu; Ouyang, Liang; Xiang, Mingli; Peng, Cheng; Jiang, Qinglin

2014-01-01

Compared with normal differentiated cells, cancer cells upregulate the expression of pyruvate kinase isozyme M2 (PKM2) to support glycolytic intermediates for anabolic processes, including the synthesis of nucleic acids, amino acids, and lipids. In this study, a combination of the structure-based pharmacophore modeling and a hybrid protocol of virtual screening methods comprised of pharmacophore model-based virtual screening, docking-based virtual screening, and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis were used to retrieve novel PKM2 activators from commercially available chemical databases. Tetrahydroquinoline derivatives were identified as potential scaffolds of PKM2 activators. Thus, the hybrid virtual screening approach was applied to screen the focused tetrahydroquinoline derivatives embedded in the ZINC database. Six hit compounds were selected from the final hits and experimental studies were then performed. Compound 8 displayed a potent inhibitory effect on human lung cancer cells. Following treatment with Compound 8, cell viability, apoptosis, and reactive oxygen species (ROS) production were examined in A549 cells. Finally, we evaluated the effects of Compound 8 on mice xenograft tumor models in vivo. These results may provide important information for further research on novel PKM2 activators as antitumor agents. PMID:25214764
Cells of Origin of Epithelial Ovarian Cancers

DTIC Science & Technology

2015-09-01

cells in oral squamous cell carcinomas by a novel pathway-based lineage tracing approach in a murine model. ! 13! Specific aims: 1. Determine...SUNDARESAN Lineage tracing and clonal analysis of oral cancer initiating cells The goal of this project is to study cancer stem cells /cancer initiating...whether oral cancer cells genetically marked based on their activities for stem cell -related pathways exhibit cancer stem cell properties in vivo by
Accelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screening.

PubMed

O'Duibhir, Eoghan; Carragher, Neil O; Pollard, Steven M

2017-04-01

Patients diagnosed with glioblastoma (GBM) continue to face a bleak prognosis. It is critical that new effective therapeutic strategies are developed. GBM stem cells have molecular hallmarks of neural stem and progenitor cells and it is possible to propagate both non-transformed normal neural stem cells and GBM stem cells, in defined, feeder-free, adherent culture. These primary stem cell lines provide an experimental model that is ideally suited to cell-based drug discovery or genetic screens in order to identify tumour-specific vulnerabilities. For many solid tumours, including GBM, the genetic disruptions that drive tumour initiation and growth have now been catalogued. CRISPR/Cas-based genome editing technologies have recently emerged, transforming our ability to functionally annotate the human genome. Genome editing opens prospects for engineering precise genetic changes in normal and GBM-derived neural stem cells, which will provide more defined and reliable genetic models, with critical matched pairs of isogenic cell lines. Generation of more complex alleles such as knock in tags or fluorescent reporters is also now possible. These new cellular models can be deployed in cell-based phenotypic drug discovery (PDD). Here we discuss the convergence of these advanced technologies (iPS cells, neural stem cell culture, genome editing and high content phenotypic screening) and how they herald a new era in human cellular genetics that should have a major impact in accelerating glioblastoma drug discovery. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
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.
Chaos in an Eulerian Based Model of Sickle Cell Blood Flow

NASA Astrophysics Data System (ADS)

Apori, Akwasi; Harris, Wesley

2001-11-01

A novel Eulerian model describing the manifestation of sickle cell blood flow in the capillaries has been formulated to study the apparently chaotic onset of sickle cell crises. This Eulerian model was based on extending previous models of sickle cell blood flow which were limited due to their Lagrangian formulation. Oxygen concentration, red blood cell velocity, cell stiffness, and plasma viscosity were modeled as system state variables. The governing equations of the system were expressed in canonical form. The non-linear coupling of velocity-viscosity and viscosity- stiffness proved to be the origin of chaos in the system. The system was solved with respect to a control parameter representing the unique rheology of the sickle cell erythrocytes. Results of chaos tests proved positive for various ranges of the control parameter. The results included con-tinuous patterns found in the Poincare section, spectral broadening of the Fourier power spectrum, and positive Lyapunov exponent values. The onset of chaos predicted by this sickle cell flow model as the control parameter was varied appeared to coincide with the change from a healthy state to a crisis state in a sickle cell patient. This finding that sickle cell crises may be caused from the well understood change of a solution from a steady state to chaotic could point to new ways in preventing and treating crises and should be validated in clinical trials.
Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

PubMed Central

Wienecke, Sarah; Ishwarbhai, Alka; Tsipa, Argyro; Aw, Rochelle; Kylilis, Nicolas; Bell, David J.; McClymont, David W.; Jensen, Kirsten; Biedendieck, Rebekka

2018-01-01

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. PMID:29666238
The use of collagen-based scaffolds to simulate prostate cancer bone metastases with potential for evaluating delivery of nanoparticulate gene therapeutics.

PubMed

Fitzgerald, Kathleen A; Guo, Jianfeng; Tierney, Erica G; Curtin, Caroline M; Malhotra, Meenakshi; Darcy, Raphael; O'Brien, Fergal J; O'Driscoll, Caitriona M

2015-10-01

Prostate cancer bone metastases are a leading cause of cancer-related death in men with current treatments offering only marginally improved rates of survival. Advances in the understanding of the genetic basis of prostate cancer provide the opportunity to develop gene-based medicines capable of treating metastatic disease. The aim of this work was to establish a 3D cell culture model of prostate cancer bone metastasis using collagen-based scaffolds, to characterise this model, and to assess the potential of the model to evaluate delivery of gene therapeutics designed to target bone metastases. Two prostate cancer cell lines (PC3 and LNCaP) were cultured in 2D standard culture and compared to 3D cell growth on three different collagen-based scaffolds (collagen and composites of collagen containing either glycosaminoglycan or nanohydroxyapatite). The 3D model was characterised for cell proliferation, viability and for matrix metalloproteinase (MMP) enzyme and Prostate Specific Antigen (PSA) secretion. Chemosensitivity to docetaxel treatment was assessed in 2D in comparison to 3D. Nanoparticles (NPs) containing siRNA formulated using a modified cyclodextrin were delivered to the cells on the scaffolds and gene silencing was quantified. Both prostate cancer cell lines actively infiltrated and proliferated on the scaffolds. Cell culture in 3D resulted in reduced levels of MMP1 and MMP9 secretion in PC3 cells. In contrast, LNCaP cells grown in 3D secreted elevated levels of PSA, particularly on the scaffold composed of collagen and glycosaminoglycans. Both cell lines grown in 3D displayed increased resistance to docetaxel treatment. The cyclodextrin.siRNA nanoparticles achieved cellular uptake and knocked down the endogenous GAPDH gene in the 3D model. In conclusion, development of a novel 3D cell culture model of prostate cancer bone metastasis has been initiated resulting, for the first time, in the successful delivery of gene therapeutics in a 3D in vitro model. Further enhancement of this model will help elucidate the pathogenesis of prostate cancer and also accelerate the design of effective therapies which can penetrate into the bone microenvironment for prostate cancer therapy. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
Mechanism for Collective Cell Alignment in Myxococcus xanthus Bacteria

PubMed Central

Balagam, Rajesh; Igoshin, Oleg A.

2015-01-01

Myxococcus xanthus cells self-organize into aligned groups, clusters, at various stages of their lifecycle. Formation of these clusters is crucial for the complex dynamic multi-cellular behavior of these bacteria. However, the mechanism underlying the cell alignment and clustering is not fully understood. Motivated by studies of clustering in self-propelled rods, we hypothesized that M. xanthus cells can align and form clusters through pure mechanical interactions among cells and between cells and substrate. We test this hypothesis using an agent-based simulation framework in which each agent is based on the biophysical model of an individual M. xanthus cell. We show that model agents, under realistic cell flexibility values, can align and form cell clusters but only when periodic reversals of cell directions are suppressed. However, by extending our model to introduce the observed ability of cells to deposit and follow slime trails, we show that effective trail-following leads to clusters in reversing cells. Furthermore, we conclude that mechanical cell alignment combined with slime-trail-following is sufficient to explain the distinct clustering behaviors observed for wild-type and non-reversing M. xanthus mutants in recent experiments. Our results are robust to variation in model parameters, match the experimentally observed trends and can be applied to understand surface motility patterns of other bacterial species. PMID:26308508
A voxel-based multiscale model to simulate the radiation response of hypoxic tumors

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

Espinoza, I., E-mail: iespinoza@fis.puc.cl; Peschke, P.; Karger, C. P.

2015-01-15

Purpose: In radiotherapy, it is important to predict the response of tumors to irradiation prior to the treatment. This is especially important for hypoxic tumors, which are known to be highly radioresistant. Mathematical modeling based on the dose distribution, biological parameters, and medical images may help to improve this prediction and to optimize the treatment plan. Methods: A voxel-based multiscale tumor response model for simulating the radiation response of hypoxic tumors was developed. It considers viable and dead tumor cells, capillary and normal cells, as well as the most relevant biological processes such as (i) proliferation of tumor cells, (ii)more » hypoxia-induced angiogenesis, (iii) spatial exchange of cells leading to tumor growth, (iv) oxygen-dependent cell survival after irradiation, (v) resorption of dead cells, and (vi) spatial exchange of cells leading to tumor shrinkage. Oxygenation is described on a microscopic scale using a previously published tumor oxygenation model, which calculates the oxygen distribution for each voxel using the vascular fraction as the most important input parameter. To demonstrate the capabilities of the model, the dependence of the oxygen distribution on tumor growth and radiation-induced shrinkage is investigated. In addition, the impact of three different reoxygenation processes is compared and tumor control probability (TCP) curves for a squamous cells carcinoma of the head and neck (HNSSC) are simulated under normoxic and hypoxic conditions. Results: The model describes the spatiotemporal behavior of the tumor on three different scales: (i) on the macroscopic scale, it describes tumor growth and shrinkage during radiation treatment, (ii) on a mesoscopic scale, it provides the cell density and vascular fraction for each voxel, and (iii) on the microscopic scale, the oxygen distribution may be obtained in terms of oxygen histograms. With increasing tumor size, the simulated tumors develop a hypoxic core. Within the model, tumor shrinkage was found to be significantly more important for reoxygenation than angiogenesis or decreased oxygen consumption due to an increased fraction of dead cells. In the studied HNSSC-case, the TCD{sub 50} values (dose at 50% TCP) decreased from 71.0 Gy under hypoxic to 53.6 Gy under the oxic condition. Conclusions: The results obtained with the developed multiscale model are in accordance with expectations based on radiobiological principles and clinical experience. As the model is voxel-based, radiological imaging methods may help to provide the required 3D-characterization of the tumor prior to irradiation. For clinical application, the model has to be further validated with experimental and clinical data. If this is achieved, the model may be used to optimize fractionation schedules and dose distributions for the treatment of hypoxic tumors.« less
Empirical membrane lifetime model for heavy duty fuel cell systems

NASA Astrophysics Data System (ADS)

Macauley, Natalia; Watson, Mark; Lauritzen, Michael; Knights, Shanna; Wang, G. Gary; Kjeang, Erik

2016-12-01

Heavy duty fuel cells used in transportation system applications such as transit buses expose the fuel cell membranes to conditions that can lead to lifetime-limiting membrane failure via combined chemical and mechanical degradation. Highly durable membranes and reliable predictive models are therefore needed in order to achieve the ultimate heavy duty fuel cell lifetime target of 25,000 h. In the present work, an empirical membrane lifetime model was developed based on laboratory data from a suite of accelerated membrane durability tests. The model considers the effects of cell voltage, temperature, oxygen concentration, humidity cycling, humidity level, and platinum in the membrane using inverse power law and exponential relationships within the framework of a general log-linear Weibull life-stress statistical distribution. The obtained model is capable of extrapolating the membrane lifetime from accelerated test conditions to use level conditions during field operation. Based on typical conditions for the Whistler, British Columbia fuel cell transit bus fleet, the model predicts a stack lifetime of 17,500 h and a membrane leak initiation time of 9200 h. Validation performed with the aid of a field operated stack confirmed the initial goal of the model to predict membrane lifetime within 20% of the actual operating time.
Cellular models and therapies for age-related macular degeneration

PubMed Central

Forest, David L.; Johnson, Lincoln V.; Clegg, Dennis O.

2015-01-01

ABSTRACT Age-related macular degeneration (AMD) is a complex neurodegenerative visual disorder that causes profound physical and psychosocial effects. Visual impairment in AMD is caused by the loss of retinal pigmented epithelium (RPE) cells and the light-sensitive photoreceptor cells that they support. There is currently no effective treatment for the most common form of this disease (dry AMD). A new approach to treating AMD involves the transplantation of RPE cells derived from either human embryonic or induced pluripotent stem cells. Multiple clinical trials are being initiated using a variety of cell therapies. Although many animal models are available for AMD research, most do not recapitulate all aspects of the disease, hampering progress. However, the use of cultured RPE cells in AMD research is well established and, indeed, some of the more recently described RPE-based models show promise for investigating the molecular mechanisms of AMD and for screening drug candidates. Here, we discuss innovative cell-culture models of AMD and emerging stem-cell-based therapies for the treatment of this vision-robbing disease. PMID:26035859
Comparison between cylindrical and prismatic lithium-ion cell costs using a process based cost model

NASA Astrophysics Data System (ADS)

Ciez, Rebecca E.; Whitacre, J. F.

2017-02-01

The relative size and age of the US electric vehicle market means that a few vehicles are able to drive market-wide trends in the battery chemistries and cell formats on the road today. Three lithium-ion chemistries account for nearly all of the storage capacity, and half of the cells are cylindrical. However, no specific model exists to examine the costs of manufacturing these cylindrical cells. Here we present a process-based cost model tailored to the cylindrical lithium-ion cells currently used in the EV market. We examine the costs for varied cell dimensions, electrode thicknesses, chemistries, and production volumes. Although cost savings are possible from increasing cell dimensions and electrode thicknesses, economies of scale have already been reached, and future cost reductions from increased production volumes are minimal. Prismatic cells, which are able to further capitalize on the cost reduction from larger formats, can offer further reductions than those possible for cylindrical cells.
Concise Review: Apoptotic Cell-Based Therapies-Rationale, Preclinical Results and Future Clinical Developments.

PubMed

Saas, Philippe; Daguindau, Etienne; Perruche, Sylvain

2016-06-01

The objectives of this review are to summarize the experimental data obtained using apoptotic cell-based therapies, and then to discuss future clinical developments. Indeed, apoptotic cells exhibit immunomodulatory properties that are reviewed here by focusing on more recent mechanisms. These immunomodulatory mechanisms are in particular linked to the clearance of apoptotic cells (called also efferocytosis) by phagocytes, such as macrophages, and the induction of regulatory T cells. Thus, apoptotic cell-based therapies have been used to prevent or treat experimental inflammatory diseases. Based on these studies, we have identified critical steps to design future clinical trials. This includes: the administration route, the number and schedule of administration, the appropriate apoptotic cell type to be used, as well as the apoptotic signal. We also have analyzed the clinical relevancy of apoptotic-cell-based therapies in experimental models. Additional experimental data are required concerning the treatment of inflammatory diseases (excepted for sepsis) before considering future clinical trials. In contrast, apoptotic cells have been shown to favor engraftment and to reduce acute graft-versus-host disease (GvHD) in different relevant models of transplantation. This has led to the conduct of a phase 1/2a clinical trial to alleviate GvHD. The absence of toxic effects obtained in this trial may support the development of other clinical studies based on this new cell therapy. Stem Cells 2016;34:1464-1473. © 2016 AlphaMed Press.
Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

PubMed Central

Pilly, Praveen K.; Grossberg, Stephen

2013-01-01

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. PMID:23577130
Mouse neuroblastoma cell-based model and the effect of epileptic events on calcium oscillations and neural spikes

NASA Astrophysics Data System (ADS)

Kim, Suhwan; Jung, Unsang; Baek, Juyoung; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

2013-01-01

Recently, mouse neuroblastoma cells have been considered as an attractive model for the study of human neurological and prion diseases, and they have been intensively used as a model system in different areas. For example, the differentiation of neuro2a (N2A) cells, receptor-mediated ion current, and glutamate-induced physiological responses have been actively investigated with these cells. These mouse neuroblastoma N2A cells are of interest because they grow faster than other cells of neural origin and have a number of other advantages. The calcium oscillations and neural spikes of mouse neuroblastoma N2A cells in epileptic conditions are evaluated. Based on our observations of neural spikes in these cells with our proposed imaging modality, we reported that they can be an important model in epileptic activity studies. We concluded that mouse neuroblastoma N2A cells produce epileptic spikes in vitro in the same way as those produced by neurons or astrocytes. This evidence suggests that increased levels of neurotransmitter release due to the enhancement of free calcium from 4-aminopyridine causes the mouse neuroblastoma N2A cells to produce epileptic spikes and calcium oscillations.
A new model for CD8+ T cell memory inflation based upon a recombinant adenoviral vector1

PubMed Central

Bolinger, Beatrice; Sims, Stuart; O’Hara, Geraldine; de Lara, Catherine; Tchilian, Elma; Firner, Sonja; Engeler, Daniel; Ludewig, Burkhard; Klenerman, Paul

2013-01-01

CD8+ T cell memory inflation, first described in murine cytomegalovirus (MCMV) infection, is characterized by the accumulation of high-frequency, functional antigen-specific CD8+ T cell pools with an effector-memory phenotype and enrichment in peripheral organs. Although persistence of antigen is considered essential, the rules underpinning memory inflation are still unclear. The MCMV model is, however, complicated by the virus’s low-level persistence, and stochastic reactivation. We developed a new model of memory inflation based upon a βgal-recombinant adenovirus vector (Ad-LacZ). After i.v. administration in C57BL/6 mice we observe marked memory inflation in the βgal96 epitope, while a second epitope, βgal497, undergoes classical memory formation. The inflationary T cell responses show kinetics, distribution, phenotype and functions similar to those seen in MCMV and are reproduced using alternative routes of administration. Memory inflation in this model is dependent on MHC Class II. As in MCMV, only the inflating epitope showed immunoproteasome-independence. These data define a new model for memory inflation, which is fully replication-independent, internally controlled and reproduces the key immunologic features of the CD8+ T cell response. This model provides insight into the mechanisms responsible for memory inflation, and since it is based on a vaccine vector, also is relevant to novel T cell-inducing vaccines in humans. PMID:23509359
In Silico Neuro-Oncology: Brownian Motion-Based Mathematical Treatment as a Potential Platform for Modeling the Infiltration of Glioma Cells into Normal Brain Tissue.

PubMed

Antonopoulos, Markos; Stamatakos, Georgios

2015-01-01

Intensive glioma tumor infiltration into the surrounding normal brain tissues is one of the most critical causes of glioma treatment failure. To quantitatively understand and mathematically simulate this phenomenon, several diffusion-based mathematical models have appeared in the literature. The majority of them ignore the anisotropic character of diffusion of glioma cells since availability of pertinent truly exploitable tomographic imaging data is limited. Aiming at enriching the anisotropy-enhanced glioma model weaponry so as to increase the potential of exploiting available tomographic imaging data, we propose a Brownian motion-based mathematical analysis that could serve as the basis for a simulation model estimating the infiltration of glioblastoma cells into the surrounding brain tissue. The analysis is based on clinical observations and exploits diffusion tensor imaging (DTI) data. Numerical simulations and suggestions for further elaboration are provided.
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

Myeloma Cell Dynamics in Response to Treatment Supports a Model of Hierarchical Differentiation and Clonal Evolution.

PubMed

Tang, Min; Zhao, Rui; van de Velde, Helgi; Tross, Jennifer G; Mitsiades, Constantine; Viselli, Suzanne; Neuwirth, Rachel; Esseltine, Dixie-Lee; Anderson, Kenneth; Ghobrial, Irene M; San Miguel, Jesús F; Richardson, Paul G; Tomasson, Michael H; Michor, Franziska

2016-08-15

Since the pioneering work of Salmon and Durie, quantitative measures of tumor burden in multiple myeloma have been used to make clinical predictions and model tumor growth. However, such quantitative analyses have not yet been performed on large datasets from trials using modern chemotherapy regimens. We analyzed a large set of tumor response data from three randomized controlled trials of bortezomib-based chemotherapy regimens (total sample size n = 1,469 patients) to establish and validate a novel mathematical model of multiple myeloma cell dynamics. Treatment dynamics in newly diagnosed patients were most consistent with a model postulating two tumor cell subpopulations, "progenitor cells" and "differentiated cells." Differential treatment responses were observed with significant tumoricidal effects on differentiated cells and less clear effects on progenitor cells. We validated this model using a second trial of newly diagnosed patients and a third trial of refractory patients. When applying our model to data of relapsed patients, we found that a hybrid model incorporating both a differentiation hierarchy and clonal evolution best explains the response patterns. The clinical data, together with mathematical modeling, suggest that bortezomib-based therapy exerts a selection pressure on myeloma cells that can shape the disease phenotype, thereby generating further inter-patient variability. This model may be a useful tool for improving our understanding of disease biology and the response to chemotherapy regimens. Clin Cancer Res; 22(16); 4206-14. ©2016 AACR. ©2016 American Association for Cancer Research.
Agent-based modeling of the interaction between CD8+ T cells and Beta cells in type 1 diabetes.

PubMed

Ozturk, Mustafa Cagdas; Xu, Qian; Cinar, Ali

2018-01-01

We propose an agent-based model for the simulation of the autoimmune response in T1D. The model incorporates cell behavior from various rules derived from the current literature and is implemented on a high-performance computing system, which enables the simulation of a significant portion of the islets in the mouse pancreas. Simulation results indicate that the model is able to capture the trends that emerge during the progression of the autoimmunity. The multi-scale nature of the model enables definition of rules or equations that govern cellular or sub-cellular level phenomena and observation of the outcomes at the tissue scale. It is expected that such a model would facilitate in vivo clinical studies through rapid testing of hypotheses and planning of future experiments by providing insight into disease progression at different scales, some of which may not be obtained easily in clinical studies. Furthermore, the modular structure of the model simplifies tasks such as the addition of new cell types, and the definition or modification of different behaviors of the environment and the cells with ease.
Nonlinear empirical model of gas humidity-related voltage dynamics of a polymer-electrolyte-membrane fuel cell stack

NASA Astrophysics Data System (ADS)

Meiler, M.; Andre, D.; Schmid, O.; Hofer, E. P.

Intelligent energy management is a cost-effective key path to realize efficient automotive drive trains [R. O'Hayre, S.W. Cha, W. Colella, F.B. Prinz. Fuel Cell Fundamentals, John Wiley & Sons, Hoboken, 2006]. To develop operating strategy in fuel cell drive trains, precise and computational efficient models of all system components, especially the fuel cell stack, are needed. Should these models further be used in diagnostic or control applications, then some major requirements must be fulfilled. First, the model must predict the mean fuel cell voltage very precisely in all possible operating conditions, even during transients. The model output should be as smooth as possible to support best efficient optimization strategies of the complete system. At least, the model must be computational efficient. For most applications, a difference between real fuel cell voltage and model output of less than 10 mV and 1000 calculations per second will be sufficient. In general, empirical models based on system identification offer a better accuracy and consume less calculation resources than detailed models derived from theoretical considerations [J. Larminie, A. Dicks. Fuel Cell Systems Explained, John Wiley & Sons, West Sussex, 2003]. In this contribution, the dynamic behaviour of the mean cell voltage of a polymer-electrolyte-membrane fuel cell (PEMFC) stack due to variations in humidity of cell's reactant gases is investigated. The validity of the overall model structure, a so-called general Hammerstein model (or Uryson model), was introduced recently in [M. Meiler, O. Schmid, M. Schudy, E.P. Hofer. Dynamic fuel cell stack model for real-time simulation based on system identification, J. Power Sources 176 (2007) 523-528]. Fuel cell mean voltage is calculated as the sum of a stationary and a dynamic voltage component. The stationary component of cell voltage is represented by a lookup-table and the dynamic voltage by a parallel placed, nonlinear transfer function. A suitable experimental setup to apply fast variations of gas humidity is introduced and is used to investigate a 10 cell PEMFC stack under various operation conditions. Using methods like stepwise multiple-regression a good mathematical description with reduced free parameters is achieved.
A novel multitarget model of radiation-induced cell killing based on the Gaussian distribution.

PubMed

Zhao, Lei; Mi, Dong; Sun, Yeqing

2017-05-07

The multitarget version of the traditional target theory based on the Poisson distribution is still used to describe the dose-survival curves of cells after ionizing radiation in radiobiology and radiotherapy. However, noting that the usual ionizing radiation damage is the result of two sequential stochastic processes, the probability distribution of the damage number per cell should follow a compound Poisson distribution, like e.g. Neyman's distribution of type A (N. A.). In consideration of that the Gaussian distribution can be considered as the approximation of the N. A. in the case of high flux, a multitarget model based on the Gaussian distribution is proposed to describe the cell inactivation effects in low linear energy transfer (LET) radiation with high dose-rate. Theoretical analysis and experimental data fitting indicate that the present theory is superior to the traditional multitarget model and similar to the Linear - Quadratic (LQ) model in describing the biological effects of low-LET radiation with high dose-rate, and the parameter ratio in the present model can be used as an alternative indicator to reflect the radiation damage and radiosensitivity of the cells. Copyright © 2017 Elsevier Ltd. All rights reserved.
Heading-vector navigation based on head-direction cells and path integration.

PubMed

Kubie, John L; Fenton, André A

2009-05-01

Insect navigation is guided by heading vectors that are computed by path integration. Mammalian navigation models, on the other hand, are typically based on map-like place representations provided by hippocampal place cells. Such models compute optimal routes as a continuous series of locations that connect the current location to a goal. We propose a "heading-vector" model in which head-direction cells or their derivatives serve both as key elements in constructing the optimal route and as the straight-line guidance during route execution. The model is based on a memory structure termed the "shortcut matrix," which is constructed during the initial exploration of an environment when a set of shortcut vectors between sequential pairs of visited waypoint locations is stored. A mechanism is proposed for calculating and storing these vectors that relies on a hypothesized cell type termed an "accumulating head-direction cell." Following exploration, shortcut vectors connecting all pairs of waypoint locations are computed by vector arithmetic and stored in the shortcut matrix. On re-entry, when local view or place representations query the shortcut matrix with a current waypoint and goal, a shortcut trajectory is retrieved. Since the trajectory direction is in head-direction compass coordinates, navigation is accomplished by tracking the firing of head-direction cells that are tuned to the heading angle. Section 1 of the manuscript describes the properties of accumulating head-direction cells. It then shows how accumulating head-direction cells can store local vectors and perform vector arithmetic to perform path-integration-based homing. Section 2 describes the construction and use of the shortcut matrix for computing direct paths between any pair of locations that have been registered in the shortcut matrix. In the discussion, we analyze the advantages of heading-based navigation over map-based navigation. Finally, we survey behavioral evidence that nonhippocampal, heading-based navigation is used in small mammals and humans. Copyright 2008 Wiley-Liss, Inc.
A mathematical model for mesenchymal and chemosensitive cell dynamics.

PubMed

Häcker, Anita

2012-01-01

The structure of an underlying tissue network has a strong impact on cell dynamics. If, in addition, cells alter the network by mechanical and chemical interactions, their movement is called mesenchymal. Important examples for mesenchymal movement include fibroblasts in wound healing and metastatic tumour cells. This paper is focused on the latter. Based on the anisotropic biphasic theory of Barocas and Tranquillo, which models a fibre network and interstitial solution as two-component fluid, a mathematical model for the interactions of cells with a fibre network is developed. A new description for fibre reorientation is given and orientation-dependent proteolysis is added to the model. With respect to cell dynamics, the equation, based on anisotropic diffusion, is extended by haptotaxis and chemotaxis. The chemoattractants are the solute network fragments, emerging from proteolysis, and the epidermal growth factor which may guide the cells to a blood vessel. Moreover the cell migration is impeded at either high or low network density. This new model enables us to study chemotactic cell migration in a complex fibre network and the consequential network deformation. Numerical simulations for the cell migration and network deformation are carried out in two space dimensions. Simulations of cell migration in underlying tissue networks visualise the impact of the network structure on cell dynamics. In a scenario for fibre reorientation between cell clusters good qualitative agreement with experimental results is achieved. The invasion speeds of cells in an aligned and an isotropic fibre network are compared. © Springer-Verlag 2011
A new level set model for cell image segmentation

NASA Astrophysics Data System (ADS)

Ma, Jing-Feng; Hou, Kai; Bao, Shang-Lian; Chen, Chun

2011-02-01

In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.
3D in vitro co-culture models based on normal cells and tumor spheroids formed by cyclic RGD-peptide induced cell self-assembly.

PubMed

Akasov, Roman; Gileva, Anastasia; Zaytseva-Zotova, Daria; Burov, Sergey; Chevalot, Isabelle; Guedon, Emmanuel; Markvicheva, Elena

2017-01-01

To design novel 3D in vitro co-culture models based on the RGD-peptide-induced cell self-assembly technique. Multicellular spheroids from M-3 murine melanoma cells and L-929 murine fibroblasts were obtained directly from monolayer culture by addition of culture medium containing cyclic RGD-peptide. To reach reproducible architecture of co-culture spheroids, two novel 3D in vitro models with well pronounced core-shell structure from tumor spheroids and single mouse fibroblasts were developed based on this approach. The first was a combination of a RGD-peptide platform with the liquid overlay technique with further co-cultivation for 1-2 days. The second allowed co-culture spheroids to generate within polyelectrolyte microcapsules by cultivation for 2 weeks. M-3 cells (a core) and L-929 fibroblasts (a shell) were easily distinguished by confocal microscopy due to cell staining with DiO and DiI dyes, respectively. The 3D co-culture spheroids are proposed as a tool in tumor biology to study cell-cell interactions as well as for testing novel anticancer drugs and drug delivery vehicles.
The Use of the Humanized Mouse Model in Gene Therapy and Immunotherapy for HIV and Cancer

PubMed Central

Carrillo, Mayra A.; Zhen, Anjie; Kitchen, Scott G.

2018-01-01

HIV and cancer remain prevailing sources of morbidity and mortality worldwide. There are current efforts to discover novel therapeutic strategies for the treatment or cure of these diseases. Humanized mouse models provide the investigative tool to study the interaction between HIV or cancer and the human immune system in vivo. These humanized models consist of immunodeficient mice transplanted with human cells, tissues, or hematopoietic stem cells that result in reconstitution with a nearly full human immune system. In this review, we discuss preclinical studies evaluating therapeutic approaches in stem cell-based gene therapy and T cell-based immunotherapies for HIV and cancer using a humanized mouse model and some recent advances in using checkpoint inhibitors to improve antiviral or antitumor responses. PMID:29755454
A Liver-centric Multiscale Modeling Framework for Xenobiotics ...

EPA Pesticide Factsheets

We describe a multi-scale framework for modeling acetaminophen-induced liver toxicity. Acetaminophen is a widely used analgesic. Overdose of acetaminophen can result in liver injury via its biotransformation into toxic product, which further induce massive necrosis. Our study focuses on developing a multi-scale computational model to characterize both phase I and phase II metabolism of acetaminophen, by bridging Physiologically Based Pharmacokinetic (PBPK) modeling at the whole body level, cell movement and blood flow at the tissue level and cell signaling and drug metabolism at the sub-cellular level. To validate the model, we estimated our model parameters by fi?tting serum concentrations of acetaminophen and its glucuronide and sulfate metabolites to experiments, and carried out sensitivity analysis on 35 parameters selected from three modules. Our study focuses on developing a multi-scale computational model to characterize both phase I and phase II metabolism of acetaminophen, by bridging Physiologically Based Pharmacokinetic (PBPK) modeling at the whole body level, cell movement and blood flow at the tissue level and cell signaling and drug metabolism at the sub-cellular level. This multiscale model bridges the CompuCell3D tool used by the Virtual Tissue project with the httk tool developed by the Rapid Exposure and Dosimetry project.
Low-high junction theory applied to solar cells

NASA Technical Reports Server (NTRS)

Godlewski, M. P.; Baraona, C. R.; Brandhorst, H. W., Jr.

1973-01-01

Recent use of alloying techniques for rear contact formation has yielded a new kind of silicon solar cell, the back surface field (BSF) cell, with abnormally high open circuit voltage and improved radiation resistance. Several analytical models for open circuit voltage based on the reverse saturation current are formulated to explain these observations. The zero SRV case of the conventional cell model, the drift field model, and the low-high junction (LHJ) model can predict the experimental trends. The LHJ model applies the theory of the low-high junction and is considered to reflect a more realistic view of cell fabrication. This model can predict the experimental trends observed for BSF cells. Detailed descriptions and derivations for the models are included. The correspondences between them are discussed. This modeling suggests that the meaning of minority carrier diffusion length measured in BSF cells be reexamined.
Data-based mathematical modeling of vectorial transport across double-transfected polarized cells.

PubMed

Bartholomé, Kilian; Rius, Maria; Letschert, Katrin; Keller, Daniela; Timmer, Jens; Keppler, Dietrich

2007-09-01

Vectorial transport of endogenous small molecules, toxins, and drugs across polarized epithelial cells contributes to their half-life in the organism and to detoxification. To study vectorial transport in a quantitative manner, an in vitro model was used that includes polarized MDCKII cells stably expressing the recombinant human uptake transporter OATP1B3 in their basolateral membrane and the recombinant ATP-driven efflux pump ABCC2 in their apical membrane. These double-transfected cells enabled mathematical modeling of the vectorial transport of the anionic prototype substance bromosulfophthalein (BSP) that has frequently been used to examine hepatobiliary transport. Time-dependent analyses of (3)H-labeled BSP in the basolateral, intracellular, and apical compartments of cells cultured on filter membranes and efflux experiments in cells preloaded with BSP were performed. A mathematical model was fitted to the experimental data. Data-based modeling was optimized by including endogenous transport processes in addition to the recombinant transport proteins. The predominant contributions to the overall vectorial transport of BSP were mediated by OATP1B3 (44%) and ABCC2 (28%). Model comparison predicted a previously unrecognized endogenous basolateral efflux process as a negative contribution to total vectorial transport, amounting to 19%, which is in line with the detection of the basolateral efflux pump Abcc4 in MDCKII cells. Rate-determining steps in the vectorial transport were identified by calculating control coefficients. Data-based mathematical modeling of vectorial transport of BSP as a model substance resulted in a quantitative description of this process and its components. The same systems biology approach may be applied to other cellular systems and to different substances.
Microengineering methods for cell-based microarrays and high-throughput drug-screening applications.

PubMed

Xu, Feng; Wu, JinHui; Wang, ShuQi; Durmus, Naside Gozde; Gurkan, Umut Atakan; Demirci, Utkan

2011-09-01

Screening for effective therapeutic agents from millions of drug candidates is costly, time consuming, and often faces concerns due to the extensive use of animals. To improve cost effectiveness, and to minimize animal testing in pharmaceutical research, in vitro monolayer cell microarrays with multiwell plate assays have been developed. Integration of cell microarrays with microfluidic systems has facilitated automated and controlled component loading, significantly reducing the consumption of the candidate compounds and the target cells. Even though these methods significantly increased the throughput compared to conventional in vitro testing systems and in vivo animal models, the cost associated with these platforms remains prohibitively high. Besides, there is a need for three-dimensional (3D) cell-based drug-screening models which can mimic the in vivo microenvironment and the functionality of the native tissues. Here, we present the state-of-the-art microengineering approaches that can be used to develop 3D cell-based drug-screening assays. We highlight the 3D in vitro cell culture systems with live cell-based arrays, microfluidic cell culture systems, and their application to high-throughput drug screening. We conclude that among the emerging microengineering approaches, bioprinting holds great potential to provide repeatable 3D cell-based constructs with high temporal, spatial control and versatility.
Microengineering Methods for Cell Based Microarrays and High-Throughput Drug Screening Applications

PubMed Central

Xu, Feng; Wu, JinHui; Wang, ShuQi; Durmus, Naside Gozde; Gurkan, Umut Atakan; Demirci, Utkan

2011-01-01

Screening for effective therapeutic agents from millions of drug candidates is costly, time-consuming and often face ethical concerns due to extensive use of animals. To improve cost-effectiveness, and to minimize animal testing in pharmaceutical research, in vitro monolayer cell microarrays with multiwell plate assays have been developed. Integration of cell microarrays with microfluidic systems have facilitated automated and controlled component loading, significantly reducing the consumption of the candidate compounds and the target cells. Even though these methods significantly increased the throughput compared to conventional in vitro testing systems and in vivo animal models, the cost associated with these platforms remains prohibitively high. Besides, there is a need for three-dimensional (3D) cell based drug-screening models, which can mimic the in vivo microenvironment and the functionality of the native tissues. Here, we present the state-of-the-art microengineering approaches that can be used to develop 3D cell based drug screening assays. We highlight the 3D in vitro cell culture systems with live cell-based arrays, microfluidic cell culture systems, and their application to high-throughput drug screening. We conclude that among the emerging microengineering approaches, bioprinting holds a great potential to provide repeatable 3D cell based constructs with high temporal, spatial control and versatility. PMID:21725152
Reprogramming cellular identity for regenerative medicine

PubMed Central

Cherry, Anne B.C.; Daley, George Q.

2012-01-01

The choreographed development of over 200 distinct differentiated cell types from a single zygote is a complex and poorly understood process. Whereas development leads unidirectionally towards more restricted cell fates, recent work in cellular reprogramming has proven that striking conversions of one cellular identity into another can be engineered, promising countless applications in biomedical research and paving the way for modeling disease with patient-derived stem cells. To date, there has been little discussion of which disease models are likely to be most informative. We here review evidence demonstrating that because environmental influences and epigenetic signatures are largely erased during reprogramming, patient-specific models of diseases with strong genetic bases and high penetrance are likely to prove most informative in the near term. However, manipulating in vitro culture conditions may ultimately enable cell-based models to recapitulate gene-environment interactions. Here, we discuss the implications of the new reprogramming paradigm in biomedicine and outline how reprogramming of cell identities is enhancing our understanding of cell differentiation and prospects for cellular therapies and in vivo regeneration. PMID:22424223
Engineered reversal of drug resistance in cancer cells--metastases suppressor factors as change agents.

PubMed

Yadav, Vinod Kumar; Kumar, Akinchan; Mann, Anita; Aggarwal, Suruchi; Kumar, Maneesh; Roy, Sumitabho Deb; Pore, Subrata Kumar; Banerjee, Rajkumar; Mahesh Kumar, Jerald; Thakur, Ram Krishna; Chowdhury, Shantanu

2014-01-01

Building molecular correlates of drug resistance in cancer and exploiting them for therapeutic intervention remains a pressing clinical need. To identify factors that impact drug resistance herein we built a model that couples inherent cell-based response toward drugs with transcriptomes of resistant/sensitive cells. To test this model, we focused on a group of genes called metastasis suppressor genes (MSGs) that influence aggressiveness and metastatic potential of cancers. Interestingly, modeling of 84 000 drug response transcriptome combinations predicted multiple MSGs to be associated with resistance of different cell types and drugs. As a case study, on inducing MSG levels in a drug resistant breast cancer line resistance to anticancer drugs caerulomycin, camptothecin and topotecan decreased by more than 50-60%, in both culture conditions and also in tumors generated in mice, in contrast to control un-induced cells. To our knowledge, this is the first demonstration of engineered reversal of drug resistance in cancer cells based on a model that exploits inherent cellular response profiles.
Mimicking Metastases Including Tumor Stroma: A New Technique to Generate a Three-Dimensional Colorectal Cancer Model Based on a Biological Decellularized Intestinal Scaffold.

PubMed

Nietzer, Sarah; Baur, Florentin; Sieber, Stefan; Hansmann, Jan; Schwarz, Thomas; Stoffer, Carolin; Häfner, Heide; Gasser, Martin; Waaga-Gasser, Ana Maria; Walles, Heike; Dandekar, Gudrun

2016-07-01

Tumor models based on cancer cell lines cultured two-dimensionally (2D) on plastic lack histological complexity and functionality compared to the native microenvironment. Xenogenic mouse tumor models display higher complexity but often do not predict human drug responses accurately due to species-specific differences. We present here a three-dimensional (3D) in vitro colon cancer model based on a biological scaffold derived from decellularized porcine jejunum (small intestine submucosa+mucosa, SISmuc). Two different cell lines were used in monoculture or in coculture with primary fibroblasts. After 14 days of culture, we demonstrated a close contact of human Caco2 colon cancer cells with the preserved basement membrane on an ultrastructural level as well as morphological characteristics of a well-differentiated epithelium. To generate a tissue-engineered tumor model, we chose human SW480 colon cancer cells, a reportedly malignant cell line. Malignant characteristics were confirmed in 2D cell culture: SW480 cells showed higher vimentin and lower E-cadherin expression than Caco2 cells. In contrast to Caco2, SW480 cells displayed cancerous characteristics such as delocalized E-cadherin and nuclear location of β-catenin in a subset of cells. One central drawback of 2D cultures-especially in consideration of drug testing-is their artificially high proliferation. In our 3D tissue-engineered tumor model, both cell lines showed decreased numbers of proliferating cells, thus correlating more precisely with observations of primary colon cancer in all stages (UICC I-IV). Moreover, vimentin decreased in SW480 colon cancer cells, indicating a mesenchymal to epithelial transition process, attributed to metastasis formation. Only SW480 cells cocultured with fibroblasts induced the formation of tumor-like aggregates surrounded by fibroblasts, whereas in Caco2 cocultures, a separate Caco2 cell layer was formed separated from the fibroblast compartment beneath. To foster tissue generation, a bioreactor was constructed for dynamic culture approaches. This induced a close tissue-like association of cultured tumor cells with fibroblasts reflecting tumor biopsies. Therapy with 5-fluorouracil (5-FU) was effective only in 3D coculture. In conclusion, our 3D tumor model reflects human tissue-related tumor characteristics, including lower tumor cell proliferation. It is now available for drug testing in metastatic context-especially for substances targeting tumor-stroma interactions.
Establishment of a Human Blood-Brain Barrier Co-culture Model Mimicking the Neurovascular Unit Using Induced Pluri- and Multipotent Stem Cells.

PubMed

Appelt-Menzel, Antje; Cubukova, Alevtina; Günther, Katharina; Edenhofer, Frank; Piontek, Jörg; Krause, Gerd; Stüber, Tanja; Walles, Heike; Neuhaus, Winfried; Metzger, Marco

2017-04-11

In vitro models of the human blood-brain barrier (BBB) are highly desirable for drug development. This study aims to analyze a set of ten different BBB culture models based on primary cells, human induced pluripotent stem cells (hiPSCs), and multipotent fetal neural stem cells (fNSCs). We systematically investigated the impact of astrocytes, pericytes, and NSCs on hiPSC-derived BBB endothelial cell function and gene expression. The quadruple culture models, based on these four cell types, achieved BBB characteristics including transendothelial electrical resistance (TEER) up to 2,500 Ω cm 2 and distinct upregulation of typical BBB genes. A complex in vivo-like tight junction (TJ) network was detected by freeze-fracture and transmission electron microscopy. Treatment with claudin-specific TJ modulators caused TEER decrease, confirming the relevant role of claudin subtypes for paracellular tightness. Drug permeability tests with reference substances were performed and confirmed the suitability of the models for drug transport studies. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
Anion exchange membrane fuel cell modelling

NASA Astrophysics Data System (ADS)

Fragiacomo, P.; Astorino, E.; Chippari, G.; De Lorenzo, G.; Czarnetzki, W. T.; Schneider, W.

2018-04-01

A parametric model predicting the performance of a solid polymer electrolyte, anion exchange membrane fuel cell (AEMFC), has been developed, in Matlab environment, based on interrelated electrical and thermal models. The electrical model proposed is developed by modelling an AEMFC open-circuit output voltage, irreversible voltage losses along with a mass balance, while the thermal model is based on the energy balance. The proposed model of the AEMFC stack estimates its dynamic behaviour, in particular the operating temperature variation for different discharge current values. The results of the theoretical fuel cell (FC) stack are reported and analysed in order to highlight the FC performance and how it varies by changing the values of some parameters such as temperature and pressure. Both the electrical and thermal FC models were validated by comparing the model results with experimental data and the results of other models found in the literature.
IEEE Photovoltaic Specialists Conference, 20th, Las Vegas, NV, Sept. 26-30, 1988, Conference Record. Volumes 1 & 2

NASA Astrophysics Data System (ADS)

Various papers on photovoltaics are presented. The general topics considered include: amorphous materials and cells; amorphous silicon-based solar cells and modules; amorphous silicon-based materials and processes; amorphous materials characterization; amorphous silicon; high-efficiency single crystal solar cells; multijunction and heterojunction cells; high-efficiency III-V cells; modeling and characterization of high-efficiency cells; LIPS flight experience; space mission requirements and technology; advanced space solar cell technology; space environmental effects and modeling; space solar cell and array technology; terrestrial systems and array technology; terrestrial utility and stand-alone applications and testing; terrestrial concentrator and storage technology; terrestrial stand-alone systems applications; terrestrial systems test and evaluation; terrestrial flatplate and concentrator technology; use of polycrystalline materials; polycrystalline II-VI compound solar cells; analysis of and fabrication procedures for compound solar cells.

Model-based cell number quantification using online single-oxygen sensor data for tissue engineering perfusion bioreactors.

PubMed

Lambrechts, T; Papantoniou, I; Sonnaert, M; Schrooten, J; Aerts, J-M

2014-10-01

Online and non-invasive quantification of critical tissue engineering (TE) construct quality attributes in TE bioreactors is indispensable for the cost-effective up-scaling and automation of cellular construct manufacturing. However, appropriate monitoring techniques for cellular constructs in bioreactors are still lacking. This study presents a generic and robust approach to determine cell number and metabolic activity of cell-based TE constructs in perfusion bioreactors based on single oxygen sensor data in dynamic perfusion conditions. A data-based mechanistic modeling technique was used that is able to correlate the number of cells within the scaffold (R(2) = 0.80) and the metabolic activity of the cells (R(2) = 0.82) to the dynamics of the oxygen response to step changes in the perfusion rate. This generic non-destructive measurement technique is effective for a large range of cells, from as low as 1.0 × 10(5) cells to potentially multiple millions of cells, and can open-up new possibilities for effective bioprocess monitoring. © 2014 Wiley Periodicals, Inc.
Computational model, method, and system for kinetically-tailoring multi-drug chemotherapy for individuals

DOEpatents

Gardner, Shea Nicole

2007-10-23

A method and system for tailoring treatment regimens to individual patients with diseased cells exhibiting evolution of resistance to such treatments. A mathematical model is provided which models rates of population change of proliferating and quiescent diseased cells using cell kinetics and evolution of resistance of the diseased cells, and pharmacokinetic and pharmacodynamic models. Cell kinetic parameters are obtained from an individual patient and applied to the mathematical model to solve for a plurality of treatment regimens, each having a quantitative efficacy value associated therewith. A treatment regimen may then be selected from the plurlaity of treatment options based on the efficacy value.
Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells.

PubMed

Provost, A; Bastin, G; Agathos, S N; Schneider, Y-J

2006-12-01

The aim of this paper is to present a systematic methodology to design macroscopic bioreaction models for cell cultures based upon metabolic networks. The cell culture is seen as a succession of phases. During each phase, a metabolic network represents the set of reactions occurring in the cell. Then, through the use of the elementary flux modes, these metabolic networks are used to derive macroscopic bioreactions linking the extracellular substrates and products. On this basis, as many separate models are obtained as there are phases. Then, a complete model is obtained by smoothly switching from model to model. This is illustrated with batch cultures of Chinese hamster ovary cells.
An Off-Lattice Hybrid Discrete-Continuum Model of Tumor Growth and Invasion

PubMed Central

Jeon, Junhwan; Quaranta, Vito; Cummings, Peter T.

2010-01-01

Abstract We have developed an off-lattice hybrid discrete-continuum (OLHDC) model of tumor growth and invasion. The continuum part of the OLHDC model describes microenvironmental components such as matrix-degrading enzymes, nutrients or oxygen, and extracellular matrix (ECM) concentrations, whereas the discrete portion represents individual cell behavior such as cell cycle, cell-cell, and cell-ECM interactions and cell motility by the often-used persistent random walk, which can be depicted by the Langevin equation. Using this framework of the OLHDC model, we develop a phenomenologically realistic and bio/physically relevant model that encompasses the experimentally observed superdiffusive behavior (at short times) of mammalian cells. When systemic simulations based on the OLHDC model are performed, tumor growth and its morphology are found to be strongly affected by cell-cell adhesion and haptotaxis. There is a combination of the strength of cell-cell adhesion and haptotaxis in which fingerlike shapes, characteristic of invasive tumor, are observed. PMID:20074513
Identification of growth phases and influencing factors in cultivations with AGE1.HN cells using set-based methods.

PubMed

Borchers, Steffen; Freund, Susann; Rath, Alexander; Streif, Stefan; Reichl, Udo; Findeisen, Rolf

2013-01-01

Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty.
Identification of Growth Phases and Influencing Factors in Cultivations with AGE1.HN Cells Using Set-Based Methods

PubMed Central

Borchers, Steffen; Freund, Susann; Rath, Alexander; Streif, Stefan; Reichl, Udo; Findeisen, Rolf

2013-01-01

Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty. PMID:23936299
Cellular automata model for human articular chondrocytes migration, proliferation and cell death: An in vitro validation.

PubMed

Vaca-González, J J; Gutiérrez, M L; Guevara, J M; Garzón-Alvarado, D A

2017-01-01

Articular cartilage is characterized by low cell density of only one cell type, chondrocytes, and has limited self-healing properties. When articular cartilage is affected by traumatic injuries, a therapeutic strategy such as autologous chondrocyte implantation is usually proposed for its treatment. This approach requires in vitro chondrocyte expansion to yield high cell number for cell transplantation. To improve the efficiency of this procedure, it is necessary to assess cell dynamics such as migration, proliferation and cell death during culture. Computational models such as cellular automata can be used to simulate cell dynamics in order to enhance the result of cell culture procedures. This methodology has been implemented for several cell types; however, an experimental validation is required for each one. For this reason, in this research a cellular automata model, based on random-walk theory, was devised in order to predict articular chondrocyte behavior in monolayer culture during cell expansion. Results demonstrated that the cellular automata model corresponded to cell dynamics and computed-accurate quantitative results. Moreover, it was possible to observe that cell dynamics depend on weighted probabilities derived from experimental data and cell behavior varies according to the cell culture period. Thus, depending on whether cells were just seeded or proliferated exponentially, culture time probabilities differed in percentages in the CA model. Furthermore, in the experimental assessment a decreased chondrocyte proliferation was observed along with increased passage number. This approach is expected to having other uses as in enhancing articular cartilage therapies based on tissue engineering and regenerative medicine.
Cell survival fraction estimation based on the probability densities of domain and cell nucleus specific energies using improved microdosimetric kinetic models.

PubMed

Sato, Tatsuhiko; Furusawa, Yoshiya

2012-10-01

Estimation of the survival fractions of cells irradiated with various particles over a wide linear energy transfer (LET) range is of great importance in the treatment planning of charged-particle therapy. Two computational models were developed for estimating survival fractions based on the concept of the microdosimetric kinetic model. They were designated as the double-stochastic microdosimetric kinetic and stochastic microdosimetric kinetic models. The former model takes into account the stochastic natures of both domain and cell nucleus specific energies, whereas the latter model represents the stochastic nature of domain specific energy by its approximated mean value and variance to reduce the computational time. The probability densities of the domain and cell nucleus specific energies are the fundamental quantities for expressing survival fractions in these models. These densities are calculated using the microdosimetric and LET-estimator functions implemented in the Particle and Heavy Ion Transport code System (PHITS) in combination with the convolution or database method. Both the double-stochastic microdosimetric kinetic and stochastic microdosimetric kinetic models can reproduce the measured survival fractions for high-LET and high-dose irradiations, whereas a previously proposed microdosimetric kinetic model predicts lower values for these fractions, mainly due to intrinsic ignorance of the stochastic nature of cell nucleus specific energies in the calculation. The models we developed should contribute to a better understanding of the mechanism of cell inactivation, as well as improve the accuracy of treatment planning of charged-particle therapy.
A biologically based model of growth and senescence of Syrian hamster embryo (SHE) cells after exposure to arsenic.

PubMed Central

Liao, K H; Gustafson, D L; Fox, M H; Chubb, L S; Reardon, K F; Yang, R S

2001-01-01

We modified the two-stage Moolgavkar-Venzon-Knudson (MVK) model for use with Syrian hamster embryo (SHE) cell neoplastic progression. Five phenotypic stages are proposed in this model: Normal cells can either become senescent or mutate into immortal cells followed by anchorage-independent growth and tumorigenic stages. The growth of normal SHE cells was controlled by their division, death, and senescence rates, and all senescent cells were converted from normal cells. In this report, we tested the modeling of cell kinetics of the first two phenotypic stages against experimental data evaluating the effects of arsenic on SHE cells. We assessed cell division and death rates using flow cytometry and correlated cell division rates to the degree of confluence of cell cultures. The mean cell death rate was approximately equal to 1% of the average division rate. Arsenic did not induce immortalization or further mutations of SHE cells at concentrations of 2 microM and below, and chromium (3.6 microM) and lead (100 microM) had similar negative results. However, the growth of SHE cells was inhibited by 5.4 microM arsenic after a 2-day exposure, with cells becoming senescent after only 16 population doublings. In contrast, normal cells and cells exposed to lower arsenic concentrations grew normally for at least 30 population doublings. The biologically based model successfully predicted the growth of normal and arsenic-treated cells, as well as the senescence rates. Mechanisms responsible for inducing cellular senescence in SHE cells exposed to arsenic may help explain the apparent inability of arsenic to induce neoplasia in experimental animals. PMID:11748027
Systems, methods and computer-readable media for modeling cell performance fade of rechargeable electrochemical devices

DOEpatents

Gering, Kevin L

2013-08-27

A system includes an electrochemical cell, monitoring hardware, and a computing system. The monitoring hardware periodically samples performance characteristics of the electrochemical cell. The computing system determines cell information from the performance characteristics of the electrochemical cell. The computing system also develops a mechanistic level model of the electrochemical cell to determine performance fade characteristics of the electrochemical cell and analyzing the mechanistic level model to estimate performance fade characteristics over aging of a similar electrochemical cell. The mechanistic level model uses first constant-current pulses applied to the electrochemical cell at a first aging period and at three or more current values bracketing a first exchange current density. The mechanistic level model also is based on second constant-current pulses applied to the electrochemical cell at a second aging period and at three or more current values bracketing the second exchange current density.
Image-based evaluations of distribution and cytotoxicity of Irinotecan (CPT-11) in a multi-compartment micro-cell coculture device.

PubMed

Nakayama, Hidenari; Kimura, Hiroshi; Fujii, Teruo; Sakai, Yasuyuki

2014-06-01

We recently developed a polydimethylsiloxane (PDMS)-based three-compartment microfluidic cocultivation device enabling real-time interactions of different cell populations as an advanced physiologically-relevant cell-based assay. This device had valves and small magnetic stirrer-based internal pumps for easy and flexible perfusion operations. In this study, we applied this device for the evaluation of Irinotecan (CPT-11) toxicity to the lung, because it is detoxified by the liver and accumulated in the fat in humans. We successfully cultured representative three different tissue model cells in each compartment under the individual culture conditions and also in entire perfusion. Growth inhibition of rat lung epithelial cell line L-2, was measured when administered with 50 μM CPT-11 under various cocultivation conditions with respect to the presences and absence of primary rat hepatocytes (liver tissue model) and adipocyte-like cells (fat tissue model) induced from a mouse fibroblast cell line, 3T3-L1. Although CPT-11 showed moderate toxicity to the pure culture of L-2 cells in the device after 72 h of perfusion culture, this was lowered mainly in the presence of the liver tissue. Inhibition of the L-2 cell growth agreed with the area under curve (AUC) values obtained from fluorescent image-based analyses in each compartment. These results demonstrate that developed simple and flexible microfluidic cocultivation device, with appropriate image-based analyses, can be used in evaluating toxicokinetic behaviors of drug candidates in systemic levels. Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.
IAP-Based Cell Sorting Results in Homogeneous Transplantable Dopaminergic Precursor Cells Derived from Human Pluripotent Stem Cells.

PubMed

Lehnen, Daniela; Barral, Serena; Cardoso, Tiago; Grealish, Shane; Heuer, Andreas; Smiyakin, Andrej; Kirkeby, Agnete; Kollet, Jutta; Cremer, Harold; Parmar, Malin; Bosio, Andreas; Knöbel, Sebastian

2017-10-10

Human pluripotent stem cell (hPSC)-derived mesencephalic dopaminergic (mesDA) neurons can relieve motor deficits in animal models of Parkinson's disease (PD). Clinical translation of differentiation protocols requires standardization of production procedures, and surface-marker-based cell sorting is considered instrumental for reproducible generation of defined cell products. Here, we demonstrate that integrin-associated protein (IAP) is a cell surface marker suitable for enrichment of hPSC-derived mesDA progenitor cells. Immunomagnetically sorted IAP + mesDA progenitors showed increased expression of ventral midbrain floor plate markers, lacked expression of pluripotency markers, and differentiated into mature dopaminergic (DA) neurons in vitro. Intrastriatal transplantation of IAP + cells sorted at day 16 of differentiation in a rat model of PD resulted in functional recovery. Grafts from sorted IAP + mesDA progenitors were more homogeneous in size and DA neuron density. Thus, we suggest IAP-based sorting for reproducible prospective enrichment of mesDA progenitor cells in clinical cell replacement strategies. Copyright © 2017 Miltenyi Biotec GmbH. Published by Elsevier Inc. All rights reserved.
Space-time dynamics of Stem Cell Niches: a unified approach for Plants.

PubMed

Pérez, Maria Del Carmen; López, Alejandro; Padilla, Pablo

2013-06-01

Many complex systems cannot be analyzed using traditional mathematical tools, due to their irreducible nature. This makes it necessary to develop models that can be implemented computationally to simulate their evolution. Examples of these models are cellular automata, evolutionary algorithms, complex networks, agent-based models, symbolic dynamics and dynamical systems techniques. We review some representative approaches to model the stem cell niche in Arabidopsis thaliana and the basic biological mechanisms that underlie its formation and maintenance. We propose a mathematical model based on cellular automata for describing the space-time dynamics of the stem cell niche in the root. By making minimal assumptions on the cell communication process documented in experiments, we classify the basic developmental features of the stem-cell niche, including the basic structural architecture, and suggest that they could be understood as the result of generic mechanisms given by short and long range signals. This could be a first step in understanding why different stem cell niches share similar topologies, not only in plants. Also the fact that this organization is a robust consequence of the way information is being processed by the cells and to some extent independent of the detailed features of the signaling mechanism.
Space-time dynamics of stem cell niches: a unified approach for plants.

PubMed

Pérez, Maria del Carmen; López, Alejandro; Padilla, Pablo

2013-04-02

Many complex systems cannot be analyzed using traditional mathematical tools, due to their irreducible nature. This makes it necessary to develop models that can be implemented computationally to simulate their evolution. Examples of these models are cellular automata, evolutionary algorithms, complex networks, agent-based models, symbolic dynamics and dynamical systems techniques. We review some representative approaches to model the stem cell niche in Arabidopsis thaliana and the basic biological mechanisms that underlie its formation and maintenance. We propose a mathematical model based on cellular automata for describing the space-time dynamics of the stem cell niche in the root. By making minimal assumptions on the cell communication process documented in experiments, we classify the basic developmental features of the stem-cell niche, including the basic structural architecture, and suggest that they could be understood as the result of generic mechanisms given by short and long range signals. This could be a first step in understanding why different stem cell niches share similar topologies, not only in plants. Also the fact that this organization is a robust consequence of the way information is being processed by the cells and to some extent independent of the detailed features of the signaling mechanism.
An adaptive state of charge estimation approach for lithium-ion series-connected battery system

NASA Astrophysics Data System (ADS)

Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael

2018-07-01

Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.
A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment. PMID:29303160
A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.
Development & experimental validation of a SINDA/FLUINT thermal/fluid/electrical model of a multi-tube AMTEC cell

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

Hendricks, T.J.; Borkowski, C.A.; Huang, C.

1998-01-01

AMTEC (Alkali Metal Thermal-to-Electric Conversion) cell development has received increased attention and funding in the space power community because of several desirable performance characteristics compared to current radioisotope thermoelectric generation and solar photovoltaic (PV) power generation. AMTEC cell development is critically dependent upon the ability to predict thermal, fluid dynamic and electrical performance of an AMTEC cell which has many complex thermal, fluid dynamic and electrical processes and interactions occurring simultaneously. Development of predictive capability is critical to understanding the complex processes and interactions within the AMTEC cell, and thereby creating the ability to design high-performance, cost-effective AMTEC cells. Amore » flexible, sophisticated thermal/fluid/electrical model of an operating AMTEC cell has been developed using the SINDA/FLUINT analysis software. This model can accurately simulate AMTEC cell performance at any hot side and cold side temperature combination desired, for any voltage and current conditions, and for a broad range of cell design parameters involving the cell dimensions, current collector and electrode design, electrode performance parameters, and cell wall and thermal shield emissivity. The model simulates the thermal radiation network within the AMTEC cell using RadCAD thermal radiation analysis; hot side, cold side and cell wall conductive and radiative coupling; BASE (Beta Alumina Solid Electrode) tube electrochemistry, including electrode over-potentials; the fluid dynamics of the low-pressure sodium vapor flow to the condenser and liquid sodium flow in the wick; sodium condensation at the condenser; and high-temperature sodium evaporation in the wick. The model predicts the temperature profiles within the AMTEC cell walls, the BASE tube temperature profiles, the sodium temperature profile in the artery return, temperature profiles in the evaporator, thermal energy flows throughout the AMTEC cell, all sodium pressure drops from hot BASE tubes to the condenser, the current, voltage, and power output from the cell, and the cell efficiency. This AMTEC cell model is so powerful and flexible that it is used in radioisotope AMTEC power system design, solar AMTEC power system design, and combustion-driven power system design on several projects at Advanced Modular Power Systems, Inc. (AMPS). The model has been successfully validated against actual cell experimental data and its performance predictions agree very well with experimental data on PX-5B cells and other test cells at AMPS. {copyright} {ital 1998 American Institute of Physics.}« less
Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406
Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

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.
Three-dimensional finite element modeling of pericellular matrix and cell mechanics in the nucleus pulposus of the intervertebral disk based on in situ morphology.

PubMed

Cao, Li; Guilak, Farshid; Setton, Lori A

2011-02-01

Nucleus pulposus (NP) cells of the intervertebral disk (IVD) have unique morphological characteristics and biologic responses to mechanical stimuli that may regulate maintenance and health of the IVD. NP cells reside as single cell, paired or multiple cells in a contiguous pericellular matrix (PCM), whose structure and properties may significantly influence cell and extracellular matrix mechanics. In this study, a computational model was developed to predict the stress-strain, fluid pressure and flow fields for cells and their surrounding PCM in the NP using three-dimensional (3D) finite element models based on the in situ morphology of cell-PCM regions of the mature rat NP, measured using confocal microscopy. Three-dimensional geometries of the extracellular matrix and representative cell-matrix units were used to construct 3D finite element models of the structures as isotropic and biphasic materials. In response to compressive strain of the extracellular matrix, NP cells and PCM regions were predicted to experience volumetric strains that were 1.9-3.7 and 1.4-2.1 times greater than the extracellular matrix, respectively. Volumetric and deviatoric strain concentrations were generally found at the cell/PCM interface, while von Mises stress concentrations were associated with the PCM/extracellular matrix interface. Cell-matrix units containing greater cell numbers were associated with higher peak cell strains and lower rates of fluid pressurization upon loading. These studies provide new model predictions for micromechanics of NP cells that can contribute to an understanding of mechanotransduction in the IVD and its changes with aging and degeneration.
Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells.

PubMed

Jang, Minjeong; Koh, Ilkyoo; Lee, Seok Jae; Cheong, Jae-Ho; Kim, Pilnam

2017-01-27

Gastric cancer (GC) is a common aggressive malignant tumor with high incidence and mortality worldwide. GC is classified into intestinal and diffuse types according to the histo-morphological features. Because of distinctly different clinico-pathological features, new cancer therapy strategies and in vitro preclinical models for the two pathological variants of GC is necessary. Since extracellular matrix (ECM) influence the biological behavior of tumor cells, we hypothesized that GC might be more similarly modeled in 3D with matrix rather than in 2D. Herein, we developed a microfluidic-based a three-dimensional (3D) in vitro gastric cancer model, with subsequent drug resistance assay. AGS (intestinal type) and Hs746T (diffuse type) gastric cancer cell lines were encapsulated in collagen beads with high cellular viability. AGS exhibited an aggregation pattern with expansive growth, whereas Hs746T showed single-cell-level infiltration. Importantly, in microtumor models, epithelial-mesenchymal transition (EMT) and metastatic genes were upregulated, whereas E-cadherin was downregulated. Expression of ß-catenin was decreased in drug-resistant cells, and chemosensitivity toward the anticancer drug (5-FU) was observed in microtumors. These results suggest that in vitro microtumor models may represent a biologically relevant platform for studying gastric cancer cell biology and tumorigenesis, and for accelerating the development of novel therapeutic targets.
Sonication reduces the attachment of Salmonella Typhimurium ATCC 14028 cells to bacterial cellulose-based plant cell wall models and cut plant material.

PubMed

Tan, Michelle S F; Rahman, Sadequr; Dykes, Gary A

2017-04-01

This study investigated the removal of bacterial surface structures, particularly flagella, using sonication, and examined its effect on the attachment of Salmonella Typhimurium ATCC 14028 cells to plant cell walls. S. Typhimurium ATCC 14028 cells were subjected to sonication at 20 kHz to remove surface structures without affecting cell viability. Effective removal of flagella was determined by staining flagella of sonicated cells with Ryu's stain and enumerating the flagella remaining by direct microscopic counting. The attachment of sonicated S. Typhimurium cells to bacterial cellulose-based plant cell wall models and cut plant material (potato, apple, lettuce) was then evaluated. Varying concentrations of pectin and/or xyloglucan were used to produce a range of bacterial cellulose-based plant cell wall models. As compared to the non-sonicated controls, sonicated S. Typhimurium cells attached in significantly lower numbers (between 0.5 and 1.0 log CFU/cm 2 ) to all surfaces except to the bacterial cellulose-only composite without pectin and xyloglucan. Since attachment of S. Typhimurium to the bacterial cellulose-only composite was not affected by sonication, this suggests that bacterial surface structures, particularly flagella, could have specific interactions with pectin and xyloglucan. This study indicates that sonication may have potential applications for reducing Salmonella attachment during the processing of fresh produce. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Modeling the Chagas’ disease after stem cell transplantation

NASA Astrophysics Data System (ADS)

Galvão, Viviane; Miranda, José Garcia Vivas

2009-04-01

A recent model for Chagas’ disease after stem cell transplantation is extended for a three-dimensional multi-agent-based model. The computational model includes six different types of autonomous agents: inflammatory cell, fibrosis, cardiomyocyte, proinflammatory cytokine tumor necrosis factor- α, Trypanosoma cruzi, and bone marrow stem cell. Only fibrosis is fixed and the other types of agents can move randomly through the empty spaces using the three-dimensional Moore neighborhood. Bone marrow stem cells can promote apoptosis in inflammatory cells, fibrosis regression and can differentiate in cardiomyocyte. T. cruzi can increase the number of inflammatory cells. Inflammatory cells and tumor necrosis factor- α can increase the quantity of fibrosis. Our results were compared with experimental data giving a fairly fit and they suggest that the inflammatory cells are important for the development of fibrosis.
A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure.

PubMed

Zhou, Yang; Wu, Dewei

2016-01-01

It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs' generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs' firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate's threshold (FRT).
A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure

PubMed Central

2016-01-01

It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs' generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs' firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate's threshold (FRT). PMID:27597859
Variable cycle control model for intersection based on multi-source information

NASA Astrophysics Data System (ADS)

Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan

2018-05-01

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.
Polyglutamine Disease Modeling: Epitope Based Screen for Homologous Recombination using CRISPR/Cas9 System.

PubMed

An, Mahru C; O'Brien, Robert N; Zhang, Ningzhe; Patra, Biranchi N; De La Cruz, Michael; Ray, Animesh; Ellerby, Lisa M

2014-04-15

We have previously reported the genetic correction of Huntington's disease (HD) patient-derived induced pluripotent stem cells using traditional homologous recombination (HR) approaches. To extend this work, we have adopted a CRISPR-based genome editing approach to improve the efficiency of recombination in order to generate allelic isogenic HD models in human cells. Incorporation of a rapid antibody-based screening approach to measure recombination provides a powerful method to determine relative efficiency of genome editing for modeling polyglutamine diseases or understanding factors that modulate CRISPR/Cas9 HR.
Probing eukaryotic cell mechanics via mesoscopic simulations

PubMed Central

Shang, Menglin; Lim, Chwee Teck

2017-01-01

Cell mechanics has proven to be important in many biological processes. Although there is a number of experimental techniques which allow us to study mechanical properties of cell, there is still a lack of understanding of the role each sub-cellular component plays during cell deformations. We present a new mesoscopic particle-based eukaryotic cell model which explicitly describes cell membrane, nucleus and cytoskeleton. We employ Dissipative Particle Dynamics (DPD) method that provides us with the unified framework for modeling of a cell and its interactions in the flow. Data from micropipette aspiration experiments were used to define model parameters. The model was validated using data from microfluidic experiments. The validated model was then applied to study the impact of the sub-cellular components on the cell viscoelastic response in micropipette aspiration and microfluidic experiments. PMID:28922399
Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling.

PubMed

Povey, Jane F; O'Malley, Christopher J; Root, Tracy; Martin, Elaine B; Montague, Gary A; Feary, Marc; Trim, Carol; Lang, Dietmar A; Alldread, Richard; Racher, Andrew J; Smales, C Mark

2014-08-20

Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10L bioreactor model of Lonza's large-scale (up to 20,000L) fed-batch cell culture processes. Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements. Copyright © 2014 Elsevier B.V. All rights reserved.
Spin glass model for dynamics of cell reprogramming

NASA Astrophysics Data System (ADS)

Pusuluri, Sai Teja; Lang, Alex H.; Mehta, Pankaj; Castillo, Horacio E.

2015-03-01

Recent experiments show that differentiated cells can be reprogrammed to become pluripotent stem cells. The possible cell fates can be modeled as attractors in a dynamical system, the ``epigenetic landscape.'' Both cellular differentiation and reprogramming can be described in the landscape picture as motion from one attractor to another attractor. We perform Monte Carlo simulations in a simple model of the landscape. This model is based on spin glass theory and it can be used to construct a simulated epigenetic landscape starting from the experimental genomic data. We re-analyse data from several cell reprogramming experiments and compare with our simulation results. We find that the model can reproduce some of the main features of the dynamics of cell reprogramming.
Induction of pluripotent stem cells from a cynomolgus monkey using a polycistronic simian immunodeficiency virus-based vector, differentiation toward functional cardiomyocytes, and generation of stably expressing reporter lines.

PubMed

Wunderlich, Stephanie; Haase, Alexandra; Merkert, Sylvia; Beier, Jennifer; Schwanke, Kristin; Schambach, Axel; Glage, Silke; Göhring, Gudrun; Curnow, Eliza C; Martin, Ulrich

2012-12-01

Induced pluripotent stem cells (iPSCs) represent a novel cell source for regenerative therapies. Many emerging iPSC-based therapeutic concepts will require preclinical evaluation in suitable large animal models. Among the large animal species frequently used in preclinical efficacy and safety studies, macaques show the highest similarities to humans at physiological, cellular, and molecular levels. We have generated iPSCs from cynomolgus monkeys (Macaca fascicularis) as a segue to regenerative therapy model development in this species. Because typical human immunodeficiency virus type 1 (HIV-1)-based lentiviral vectors show poor transduction of simian cells, a simian immunodeficiency virus (SIV)-based vector was chosen for efficient transduction of cynomolgus skin fibroblasts. A corresponding polycistronic vector with codon-optimized reprogramming factors was constructed for reprogramming. Growth characteristics as well as cell and colony morphology of the resulting cynomolgus iPSCs (cyiPSCs) were demonstrated to be almost identical to cynomolgus embryonic stem cells (cyESCs), and cyiPSCs expressed typical pluripotency markers including OCT4, SOX2, and NANOG. Furthermore, differentiation in vivo and in vitro into derivatives of all three germ layers, as well as generation of functional cardiomyocytes, could be demonstrated. Finally, a highly efficient technique for generation of transgenic cyiPSC clones with stable reporter expression in undifferentiated cells as well as differentiated transgenic cyiPSC progeny was developed to enable cell tracking in recipient animals. In conclusion, our data indicate that cyiPSCs represent a valuable cell source for establishment of macaque-based allogeneic and autologous preclinical cell transplantation models for various fields of regenerative medicine.
Parameter variation of the one-diode model of a-Si and a- Si/μc-Si solar cells for modeling light-induced degradation

NASA Astrophysics Data System (ADS)

Weicht, J. A.; Hamelmann, F. U.; Behrens, G.

2014-11-01

For analyzing the long-term behavior of thin film a-Si/μc-Si photovoltaic modules, it is important to observe the light-induced degradation (LID) in dependence of the temperature for the parameters of the one-diode model for solar cells. According to the IEC 61646 standard, the impact of LID on module parameters of these thin film cells is determined at a constant temperature of 50°C with an irradiation of 1000 W/m2 at open circuit conditions. Previous papers examined the LID of thin film a-Si cells with different temperatures and some others are about a-Si/μc-Si. In these previous papers not all parameters of the one-diode model are examined. We observed the serial resistance (Rs), parallel resistance (Rp), short circuit current (Isc), open circuit voltage (Uoc), the maximum power point (MPP: Umpp, Impp and Pmpp) and the diode factor (n). Since the main reason for the LID of silicon-based thin films is the Staebler Wronski effect in the a-Si part of the cell, the temperature dependence of the healing of defects for all parameters of the one-diode model is also taken into account. We are also measuring modules with different kind of transparent conductive oxides: In a-Si thin film solar cells fluorine-doped tin oxide (FTO) is used and for thin film solar cells of a-Si/μc-Si boron- doped zinc oxide is used. In our work we describe an approach for transferring the parameters of a one-diode model for tandem thin film solar cells into the one-diode model for each part of the solar cell. The measurement of degradation and regeneration at higher temperatures is the necessary base for optimization of the different silicon-based thin films in warm hot climate.
VirtualLeaf: an open-source framework for cell-based modeling of plant tissue growth and development.

PubMed

Merks, Roeland M H; Guravage, Michael; Inzé, Dirk; Beemster, Gerrit T S

2011-02-01

Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http://virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux.
The development of a 3D mesoscopic model of metallic foam based on an improved watershed algorithm

NASA Astrophysics Data System (ADS)

Zhang, Jinhua; Zhang, Yadong; Wang, Guikun; Fang, Qin

2018-06-01

The watershed algorithm has been used widely in the x-ray computed tomography (XCT) image segmentation. It provides a transformation defined on a grayscale image and finds the lines that separate adjacent images. However, distortion occurs in developing a mesoscopic model of metallic foam based on XCT image data. The cells are oversegmented at some events when the traditional watershed algorithm is used. The improved watershed algorithm presented in this paper can avoid oversegmentation and is composed of three steps. Firstly, it finds all of the connected cells and identifies the junctions of the corresponding cell walls. Secondly, the image segmentation is conducted to separate the adjacent cells. It generates the lost cell walls between the adjacent cells. Optimization is then performed on the segmentation image. Thirdly, this improved algorithm is validated when it is compared with the image of the metallic foam, which shows that it can avoid the image segmentation distortion. A mesoscopic model of metallic foam is thus formed based on the improved algorithm, and the mesoscopic characteristics of the metallic foam, such as cell size, volume and shape, are identified and analyzed.
Integument pattern formation involves genetic and epigenetic controls: feather arrays simulated by digital hormone models

PubMed Central

Jiang, Ting-Xin; Widelitz, Randall B.; Shen, Wei-Min; Will, Peter; Wu, Da-Yu; Lin, Chih-Min; Jung, Han-Sung; Chuong, Cheng-Ming

2015-01-01

Pattern formation is a fundamental morphogenetic process. Models based on genetic and epigenetic control have been proposed but remain controversial. Here we use feather morphogenesis for further evaluation. Adhesion molecules and/or signaling molecules were first expressed homogenously in feather tracts (restrictive mode, appear earlier) or directly in bud or inter-bud regions (de novo mode, appear later). They either activate or inhibit bud formation, but paradoxically co-localize in the bud. Using feather bud reconstitution, we showed that completely dissociated cells can reform periodic patterns without reference to previous positional codes. The patterning process has the characteristics of being self-organizing, dynamic and plastic. The final pattern is an equilibrium state reached by competition, and the number and size of buds can be altered based on cell number and activator/inhibitor ratio, respectively. We developed a Digital Hormone Model which consists of (1) competent cells without identity that move randomly in a space, (2) extracellular signaling hormones which diffuse by a reaction-diffusion mechanism and activate or inhibit cell adhesion, and (3) cells which respond with topological stochastic actions manifested as changes in cell adhesion. Based on probability, the results are cell clusters arranged in dots or stripes. Thus genetic control provides combinational molecular information which defines the properties of the cells but not the final pattern. Epigenetic control governs interactions among cells and their environment based on physical-chemical rules (such as those described in the Digital Hormone Model). Complex integument patterning is the sum of these two components of control and that is why integument patterns are usually similar but non-identical. These principles may be shared by other pattern formation processes such as barb ridge formation, fingerprints, pigmentation patterning, etc. The Digital Hormone Model can also be applied to swarming robot navigation, reaching intelligent automata and representing a self-re-configurable type of control rather than a follow-the-instruction type of control. PMID:15272377
Hybrid multiscale modeling and prediction of cancer cell behavior

PubMed Central

Habibi, Jafar

2017-01-01

Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. PMID:28846712
Hybrid multiscale modeling and prediction of cancer cell behavior.

PubMed

Zangooei, Mohammad Hossein; Habibi, Jafar

2017-01-01

Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.

Image-based model of the spectrin cytoskeleton for red blood cell simulation.

PubMed

Fai, Thomas G; Leo-Macias, Alejandra; Stokes, David L; Peskin, Charles S

2017-10-01

We simulate deformable red blood cells in the microcirculation using the immersed boundary method with a cytoskeletal model that incorporates structural details revealed by tomographic images. The elasticity of red blood cells is known to be supplied by both their lipid bilayer membranes, which resist bending and local changes in area, and their cytoskeletons, which resist in-plane shear. The cytoskeleton consists of spectrin tetramers that are tethered to the lipid bilayer by ankyrin and by actin-based junctional complexes. We model the cytoskeleton as a random geometric graph, with nodes corresponding to junctional complexes and with edges corresponding to spectrin tetramers such that the edge lengths are given by the end-to-end distances between nodes. The statistical properties of this graph are based on distributions gathered from three-dimensional tomographic images of the cytoskeleton by a segmentation algorithm. We show that the elastic response of our model cytoskeleton, in which the spectrin polymers are treated as entropic springs, is in good agreement with the experimentally measured shear modulus. By simulating red blood cells in flow with the immersed boundary method, we compare this discrete cytoskeletal model to an existing continuum model and predict the extent to which dynamic spectrin network connectivity can protect against failure in the case of a red cell subjected to an applied strain. The methods presented here could form the basis of disease- and patient-specific computational studies of hereditary diseases affecting the red cell cytoskeleton.
Image-based model of the spectrin cytoskeleton for red blood cell simulation

PubMed Central

Stokes, David L.; Peskin, Charles S.

2017-01-01

We simulate deformable red blood cells in the microcirculation using the immersed boundary method with a cytoskeletal model that incorporates structural details revealed by tomographic images. The elasticity of red blood cells is known to be supplied by both their lipid bilayer membranes, which resist bending and local changes in area, and their cytoskeletons, which resist in-plane shear. The cytoskeleton consists of spectrin tetramers that are tethered to the lipid bilayer by ankyrin and by actin-based junctional complexes. We model the cytoskeleton as a random geometric graph, with nodes corresponding to junctional complexes and with edges corresponding to spectrin tetramers such that the edge lengths are given by the end-to-end distances between nodes. The statistical properties of this graph are based on distributions gathered from three-dimensional tomographic images of the cytoskeleton by a segmentation algorithm. We show that the elastic response of our model cytoskeleton, in which the spectrin polymers are treated as entropic springs, is in good agreement with the experimentally measured shear modulus. By simulating red blood cells in flow with the immersed boundary method, we compare this discrete cytoskeletal model to an existing continuum model and predict the extent to which dynamic spectrin network connectivity can protect against failure in the case of a red cell subjected to an applied strain. The methods presented here could form the basis of disease- and patient-specific computational studies of hereditary diseases affecting the red cell cytoskeleton. PMID:28991926
A novel methodology for non-linear system identification of battery cells used in non-road hybrid electric vehicles

NASA Astrophysics Data System (ADS)

Unger, Johannes; Hametner, Christoph; Jakubek, Stefan; Quasthoff, Marcus

2014-12-01

An accurate state of charge (SoC) estimation of a traction battery in hybrid electric non-road vehicles, which possess higher dynamics and power densities than on-road vehicles, requires a precise battery cell terminal voltage model. This paper presents a novel methodology for non-linear system identification of battery cells to obtain precise battery models. The methodology comprises the architecture of local model networks (LMN) and optimal model based design of experiments (DoE). Three main novelties are proposed: 1) Optimal model based DoE, which aims to high dynamically excite the battery cells at load ranges frequently used in operation. 2) The integration of corresponding inputs in the LMN to regard the non-linearities SoC, relaxation, hysteresis as well as temperature effects. 3) Enhancements to the local linear model tree (LOLIMOT) construction algorithm, to achieve a physical appropriate interpretation of the LMN. The framework is applicable for different battery cell chemistries and different temperatures, and is real time capable, which is shown on an industrial PC. The accuracy of the obtained non-linear battery model is demonstrated on cells with different chemistries and temperatures. The results show significant improvement due to optimal experiment design and integration of the battery non-linearities within the LMN structure.
Population pharmacokinetics of busulfan in pediatric and young adult patients undergoing hematopoietic cell transplant: a model-based dosing algorithm for personalized therapy and implementation into routine clinical use.

PubMed

Long-Boyle, Janel R; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J; Dvorak, Christopher C

2015-04-01

Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared with conventional dose guidelines. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration at steady state) and implement a simple model-based tool for the initial dosing of busulfan in children undergoing hematopoietic cell transplantation. Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone hematopoietic cell transplantation with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the nonlinear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Modeling of busulfan time-concentration data indicates that busulfan clearance displays nonlinearity in children, decreasing up to approximately 20% between the concentrations of 250-2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan clearance were actual body weight and age. The percentage of individuals achieving a therapeutic concentration at steady state was significantly higher in subjects receiving initial doses based on the population PK model (81%) than in historical controls dosed on conventional guidelines (52%) (P = 0.02). When compared with the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults.
Mouse neuroblastoma cell based model and the effect of epileptic events on calcium oscillations and neural spikes

NASA Astrophysics Data System (ADS)

Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

2013-05-01

Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.
ASSESSMENT OF TWO PHYSICALLY-BASED WATERSHED MODELS BASED ON THEIR PERFORMANCES OF SIMULATING WATER AND SEDIMENT MOVEMENT

EPA Science Inventory

Two physically based watershed models, GSSHA and KINEROS-2 are evaluated and compared for their performances on modeling flow and sediment movement. Each model has a different watershed conceptualization. GSSHA divides the watershed into cells, and flow and sediments are routed t...
A Micromechanics Finite Element Model for Studying the Mechanical Behavior of Spray-On Foam Insulation (SOFI)

NASA Technical Reports Server (NTRS)

Ghosn, Louis J.; Sullivan, Roy M.; Lerch, Bradley A.

2006-01-01

A micromechanics model has been constructed to study the mechanical behavior of spray-on foam insulation (SOFI) for the external tank. The model was constructed using finite elements representing the fundamental repeating unit of the SOFI microstructure. The details of the micromechanics model were based on cell observations and measured average cell dimensions discerned from photomicrographs. The unit cell model is an elongated Kelvin model (fourteen-sided polyhedron with 8 hexagonal and six quadrilateral faces), which will pack to a 100% density. The cell faces and cell edges are modeled using three-dimensional 20-node brick elements. Only one-eighth of the cell is modeled due to symmetry. By exercising the model and correlating the results with the macro-mechanical foam behavior obtained through material characterization testing, the intrinsic stiffness and Poisson s Ratio of the polymeric cell walls and edges are determined as a function of temperature. The model is then exercised to study the unique and complex temperature-dependent mechanical behavior as well as the fracture initiation and propagation at the microscopic unit cell level.
Attachment of Salmonella strains to a plant cell wall model is modulated by surface characteristics and not by specific carbohydrate interactions.

PubMed

Tan, Michelle Sze-Fan; Moore, Sean C; Tabor, Rico F; Fegan, Narelle; Rahman, Sadequr; Dykes, Gary A

2016-09-15

Processing of fresh produce exposes cut surfaces of plant cell walls that then become vulnerable to human foodborne pathogen attachment and contamination, particularly by Salmonella enterica. Plant cell walls are mainly composed of the polysaccharides cellulose, pectin and hemicelluloses (predominantly xyloglucan). Our previous work used bacterial cellulose-based plant cell wall models to study the interaction between Salmonella and the various plant cell wall components. We demonstrated that Salmonella attachment was favoured in the presence of pectin while xyloglucan had no effect on its attachment. Xyloglucan significantly increased the attachment of Salmonella cells to the plant cell wall model only when it was in association with pectin. In this study, we investigate whether the plant cell wall polysaccharides mediate Salmonella attachment to the bacterial cellulose-based plant cell wall models through specific carbohydrate interactions or through the effects of carbohydrates on the physical characteristics of the attachment surface. We found that none of the monosaccharides that make up the plant cell wall polysaccharides specifically inhibit Salmonella attachment to the bacterial cellulose-based plant cell wall models. Confocal laser scanning microscopy showed that Salmonella cells can penetrate and attach within the tightly arranged bacterial cellulose network. Analysis of images obtained from atomic force microscopy revealed that the bacterial cellulose-pectin-xyloglucan composite with 0.3 % (w/v) xyloglucan, previously shown to have the highest number of Salmonella cells attached to it, had significantly thicker cellulose fibrils compared to other composites. Scanning electron microscopy images also showed that the bacterial cellulose and bacterial cellulose-xyloglucan composites were more porous when compared to the other composites containing pectin. Our study found that the attachment of Salmonella cells to cut plant cell walls was not mediated by specific carbohydrate interactions. This suggests that the attachment of Salmonella strains to the plant cell wall models were more dependent on the structural characteristics of the attachment surface. Pectin reduces the porosity and space between cellulose fibrils, which then forms a matrix that is able to retain Salmonella cells within the bacterial cellulose network. When present with pectin, xyloglucan provides a greater surface for Salmonella cells to attach through the thickening of cellulose fibrils.
A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances.

PubMed

Pisu, Massimo; Concas, Alessandro; Cao, Giacomo

2015-04-01

Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation-dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering. Copyright © 2015 Elsevier Ltd. All rights reserved.
Mechanical positioning of multiple nuclei in muscle cells.

PubMed

Manhart, Angelika; Windner, Stefanie; Baylies, Mary; Mogilner, Alex

2018-06-01

Many types of large cells have multiple nuclei. In skeletal muscle fibers, the nuclei are distributed along the cell to maximize their internuclear distances. This myonuclear positioning is crucial for cell function. Although microtubules, microtubule associated proteins, and motors have been implicated, mechanisms responsible for myonuclear positioning remain unclear. We used a combination of rough interacting particle and detailed agent-based modeling to examine computationally the hypothesis that a force balance generated by microtubules positions the muscle nuclei. Rather than assuming the nature and identity of the forces, we simulated various types of forces between the pairs of nuclei and between the nuclei and cell boundary to position the myonuclei according to the laws of mechanics. We started with a large number of potential interacting particle models and computationally screened these models for their ability to fit biological data on nuclear positions in hundreds of Drosophila larval muscle cells. This reverse engineering approach resulted in a small number of feasible models, the one with the best fit suggests that the nuclei repel each other and the cell boundary with forces that decrease with distance. The model makes nontrivial predictions about the increased nuclear density near the cell poles, the zigzag patterns of the nuclear positions in wider cells, and about correlations between the cell width and elongated nuclear shapes, all of which we confirm by image analysis of the biological data. We support the predictions of the interacting particle model with simulations of an agent-based mechanical model. Taken together, our data suggest that microtubules growing from nuclear envelopes push on the neighboring nuclei and the cell boundaries, which is sufficient to establish the nearly-uniform nuclear spreading observed in muscle fibers.
Finite state projection based bounds to compare chemical master equation models using single-cell data

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

Fox, Zachary; Neuert, Gregor; Department of Pharmacology, School of Medicine, Vanderbilt University, Nashville, Tennessee 37232

2016-08-21

Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort.more » In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.« less
Serum-free keloid fibroblast cell culture: an in vitro model for the study of aberrant wound healing.

PubMed

Koch, R J; Goode, R L; Simpson, G T

1997-04-01

The purpose of this study was to develop an in vitro serum-free keloid fibroblast model. Keloid formation remains a problem for every surgeon. Prior evaluations of fibroblast characteristics in vitro, especially those of growth factor measurement, have been confounded by the presence of serum-containing tissue culture media. The serum itself contains growth factors, yet has been a "necessary evil" to sustain cell growth. The design of this study is laboratory-based and uses keloid fibroblasts obtained from five patients undergoing facial (ear lobule) keloid removal in a university-affiliated clinic. Keloid fibroblasts were established in primary cell culture and then propagated in a serum-free environment. The main outcome measures included sustained keloid fibroblast growth and viability, which was comparable to serum-based models. The keloid fibroblast cell cultures exhibited logarithmic growth, sustained a high cellular viability, maintained a monolayer, and displayed contact inhibition. Demonstrating model consistency, there was no statistically significant difference between the mean cell counts of the five keloid fibroblast cell lines at each experimental time point. The in vitro growth of keloid fibroblasts in a serum-free model has not been done previous to this study. The results of this study indicate that the proliferative characteristics described are comparable to those of serum-based models. The described model will facilitate the evaluation of potential wound healing modulators, and cellular effects and collagen modifications of laser resurfacing techniques, and may serve as a harvest source for contaminant-free fibroblast autoimplants. Perhaps its greatest utility will be in the evaluation of endogenous and exogenous growth factors.
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
Analysis and optimization of solid oxide fuel cell-based auxiliary power units using a generic zero-dimensional fuel cell model

NASA Astrophysics Data System (ADS)

Göll, S.; Samsun, R. C.; Peters, R.

Fuel-cell-based auxiliary power units can help to reduce fuel consumption and emissions in transportation. For this application, the combination of solid oxide fuel cells (SOFCs) with upstream fuel processing by autothermal reforming (ATR) is seen as a highly favorable configuration. Notwithstanding the necessity to improve each single component, an optimized architecture of the fuel cell system as a whole must be achieved. To enable model-based analyses, a system-level approach is proposed in which the fuel cell system is modeled as a multi-stage thermo-chemical process using the "flowsheeting" environment PRO/II™. Therein, the SOFC stack and the ATR are characterized entirely by corresponding thermodynamic processes together with global performance parameters. The developed model is then used to achieve an optimal system layout by comparing different system architectures. A system with anode and cathode off-gas recycling was identified to have the highest electric system efficiency. Taking this system as a basis, the potential for further performance enhancement was evaluated by varying four parameters characterizing different system components. Using methods from the design and analysis of experiments, the effects of these parameters and of their interactions were quantified, leading to an overall optimized system with encouraging performance data.
Chromatin conformation in living cells: support for a zig-zag model of the 30 nm chromatin fiber

NASA Technical Reports Server (NTRS)

Rydberg, B.; Holley, W. R.; Mian, I. S.; Chatterjee, A.

1998-01-01

A new method was used to probe the conformation of chromatin in living mammalian cells. The method employs ionizing radiation and is based on the concept that such radiation induces correlated breaks in DNA strands that are in spatial proximity. Human dermal fibroblasts in G0 phase of the cell cycle and Chinese hamster ovary cells in mitosis were irradiated by X-rays or accelerated ions. Following lysis of the cells, DNA fragments induced by correlated breaks were end-labeled and separated according to size on denaturing polyacrylamide gels. A characteristic peak was obtained for a fragment size of 78 bases, which is the size that corresponds to one turn of DNA around the nucleosome. Additional peaks between 175 and 450 bases reflect the relative position of nearest-neighbor nucleosomes. Theoretical calculations that simulate the indirect and direct effect of radiation on DNA demonstrate that the fragment size distributions are closely related to the chromatin structure model used. Comparison of the experimental data with theoretical results support a zig-zag model of the chromatin fiber rather than a simple helical model. Thus, radiation-induced damage analysis can provide information on chromatin structure in the living cell. Copyright 1998 Academic Press.
Battery Pack Life Estimation through Cell Degradation Data and Pack Thermal Modeling for BAS+ Li-Ion Batteries. Cooperative Research and Development Final Report, CRADA Number CRD-12-489

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

Smith, Kandler

Battery Life estimation is one of the key inputs required for Hybrid applications for all GM Hybrid/EV/EREV/PHEV programs. For each Hybrid vehicle program, GM has instituted multi-parameter Design of Experiments generating test data at Cell level and also Pack level on a reduced basis. Based on experience, generating test data on a pack level is found to be very expensive, resource intensive and sometimes less reliable. The proposed collaborative project will focus on a methodology to estimate Battery life based on cell degradation data combined with pack thermal modeling. NREL has previously developed cell-level battery aging models and pack-level thermal/electricalmore » network models, though these models are currently not integrated. When coupled together, the models are expected to describe pack-level thermal and aging response of individual cells. GM and NREL will use data collected for GM's Bas+ battery system for evaluation of the proposed methodology and assess to what degree these models can replace pack-level aging experiments in the future.« less
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.
Theoretical aspects and modelling of cellular decision making, cell killing and information-processing in photodynamic therapy of cancer.

PubMed

Gkigkitzis, Ioannis

2013-01-01

The aim of this report is to provide a mathematical model of the mechanism for making binary fate decisions about cell death or survival, during and after Photodynamic Therapy (PDT) treatment, and to supply the logical design for this decision mechanism as an application of rate distortion theory to the biochemical processing of information by the physical system of a cell. Based on system biology models of the molecular interactions involved in the PDT processes previously established, and regarding a cellular decision-making system as a noisy communication channel, we use rate distortion theory to design a time dependent Blahut-Arimoto algorithm where the input is a stimulus vector composed of the time dependent concentrations of three PDT related cell death signaling molecules and the output is a cell fate decision. The molecular concentrations are determined by a group of rate equations. The basic steps are: initialize the probability of the cell fate decision, compute the conditional probability distribution that minimizes the mutual information between input and output, compute the cell probability of cell fate decision that minimizes the mutual information and repeat the last two steps until the probabilities converge. Advance to the next discrete time point and repeat the process. Based on the model from communication theory described in this work, and assuming that the activation of the death signal processing occurs when any of the molecular stimulants increases higher than a predefined threshold (50% of the maximum concentrations), for 1800s of treatment, the cell undergoes necrosis within the first 30 minutes with probability range 90.0%-99.99% and in the case of repair/survival, it goes through apoptosis within 3-4 hours with probability range 90.00%-99.00%. Although, there is no experimental validation of the model at this moment, it reproduces some patterns of survival ratios of predicted experimental data. Analytical modeling based on cell death signaling molecules has been shown to be an independent and useful tool for prediction of cell surviving response to PDT. The model can be adjusted to provide important insights for cellular response to other treatments such as hyperthermia, and diseases such as neurodegeneration.
Insulator-based dielectrophoresis of microorganisms: theoretical and experimental results.

PubMed

Moncada-Hernandez, Hector; Baylon-Cardiel, Javier L; Pérez-González, Victor H; Lapizco-Encinas, Blanca H

2011-09-01

Dielectrophoresis (DEP) is the motion of particles due to polarization effects in nonuniform electric fields. DEP has great potential for handling cells and is a non-destructive phenomenon. It has been utilized for different cell analysis, from viability assessments to concentration enrichment and separation. Insulator-based DEP (iDEP) provides an attractive alternative to conventional electrode-based systems; in iDEP, insulating structures are used to generate nonuniform electric fields, resulting in simpler and more robust devices. Despite the rapid development of iDEP microdevices for applications with cells, the fundamentals behind the dielectrophoretic behavior of cells has not been fully elucidated. Understanding the theory behind iDEP is necessary to continue the progress in this field. This work presents the manipulation and separation of bacterial and yeast cells with iDEP. A computational model in COMSOL Multiphysics was employed to predict the effect of direct current-iDEP on cells suspended in a microchannel containing an array of insulating structures. The model allowed predicting particle behavior, pathlines and the regions where dielectrophoretic immobilization should occur. Experimental work was performed at the same operating conditions employed with the model and results were compared, obtaining good agreement. This is the first report on the mathematical modeling of the dielectrophoretic response of yeast and bacterial cells in a DC-iDEP microdevice. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
RCCS bioreactor-based modelled microgravity induces significant changes on in vitro 3D neuroglial cell cultures.

PubMed

Morabito, Caterina; Steimberg, Nathalie; Mazzoleni, Giovanna; Guarnieri, Simone; Fanò-Illic, Giorgio; Mariggiò, Maria A

2015-01-01

We propose a human-derived neuro-/glial cell three-dimensional in vitro model to investigate the effects of microgravity on cell-cell interactions. A rotary cell-culture system (RCCS) bioreactor was used to generate a modelled microgravity environment, and morphofunctional features of glial-like GL15 and neuronal-like SH-SY5Y cells in three-dimensional individual cultures (monotypic aggregates) and cocultures (heterotypic aggregates) were analysed. Cell survival was maintained within all cell aggregates over 2 weeks of culture. Moreover, compared to cells as traditional static monolayers, cell aggregates cultured under modelled microgravity showed increased expression of specific differentiation markers (e.g., GL15 cells: GFAP, S100B; SH-SY5Y cells: GAP43) and modulation of functional cell-cell interactions (e.g., N-CAM and Cx43 expression and localisation). In conclusion, this culture model opens a wide range of specific investigations at the molecular, biochemical, and morphological levels, and it represents an important tool for in vitro studies into dynamic interactions and responses of nervous system cell components to microgravity environmental conditions.

RCCS Bioreactor-Based Modelled Microgravity Induces Significant Changes on In Vitro 3D Neuroglial Cell Cultures

PubMed Central

Mazzoleni, Giovanna; Fanò-Illic, Giorgio; Mariggiò, Maria A.

2015-01-01

We propose a human-derived neuro-/glial cell three-dimensional in vitro model to investigate the effects of microgravity on cell-cell interactions. A rotary cell-culture system (RCCS) bioreactor was used to generate a modelled microgravity environment, and morphofunctional features of glial-like GL15 and neuronal-like SH-SY5Y cells in three-dimensional individual cultures (monotypic aggregates) and cocultures (heterotypic aggregates) were analysed. Cell survival was maintained within all cell aggregates over 2 weeks of culture. Moreover, compared to cells as traditional static monolayers, cell aggregates cultured under modelled microgravity showed increased expression of specific differentiation markers (e.g., GL15 cells: GFAP, S100B; SH-SY5Y cells: GAP43) and modulation of functional cell-cell interactions (e.g., N-CAM and Cx43 expression and localisation). In conclusion, this culture model opens a wide range of specific investigations at the molecular, biochemical, and morphological levels, and it represents an important tool for in vitro studies into dynamic interactions and responses of nervous system cell components to microgravity environmental conditions. PMID:25654124
Assessment of Cell Line Models of Primary Human Cells by Raman Spectral Phenotyping

PubMed Central

Swain, Robin J.; Kemp, Sarah J.; Goldstraw, Peter; Tetley, Teresa D.; Stevens, Molly M.

2010-01-01

Abstract Researchers have previously questioned the suitability of cell lines as models for primary cells. In this study, we used Raman microspectroscopy to characterize live A549 cells from a unique molecular biochemical perspective to shed light on their suitability as a model for primary human pulmonary alveolar type II (ATII) cells. We also investigated a recently developed transduced type I (TT1) cell line as a model for alveolar type I (ATI) cells. Single-cell Raman spectra provide unique biomolecular fingerprints that can be used to characterize cellular phenotypes. A multivariate statistical analysis of Raman spectra indicated that the spectra of A549 and TT1 cells are characterized by significantly lower phospholipid content compared to ATII and ATI spectra because their cytoplasm contains fewer surfactant lamellar bodies. Furthermore, we found that A549 spectra are statistically more similar to ATI spectra than to ATII spectra. The spectral variation permitted phenotypic classification of cells based on Raman spectral signatures with >99% accuracy. These results suggest that A549 cells are not a good model for ATII cells, but TT1 cells do provide a reasonable model for ATI cells. The findings have far-reaching implications for the assessment of cell lines as suitable primary cellular models in live cultures. PMID:20409492
Stochastic cellular automata model of cell migration, proliferation and differentiation: validation with in vitro cultures of muscle satellite cells.

PubMed

Garijo, N; Manzano, R; Osta, R; Perez, M A

2012-12-07

Cell migration and proliferation has been modelled in the literature as a process similar to diffusion. However, using diffusion models to simulate the proliferation and migration of cells tends to create a homogeneous distribution in the cell density that does not correlate to empirical observations. In fact, the mechanism of cell dispersal is not diffusion. Cells disperse by crawling or proliferation, or are transported in a moving ﬂuid. The use of cellular automata, particle models or cell-based models can overcome this limitation. This paper presents a stochastic cellular automata model to simulate the proliferation, migration and differentiation of cells. These processes are considered as completely stochastic as well as discrete. The model developed was applied to predict the behaviour of in vitro cell cultures performed with adult muscle satellite cells. Moreover, non homogeneous distribution of cells has been observed inside the culture well and, using the above mentioned stochastic cellular automata model, we have been able to predict this heterogeneous cell distribution and compute accurate quantitative results. Differentiation was also incorporated into the computational simulation. The results predicted the myotube formation that typically occurs with adult muscle satellite cells. In conclusion, we have shown how a stochastic cellular automata model can be implemented and is capable of reproducing the in vitro behaviour of adult muscle satellite cells. Copyright © 2012 Elsevier Ltd. All rights reserved.
Treatment Analysis in a Cancer Stem Cell Context Using a Tumor Growth Model Based on Cellular Automata.

PubMed

Monteagudo, Ángel; Santos, José

2015-01-01

Cancer can be viewed as an emergent behavior in terms of complex system theory and artificial life, Cellular Automata (CA) being the tool most used for studying and characterizing the emergent behavior. Different approaches with CA models were used to model cancer growth. The use of the abstract model of acquired cancer hallmarks permits the direct modeling at cellular level, where a cellular automaton defines the mitotic and apoptotic behavior of cells, and allows for an analysis of different dynamics of the cellular system depending on the presence of the different hallmarks. A CA model based on the presence of hallmarks in the cells, which includes a simulation of the behavior of Cancer Stem Cells (CSC) and their implications for the resultant growth behavior of the multicellular system, was employed. This modeling of cancer growth, in the avascular phase, was employed to analyze the effect of cancer treatments in a cancer stem cell context. The model clearly explains why, after treatment against non-stem cancer cells, the regrowth capability of CSCs generates a faster regrowth of tumor behavior, and also shows that a continuous low-intensity treatment does not favor CSC proliferation and differentiation, thereby allowing an unproblematic control of future tumor regrowth. The analysis performed indicates that, contrary to the current attempts at CSC control, trying to make CSC proliferation more difficult is an important point to consider, especially in the immediate period after a standard treatment for controlling non-stem cancer cell proliferation.
Predicting lymph node output efficiency using systems biology

PubMed Central

Gong, Chang; Mattila, Joshua T.; Miller, Mark; Flynn, JoAnne L.; Linderman, Jennifer J.; Kirschner, D.

2013-01-01

Dendritic cells (DCs) capture pathogens and foreign antigen (Ag) in peripheral tissues and migrate to secondary lymphoid tissues, such as lymph nodes (LNs), where they present processed Ag as MHC-bound peptide (pMHC) to naïve T cells. Interactions between DCs and T cells result, over periods of hours, in activation, clonal expansion and differentiation of antigen-specific T cells, leading to primed cells that can now participate in immune responses. Two-photon microscopy (2PM) has been widely adopted to analyze lymphocyte dynamics and can serve as a powerful in vivo assay for cell trafficking and activation over short length and time scales. Linking biological phenomena between vastly different spatiotemporal scales can be achieved using a systems biology approach. We developed a 3D agent-based cellular model of a LN that allows for the simultaneous in silico simulation of T cell trafficking, activation and production of effector cells under different antigen (Ag) conditions. The model anatomy is based on in situ analysis of LN sections (from primates and mice) and cell dynamics based on quantitative measurements from 2PM imaging of mice. Our simulations make three important predictions. First, T cell encounters by DCs and T cell receptor (TCR) repertoire scanning are more efficient in a 3D model compared with 2D, suggesting that a 3D model is needed to analyze LN function. Second, LNs are able to produce primed CD4+T cells at the same efficiency over broad ranges of cognate frequencies (from 10−5 to 10−2). Third, reducing the time that naïve T cells are required to bind DCs before becoming activated will increase the rate at which effector cells are produced. This 3D model provides a robust platform to study how T cell trafficking and activation dynamics relate to the efficiency of T cell priming and clonal expansion. We envision that this systems biology approach will provide novel insights for guiding vaccine development and understanding immune responses to infection. PMID:23816876
An Illumination- and Temperature-Dependent Analytical Model for Copper Indium Gallium Diselenide (CIGS) Solar Cells

DOE PAGES

Sun, Xingshu; Silverman, Timothy; Garris, Rebekah; ...

2016-07-18

In this study, we present a physics-based analytical model for copper indium gallium diselenide (CIGS) solar cells that describes the illumination- and temperature-dependent current-voltage (I-V) characteristics and accounts for the statistical shunt variation of each cell. The model is derived by solving the drift-diffusion transport equation so that its parameters are physical and, therefore, can be obtained from independent characterization experiments. The model is validated against CIGS I-V characteristics as a function of temperature and illumination intensity. This physics-based model can be integrated into a large-scale simulation framework to optimize the performance of solar modules, as well as predict themore » long-term output yields of photovoltaic farms under different environmental conditions.« less
A Prestressed Cable Network Model of the Adherent Cell Cytoskeleton

PubMed Central

Coughlin, Mark F.; Stamenović, Dimitrije

2003-01-01

A prestressed cable network is used to model the deformability of the adherent cell actin cytoskeleton. The overall and microstructural model geometries and cable mechanical properties were assigned values based on observations from living cells and mechanical measurements on isolated actin filaments, respectively. The models were deformed to mimic cell poking (CP), magnetic twisting cytometry (MTC) and magnetic bead microrheometry (MBM) measurements on living adherent cells. The models qualitatively and quantitatively captured the fibroblast cell response to the deformation imposed by CP while exhibiting only some qualitative features of the cell response to MTC and MBM. The model for CP revealed that the tensed peripheral actin filaments provide the key resistance to indentation. The actin filament tension that provides mechanical integrity to the network was estimated at ∼158 pN, and the nonlinear mechanical response during CP originates from filament kinematics. The MTC and MBM simulations revealed that the model is incomplete, however, these simulations show cable tension as a key determinant of the model response. PMID:12547813
A prestressed cable network model of the adherent cell cytoskeleton.

PubMed

Coughlin, Mark F; Stamenović, Dimitrije

2003-02-01

A prestressed cable network is used to model the deformability of the adherent cell actin cytoskeleton. The overall and microstructural model geometries and cable mechanical properties were assigned values based on observations from living cells and mechanical measurements on isolated actin filaments, respectively. The models were deformed to mimic cell poking (CP), magnetic twisting cytometry (MTC) and magnetic bead microrheometry (MBM) measurements on living adherent cells. The models qualitatively and quantitatively captured the fibroblast cell response to the deformation imposed by CP while exhibiting only some qualitative features of the cell response to MTC and MBM. The model for CP revealed that the tensed peripheral actin filaments provide the key resistance to indentation. The actin filament tension that provides mechanical integrity to the network was estimated at approximately 158 pN, and the nonlinear mechanical response during CP originates from filament kinematics. The MTC and MBM simulations revealed that the model is incomplete, however, these simulations show cable tension as a key determinant of the model response.
Computational analysis of the human sinus node action potential: model development and effects of mutations

PubMed Central

Fabbri, Alan; Fantini, Matteo; Wilders, Ronald

2017-01-01

Key points We constructed a comprehensive mathematical model of the spontaneous electrical activity of a human sinoatrial node (SAN) pacemaker cell, starting from the recent Severi–DiFrancesco model of rabbit SAN cells.Our model is based on electrophysiological data from isolated human SAN pacemaker cells and closely matches the action potentials and calcium transient that were recorded experimentally.Simulated ion channelopathies explain the clinically observed changes in heart rate in corresponding mutation carriers, providing an independent qualitative validation of the model.The model shows that the modulatory role of the ‘funny current’ (I f) in the pacing rate of human SAN pacemaker cells is highly similar to that of rabbit SAN cells, despite its considerably lower amplitude.The model may prove useful in the design of experiments and the development of heart‐rate modulating drugs. Abstract The sinoatrial node (SAN) is the normal pacemaker of the mammalian heart. Over several decades, a large amount of data on the ionic mechanisms underlying the spontaneous electrical activity of SAN pacemaker cells has been obtained, mostly in experiments on single cells isolated from rabbit SAN. This wealth of data has allowed the development of mathematical models of the electrical activity of rabbit SAN pacemaker cells. The present study aimed to construct a comprehensive model of the electrical activity of a human SAN pacemaker cell using recently obtained electrophysiological data from human SAN pacemaker cells. We based our model on the recent Severi–DiFrancesco model of a rabbit SAN pacemaker cell. The action potential and calcium transient of the resulting model are close to the experimentally recorded values. The model has a much smaller ‘funny current’ (I f) than do rabbit cells, although its modulatory role is highly similar. Changes in pacing rate upon the implementation of mutations associated with sinus node dysfunction agree with the clinical observations. This agreement holds for both loss‐of‐function and gain‐of‐function mutations in the HCN4, SCN5A and KCNQ1 genes, underlying ion channelopathies in I f, fast sodium current and slow delayed rectifier potassium current, respectively. We conclude that our human SAN cell model can be a useful tool in the design of experiments and the development of drugs that aim to modulate heart rate. PMID:28185290
Statistical Modeling of Single Target Cell Encapsulation

PubMed Central

Moon, SangJun; Ceyhan, Elvan; Gurkan, Umut Atakan; Demirci, Utkan

2011-01-01

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems. PMID:21814548
The use of experimental design to find the operating maximum power point of PEM fuel cells

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

Crăciunescu, Aurelian; Pătularu, Laurenţiu; Ciumbulea, Gloria

2015-03-10

Proton Exchange Membrane (PEM) Fuel Cells are difficult to model due to their complex nonlinear nature. In this paper, the development of a PEM Fuel Cells mathematical model based on the Design of Experiment methodology is described. The Design of Experiment provides a very efficient methodology to obtain a mathematical model for the studied multivariable system with only a few experiments. The obtained results can be used for optimization and control of the PEM Fuel Cells systems.
Use of CellNetAnalyzer in biotechnology and metabolic engineering.

PubMed

von Kamp, Axel; Thiele, Sven; Hädicke, Oliver; Klamt, Steffen

2017-11-10

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Temporal Noise Analysis of Charge-Domain Sampling Readout Circuits for CMOS Image Sensors.

PubMed

Ge, Xiaoliang; Theuwissen, Albert J P

2018-02-27

This paper presents a temporal noise analysis of charge-domain sampling readout circuits for Complementary Metal-Oxide Semiconductor (CMOS) image sensors. In order to address the trade-off between the low input-referred noise and high dynamic range, a Gm-cell-based pixel together with a charge-domain correlated-double sampling (CDS) technique has been proposed to provide a way to efficiently embed a tunable conversion gain along the read-out path. Such readout topology, however, operates in a non-stationery large-signal behavior, and the statistical properties of its temporal noise are a function of time. Conventional noise analysis methods for CMOS image sensors are based on steady-state signal models, and therefore cannot be readily applied for Gm-cell-based pixels. In this paper, we develop analysis models for both thermal noise and flicker noise in Gm-cell-based pixels by employing the time-domain linear analysis approach and the non-stationary noise analysis theory, which help to quantitatively evaluate the temporal noise characteristic of Gm-cell-based pixels. Both models were numerically computed in MATLAB using design parameters of a prototype chip, and compared with both simulation and experimental results. The good agreement between the theoretical and measurement results verifies the effectiveness of the proposed noise analysis models.
Temporal Noise Analysis of Charge-Domain Sampling Readout Circuits for CMOS Image Sensors †

PubMed Central

Theuwissen, Albert J. P.

2018-01-01

This paper presents a temporal noise analysis of charge-domain sampling readout circuits for Complementary Metal-Oxide Semiconductor (CMOS) image sensors. In order to address the trade-off between the low input-referred noise and high dynamic range, a Gm-cell-based pixel together with a charge-domain correlated-double sampling (CDS) technique has been proposed to provide a way to efficiently embed a tunable conversion gain along the read-out path. Such readout topology, however, operates in a non-stationery large-signal behavior, and the statistical properties of its temporal noise are a function of time. Conventional noise analysis methods for CMOS image sensors are based on steady-state signal models, and therefore cannot be readily applied for Gm-cell-based pixels. In this paper, we develop analysis models for both thermal noise and flicker noise in Gm-cell-based pixels by employing the time-domain linear analysis approach and the non-stationary noise analysis theory, which help to quantitatively evaluate the temporal noise characteristic of Gm-cell-based pixels. Both models were numerically computed in MATLAB using design parameters of a prototype chip, and compared with both simulation and experimental results. The good agreement between the theoretical and measurement results verifies the effectiveness of the proposed noise analysis models. PMID:29495496
A Miniature Swine Model for Stem Cell-Based De Novo Regeneration of Dental Pulp and Dentin-Like Tissue.

PubMed

Zhu, Xiaofei; Liu, Jie; Yu, Zongdong; Chen, Chao-An; Aksel, Hacer; Azim, Adham A; Huang, George T-J

2018-02-01

The goal of this study was to establish mini-swine as a large animal model for stem cell-based pulp regeneration studies. Swine dental pulp stem cells (sDPSCs) were isolated from mini-swine and characterized in vitro. For in vivo studies, we first employed both ectopic and semi-orthotopic study models using severe combined immunodeficiency mice. One is hydroxyapatite-tricalcium phosphate (HA/TCP) model for pulp-dentin complex formation, and the other is tooth fragment model for complete pulp regeneration with new dentin depositing along the canal walls. We found that sDPSCs are similar to their human counterparts exhibiting mesenchymal stem cell characteristics with ability to form colony forming unit-fibroblastic and odontogenic differentiation potential. sDPSCs formed pulp-dentin complex in the HA/TCP model and showed pulp regeneration capacity in the tooth fragment model. We then tested orthotopic pulp regeneration on mini-swine including the use of multi-rooted teeth. Using autologous sDPSCs carried by hydrogel and transplanted into the mini-swine root canal space, we observed regeneration of vascularized pulp-like tissue with a layer of newly deposited dentin-like (rD) tissue or osteodentin along the canal walls. In some cases, dentin bridge-like structure was observed. Immunohistochemical analysis detected the expression of nestin, dentin sialophosphoprotein, dentin matrix protein 1, and bone sialoprotein in odontoblast-like cells lining against the produced rD. We also tested the use of allogeneic sDPSCs for the same procedures. Similar findings were observed in allogeneic transplantation. This study is the first to show an establishment of mini-swine as a suitable large animal model utilizing multi-rooted teeth for further cell-based pulp regeneration studies.
Surface tension and modeling of cellular intercalation during zebrafish gastrulation.

PubMed

Calmelet, Colette; Sepich, Diane

2010-04-01

In this paper we discuss a model of zebrafish embryo notochord development based on the effect of surface tension of cells at the boundaries. We study the process of interaction of mesodermal cells at the boundaries due to adhesion and cortical tension, resulting in cellular intercalation. From in vivo experiments, we obtain cell outlines of time-lapse images of cell movements during zebrafish embryo development. Using Cellular Potts Model, we calculate the total surface energy of the system of cells at different time intervals at cell contacts. We analyze the variations of total energy depending on nature of cell contacts. We demonstrate that our model can be viable by calculating the total surface energy value for experimentally observed configurations of cells and showing that in our model these configurations correspond to a decrease in total energy values in both two and three dimensions.
Simulation analysis of receptive-field size of retinal horizontal cells by ionic current model.

PubMed

Aoyama, Toshihiro; Kamiyama, Yoshimi; Usui, Shiro

2005-01-01

The size of the receptive field of retinal horizontal cells changes with the state of dark/light adaptation. We have used a mathematical model to determine how changes in the membrane conductance affect the receptive-field properties of horizontal cells. We first modeled the nonlinear membrane properties of horizontal cells based on ionic current mechanisms. The dissociated horizontal cell model reproduced the voltage-current (V-I) relationships for various extracellular glutamate concentrations measured in electrophysiological studies. Second, a network horizontal cell model was also described, and it reproduced the V-I relationship observed in vivo. The network model showed a bell-shaped relationship between the receptive-field size and constant glutamate concentration. The simulated results suggest that the calcium current is a candidate for the bell-shaped length constant relationship.
Description and modelling of the solar-hydrogen-biogas-fuel cell system in GlashusEtt

NASA Astrophysics Data System (ADS)

Hedström, L.; Wallmark, C.; Alvfors, P.; Rissanen, M.; Stridh, B.; Ekman, J.

The need to reduce pollutant emissions and utilise the world's available energy resources more efficiently has led to increased attention towards e.g. fuel cells, but also to other alternative energy solutions. In order to further understand and evaluate the prerequisites for sustainable and energy-saving systems, ABB and Fortum have equipped an environmental information centre, located in Hammarby Sjöstad, Stockholm, Sweden, with an alternative energy system. The system is being used to demonstrate and evaluate how a system based on fuel cells and solar cells can function as a complement to existing electricity and heat production. The stationary energy system is situated on the top level of a three-floor glass building and is open to the public. The alternative energy system consists of a fuel cell system, a photovoltaic (PV) cell array, an electrolyser, hydrogen storage tanks, a biogas burner, dc/ac inverters, heat exchangers and an accumulator tank. The fuel cell system includes a reformer and a polymer electrolyte fuel cell (PEFC) with a maximum rated electrical output of 4 kW el and a maximum thermal output of 6.5 kW th. The fuel cell stack can be operated with reformed biogas, or directly using hydrogen produced by the electrolyser. The cell stack in the electrolyser consists of proton exchange membrane (PEM) cells. To evaluate different automatic control strategies for the system, a simplified dynamic model has been developed in MATLAB Simulink. The model based on measurement data taken from the actual system. The evaluation is based on demand curves, investment costs, electricity prices and irradiation. Evaluation criteria included in the model are electrical and total efficiencies as well as economic parameters.
A recursive vesicle-based model protocell with a primitive model cell cycle

NASA Astrophysics Data System (ADS)

Kurihara, Kensuke; Okura, Yusaku; Matsuo, Muneyuki; Toyota, Taro; Suzuki, Kentaro; Sugawara, Tadashi

2015-09-01

Self-organized lipid structures (protocells) have been proposed as an intermediate between nonliving material and cellular life. Synthetic production of model protocells can demonstrate the potential processes by which living cells first arose. While we have previously described a giant vesicle (GV)-based model protocell in which amplification of DNA was linked to self-reproduction, the ability of a protocell to recursively self-proliferate for multiple generations has not been demonstrated. Here we show that newborn daughter GVs can be restored to the status of their parental GVs by pH-induced vesicular fusion of daughter GVs with conveyer GVs filled with depleted substrates. We describe a primitive model cell cycle comprising four discrete phases (ingestion, replication, maturity and division), each of which is selectively activated by a specific external stimulus. The production of recursive self-proliferating model protocells represents a step towards eventual production of model protocells that are able to mimic evolution.
Argument Based Science Inquiry (ABSI) Learning Model in Voltaic Cell Concept

NASA Astrophysics Data System (ADS)

Subarkah, C. Z.; Fadilah, A.; Aisyah, R.

2017-09-01

Voltaic Cell is a sub-concept of electrochemistry that is considered difficult to be comprehended by learners Voltaic Cell is a sub concept of electrochemistry that is considered difficult to be understood by learners so that impacts on student activity in learning process. Therefore the learning model Argument Based Science Inquiry (ABSI) will be applied to the concept of Voltaic cell. This research aims to describe students’ activities during learning process using ABSI model and to analyze students’ competency to solve ABSI-based worksheets (LK) of Voltaic Cell concept. The method used in this research was the “mix-method-quantitative-embedded” method with subjects of the study: 39 second-semester students of Chemistry Education study program. The student activity is quite good during ABSI learning. The students’ ability to complete worksheet (LK) for every average phase is good. In the phase of exploration of post instruction understanding, it is categorized very good, and in the phase of negotiation shape III: comparing science ideas to textbooks or other printed resources merely reach enough category. Thus, the ABSI learning has improved the student levels of activity and students’ competency to solve the ABSI-based worksheet (LK).

An Observation-Driven Agent-Based Modeling and Analysis Framework for C. elegans Embryogenesis.

PubMed

Wang, Zi; Ramsey, Benjamin J; Wang, Dali; Wong, Kwai; Li, Husheng; Wang, Eric; Bao, Zhirong

2016-01-01

With cutting-edge live microscopy and image analysis, biologists can now systematically track individual cells in complex tissues and quantify cellular behavior over extended time windows. Computational approaches that utilize the systematic and quantitative data are needed to understand how cells interact in vivo to give rise to the different cell types and 3D morphology of tissues. An agent-based, minimum descriptive modeling and analysis framework is presented in this paper to study C. elegans embryogenesis. The framework is designed to incorporate the large amounts of experimental observations on cellular behavior and reserve data structures/interfaces that allow regulatory mechanisms to be added as more insights are gained. Observed cellular behaviors are organized into lineage identity, timing and direction of cell division, and path of cell movement. The framework also includes global parameters such as the eggshell and a clock. Division and movement behaviors are driven by statistical models of the observations. Data structures/interfaces are reserved for gene list, cell-cell interaction, cell fate and landscape, and other global parameters until the descriptive model is replaced by a regulatory mechanism. This approach provides a framework to handle the ongoing experiments of single-cell analysis of complex tissues where mechanistic insights lag data collection and need to be validated on complex observations.
Population-expression models of immune response

NASA Astrophysics Data System (ADS)

Stromberg, Sean P.; Antia, Rustom; Nemenman, Ilya

2013-06-01

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.
An overview of the CellML API and its implementation

PubMed Central

2010-01-01

Background CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models. However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML. Results We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages. We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. Conclusions Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions. PMID:20377909
An overview of the CellML API and its implementation.

PubMed

Miller, Andrew K; Marsh, Justin; Reeve, Adam; Garny, Alan; Britten, Randall; Halstead, Matt; Cooper, Jonathan; Nickerson, David P; Nielsen, Poul F

2010-04-08

CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models.However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML. We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages.We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.
Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search

PubMed Central

2014-01-01

Background The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. Results We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. Conclusion The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology. PMID:24917489
Material Modeling of Stony Meteorites for Mechanical Properties

NASA Astrophysics Data System (ADS)

Agrawal, P.

2016-12-01

To assess the threat posed by an asteroid entering Earth's atmosphere, one must predict if, when, and how it fragments during entry. A comprehensive understanding of the asteroid material properties is needed to achieve this objective. At present, the meteorite material found on earth are the only objects (other than synthetic meteorites) from an entering asteroid that can be used as representative material and be tested inside a laboratory setting. Due to limited number of meteorites available for testing it is difficult to develop a material model that can be purely based on statistics from the test data. Therefore, we are developing computational models to determine the effective material properties of stony meteorites and in turn deduce the properties of asteroids. The internal structure of meteorites are very complex. They consists of several minerals that include the silica based materials such as Olivine, Pyroxene, Feldspar that are found in terrestrial rocks, as well as Fe-Ni based minerals such as Kamacite, Troilite and Taenite that are unique to meteorites. Each of these minerals have different densities and mechanical properties. In addition, the meteorites have different phases that can be summarized as chondrules, metal and matrix. The meteorites have varying degree of porosity and pre-cracked structure. In order to account for diverse petrology of the meteorites a unique methodology is developed the form of unit cell model. The unit cell is representative volume that accounts for diverse minerals, porosity, and matrix composition inside a meteorite. All the minerals and phases inside these unit cells are randomly distributed. Several hundreds of Monte-Carlo simulations are performed to generate the effective mechanical properties such as Young's Modulus and Poisson's Ratio of the unit cell. Stress-strain curves as well as strength estimates are generated based on the unit cell models. These estimates will used as material models for full scale modeling of atmospheric entry for asteroids. Terrestrial analogs such as Basalt and Gabbro are being used to validate the unit cell methodology. Structural tests are also being performed on some of the meteorites including Tamdakht and Mbole to validate the predictions from unit cell models.
Cell Migration in 1D and 2D Nanofiber Microenvironments.

PubMed

Estabridis, Horacio M; Jana, Aniket; Nain, Amrinder; Odde, David J

2018-03-01

Understanding how cells migrate in fibrous environments is important in wound healing, immune function, and cancer progression. A key question is how fiber orientation and network geometry influence cell movement. Here we describe a quantitative, modeling-based approach toward identifying the mechanisms by which cells migrate in fibrous geometries having well controlled orientation. Specifically, U251 glioblastoma cells were seeded onto non-electrospinning Spinneret based tunable engineering parameters fiber substrates that consist of networks of suspended 400 nm diameter nanofibers. Cells were classified based on the local fiber geometry and cell migration dynamics observed by light microscopy. Cells were found in three distinct geometries: adhering two a single fiber, adhering to two parallel fibers, and adhering to a network of orthogonal fibers. Cells adhering to a single fiber or two parallel fibers can only move in one dimension along the fiber axis, whereas cells on a network of orthogonal fibers can move in two dimensions. We found that cells move faster and more persistently in 1D geometries than in 2D, with cell migration being faster on parallel fibers than on single fibers. To explain these behaviors mechanistically, we simulated cell migration in the three different geometries using a motor-clutch based model for cell traction forces. Using nearly identical parameter sets for each of the three cases, we found that the simulated cells naturally replicated the reduced migration in 2D relative to 1D geometries. In addition, the modestly faster 1D migration on parallel fibers relative to single fibers was captured using a correspondingly modest increase in the number of clutches to reflect increased surface area of adhesion on parallel fibers. Overall, the integrated modeling and experimental analysis shows that cell migration in response to varying fibrous geometries can be explained by a simple mechanical readout of geometry via a motor-clutch mechanism.
Potential-based and non-potential-based cohesive zone formulations under mixed-mode separation and over-closure-Part II: Finite element applications

NASA Astrophysics Data System (ADS)

Máirtín, Éamonn Ó.; Parry, Guillaume; Beltz, Glenn E.; McGarry, J. Patrick

2014-02-01

This paper, the second of two parts, presents three novel finite element case studies to demonstrate the importance of normal-tangential coupling in cohesive zone models (CZMs) for the prediction of mixed-mode interface debonding. Specifically, four new CZMs proposed in Part I of this study are implemented, namely the potential-based MP model and the non-potential-based NP1, NP2 and SMC models. For comparison, simulations are also performed for the well established potential-based Xu-Needleman (XN) model and the non-potential-based model of van den Bosch, Schreurs and Geers (BSG model). Case study 1: Debonding and rebonding of a biological cell from a cyclically deforming silicone substrate is simulated when the mode II work of separation is higher than the mode I work of separation at the cell-substrate interface. An active formulation for the contractility and remodelling of the cell cytoskeleton is implemented. It is demonstrated that when the XN potential function is used at the cell-substrate interface repulsive normal tractions are computed, preventing rebonding of significant regions of the cell to the substrate. In contrast, the proposed MP potential function at the cell-substrate interface results in negligible repulsive normal tractions, allowing for the prediction of experimentally observed patterns of cell cytoskeletal remodelling. Case study 2: Buckling of a coating from the compressive surface of a stent is simulated. It is demonstrated that during expansion of the stent the coating is initially compressed into the stent surface, while simultaneously undergoing tangential (shear) tractions at the coating-stent interface. It is demonstrated that when either the proposed NP1 or NP2 model is implemented at the stent-coating interface mixed-mode over-closure is correctly penalised. Further expansion of the stent results in the prediction of significant buckling of the coating from the stent surface, as observed experimentally. In contrast, the BSG model does not correctly penalise mixed-mode over-closure at the stent-coating interface, significantly altering the stress state in the coating and preventing the prediction of buckling. Case study 3: Application of a displacement to the base of a bi-layered composite arch results in a symmetric sinusoidal distribution of normal and tangential traction at the arch interface. The traction defined mode mixity at the interface ranges from pure mode II at the base of the arch to pure mode I at the top of the arch. It is demonstrated that predicted debonding patterns are highly sensitive to normal-tangential coupling terms in a CZM. The NP2, XN, and BSG models exhibit a strong bias towards mode I separation at the top of the arch, while the NP1 model exhibits a bias towards mode II debonding at the base of the arch. Only the SMC model provides mode-independent behaviour in the early stages of debonding. This case study provides a practical example of the importance of the behaviour of CZMs under conditions of traction controlled mode mixity, following from the theoretical analysis presented in Part I of this study.
A biochemically semi-detailed model of auxin-mediated vein formation in plant leaves.

PubMed

Roussel, Marc R; Slingerland, Martin J

2012-09-01

We present here a model intended to capture the biochemistry of vein formation in plant leaves. The model consists of three modules. Two of these modules, those describing auxin signaling and transport in plant cells, are biochemically detailed. We couple these modules to a simple model for PIN (auxin efflux carrier) protein localization based on an extracellular auxin sensor. We study the single-cell responses of this combined model in order to verify proper functioning of the modeled biochemical network. We then assemble a multicellular model from the single-cell building blocks. We find that the model can, under some conditions, generate files of polarized cells, but not true veins. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Stochastic model of cell rearrangements in convergent extension of ascidian notochord

NASA Astrophysics Data System (ADS)

Lubkin, Sharon; Backes, Tracy; Latterman, Russell; Small, Stephen

2007-03-01

We present a discrete stochastic cell based model of convergent extension of the ascidian notochord. Our work derives from research that clarifies the coupling of invagination and convergent extension in ascidian notochord morphogenesis (Odell and Munro, 2002). We have tested the roles of cell-cell adhesion, cell-extracellular matrix adhesion, random motion, and extension of individual cells, as well as the presence or absence of various tissue types, and determined which factors are necessary and/or sufficient for convergent extension.
Streamline three-dimensional thermal model of a lithium titanate pouch cell battery in extreme temperature conditions with module simulation

NASA Astrophysics Data System (ADS)

Jaguemont, Joris; Omar, Noshin; Martel, François; Van den Bossche, Peter; Van Mierlo, Joeri

2017-11-01

In this paper, the development of a three-dimensional (3D) lithium titanium oxide (LTO) pouch cell is presented to first better comprehend its thermal behavior within electrified vehicle applications, but also to propose a strong modeling base for future thermal management system. Current 3D-thermal models are based on electrochemical reactions which are in need for elaborated meshing effort and long computational time. There lacks a fast electro-thermal model which can capture voltage, current and thermal distribution variation during the whole process. The proposed thermal model is a reduce-effort temperature simulation approach involving a 0D-electrical model accommodating a 3D-thermal model to exclude electrochemical processes. The thermal model is based on heat-transfer theory and its temperature distribution prediction incorporates internal conduction and heat generation effect as well as convection. In addition, experimental tests are conducted to validate the model. Results show that both the heat dissipation rate and surface temperature uniformity data are in agreement with simulation results, which satisfies the application requirements for electrified vehicles. Additionally, a LTO battery pack sizing and modeling is also designed, applied and displays a non-uniformity of the cells under driving operation. Ultimately, the model will serve as a basis for the future development of a thermal strategy for LTO cells that operate in a large temperature range, which is a strong contribution to the existing body of scientific literature.
The fused in sarcoma protein forms cytoplasmic aggregates in motor neurons derived from integration-free induced pluripotent stem cells generated from a patient with familial amyotrophic lateral sclerosis carrying the FUS-P525L mutation.

PubMed

Liu, Xinxiu; Chen, Jiayu; Liu, Wenchao; Li, Xiaogang; Chen, Qi; Liu, Tao; Gao, Shaorong; Deng, Min

2015-07-01

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that primarily affects motor neurons (MNs) and has no effective treatment. Mutations in the fused in sarcoma (FUS) gene and abnormal aggregation of FUS protein have been reported in ALS. However, the mechanisms involved in ALS are poorly understood. Clinical drug trails have failed due to a lack of appropriate disease models, including a lack of access to MNs from ALS patients. Induced pluripotent stem (iPS) cells derived from patients with ALS provide an indispensable resource for in vitro mechanistic studies and for future patient-specific cell-based therapies. Previous reports demonstrated that viral-based ALS-iPS cells generated from fibroblasts harvested from Caucasian populations are ideal for basic research; however, ALS-iPS cells are precluded from cell-based therapeutic applications because of the risks associated with the integration of viral sequences into the genome and inconvenience associated with dermal biopsies. To establish a model for use in clinical applications, using episomal vectors, we generated an integration-free iPS cell line from peripheral blood mononuclear cells (PBMCs) harvested from a familial ALS (FALS) patient carrying the FUS-P525L mutation and a healthy control. Furthermore, we successfully differentiated ALS patient-specific iPS cells into MNs and subsequently detected cytoplasmic mislocalization and formation of FUS protein aggregates in MNs due to the FUS-P525L mutation. Our findings offer a cell-based disease model for use in further elucidating ALS pathogenesis and provide a tool for exploring gene repair coupled with cell replacement therapy.
Measured and Modeled Toxicokinetics in Cultured Fish Cells and Application to In Vitro - In Vivo Toxicity Extrapolation

PubMed Central

Stadnicka-Michalak, Julita; Tanneberger, Katrin; Schirmer, Kristin; Ashauer, Roman

2014-01-01

Effect concentrations in the toxicity assessment of chemicals with fish and fish cells are generally based on external exposure concentrations. External concentrations as dose metrics, may, however, hamper interpretation and extrapolation of toxicological effects because it is the internal concentration that gives rise to the biological effective dose. Thus, we need to understand the relationship between the external and internal concentrations of chemicals. The objectives of this study were to: (i) elucidate the time-course of the concentration of chemicals with a wide range of physicochemical properties in the compartments of an in vitro test system, (ii) derive a predictive model for toxicokinetics in the in vitro test system, (iii) test the hypothesis that internal effect concentrations in fish (in vivo) and fish cell lines (in vitro) correlate, and (iv) develop a quantitative in vitro to in vivo toxicity extrapolation method for fish acute toxicity. To achieve these goals, time-dependent amounts of organic chemicals were measured in medium, cells (RTgill-W1) and the plastic of exposure wells. Then, the relation between uptake, elimination rate constants, and log KOW was investigated for cells in order to develop a toxicokinetic model. This model was used to predict internal effect concentrations in cells, which were compared with internal effect concentrations in fish gills predicted by a Physiologically Based Toxicokinetic model. Our model could predict concentrations of non-volatile organic chemicals with log KOW between 0.5 and 7 in cells. The correlation of the log ratio of internal effect concentrations in fish gills and the fish gill cell line with the log KOW was significant (r>0.85, p = 0.0008, F-test). This ratio can be predicted from the log KOW of the chemical (77% of variance explained), comprising a promising model to predict lethal effects on fish based on in vitro data. PMID:24647349
Centrosome-Based Mechanisms, Prognostics and Therapeutics in Prostate Cancer

DTIC Science & Technology

2006-12-01

progression of prostate carcinomas. The specific aims of the original proposal were designed to test several features of this model . 1. Are centrosome...features of this model . 1. Are centrosome defects present in early prostate cancer and can they predict aggressive disease? 2. Do pericentrin’s...cells, supports this model . The ability to block the cell cycle in prostate cells by depletion of any of 14 centrosome proteins identifies several
Modelling and simulation of two-chamber microbial fuel cell

NASA Astrophysics Data System (ADS)

Zeng, Yingzhi; Choo, Yeng Fung; Kim, Byung-Hong; Wu, Ping

Microbial fuel cells (MFCs) offer great promise for simultaneous treatment of wastewater and energy recovery. While past research has been based extensively on experimental studies, modelling and simulation remains scarce. A typical MFC shares many similarities with chemical fuel cells such as direct ascorbic acid fuel cells and direct methanol fuel cells. Therefore, an attempt is made to develop a MFC model similar to that for chemical fuel cells. By integrating biochemical reactions, Butler-Volmer expressions and mass/charge balances, a MFC model based on a two-chamber configuration is developed that simulates both steady and dynamic behaviour of a MFC, including voltage, power density, fuel concentration, and the influence of various parameters on power generation. Results show that the cathodic reaction is the most significant limiting factor of MFC performance. Periodic changes in the flow rate of fuel result in a boost of power output; this offers further insight into MFC behaviour. In addition to a MFC fuelled by acetate, the present method is also successfully extended to using artificial wastewater (solution of glucose and glutamic acid) as fuel. Since the proposed modelling method is easy to implement, it can serve as a framework for modelling other types of MFC and thereby will facilitate the development and scale-up of more efficient MFCs.
A reliability design method for a lithium-ion battery pack considering the thermal disequilibrium in electric vehicles

NASA Astrophysics Data System (ADS)

Xia, Quan; Wang, Zili; Ren, Yi; Sun, Bo; Yang, Dezhen; Feng, Qiang

2018-05-01

With the rapid development of lithium-ion battery technology in the electric vehicle (EV) industry, the lifetime of the battery cell increases substantially; however, the reliability of the battery pack is still inadequate. Because of the complexity of the battery pack, a reliability design method for a lithium-ion battery pack considering the thermal disequilibrium is proposed in this paper based on cell redundancy. Based on this method, a three-dimensional electric-thermal-flow-coupled model, a stochastic degradation model of cells under field dynamic conditions and a multi-state system reliability model of a battery pack are established. The relationships between the multi-physics coupling model, the degradation model and the system reliability model are first constructed to analyze the reliability of the battery pack and followed by analysis examples with different redundancy strategies. By comparing the reliability of battery packs of different redundant cell numbers and configurations, several conclusions for the redundancy strategy are obtained. More notably, the reliability does not monotonically increase with the number of redundant cells for the thermal disequilibrium effects. In this work, the reliability of a 6 × 5 parallel-series configuration is the optimal system structure. In addition, the effect of the cell arrangement and cooling conditions are investigated.
Biomimetic three-dimensional tissue models for advanced high-throughput drug screening

PubMed Central

Nam, Ki-Hwan; Smith, Alec S.T.; Lone, Saifullah; Kwon, Sunghoon; Kim, Deok-Ho

2015-01-01

Most current drug screening assays used to identify new drug candidates are 2D cell-based systems, even though such in vitro assays do not adequately recreate the in vivo complexity of 3D tissues. Inadequate representation of the human tissue environment during a preclinical test can result in inaccurate predictions of compound effects on overall tissue functionality. Screening for compound efficacy by focusing on a single pathway or protein target, coupled with difficulties in maintaining long-term 2D monolayers, can serve to exacerbate these issues when utilizing such simplistic model systems for physiological drug screening applications. Numerous studies have shown that cell responses to drugs in 3D culture are improved from those in 2D, with respect to modeling in vivo tissue functionality, which highlights the advantages of using 3D-based models for preclinical drug screens. In this review, we discuss the development of microengineered 3D tissue models which accurately mimic the physiological properties of native tissue samples, and highlight the advantages of using such 3D micro-tissue models over conventional cell-based assays for future drug screening applications. We also discuss biomimetic 3D environments, based-on engineered tissues as potential preclinical models for the development of more predictive drug screening assays for specific disease models. PMID:25385716
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.
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.
Modeling of Sustainable Base Production by Microbial Electrolysis Cell.

PubMed

Blatter, Maxime; Sugnaux, Marc; Comninellis, Christos; Nealson, Kenneth; Fischer, Fabian

2016-07-07

A predictive model for the microbial/electrochemical base formation from wastewater was established and compared to experimental conditions within a microbial electrolysis cell. A Na2 SO4 /K2 SO4 anolyte showed that model prediction matched experimental results. Using Shewanella oneidensis MR-1, a strong base (pH≈13) was generated using applied voltages between 0.3 and 1.1 V. Due to the use of bicarbonate, the pH value in the anolyte remained unchanged, which is required to maintain microbial activity. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Density-based clustering analyses to identify heterogeneous cellular sub-populations

NASA Astrophysics Data System (ADS)

Heaster, Tiffany M.; Walsh, Alex J.; Landman, Bennett A.; Skala, Melissa C.

2017-02-01

Autofluorescence microscopy of NAD(P)H and FAD provides functional metabolic measurements at the single-cell level. Here, density-based clustering algorithms were applied to metabolic autofluorescence measurements to identify cell-level heterogeneity in tumor cell cultures. The performance of the density-based clustering algorithm, DENCLUE, was tested in samples with known heterogeneity (co-cultures of breast carcinoma lines). DENCLUE was found to better represent the distribution of cell clusters compared to Gaussian mixture modeling. Overall, DENCLUE is a promising approach to quantify cell-level heterogeneity, and could be used to understand single cell population dynamics in cancer progression and treatment.
A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

PubMed

Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T; Tyson, John J

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.
A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

PubMed Central

Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804
Computational Modeling of Single-Cell Migration: The Leading Role of Extracellular Matrix Fibers

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A.J.

2012-01-01

Cell migration is vitally important in a wide variety of biological contexts ranging from embryonic development and wound healing to malignant diseases such as cancer. It is a very complex process that is controlled by intracellular signaling pathways as well as the cell’s microenvironment. Due to its importance and complexity, it has been studied for many years in the biomedical sciences, and in the last 30 years it also received an increasing amount of interest from theoretical scientists and mathematical modelers. Here we propose a force-based, individual-based modeling framework that links single-cell migration with matrix fibers and cell-matrix interactions through contact guidance and matrix remodelling. With this approach, we can highlight the effect of the cell’s environment on its migration. We investigate the influence of matrix stiffness, matrix architecture, and cell speed on migration using quantitative measures that allow us to compare the results to experiments. PMID:22995486
GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

PubMed

Arakawa, Kazuharu; Yamada, Yohei; Shinoda, Kosaku; Nakayama, Yoichi; Tomita, Masaru

2006-03-23

Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.
Identifying Developmental Zones in Maize Lateral Root Cell Length Profiles using Multiple Change-Point Models

PubMed Central

Moreno-Ortega, Beatriz; Fort, Guillaume; Muller, Bertrand; Guédon, Yann

2017-01-01

The identification of the limits between the cell division, elongation and mature zones in the root apex is still a matter of controversy when methods based on cellular features, molecular markers or kinematics are compared while methods based on cell length profiles have been comparatively underexplored. Segmentation models were developed to identify developmental zones within a root apex on the basis of epidermal cell length profiles. Heteroscedastic piecewise linear models were estimated for maize lateral roots of various lengths of both wild type and two mutants affected in auxin signaling (rtcs and rum-1). The outputs of these individual root analyses combined with morphological features (first root hair position and root diameter) were then globally analyzed using principal component analysis. Three zones corresponding to the division zone, the elongation zone and the mature zone were identified in most lateral roots while division zone and sometimes elongation zone were missing in arrested roots. Our results are consistent with an auxin-dependent coordination between cell flux, cell elongation and cell differentiation. The proposed segmentation models could extend our knowledge of developmental regulations in longitudinally organized plant organs such as roots, monocot leaves or internodes. PMID:29123533
Elasticity-based development of functionally enhanced multicellular 3D liver encapsulated in hybrid hydrogel.

PubMed

Lee, Ho-Joon; Son, Myung Jin; Ahn, Jiwon; Oh, Soo Jin; Lee, Mihee; Kim, Ansoon; Jeung, Yun-Ji; Kim, Han-Gyeul; Won, Misun; Lim, Jung Hwa; Kim, Nam-Soon; Jung, Cho-Rock; Chung, Kyung-Sook

2017-12-01

Current in vitro liver models provide three-dimensional (3-D) microenvironments in combination with tissue engineering technology and can perform more accurate in vivo mimicry than two-dimensional models. However, a human cell-based, functionally mature liver model is still desired, which would provide an alternative to animal experiments and resolve low-prediction issues on species differences. Here, we prepared hybrid hydrogels of varying elasticity and compared them with a normal liver, to develop a more mature liver model that preserves liver properties in vitro. We encapsulated HepaRG cells, either alone or with supporting cells, in a biodegradable hybrid hydrogel. The elastic modulus of the 3D liver dynamically changed during culture due to the combined effects of prolonged degradation of hydrogel and extracellular matrix formation provided by the supporting cells. As a result, when the elastic modulus of the 3D liver model converges close to that of the in vivo liver (≅ 2.3 to 5.9 kPa), both phenotypic and functional maturation of the 3D liver were realized, while hepatic gene expression, albumin secretion, cytochrome p450-3A4 activity, and drug metabolism were enhanced. Finally, the 3D liver model was expanded to applications with embryonic stem cell-derived hepatocytes and primary human hepatocytes, and it supported prolonged hepatocyte survival and functionality in long-term culture. Our model represents critical progress in developing a biomimetic liver system to simulate liver tissue remodeling, and provides a versatile platform in drug development and disease modeling, ranging from physiology to pathology. We provide a functionally improved 3D liver model that recapitulates in vivo liver stiffness. We have experimentally addressed the issues of orchestrated effects of mechanical compliance, controlled matrix formation by stromal cells in conjunction with hepatic differentiation, and functional maturation of hepatocytes in a dynamic 3D microenvironment. Our model represents critical progress in developing a biomimetic liver system to simulate liver tissue remodeling, and provides a versatile platform in drug development and disease modeling, ranging from physiology to pathology. Additionally, recent advances in the stem-cell technologies have made the development of 3D organoid possible, and thus, our study also provides further contribution to the development of physiologically relevant stem-cell-based 3D tissues that provide an elasticity-based predefined biomimetic 3D microenvironment. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Experimental characterization and numerical modeling of tissue electrical conductivity during pulsed electric fields for irreversible electroporation treatment planning.

PubMed

Neal, Robert E; Garcia, Paulo A; Robertson, John L; Davalos, Rafael V

2012-04-01

Irreversible electroporation is a new technique to kill cells in targeted tissue, such as tumors, through a nonthermal mechanism using electric pulses to irrecoverably disrupt the cell membrane. Treatment effects relate to the tissue electric field distribution, which can be predicted with numerical modeling for therapy planning. Pulse effects will change the cell and tissue properties through thermal and electroporation (EP)-based processes. This investigation characterizes these changes by measuring the electrical conductivity and temperature of ex vivo renal porcine tissue within a single pulse and for a 200 pulse protocol. These changes are incorporated into an equivalent circuit model for cells and tissue with a variable EP-based resistance, providing a potential method to estimate conductivity as a function of electric field and pulse length for other tissues. Finally, a numerical model using a human kidney volumetric mesh evaluated how treatment predictions vary when EP- and temperature-based electrical conductivity changes are incorporated. We conclude that significant changes in predicted outcomes will occur when the experimental results are applied to the numerical model, where the direction and degree of change varies with the electric field considered.
Myxobacteria Fruiting Body Formation

NASA Astrophysics Data System (ADS)

Jiang, Yi

2006-03-01

Myxobacteria are social bacteria that swarm and glide on surfaces, and feed cooperatively. When starved, tens of thousands of cells change their movement pattern from outward spreading to inward concentration; they form aggregates that become fruiting bodies, inside which cells differentiate into nonmotile, environmentally resistant spores. Traditionally, cell aggregation has been considered to imply chemotaxis, a long-range cell interaction mediated by diffusing chemicals. However, myxobacteria aggregation is the consequence of direct cell-contact interactions. I will review our recent efforts in modeling the fruiting body formation of Myxobacteria, using lattice gas cellular automata models that are based on local cell-cell contact signaling. These models have reproduced the individual phases in Myxobacteria development such as the rippling, streaming, early aggregation and the final sporulation; the models can be unified to simulate the whole developmental process of Myxobacteria.
A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments.

PubMed

Bravo, Rafael; Axelrod, David E

2013-11-18

Normal colon crypts consist of stem cells, proliferating cells, and differentiated cells. Abnormal rates of proliferation and differentiation can initiate colon cancer. We have measured the variation in the number of each of these cell types in multiple crypts in normal human biopsy specimens. This has provided the opportunity to produce a calibrated computational model that simulates cell dynamics in normal human crypts, and by changing model parameter values, to simulate the initiation and treatment of colon cancer. An agent-based model of stochastic cell dynamics in human colon crypts was developed in the multi-platform open-source application NetLogo. It was assumed that each cell's probability of proliferation and probability of death is determined by its position in two gradients along the crypt axis, a divide gradient and in a die gradient. A cell's type is not intrinsic, but rather is determined by its position in the divide gradient. Cell types are dynamic, plastic, and inter-convertible. Parameter values were determined for the shape of each of the gradients, and for a cell's response to the gradients. This was done by parameter sweeps that indicated the values that reproduced the measured number and variation of each cell type, and produced quasi-stationary stochastic dynamics. The behavior of the model was verified by its ability to reproduce the experimentally observed monocolonal conversion by neutral drift, the formation of adenomas resulting from mutations either at the top or bottom of the crypt, and by the robust ability of crypts to recover from perturbation by cytotoxic agents. One use of the virtual crypt model was demonstrated by evaluating different cancer chemotherapy and radiation scheduling protocols. A virtual crypt has been developed that simulates the quasi-stationary stochastic cell dynamics of normal human colon crypts. It is unique in that it has been calibrated with measurements of human biopsy specimens, and it can simulate the variation of cell types in addition to the average number of each cell type. The utility of the model was demonstrated with in silico experiments that evaluated cancer therapy protocols. The model is available for others to conduct additional experiments.
Practical Modeling Concepts for Connective Tissue Stem Cell and Progenitor Compartment Kinetics

PubMed Central

2003-01-01

Stem cell activation and development is central to skeletal development, maintenance, and repair, as it is for all tissues. However, an integrated model of stem cell proliferation, differentiation, and transit between functional compartments has yet to evolve. In this paper, the authors review current concepts in stem cell biology and progenitor cell growth and differentiation kinetics in the context of bone formation. A cell-based modeling strategy is developed and offered as a tool for conceptual and quantitative exploration of the key kinetic variables and possible organizational hierarchies in bone tissue development and remodeling, as well as in tissue engineering strategies for bone repair. PMID:12975533
Biointerface dynamics--Multi scale modeling considerations.

PubMed

Pajic-Lijakovic, Ivana; Levic, Steva; Nedovic, Viktor; Bugarski, Branko

2015-08-01

Irreversible nature of matrix structural changes around the immobilized cell aggregates caused by cell expansion is considered within the Ca-alginate microbeads. It is related to various effects: (1) cell-bulk surface effects (cell-polymer mechanical interactions) and cell surface-polymer surface effects (cell-polymer electrostatic interactions) at the bio-interface, (2) polymer-bulk volume effects (polymer-polymer mechanical and electrostatic interactions) within the perturbed boundary layers around the cell aggregates, (3) cumulative surface and volume effects within the parts of the microbead, and (4) macroscopic effects within the microbead as a whole based on multi scale modeling approaches. All modeling levels are discussed at two time scales i.e. long time scale (cell growth time) and short time scale (cell rearrangement time). Matrix structural changes results in the resistance stress generation which have the feedback impact on: (1) single and collective cell migrations, (2) cell deformation and orientation, (3) decrease of cell-to-cell separation distances, and (4) cell growth. Herein, an attempt is made to discuss and connect various multi scale modeling approaches on a range of time and space scales which have been proposed in the literature in order to shed further light to this complex course-consequence phenomenon which induces the anomalous nature of energy dissipation during the structural changes of cell aggregates and matrix quantified by the damping coefficients (the orders of the fractional derivatives). Deeper insight into the matrix partial disintegration within the boundary layers is useful for understanding and minimizing the polymer matrix resistance stress generation within the interface and on that base optimizing cell growth. Copyright © 2015 Elsevier B.V. All rights reserved.
Convection-enhanced delivery of etoposide is effective against murine proneural glioblastoma.

PubMed

Sonabend, Adam M; Carminucci, Arthur S; Amendolara, Benjamin; Bansal, Mukesh; Leung, Richard; Lei, Liang; Realubit, Ronald; Li, Hai; Karan, Charles; Yun, Jonathan; Showers, Christopher; Rothcock, Robert; O, Jane; Califano, Andrea; Canoll, Peter; Bruce, Jeffrey N

2014-09-01

Glioblastoma subtypes have been defined based on transcriptional profiling, yet personalized care based on molecular classification remains unexploited. Topoisomerase II (TOP2) contributes to the transcriptional signature of the proneural glioma subtype. Thus, we targeted TOP2 pharmacologically with etoposide in proneural glioma models. TOP2 gene expression was evaluated in mouse platelet derived growth factor (PDGF)(+)phosphatase and tensin homolog (PTEN)(-/-)p53(-/-) and PDGF(+)PTEN(-/-) proneural gliomas and cell lines, as well as human glioblastoma from The Cancer Genome Atlas. Correlation between TOP2 transcript levels and etoposide susceptibility was investigated in 139 human cancer cell lines from the Cancer Cell Line Encyclopedia public dataset and in mouse proneural glioma cell lines. Convection-enhanced delivery (CED) of etoposide was tested on cell-based PDGF(+)PTEN(-/-)p53(-/-) and retroviral-based PDGF(+)PTEN(-/-) mouse proneural glioma models. TOP2 expression was significantly higher in human proneural glioblastoma and in mouse proneural tumors at early as well as late stages of development compared with normal brain. TOP2B transcript correlated with susceptibility to etoposide in mouse proneural cell lines and in 139 human cancer cell lines from the Cancer Cell Line Encyclopedia. Intracranial etoposide CED treatment (680 μM) was well tolerated by mice and led to a significant survival benefit in the PDGF(+)PTEN(-/-)p53(-/-) glioma model. Moreover, etoposide CED treatment at 80 μM but not 4 μM led to a significant survival advantage in the PDGF(+)PTEN(-/-) glioma model. TOP2 is highly expressed in proneural gliomas, rendering its pharmacological targeting by intratumoral administration of etoposide by CED effective on murine proneural gliomas. We provide evidence supporting clinical testing of CED of etoposide with a molecular-based patient selection approach. Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.
Emergent cell and tissue dynamics from subcellular modeling of active biomechanical processes

NASA Astrophysics Data System (ADS)

Sandersius, S. A.; Weijer, C. J.; Newman, T. J.

2011-08-01

Cells and the tissues they form are not passive material bodies. Cells change their behavior in response to external biochemical and biomechanical cues. Behavioral changes, such as morphological deformation, proliferation and migration, are striking in many multicellular processes such as morphogenesis, wound healing and cancer progression. Cell-based modeling of these phenomena requires algorithms that can capture active cell behavior and their emergent tissue-level phenotypes. In this paper, we report on extensions of the subcellular element model to model active biomechanical subcellular processes. These processes lead to emergent cell and tissue level phenotypes at larger scales, including (i) adaptive shape deformations in cells responding to slow stretching, (ii) viscous flow of embryonic tissues, and (iii) streaming patterns of chemotactic cells in epithelial-like sheets. In each case, we connect our simulation results to recent experiments.
Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression

PubMed Central

Macklin, Paul; Edgerton, Mary E.; Thompson, Alastair M.; Cristini, Vittorio

2012-01-01

Ductal carcinoma in situ (DCIS)—a significant precursor to invasive breast cancer—is typically diagnosed as microcalcifications in mammograms. However, the effective use of mammograms and other patient data to plan treatment has been restricted by our limited understanding of DCIS growth and calcification. We develop a mechanistic, agent-based cell model and apply it to DCIS. Cell motion is determined by a balance of biomechanical forces. We use potential functions to model interactions with the basement membrane and amongst cells of unequal size and phenotype. Each cell’s phenotype is determined by genomic/proteomic- and microenvironment-dependent stochastic processes. Detailed “sub-models” describe cell volume changes during proliferation and necrosis; we are the first to account for cell calcification. We introduce the first patient-specific calibration method to fully constrain the model based upon clinically-accessible histopathology data. After simulating 45 days of solid-type DCIS with comedonecrosis, the model predicts: necrotic cell lysis acts as a biomechanical stress relief, and is responsible for the linear DCIS growth observed in mammography; the rate of DCIS advance varies with the duct radius; the tumour grows 7 to 10 mm per year—consistent with mammographic data; and the mammographic and (post-operative) pathologic sizes are linearly correlated—in quantitative agreement with the clinical literature. Patient histopathology matches the predicted DCIS microstructure: an outer proliferative rim surrounds a stratified necrotic core with nuclear debris on its outer edge and calcification in the centre. This work illustrates that computational modelling can provide new insight on the biophysical underpinnings of cancer. It may one day be possible to augment a patient’s mammography and other imaging with rigorously-calibrated models that help select optimal surgical margins based upon the patient’s histopathologic data. PMID:22342935
Modeling extracellular matrix (ECM) alterations in ovarian cancer by multiphoton excited fabrication of stromal models (Conference Presentation)

NASA Astrophysics Data System (ADS)

Campagnola, Paul J.; Ajeti, Visar; Lara, Jorge; Eliceiri, Kevin W.; Patankar, Mansh

2016-04-01

A profound remodeling of the extracellular matrix (ECM) occurs in human ovarian cancer but it unknown how this affects tumor growth, where this understanding could lead to better diagnostics and therapeutic approaches. We investigate the role of these ECM alterations by using multiphoton excited (MPE) polymerization to fabricate biomimetic models to investigate operative cell-matrix interactions in invasion/metastasis. First, we create nano/microstructured gradients mimicking the basal lamina to study adhesion/migration dynamics of ovarian cancer cells of differing metastatic potential. We find a strong haptotactic response that depends on both contact guidance and ECM binding cues. While we found enhanced migration for more invasive cells, the specifics of alignment and directed migration also depend on cell polarity. We further use MPE fabrication to create collagen scaffolds with complex, 3D submicron morphology. The stromal scaffold designs are derived directly from "blueprints" based on SHG images of normal, high risk, and malignant ovarian tissues. The models are seeded with different cancer cell lines and this allows decoupling of the roles of cell characteristics (metastatic potential) and ECM structure and composition (normal vs cancer) on adhesion/migration dynamics. We found the malignant stroma structure promotes enhanced migration and proliferation and also cytoskeletal alignment. Creating synthetic models based on fibers patterns further allows decoupling the topographic roles of the fibers themselves vs their alignment within the tissue. These models cannot be synthesized by other conventional fabrication methods and we suggest the MPE image-based fabrication method will enable a variety of studies in cancer biology.
Impact Assessment of Cigarette Smoke Exposure on Organotypic Bronchial Epithelial Tissue Cultures: A Comparison of Mono-Culture and Coculture Model Containing Fibroblasts

PubMed Central

Iskandar, Anita R.; Xiang, Yang; Frentzel, Stefan; Talikka, Marja; Leroy, Patrice; Kuehn, Diana; Guedj, Emmanuel; Martin, Florian; Mathis, Carole; Ivanov, Nikolai V.; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

Organotypic 3D cultures of epithelial cells are grown at the air–liquid interface (ALI) and resemble the in vivo counterparts. Although the complexity of in vivo cellular responses could be better manifested in coculture models in which additional cell types such as fibroblasts were incorporated, the presence of another cell type could mask the response of the other. This study reports the impact of whole cigarette smoke (CS) exposure on organotypic mono- and coculture models to evaluate the relevancy of organotypic models for toxicological assessment of aerosols. Two organotypic bronchial models were directly exposed to low and high concentrations of CS of the reference research cigarette 3R4F: monoculture of bronchial epithelial cells without fibroblasts (BR) and coculture with fibroblasts (BRF) models. Adenylate kinase (AK)-based cytotoxicity, cytochrome P450 (CYP) 1A1/1B1 activity, tissue histology, and concentrations of secreted mediators into the basolateral media, as well as transcriptomes were evaluated following the CS exposure. The results demonstrated similar impact of CS on the AK-based cytotoxicity, CYP1A1/1B1 activity, and tissue histology in both models. However, a greater number of secreted mediators was identified in the basolateral media of the monoculture than in the coculture models. Furthermore, annotation analysis and network-based systems biology analysis of the transcriptomic profiles indicated a more prominent cellular stress and tissue damage following CS in the monoculture epithelium model without fibroblasts. Finally, our results indicated that an in vivo smoking-induced xenobiotic metabolism response of bronchial epithelial cells was better reflected from the in vitro CS-exposed coculture model. PMID:26085348
Impact Assessment of Cigarette Smoke Exposure on Organotypic Bronchial Epithelial Tissue Cultures: A Comparison of Mono-Culture and Coculture Model Containing Fibroblasts.

PubMed

Iskandar, Anita R; Xiang, Yang; Frentzel, Stefan; Talikka, Marja; Leroy, Patrice; Kuehn, Diana; Guedj, Emmanuel; Martin, Florian; Mathis, Carole; Ivanov, Nikolai V; Peitsch, Manuel C; Hoeng, Julia

2015-09-01

Organotypic 3D cultures of epithelial cells are grown at the air-liquid interface (ALI) and resemble the in vivo counterparts. Although the complexity of in vivo cellular responses could be better manifested in coculture models in which additional cell types such as fibroblasts were incorporated, the presence of another cell type could mask the response of the other. This study reports the impact of whole cigarette smoke (CS) exposure on organotypic mono- and coculture models to evaluate the relevancy of organotypic models for toxicological assessment of aerosols. Two organotypic bronchial models were directly exposed to low and high concentrations of CS of the reference research cigarette 3R4F: monoculture of bronchial epithelial cells without fibroblasts (BR) and coculture with fibroblasts (BRF) models. Adenylate kinase (AK)-based cytotoxicity, cytochrome P450 (CYP) 1A1/1B1 activity, tissue histology, and concentrations of secreted mediators into the basolateral media, as well as transcriptomes were evaluated following the CS exposure. The results demonstrated similar impact of CS on the AK-based cytotoxicity, CYP1A1/1B1 activity, and tissue histology in both models. However, a greater number of secreted mediators was identified in the basolateral media of the monoculture than in the coculture models. Furthermore, annotation analysis and network-based systems biology analysis of the transcriptomic profiles indicated a more prominent cellular stress and tissue damage following CS in the monoculture epithelium model without fibroblasts. Finally, our results indicated that an in vivo smoking-induced xenobiotic metabolism response of bronchial epithelial cells was better reflected from the in vitro CS-exposed coculture model. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology.
A Computational Model Predicting Disruption of Blood Vessel Development

PubMed Central

Kleinstreuer, Nicole; Dix, David; Rountree, Michael; Baker, Nancy; Sipes, Nisha; Reif, David; Spencer, Richard; Knudsen, Thomas

2013-01-01

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology. PMID:23592958
Agent-based model of diffusion of N-acyl homoserine lactones in a multicellular environment of Pseudomonas aeruginosa and Candida albicans.

PubMed

Pérez-Rodríguez, Gael; Dias, Sónia; Pérez-Pérez, Martín; Fdez-Riverola, Florentino; Azevedo, Nuno F; Lourenço, Anália

2018-03-08

Experimental incapacity to track microbe-microbe interactions in structures like biofilms, and the complexity inherent to the mathematical modelling of those interactions, raises the need for feasible, alternative modelling approaches. This work proposes an agent-based representation of the diffusion of N-acyl homoserine lactones (AHL) in a multicellular environment formed by Pseudomonas aeruginosa and Candida albicans. Depending on the spatial location, C. albicans cells were variably exposed to AHLs, an observation that might help explain why phenotypic switching of individual cells in biofilms occurred at different time points. The simulation and algebraic results were similar for simpler scenarios, although some statistical differences could be observed (p < 0.05). The model was also successfully applied to a more complex scenario representing a small multicellular environment containing C. albicans and P. aeruginosa cells encased in a 3-D matrix. Further development of this model may help create a predictive tool to depict biofilm heterogeneity at the single-cell level.

Theoretical results on the tandem junction solar cell based on its Ebers-Moll transistor model

NASA Technical Reports Server (NTRS)

Goradia, C.; Vaughn, J.; Baraona, C. R.

1980-01-01

A one-dimensional theoretical model of the tandem junction solar cell (TJC) with base resistivity greater than about 1 ohm-cm and under low level injection has been derived. This model extends a previously published conceptual model which treats the TJC as an npn transistor. The model gives theoretical expressions for each of the Ebers-Moll type currents of the illuminated TJC and allows for the calculation of the spectral response, I(sc), V(oc), FF and eta under variation of one or more of the geometrical and material parameters and 1MeV electron fluence. Results of computer calculations based on this model are presented and discussed. These results indicate that for space applications, both a high beginning of life efficiency, greater than 15% AM0, and a high radiation tolerance can be achieved only with thin (less than 50 microns) TJC's with high base resistivity (greater than 10 ohm-cm).
Exponential growth kinetics for Polyporus versicolor and Pleurotus ostreatus in submerged culture

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

Carroad, P.A.; Wilke, C.R.

1977-04-01

Simple mathematical models for a batch culture of pellet-forming fungi in submerged culture were tested on growth data for Polyporus versicolor (ATCC 12679) and Pleurotus ostreatus (ATCC 9415). A kinetic model based on a growth rate proportional to the two-thirds power of the cell mass was shown to be satisfactory. A model based on a growth rate directly proportional to the cell mass fitted the data equally well, however, and may be preferable because of mathematical simplicity.
A new epidemic modeling approach: Multi-regions discrete-time model with travel-blocking vicinity optimal control strategy.

PubMed

Zakary, Omar; Rachik, Mostafa; Elmouki, Ilias

2017-08-01

First, we devise in this paper, a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement. We suppose homogeneous Susceptible-Infected-Removed (SIR) populations, and we consider in our simulations, a grid of colored cells, which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region. Second, in order to minimize the number of infected individuals in one region, we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells. Thus, we show the influence of the optimal control approach on the controlled cell. We should also note that the cellular modeling approach we propose here, can also describes infection dynamics of regions which are not necessarily attached one to an other, even if no empty space can be viewed between cells. The theoretical method we follow for the characterization of the travel-locking optimal controls, is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here, is based on discrete progressive-regressive iterative schemes. We illustrate our modeling and control approaches by giving an example of 100 regions.
The Applicability of the Generalized Method of Cells for Analyzing Discontinuously Reinforced Composites

NASA Technical Reports Server (NTRS)

Pahr, D. H.; Arnold, S. M.

2001-01-01

The paper begins with a short overview of the recent work done in the field of discontinuous reinforced composites, focusing on the different parameters which influence the material behavior of discontinuous reinforced composites, as well as the various analysis approaches undertaken. Based on this overview it became evident, that in order to investigate the enumerated effects in an efficient and comprehensive manner, an alternative approach to the computationally intensive finite-element based micromechanics approach is required. Therefore, an investigation is conducted to demonstrate the utility of utilizing the generalized method of cells (GMC), a semi-analytical micromechanics-based approach, to simulate the elastic and elastoplastic material behavior of aligned short fiber composites. The results are compared with (1) simulations using other micromechanical based mean field models and finite element (FE) unit cell models found in the literature given elastic material behavior, as well as (2) finite element unit cell and a new semianalytical elastoplastic shear lag model in the inelastic range. GMC is shown to definitely have a window of applicability when simulating discontinuously reinforced composite material behavior.
The Applicability of the Generalized Method of Cells for Analyzing Discontinuously Reinforced Composites

NASA Technical Reports Server (NTRS)

Pahr, D. H.; Arnold, S. M.

2001-01-01

The paper begins with a short overview of the recent work done in the field of discontinuous reinforced composites, focusing on the different parameters which influence the material behavior of discontinuous reinforced composites, as well as the various analysis approaches undertaken. Based on this overview it became evident that in order to investigate the enumerated effects in an efficient and comprehensive manner, an alternative approach to the computationally intensive finite-element based micromechanics approach is required. Therefore, an investigation is conducted to demonstrate the utility of utilizing the generalized method of cells (GMC), a semi-analytical micromechanics-based approach, to simulate the elastic and elastoplastic material behavior of aligned short fiber composites. The results are compared with simulations using other micromechanical based mean field models and finite element (FE) unit cell models found in the literature given elastic material behavior, as well as finite element unit cell and a new semianalytical elastoplastic shear lag model in the inelastic range. GMC is shown to definitely have a window of applicability when simulating discontinuously reinforced composite material behavior.
A numerical model for charge transport and energy conversion of perovskite solar cells.

PubMed

Zhou, Yecheng; Gray-Weale, Angus

2016-02-14

Based on the continuity equations and Poisson's equation, we developed a numerical model for perovskite solar cells. Due to different working mechanisms, the model for perovskite solar cells differs from that of silicon solar cells and Dye Sensitized Solar Cells. The output voltage and current are calculated differently, and in a manner suited in particular to perovskite organohalides. We report a test of our equations against experiment with good agreement. Using this numerical model, it was found that performances of solar cells increase with charge carrier's lifetimes, mobilities and diffusion lengths. The open circuit voltage (Voc) of a solar cell is dependent on light intensities, and charge carrier lifetimes. Diffusion length and light intensity determine the saturated current (Jsc). Additionally, three possible guidelines for the design and fabrication of perovskite solar cells are suggested by our calculations. Lastly, we argue that concentrator perovskite solar cells are promising.
Cell design concepts for aqueous lithium-oxygen batteries: A model-based assessment

NASA Astrophysics Data System (ADS)

Grübl, Daniel; Bessler, Wolfgang G.

2015-11-01

Seven cell design concepts for aqueous (alkaline) lithium-oxygen batteries are investigated using a multi-physics continuum model for predicting cell behavior and performance in terms of the specific energy and specific power. Two different silver-based cathode designs (a gas diffusion electrode and a flooded cathode) and three different separator designs (a porous separator, a stirred separator chamber, and a redox-flow separator) are compared. Cathode and separator thicknesses are varied over a wide range (50 μm-20 mm) in order to identify optimum configurations. All designs show a considerable capacity-rate effect due to spatiotemporally inhomogeneous precipitation of solid discharge product LiOH·H2O. In addition, a cell design with flooded cathode and redox-flow separator including oxygen uptake within the external tank is suggested. For this design, the model predicts specific power up to 33 W/kg and specific energy up to 570 Wh/kg (gravimetric values of discharged cell including all cell components and catholyte except housing and piping).
E-beam deposited Ag-nanoparticles plasmonic organic solar cell and its absorption enhancement analysis using FDTD-based cylindrical nano-particle optical model.

PubMed

Kim, Richard S; Zhu, Jinfeng; Park, Jeung Hun; Li, Lu; Yu, Zhibin; Shen, Huajun; Xue, Mei; Wang, Kang L; Park, Gyechoon; Anderson, Timothy J; Pei, Qibing

2012-06-04

We report the plasmon-assisted photocurrent enhancement in Ag-nanoparticles (Ag-NPs) embedded PEDOT:PSS/P3HT:PCBM organic solar cells, and systematically investigate the causes of the improved optical absorption based on a cylindrical Ag-NPs optical model which is simulated with a 3-Dimensional finite difference time domain (FDTD) method. The proposed cylindrical Ag-NPs optical model is able to explain the optical absorption enhancement by the localized surface plasmon resonance (LSPR) modes, and to provide a further understanding of Ag-NPs shape parameters which play an important role to determine the broadband absorption phenomena in plasmonic organic solar cells. A significant increase in the power conversion efficiency (PCE) of the plasmonic solar cell was experimentally observed and compared with that of the solar cells without Ag-NPs. Finally, our conclusion was made after briefly discussing the electrical effects of the fabricated plasmonic organic solar cells.
Present state and future perspectives of using pluripotent stem cells in toxicology research

PubMed Central

Löser, Peter

2011-01-01

The use of novel drugs and chemicals requires reliable data on their potential toxic effects on humans. Current test systems are mainly based on animals or in vitro–cultured animal-derived cells and do not or not sufficiently mirror the situation in humans. Therefore, in vitro models based on human pluripotent stem cells (hPSCs) have become an attractive alternative. The article summarizes the characteristics of pluripotent stem cells, including embryonic carcinoma and embryonic germ cells, and discusses the potential of pluripotent stem cells for safety pharmacology and toxicology. Special attention is directed to the potential application of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) for the assessment of developmental toxicology as well as cardio- and hepatotoxicology. With respect to embryotoxicology, recent achievements of the embryonic stem cell test (EST) are described and current limitations as well as prospects of embryotoxicity studies using pluripotent stem cells are discussed. Furthermore, recent efforts to establish hPSC-based cell models for testing cardio- and hepatotoxicity are presented. In this context, methods for differentiation and selection of cardiac and hepatic cells from hPSCs are summarized, requirements and implications with respect to the use of these cells in safety pharmacology and toxicology are presented, and future challenges and perspectives of using hPSCs are discussed. PMID:21225242
Encapsulating Non-Human Primate Multipotent Stromal Cells in Alginate via High Voltage for Cell-Based Therapies and Cryopreservation

PubMed Central

Gryshkov, Oleksandr; Pogozhykh, Denys; Hofmann, Nicola; Pogozhykh, Olena; Mueller, Thomas; Glasmacher, Birgit

2014-01-01

Alginate cell-based therapy requires further development focused on clinical application. To assess engraftment, risk of mutations and therapeutic benefit studies should be performed in an appropriate non-human primate model, such as the common marmoset (Callithrix jacchus). In this work we encapsulated amnion derived multipotent stromal cells (MSCs) from Callithrix jacchus in defined size alginate beads using a high voltage technique. Our results indicate that i) alginate-cell mixing procedure and cell concentration do not affect the diameter of alginate beads, ii) encapsulation of high cell numbers (up to 10×106 cells/ml) can be performed in alginate beads utilizing high voltage and iii) high voltage (15–30 kV) does not alter the viability, proliferation and differentiation capacity of MSCs post-encapsulation compared with alginate encapsulated cells produced by the traditional air-flow method. The consistent results were obtained over the period of 7 days of encapsulated MSCs culture and after cryopreservation utilizing a slow cooling procedure (1 K/min). The results of this work show that high voltage encapsulation can further be maximized to develop cell-based therapies with alginate beads in a non-human primate model towards human application. PMID:25259731
Rheological behavior of mammalian cells.

PubMed

Stamenović, D

2008-11-01

Rheological properties of living cells determine how cells interact with their mechanical microenvironment and influence their physiological functions. Numerous experimental studies have show that mechanical contractile stress borne by the cytoskeleton and weak power-law viscoelasticity are governing principles of cell rheology, and that the controlling physics is at the level of integrative cytoskeletal lattice properties. Based on these observations, two concepts have emerged as leading models of cytoskeletal mechanics. One is the tensegrity model, which explains the role of the contractile stress in cytoskeletal mechanics, and the other is the soft glass rheology model, which explains the weak power-law viscoelasticity of cells. While these two models are conceptually disparate, the phenomena that they describe are often closely associated in living cells for reasons that are largely unknown. In this review, we discuss current understanding of cell rheology by emphasizing the underlying biophysical mechanism and critically evaluating the existing rheological models.
Silicon materials task of the Low Cost Solar Array Project: Effect of impurities and processing on silicon solar cells

NASA Technical Reports Server (NTRS)

Hopkins, R. H.; Davis, J. R.; Rohatgi, A.; Hanes, M. H.; Rai-Choudhury, P.; Mollenkopf, H. C.

1982-01-01

The effects of impurities and processing on the characteristics of silicon and terrestrial silicon solar cells were defined in order to develop cost benefit relationships for the use of cheaper, less pure solar grades of silicon. The amount of concentrations of commonly encountered impurities that can be tolerated in typical p or n base solar cells was established, then a preliminary analytical model from which the cell performance could be projected depending on the kinds and amounts of contaminants in the silicon base material was developed. The impurity data base was expanded to include construction materials, and the impurity performace model was refined to account for additional effects such as base resistivity, grain boundary interactions, thermal processing, synergic behavior, and nonuniform impurity distributions. A preliminary assessment of long term (aging) behavior of impurities was also undertaken.
Sabin-to-Mahoney Transition Model of Quasispecies Replication

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

2009-05-31

Qspp is an agent-based stochastic simulation model of the Poliovirus Sabin-to-Mahoney transition. This code simulates a cell-to-cell model of Poliovirus replication. The model tracks genotypes (virus genomes) as they are replicated in cells, and as the cells burst and release particles into the medium of a culture dish. An inoculum is then taken from the pool of virions and is used to inoculate cells on a new dish. This process repeats. The Sabin genotype comprises the initial inoculum. Nucleotide positions that match the Sabin1 (vaccine strain) and Mahoney (wild type) genotypes, as well as the neurovirulent phenotype (from the literature)more » are enumerated as constants.« less
Isogenic blood-brain barrier models based on patient-derived stem cells display inter-individual differences in cell maturation and functionality.

PubMed

Patel, Ronak; Page, Shyanne; Al-Ahmad, Abraham Jacob

2017-07-01

The blood-brain barrier (BBB) constitutes an important component of the neurovascular unit formed by specialized brain microvascular endothelial cells (BMECs) surrounded by astrocytes, pericytes, and neurons. Recently, isogenic in vitro models of the BBB based on human pluripotent stem cells have been documented, yet the impact of inter-individual variability on the yield and phenotype of such models remains to be documented. In this study, we investigated the impact of inter-individual variability on the yield and phenotype of isogenic models of the BBB, using patient-derived induced pluripotent stem cells (iPSCs). Astrocytes, BMECs, and neurons were differentiated from four asymptomatic patient-derived iPSCs (two males, two females). We differentiated such cells using existing differentiation protocols and quantified expression of cell lineage markers, as well as BBB phenotype, barrier induction, and formation of neurite processes. iPSC-derived BMECs showed barrier properties better than hCMEC/D3 monolayers; however, we noted differences in the expression and activity among iPSC lines. In addition, we noted differences in the differentiation efficiency of these cells into neural stem cells and progenitor cells (as noted by differences in expression of cell lineage markers). Such differences were reflected later in the terminal differentiation, as seen as ability to induce barrier function and to form neurite processes. Although we demonstrated our ability to obtain an isogenic model of the BBB with different patients' iPSCs, we also noted subtle differences in the expression of cell lineage markers and cell maturation processes, suggesting the presence of inter-individual polymorphisms. © 2017 International Society for Neurochemistry.
Thermodynamic analysis of biofuels as fuels for high temperature fuel cells

NASA Astrophysics Data System (ADS)

Milewski, Jarosław; Bujalski, Wojciech; Lewandowski, Janusz

2011-11-01

Based on mathematical modeling and numerical simulations, applicativity of various biofuels on high temperature fuel cell performance are presented. Governing equations of high temperature fuel cell modeling are given. Adequate simulators of both solid oxide fuel cell (SOFC) and molten carbonate fuel cell (MCFC) have been done and described. Performance of these fuel cells with different biofuels is shown. Some characteristics are given and described. Advantages and disadvantages of various biofuels from the system performance point of view are pointed out. An analysis of various biofuels as potential fuels for SOFC and MCFC is presented. The results are compared with both methane and hydrogen as the reference fuels. The biofuels are characterized by both lower efficiency and lower fuel utilization factors compared with methane. The presented results are based on a 0D mathematical model in the design point calculation. The governing equations of the model are also presented. Technical and financial analysis of high temperature fuel cells (SOFC and MCFC) are shown. High temperature fuel cells can be fed by biofuels like: biogas, bioethanol, and biomethanol. Operational costs and possible incomes of those installation types were estimated and analyzed. A comparison against classic power generation units is shown. A basic indicator net present value (NPV) for projects was estimated and commented.
Thermodynamic analysis of biofuels as fuels for high temperature fuel cells

NASA Astrophysics Data System (ADS)

Milewski, Jarosław; Bujalski, Wojciech; Lewandowski, Janusz

2013-02-01

Based on mathematical modeling and numerical simulations, applicativity of various biofuels on high temperature fuel cell performance are presented. Governing equations of high temperature fuel cell modeling are given. Adequate simulators of both solid oxide fuel cell (SOFC) and molten carbonate fuel cell (MCFC) have been done and described. Performance of these fuel cells with different biofuels is shown. Some characteristics are given and described. Advantages and disadvantages of various biofuels from the system performance point of view are pointed out. An analysis of various biofuels as potential fuels for SOFC and MCFC is presented. The results are compared with both methane and hydrogen as the reference fuels. The biofuels are characterized by both lower efficiency and lower fuel utilization factors compared with methane. The presented results are based on a 0D mathematical model in the design point calculation. The governing equations of the model are also presented. Technical and financial analysis of high temperature fuel cells (SOFC and MCFC) are shown. High temperature fuel cells can be fed by biofuels like: biogas, bioethanol, and biomethanol. Operational costs and possible incomes of those installation types were estimated and analyzed. A comparison against classic power generation units is shown. A basic indicator net present value (NPV) for projects was estimated and commented.
Multiscale modeling of mucosal immune responses

PubMed Central

2015-01-01

Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM. Background Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Implementation Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. Conclusion We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation. PMID:26329787
Multiscale modeling of mucosal immune responses.

PubMed

Mei, Yongguo; Abedi, Vida; Carbo, Adria; Zhang, Xiaoying; Lu, Pinyi; Philipson, Casandra; Hontecillas, Raquel; Hoops, Stefan; Liles, Nathan; Bassaganya-Riera, Josep

2015-01-01

Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.
A novel individual-cell-based mathematical model based on multicellular tumour spheroids for evaluating doxorubicin-related delivery in avascular regions.

PubMed

Liu, Jiali; Yan, Fangrong; Chen, Hongzhu; Wang, Wenjie; Liu, Wenyue; Hao, Kun; Wang, Guangji; Zhou, Fang; Zhang, Jingwei

2017-09-01

Effective drug delivery in the avascular regions of tumours, which is crucial for the promising antitumour activity of doxorubicin-related therapy, is governed by two inseparable processes: intercellular diffusion and intracellular retention. To accurately evaluate doxorubicin-related delivery in the avascular regions, these two processes should be assessed together. Here we describe a new approach to such an assessment. An individual-cell-based mathematical model based on multicellular tumour spheroids was developed that describes the different intercellular diffusion and intracellular retention kinetics of doxorubicin in each cell layer. The different effects of a P-glycoprotein inhibitor (LY335979) and a hypoxia inhibitor (YC-1) were quantitatively evaluated and compared, in vitro (tumour spheroids) and in vivo (HepG2 tumours in mice). This approach was further tested by evaluating in these models, an experimental doxorubicin derivative, INNO 206, which is in Phase II clinical trials. Inhomogeneous, hypoxia-induced, P-glycoprotein expression compromised active transport of doxorubicin in the central area, that is, far from the vasculature. LY335979 inhibited efflux due to P-glycoprotein but limited levels of doxorubicin outside the inner cells, whereas YC-1 co-administration specifically increased doxorubicin accumulation in the inner cells without affecting the extracellular levels. INNO 206 exhibited a more effective distribution profile than doxorubicin. The individual-cell-based mathematical model accurately evaluated and predicted doxorubicin-related delivery and regulation in the avascular regions of tumours. The described framework provides a mechanistic basis for the proper development of doxorubicin-related drug co-administration profiles and nanoparticle development and could avoid unnecessary clinical trials. © 2017 The British Pharmacological Society.
Comparison of the uptake of methacrylate-based nanoparticles in static and dynamic in vitro systems as well as in vivo.

PubMed

Rinkenauer, Alexandra C; Press, Adrian T; Raasch, Martin; Pietsch, Christian; Schweizer, Simon; Schwörer, Simon; Rudolph, Karl L; Mosig, Alexander; Bauer, Michael; Traeger, Anja; Schubert, Ulrich S

2015-10-28

Polymer-based nanoparticles are promising drug delivery systems allowing the development of new drug and treatment strategies with reduced side effects. However, it remains a challenge to screen for new and effective nanoparticle-based systems in vitro. Important factors influencing the behavior of nanoparticles in vivo cannot be simulated in screening assays in vitro, which still represent the main tools in academic research and pharmaceutical industry. These systems have serious drawbacks in the development of nanoparticle-based drug delivery systems, since they do not consider the highly complex processes influencing nanoparticle clearance, distribution, and uptake in vivo. In particular, the transfer of in vitro nanoparticle performance to in vivo models often fails, demonstrating the urgent need for novel in vitro tools that can imitate aspects of the in vivo situation more accurate. Dynamic cell culture, where cells are cultured and incubated in the presence of shear stress has the potential to bridge this gap by mimicking key-features of organs and vessels. Our approach implements and compares a chip-based dynamic cell culture model to the common static cell culture and mouse model to assess its capability to predict the in vivo success more accurately, by using a well-defined poly((methyl methacrylate)-co-(methacrylic acid)) and poly((methyl methacrylate)-co-(2-dimethylamino ethylmethacrylate)) based nanoparticle library. After characterization in static and dynamic in vitro cell culture we were able to show that physiological conditions such as cell-cell communication of co-cultured endothelial cells and macrophages as well as mechanotransductive signaling through shear stress significantly alter cellular nanoparticle uptake. In addition, it could be demonstrated by using dynamic cell cultures that the in vivo situation is simulated more accurately and thereby can be applied as a novel system to investigate the performance of nanoparticle systems in vivo more reliable. Copyright © 2015. Published by Elsevier B.V.

Tracking the Invasion of Small Numbers of Cells in Paper-Based Assays with Quantitative PCR.

PubMed

Truong, Andrew S; Lochbaum, Christian A; Boyce, Matthew W; Lockett, Matthew R

2015-11-17

Paper-based scaffolds are an attractive material for culturing mammalian cells in a three-dimensional environment. There are a number of previously published studies, which utilize these scaffolds to generate models of aortic valves, cardiac ischemia and reperfusion, and solid tumors. These models have largely relied on fluorescence imaging and microscopy to quantify cells in the scaffolds. We present here a polymerase chain reaction (PCR)-based method, capable of quantifying multiple cell types in a single culture with the aid of DNA barcodes: unique sequences of DNA introduced to the genome of individual cells or cell types through lentiviral transduction. PCR-based methods are highly specific and are amenable to high-throughput and multiplexed analyses. To validate this method, we engineered two different breast cancer lines to constitutively express either a green or red fluorescent protein. These cells lines allowed us to directly compare the ability of fluorescence imaging (of the fluorescent proteins) and qPCR (of the unique DNA sequences of the fluorescent proteins) to quantify known numbers of cells in the paper based-scaffolds. We also used both methods to quantify the distribution of these breast cell lines in homotypic and heterotypic invasion assays. In the paper-based invasion assays, a single sheet of paper containing cells suspended in a hydrogel was sandwiched between sheets of paper containing only hydrogel. The stack was incubated, and the cells invaded the adjacent layers. The individual sheets of the invasion assay were then destacked and the number of cells in each layer quantified. Our results show both methods can accurately detect cell populations of greater than 500 cells. The qPCR method can repeatedly and accurately detect as few as 50 cells, allowing small populations of highly invasive cells to be detected and differentiated from other cell types.
A simplified model for dynamics of cell rolling and cell-surface adhesion

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

Cimrák, Ivan, E-mail: ivan.cimrak@fri.uniza.sk

2015-03-10

We propose a three dimensional model for the adhesion and rolling of biological cells on surfaces. We study cells moving in shear flow above a wall to which they can adhere via specific receptor-ligand bonds based on receptors from selectin as well as integrin family. The computational fluid dynamics are governed by the lattice-Boltzmann method. The movement and the deformation of the cells is described by the immersed boundary method. Both methods are fully coupled by implementing a two-way fluid-structure interaction. The adhesion mechanism is modelled by adhesive bonds including stochastic rules for their creation and rupture. We explore amore » simplified model with dissociation rate independent of the length of the bonds. We demonstrate that this model is able to resemble the mesoscopic properties, such as velocity of rolling cells.« less
A 3D Poly(ethylene glycol)-based Tumor Angiogenesis Model to Study the Influence of Vascular Cells on Lung Tumor Cell Behavior

PubMed Central

Roudsari, Laila C.; Jeffs, Sydney E.; Witt, Amber S.; Gill, Bartley J.; West, Jennifer L.

2016-01-01

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. In vitro models that mimic in vivo tumor neovascularization facilitate exploration of this role. Here we investigated lung adenocarcinoma cancer cells (344SQ) and endothelial and pericyte vascular cells encapsulated in cell-adhesive, proteolytically-degradable poly(ethylene) glycol-based hydrogels. 344SQ in hydrogels formed spheroids and secreted proangiogenic growth factors that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, we engineered a 2-layer hydrogel with 344SQ and vascular cell layers. Large, invasive 344SQ clusters (area > 5,000 μm2, circularity < 0.25) developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration. Our findings suggest vascular cells contribute to tumor progression and establish this culture system as a platform for studying tumor vascularization. PMID:27596933
A 3D Poly(ethylene glycol)-based Tumor Angiogenesis Model to Study the Influence of Vascular Cells on Lung Tumor Cell Behavior

NASA Astrophysics Data System (ADS)

Roudsari, Laila C.; Jeffs, Sydney E.; Witt, Amber S.; Gill, Bartley J.; West, Jennifer L.

2016-09-01

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. In vitro models that mimic in vivo tumor neovascularization facilitate exploration of this role. Here we investigated lung adenocarcinoma cancer cells (344SQ) and endothelial and pericyte vascular cells encapsulated in cell-adhesive, proteolytically-degradable poly(ethylene) glycol-based hydrogels. 344SQ in hydrogels formed spheroids and secreted proangiogenic growth factors that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, we engineered a 2-layer hydrogel with 344SQ and vascular cell layers. Large, invasive 344SQ clusters (area > 5,000 μm2, circularity < 0.25) developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration. Our findings suggest vascular cells contribute to tumor progression and establish this culture system as a platform for studying tumor vascularization.
Performance analysis of a potassium-base AMTEC cell

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

Huang, C.; Hendricks, T.J.; Hunt, T.K.

1998-07-01

Sodium-BASE Alkali-Metal-Thermal-to-Electric-Conversion (AMTEC) cells have been receiving increased attention and funding from the Department of Energy, NASA and the United States Air Force. Recently, sodium-BASE (Na-BASE) AMTEC cells were selected for the Advanced Radioisotope Power System (ARPS) program for the next generation of deep-space missions and spacecraft. Potassium-BASE (K-BASE) AMTEC cells have not received as much attention to date, even though the vapor pressure of potassium is higher than that of sodium at the same temperature. So that, K-BASE AMTEC cells with potentially higher open circuit voltage and higher power output than Na-BASE AMTEC cells are possible. Because the surfacemore » tension of potassium is about half of the surface tension of sodium at the same temperature, the artery and evaporator design in a potassium AMTEC cell has much more challenging pore size requirements than designs using sodium. This paper uses a flexible thermal/fluid/electrical model to predict the performance of a K-BASE AMTEC cell. Pore sizes in the artery of K-BASE AMTEC cells must be smaller by an order of magnitude than in Na-BASE AMTEC cells. The performance of a K-BASE AMTEC cell was higher than a Na-BASE AMTEC cell at low voltages/high currents. K-BASE AMTEC cells also have the potential of much better electrode performance, thereby creating another avenue for potentially better performance in K-BASE AMTEC cells.« less
The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

NASA Astrophysics Data System (ADS)

Eftimie, Raluca

2015-03-01

One of the main unsolved problems of modern physics is finding a "theory of everything" - a theory that can explain, with the help of mathematics, all physical aspects of the universe. While the laws of physics could explain some aspects of the biology of living systems (e.g., the phenomenological interpretation of movement of cells and animals), there are other aspects specific to biology that cannot be captured by physics models. For example, it is generally accepted that the evolution of a cell-based system is influenced by the activation state of cells (e.g., only activated and functional immune cells can fight diseases); on the other hand, the evolution of an animal-based system can be influenced by the psychological state (e.g., distress) of animals. Therefore, the last 10-20 years have seen also a quest for a "theory of everything"-approach extended to biology, with researchers trying to propose mathematical modelling frameworks that can explain various biological phenomena ranging from ecology to developmental biology and medicine [1,2,6]. The basic idea behind this approach can be found in a few reviews on ecology and cell biology [6,7,9-11], where researchers suggested that due to the parallel between the micro-scale dynamics and the emerging macro-scale phenomena in both cell biology and in ecology, many mathematical methods used for ecological processes could be adapted to cancer modelling [7,9] or to modelling in immunology [11]. However, this approach generally involved the use of different models to describe different biological aspects (e.g., models for cell and animal movement, models for competition between cells or animals, etc.).
Multiple scale model for cell migration in monolayers: Elastic mismatch between cells enhances motility

NASA Astrophysics Data System (ADS)

Palmieri, Benoit; Bresler, Yony; Wirtz, Denis; Grant, Martin

2015-07-01

We propose a multiscale model for monolayer of motile cells that comprise normal and cancer cells. In the model, the two types of cells have identical properties except for their elasticity; cancer cells are softer and normal cells are stiffer. The goal is to isolate the role of elasticity mismatch on the migration potential of cancer cells in the absence of other contributions that are present in real cells. The methodology is based on a phase-field description where each cell is modeled as a highly-deformable self-propelled droplet. We simulated two types of nearly confluent monolayers. One contains a single cancer cell in a layer of normal cells and the other contains normal cells only. The simulation results demonstrate that elasticity mismatch alone is sufficient to increase the motility of the cancer cell significantly. Further, the trajectory of the cancer cell is decorated by several speed “bursts” where the cancer cell quickly relaxes from a largely deformed shape and consequently increases its translational motion. The increased motility and the amplitude and frequency of the bursts are in qualitative agreement with recent experiments.
The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757
A cell-based study on pedestrian acceleration and overtaking in a transfer station corridor

NASA Astrophysics Data System (ADS)

Ji, Xiangfeng; Zhou, Xuemei; Ran, Bin

2013-04-01

Pedestrian speed in a transfer station corridor is faster than usual and sometimes running can be found among some of them. In this paper, pedestrians are divided into two categories. The first one is aggressive, and the other is conservative. Aggressive pedestrians weaving their way through crowd in the corridor are the study object of this paper. During recent decades, much attention has been paid to the pedestrians' behavior, such as overtaking (also deceleration) and collision avoidance, and that continues in this paper. After sufficiently analyzing the characteristics of pedestrian flow in transfer station corridor, a cell-based model is presented in this paper, including the acceleration (also deceleration) and overtaking analysis. Acceleration (also deceleration) in a corridor is fixed according to Newton's Law and then speed calculated with a kinematic formula is discretized into cells based on the fuzzy logic. After the speed is updated, overtaking is analyzed based on updated speed and force explicitly, compared to rule-based models, which herein we call implicit ones. During the analysis of overtaking, a threshold value to determine the overtaking direction is introduced. Actually, model in this paper is a two-step one. The first step is to update speed, which is the cells the pedestrian can move in one time interval and the other is to analyze the overtaking. Finally, a comparison between the rule-based cellular automata, the model in this paper and data in HCM 2000 is made to demonstrate our model can be used to achieve reasonable simulation of acceleration (also deceleration) and overtaking among pedestrians.
Developing global regression models for metabolite concentration prediction regardless of cell line.

PubMed

André, Silvère; Lagresle, Sylvain; Da Sliva, Anthony; Heimendinger, Pierre; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-11-01

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

NASA Astrophysics Data System (ADS)

Guha, Rajarshi; Schürer, Stephan C.

2008-06-01

Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.
Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm.

PubMed

Kaga, Chiaki; Okochi, Mina; Tomita, Yasuyuki; Kato, Ryuji; Honda, Hiroyuki

2008-03-01

We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.
Pulsed magnetic field enhances therapeutic efficiency of mesenchymal stem cells in chronic neuropathic pain model.

PubMed

Mert, Tufan; Kurt, Akif Hakan; Altun, İdiris; Celik, Ahmet; Baran, Furkan; Gunay, Ismail

2017-05-01

Cell-based or magnetic field therapies as alternative approaches to pain management have been tested in several experimental pain models. The aim of this study therefore was to investigate the actions of the cell-based therapy (adipose tissue derived mesenchymal stem cells; ADMSC) or pulsed magnetic field (PMF) therapy and magneto-cell therapy (combination of ADMSC and PMF) in chronic constriction nerve injury model (CCI). The actions of individual ADMSC (route dependent [systemic or local], time-dependent [a day or a week after surgery]), or PMF and their combination (magneto-cell) therapies on hyperalgesia and allodynia were investigated by using thermal plantar test and a dynamic plantar aesthesiometer, respectively. In addition, various cytokine levels (IL-1β, IL-6, and IL-10) of rat sciatic nerve after CCI were analyzed. Following the CCI, both latency and threshold significantly decreased. ADMSC or PMF significantly increased latencies and thresholds. The combination of ADMSC with PMF even more significantly increased latency and threshold when compared with ADMSC alone. However, ADMSC-induced decrease in pro-inflammatory or increase in anti-inflammatory cytokines levels were partially prevented by PMF treatments. Present findings may suggest that both cell-based and magnetic therapies can effectively attenuate chronic neuropathic pain symptoms. Combined magneto-cell therapy may also efficiently reverse neuropathic signs. Bioelectromagnetics. 38:255-264, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Modeling and Predicting Cancer from ToxCast Phase I Data

EPA Science Inventory

The ToxCast program is generating a diverse collection of in vitro cell free and cell based HTS data to be used for predictive modeling of in vivo toxicity. We are using this in vitro data, plus corresponding in vivo data from ToxRefDB, to develop models for prediction and priori...
Prediction of energy balance and utilization for solar electric cars

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

Solar irradiation and ambient temperature are characterized by region, season and time-domain, which directly affects the performance of solar energy based car system. In this paper, the model of solar electric cars used was based in Xi’an. Firstly, the meteorological data are modelled to simulate the change of solar irradiation and ambient temperature, and then the temperature change of solar cell is calculated using the thermal equilibrium relation. The above work is based on the driving resistance and solar cell power generation model, which is simulated under the varying radiation conditions in a day. The daily power generation and solar electric car cruise mileage can be predicted by calculating solar cell efficiency and power. The above theoretical approach and research results can be used in the future for solar electric car program design and optimization for the future developments.
Modeling mammary organogenesis from biological first principles: Cells and their physical constraints.

PubMed

Montévil, Maël; Speroni, Lucia; Sonnenschein, Carlos; Soto, Ana M

2016-10-01

In multicellular organisms, relations among parts and between parts and the whole are contextual and interdependent. These organisms and their cells are ontogenetically linked: an organism starts as a cell that divides producing non-identical cells, which organize in tri-dimensional patterns. These association patterns and cells types change as tissues and organs are formed. This contextuality and circularity makes it difficult to establish detailed cause and effect relationships. Here we propose an approach to overcome these intrinsic difficulties by combining the use of two models; 1) an experimental one that employs 3D culture technology to obtain the structures of the mammary gland, namely, ducts and acini, and 2) a mathematical model based on biological principles. The typical approach for mathematical modeling in biology is to apply mathematical tools and concepts developed originally in physics or computer sciences. Instead, we propose to construct a mathematical model based on proper biological principles. Specifically, we use principles identified as fundamental for the elaboration of a theory of organisms, namely i) the default state of cell proliferation with variation and motility and ii) the principle of organization by closure of constraints. This model has a biological component, the cells, and a physical component, a matrix which contains collagen fibers. Cells display agency and move and proliferate unless constrained; they exert mechanical forces that i) act on collagen fibers and ii) on other cells. As fibers organize, they constrain the cells on their ability to move and to proliferate. The model exhibits a circularity that can be interpreted in terms of closure of constraints. Implementing the mathematical model shows that constraints to the default state are sufficient to explain ductal and acinar formation, and points to a target of future research, namely, to inhibitors of cell proliferation and motility generated by the epithelial cells. The success of this model suggests a step-wise approach whereby additional constraints imposed by the tissue and the organism could be examined in silico and rigorously tested by in vitro and in vivo experiments, in accordance with the organicist perspective we embrace. Copyright © 2016. Published by Elsevier Ltd.
Modeling mammary organogenesis from biological first principles: Cells and their physical constraints

PubMed Central

Montévil, Maël; Speroni, Lucia; Sonnenschein, Carlos; Soto, Ana M.

2017-01-01

In multicellular organisms, relations among parts and between parts and the whole are contextual and interdependent. These organisms and their cells are ontogenetically linked: an organism starts as a cell that divides producing non-identical cells, which organize in tri-dimensional patterns. These association patterns and cells types change as tissues and organs are formed. This contextuality and circularity makes it difficult to establish detailed cause and effect relationships. Here we propose an approach to overcome these intrinsic difficulties by combining the use of two models; 1) an experimental one that employs 3D culture technology to obtain the structures of the mammary gland, namely, ducts and acini, and 2) a mathematical model based on biological principles. The typical approach for mathematical modeling in biology is to apply mathematical tools and concepts developed originally in physics or computer sciences. Instead, we propose to construct a mathematical model based on proper biological principles. Specifically, we use principles identified as fundamental for the elaboration of a theory of organisms, namely i) the default state of cell proliferation with variation and motility and ii) the principle of organization by closure of constraints. This model has a biological component, the cells, and a physical component, a matrix which contains collagen fibers. Cells display agency and move and proliferate unless constrained; they exert mechanical forces that i) act on collagen fibers and ii) on other cells. As fibers organize, they constrain the cells on their ability to move and to proliferate. The model exhibits a circularity that can be interpreted in terms of closure of constraints. Implementing the mathematical model shows that constraints to the default state are sufficient to explain ductal and acinar formation, and points to a target of future research, namely, to inhibitors of cell proliferation and motility generated by the epithelial cells. The success of this model suggests a step-wise approach whereby additional constraints imposed by the tissue and the organism could be examined in silico and rigorously tested by in vitro and in vivo experiments, in accordance with the organicist perspective we embrace. PMID:27544910
Effects of stiffness and volume on the transit time of an erythrocyte through a slit.

PubMed

Salehyar, Sara; Zhu, Qiang

2017-06-01

By using a fully coupled fluid-cell interaction model, we numerically simulate the dynamic process of a red blood cell passing through a slit driven by an incoming flow. The model is achieved by combining a multiscale model of the composite cell membrane with a boundary element fluid dynamics model based on the Stokes flow assumption. Our concentration is on the correlation between the transit time (the time it takes to finish the whole translocation process) and different conditions (flow speed, cell orientation, cell stiffness, cell volume, etc.) that are involved. According to the numerical prediction (with some exceptions), the transit time rises as the cell is stiffened. It is also highly sensitive to volume increase inside the cell. In general, even slightly swollen cells (i.e., the internal volume is increased while the surface area of the cell kept unchanged) travel dramatically slower through the slit. For these cells, there is also an increased chance of blockage.
Programmatic access to logical models in the Cell Collective modeling environment via a REST API.

PubMed

Kowal, Bryan M; Schreier, Travis R; Dauer, Joseph T; Helikar, Tomáš

2016-01-01

Cell Collective (www.cellcollective.org) is a web-based interactive environment for constructing, simulating and analyzing logical models of biological systems. Herein, we present a Web service to access models, annotations, and simulation data in the Cell Collective platform through the Representational State Transfer (REST) Application Programming Interface (API). The REST API provides a convenient method for obtaining Cell Collective data through almost any programming language. To ensure easy processing of the retrieved data, the request output from the API is available in a standard JSON format. The Cell Collective REST API is freely available at http://thecellcollective.org/tccapi. All public models in Cell Collective are available through the REST API. For users interested in creating and accessing their own models through the REST API first need to create an account in Cell Collective (http://thecellcollective.org). thelikar2@unl.edu. Technical user documentation: https://goo.gl/U52GWo. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Running and rotating: modelling the dynamics of migrating cell clusters

NASA Astrophysics Data System (ADS)

Copenhagen, Katherine; Gov, Nir; Gopinathan, Ajay

Collective motion of cells is a common occurrence in many biological systems, including tissue development and repair, and tumor formation. Recent experiments have shown cells form clusters in a chemical gradient, which display three different phases of motion: translational, rotational, and random. We present a model for cell clusters based loosely on other models seen in the literature that involves a Vicsek-like alignment as well as physical collisions and adhesions between cells. With this model we show that a mechanism for driving rotational motion in this kind of system is an increased motility of rim cells. Further, we examine the details of the relationship between rim and core cells, and find that the phases of the cluster as a whole are correlated with the creation and annihilation of topological defects in the tangential component of the velocity field.

Edge Detection Based On the Characteristic of Primary Visual Cortex Cells

NASA Astrophysics Data System (ADS)

Zhu, M. M.; Xu, Y. L.; Ma, H. Q.

2018-01-01

Aiming at the problem that it is difficult to balance the accuracy of edge detection and anti-noise performance, and referring to the dynamic and static perceptions of the primary visual cortex (V1) cells, a V1 cell model is established to perform edge detection. A spatiotemporal filter is adopted to simulate the receptive field of V1 simple cells, the model V1 cell is obtained after integrating the responses of simple cells by half-wave rectification and normalization, Then the natural image edge is detected by using static perception of V1 cells. The simulation results show that, the V1 model can basically fit the biological data and has the universality of biology. What’s more, compared with other edge detection operators, the proposed model is more effective and has better robustness
On the application of the PFEM to droplet dynamics modeling in fuel cells

NASA Astrophysics Data System (ADS)

Ryzhakov, Pavel B.; Jarauta, Alex; Secanell, Marc; Pons-Prats, Jordi

2017-07-01

The Particle Finite Element Method (PFEM) is used to develop a model to study two-phase flow in fuel cell gas channels. First, the PFEM is used to develop the model of free and sessile droplets. The droplet model is then coupled to an Eulerian, fixed-grid, model for the airflow. The resulting coupled PFEM-Eulerian algorithm is used to study droplet oscillations in an air flow and droplet growth in a low-temperature fuel cell gas channel. Numerical results show good agreement with predicted frequencies of oscillation, contact angle, and deformation of injected droplets in gas channels. The PFEM-based approach provides a novel strategy to study droplet dynamics in fuel cells.
STEM based learning to facilitate middle school students’ conceptual change, creativity and collaboration in organization of living system topic

NASA Astrophysics Data System (ADS)

Rustaman, N. Y.; Afianti, E.; Maryati, S.

2018-05-01

A study using one group pre-post-test experimental design on Life organization system topic was carried out to investigate student’s tendency in learning abstract concept, their creativity and collaboration in designing and producing cell models through STEM-based learning. A number of seventh grade students in Cianjur district were involved as research subjects (n=34). Data were collected using two tier test for tracing changes in student conception before and after the application of STEM-based learning, and rubrics in creativity design (adopted from Torrance) and product on cell models (individually, in group), and rubric for self-assessment and observed skills on collaboration adapted from Marzano’s for life-long learning. Later the data obtained were analyzed qualitatively by interpreting the tendency of data presented in matrix sorted by gender. Research findings showed that the percentage of student’s scientific concept mastery is moderate in general. Their creativity in making a cell model design varied in category (expressing, emergent, excellent, not yet evident). Student’s collaboration varied from excellent, fair, good, less once, to less category in designing cell model. It was found that STEM based learning can facilitate students conceptual change, creativity and collaboration.
A comparison of scaffold-free and scaffold-based reconstructed human skin models as alternatives to animal use.

PubMed

Kinikoglu, Beste

2017-12-01

Tissue engineered full-thickness human skin substitutes have various applications in the clinic and in the laboratory, such as in the treatment of burns or deep skin defects, and as reconstructed human skin models in the safety testing of drugs and cosmetics and in the fundamental study of skin biology and pathology. So far, different approaches have been proposed for the generation of reconstructed skin, each with its own advantages and disadvantages. Here, the classic tissue engineering approach, based on cell-seeded polymeric scaffolds, is compared with the less-studied cell self-assembly approach, where the cells are coaxed to synthesise their own extracellular matrix (ECM). The resulting full-thickness human skin substitutes were analysed by means of histological and immunohistochemical analyses. It was found that both the scaffold-free and the scaffold-based skin equivalents successfully mimicked the functionality and morphology of native skin, with complete epidermal differentiation (as determined by the expression of filaggrin), the presence of a continuous basement membrane expressing collagen VII, and new ECM deposition by dermal fibroblasts. On the other hand, the scaffold-free model had a thicker epidermis and a significantly higher number of Ki67-positive proliferative cells, indicating a higher capacity for self-renewal, as compared to the scaffold-based model. 2017 FRAME.
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
Bioprinting for cancer research.

PubMed

Knowlton, Stephanie; Onal, Sevgi; Yu, Chu Hsiang; Zhao, Jean J; Tasoglu, Savas

2015-09-01

Bioprinting offers the ability to create highly complex 3D architectures with living cells. This cutting-edge technique has significantly gained popularity and applicability in several fields. Bioprinting methods have been developed to effectively and rapidly pattern living cells, biological macromolecules, and biomaterials. These technologies hold great potential for applications in cancer research. Bioprinted cancer models represent a significant improvement over previous 2D models by mimicking 3D complexity and facilitating physiologically relevant cell-cell and cell-matrix interactions. Here we review bioprinting methods based on inkjet, microextrusion, and laser technologies and compare 3D cancer models with 2D cancer models. We discuss bioprinted models that mimic the tumor microenvironment, providing a platform for deeper understanding of cancer pathology, anticancer drug screening, and cancer treatment development. Copyright © 2015 Elsevier Ltd. All rights reserved.
Boundary-based cellwise OPC for standard-cell layouts

NASA Astrophysics Data System (ADS)

Pawlowski, David M.; Deng, Liang; Wong, Martin D. F.

2007-03-01

Model based optical proximity correction (OPC) has become necessary at 90nm technology node. Cellwise OPC is an attractive technique to reduce the mask data size as well as the prohibitive runtime of full-chip OPC. As feature dimensions have gotten smaller, the radius of influence for edge features has extended further into neighboring cells such that it is no longer sufficient to perform cellwise OPC independent of neighboring cells, especially for the critical layers. The methodology described in this work accounts for features in neighboring cells and allows a cellwise approach to be applied to cells with a printed gate length of 45nm with the projection that it can also be applied to future technology nodes. OPC-ready cells are generated at library creation (independent of placement) using a boundary-based technique. Each cell has a tractable number of OPC-ready versions due to an intelligent characterization of standard cell layout features. Results are very promising: the average edge placement error (EPE) for all metal1 features in 100 layouts is 0.731nm which is less than 1% of metal1 width; the maximum EPE for poly features reduced to 1/3, compared to cellwise OPC without considering boundaries, creating similar levels of lithographic accuracy while obviating any of the drawbacks inherent in layout specific full-chip model-based OPC.
In vitro micro-physiological immune-competent model of the human skin.

PubMed

Ramadan, Qasem; Ting, Fiona Chia Wan

2016-05-21

Skin allergy, in particular, allergic contact dermatitis and irritant contact dermatitis, are common occupational and environmental health problems affecting the quality of life of a significant proportion of the world population. Since all new ingredients to be incorporated into a product are potential skin allergens, it is essential that these ingredients be first tested for their allergenic potential. However, despite the considerable effort using animal models to understand the underlying mechanism of skin sensitization, to date, the molecular and cellular responses due to skin contact with sensitizers are still not fully understood. To replace animal testing and to improve the prediction of skin sensitization, significant attention has been directed to the use of reconstructed organotypic in vitro models of human skin. Here we describe a miniaturized immune competent in vitro model of human skin based on 3D co-culture of immortalized human keratinocytes (HaCaT) as a model of the epidermis barrier and human leukemic monocyte lymphoma cell line (U937) as a model of human dendritic cells. The biological model was fitted in a microfluidic-based cell culture system that provides a dynamic cellular environment that mimics the in vivo environment of skin. The dynamic perfusion of culture media significantly improved the tight junction formation as evidenced by measuring higher values of TEER compared to static culture. This setting also maintained the high viability of cells over extended periods of time up to 17 days. The perfusion-based culture also allows growth of the cells at the air-liquid interface by exposing the apical side of the cells to air while providing the cell nutrients through a basolateral fluidic compartment. The microsystem has been evaluated to investigate the effect of the chemical and physical (UV irradiation) stimulation on the skin barrier (i.e. the TJ integrity). Three-tiered culture differential stimulation allowed the investigation of the role of the keratinocyte layer as a protection barrier to chemical/biological hazards.
Formation of rarefaction waves in origami-based metamaterials

NASA Astrophysics Data System (ADS)

Yasuda, H.; Chong, C.; Charalampidis, E. G.; Kevrekidis, P. G.; Yang, J.

2016-04-01

We investigate the nonlinear wave dynamics of origami-based metamaterials composed of Tachi-Miura polyhedron (TMP) unit cells. These cells exhibit strain softening behavior under compression, which can be tuned by modifying their geometrical configurations or initial folded conditions. We assemble these TMP cells into a cluster of origami-based metamaterials, and we theoretically model and numerically analyze their wave transmission mechanism under external impact. Numerical simulations show that origami-based metamaterials can provide a prototypical platform for the formation of nonlinear coherent structures in the form of rarefaction waves, which feature a tensile wavefront upon the application of compression to the system. We also demonstrate the existence of numerically exact traveling rarefaction waves in an effective lumped-mass model. Origami-based metamaterials can be highly useful for mitigating shock waves, potentially enabling a wide variety of engineering applications.
A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform

PubMed Central

Rizvi, Imran; Moon, Sangjun; Hasan, Tayyaba; Demirci, Utkan

2013-01-01

In vitro 3D cancer models that provide a more accurate representation of disease in vivo are urgently needed to improve our understanding of cancer pathology and to develop better cancer therapies. However, development of 3D models that are based on manual ejection of cells from micropipettes suffer from inherent limitations such as poor control over cell density, limited repeatability, low throughput, and, in the case of coculture models, lack of reproducible control over spatial distance between cell types (e.g., cancer and stromal cells). In this study, we build on a recently introduced 3D model in which human ovarian cancer (OVCAR-5) cells overlaid on Matrigel™ spontaneously form multicellular acini. We introduce a high-throughput automated cell printing system to bioprint a 3D coculture model using cancer cells and normal fibroblasts micropatterned on Matrigel™. Two cell types were patterned within a spatially controlled microenvironment (e.g., cell density, cell-cell distance) in a high-throughput and reproducible manner; both cell types remained viable during printing and continued to proliferate following patterning. This approach enables the miniaturization of an established macro-scale 3D culture model and would allow systematic investigation into the multiple unknown regulatory feedback mechanisms between tumor and stromal cells and provide a tool for high-throughput drug screening. PMID:21298805
XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

PubMed

Weiler, Gabriele; Schwarz, Ulf; Rauch, Jochen; Rohm, Kerstin; Lehr, Thorsten; Theobald, Stefan; Kiefer, Stephan; Götz, Katharina; Och, Katharina; Pfeifer, Nico; Handl, Lisa; Smola, Sigrun; Ihle, Matthias; Turki, Amin T; Beelen, Dietrich W; Rissland, Jürgen; Bittenbring, Jörg; Graf, Norbert

2018-01-01

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.
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.
Lymphocyte-based model systems for allergy research: a historic overview.

PubMed

Neunkirchner, Alina; Schmetterer, Klaus G; Pickl, Winfried F

2014-01-01

During the last decades, a multitude of studies applying distinct in vitro and in vivo model systems have contributed greatly to our better understanding of the initiation and regulation of inflammatory processes leading to allergic diseases. Over the years, it has become evident that among lymphocytes, not only IgE-producing B cells and allergy-orchestrating CD4(+) helper cells but also cytotoxic CD8(+) T cells, γδ-T cells and innate lymphoid cells, as well as regulatory lymphocytes, might critically shape the immune response towards usually innocuous allergens. In this review, we provide a historic overview of pioneering work leading to the establishment of important lymphocyte-based model systems for allergy research. Moreover, we contrast the original findings with our currently more refined knowledge to appreciate the actual validity of the respective models and to reassess the conclusions obtained from them. Conflicting studies and interpretations are identified and discussed. The tables are intended to provide an easy overview of the field not only for scientists newly entering the field but also for the broader readership interested in updating their knowledge. Along those lines, herein we discuss in vitro and in vivo approaches to the investigation of lymphocyte effector cell activation, polarization and regulation, and describe depletion and adoptive transfer models along with gene knockout and transgenic (tg) methodologies. In addition, novel attempts to establish humanized T cell antigen receptor tg mouse models for allergy research are described and discussed. © 2014 S. Karger AG, Basel.
Highly Adaptable Triple-Negative Breast Cancer Cells as a Functional Model for Testing Anticancer Agents

PubMed Central

Singh, Balraj; Shamsnia, Anna; Raythatha, Milan R.; Milligan, Ryan D.; Cady, Amanda M.; Madan, Simran; Lucci, Anthony

2014-01-01

A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling highly abnormal and highly adaptable human triple-negative breast cancer cells, and evaluating therapies for their ability to eradicate such cells. To overcome the shortcomings often associated with cell culture models, we incorporated several features in our model including a selection of highly adaptable cancer cells based on their ability to survive a metabolic challenge. We have previously shown that metabolically adaptable cancer cells efficiently metastasize to multiple organs in nude mice. Here we show that the cancer cells modeled in our system feature an embryo-like gene expression and amplification of the fat mass and obesity associated gene FTO. We also provide evidence of upregulation of ZEB1 and downregulation of GRHL2 indicating increased epithelial to mesenchymal transition in metabolically adaptable cancer cells. Our results obtained with a variety of anticancer agents support the validity of the model of realistic panresistance and suggest that it could be used for developing anticancer agents that would overcome panresistance. PMID:25279830
Modelling Spread of Oncolytic Viruses in Heterogeneous Cell Populations

NASA Astrophysics Data System (ADS)

Ellis, Michael; Dobrovolny, Hana

2014-03-01

One of the most promising areas in current cancer research and treatment is the use of viruses to attack cancer cells. A number of oncolytic viruses have been identified to date that possess the ability to destroy or neutralize cancer cells while inflicting minimal damage upon healthy cells. Formulation of predictive models that correctly describe the evolution of infected tumor systems is critical to the successful application of oncolytic virus therapy. A number of different models have been proposed for analysis of the oncolytic virus-infected tumor system, with approaches ranging from traditional coupled differential equations such as the Lotka-Volterra predator-prey models, to contemporary modeling frameworks based on neural networks and cellular automata. Existing models are focused on tumor cells and the effects of virus infection, and offer the potential for improvement by including effects upon normal cells. We have recently extended the traditional framework to a 2-cell model addressing the full cellular system including tumor cells, normal cells, and the impacts of viral infection upon both populations. Analysis of the new framework reveals complex interaction between the populations and potential inability to simultaneously eliminate the virus and tumor populations.
Highly adaptable triple-negative breast cancer cells as a functional model for testing anticancer agents.

PubMed

Singh, Balraj; Shamsnia, Anna; Raythatha, Milan R; Milligan, Ryan D; Cady, Amanda M; Madan, Simran; Lucci, Anthony

2014-01-01

A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling highly abnormal and highly adaptable human triple-negative breast cancer cells, and evaluating therapies for their ability to eradicate such cells. To overcome the shortcomings often associated with cell culture models, we incorporated several features in our model including a selection of highly adaptable cancer cells based on their ability to survive a metabolic challenge. We have previously shown that metabolically adaptable cancer cells efficiently metastasize to multiple organs in nude mice. Here we show that the cancer cells modeled in our system feature an embryo-like gene expression and amplification of the fat mass and obesity associated gene FTO. We also provide evidence of upregulation of ZEB1 and downregulation of GRHL2 indicating increased epithelial to mesenchymal transition in metabolically adaptable cancer cells. Our results obtained with a variety of anticancer agents support the validity of the model of realistic panresistance and suggest that it could be used for developing anticancer agents that would overcome panresistance.
Computational Model of Secondary Palate Fusion and Disruption

EPA Science Inventory

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks...
Linking stem cell function and growth pattern of intestinal organoids.

PubMed

Thalheim, Torsten; Quaas, Marianne; Herberg, Maria; Braumann, Ulf-Dietrich; Kerner, Christiane; Loeffler, Markus; Aust, Gabriela; Galle, Joerg

2018-01-15

Intestinal stem cells (ISCs) require well-defined signals from their environment in order to carry out their specific functions. Most of these signals are provided by neighboring cells that form a stem cell niche, whose shape and cellular composition self-organize. Major features of this self-organization can be studied in ISC-derived organoid culture. In this system, manipulation of essential pathways of stem cell maintenance and differentiation results in well-described growth phenotypes. We here provide an individual cell-based model of intestinal organoids that enables a mechanistic explanation of the observed growth phenotypes. In simulation studies of the 3D structure of expanding organoids, we investigate interdependences between Wnt- and Notch-signaling which control the shape of the stem cell niche and, thus, the growth pattern of the organoids. Similar to in vitro experiments, changes of pathway activities alter the cellular composition of the organoids and, thereby, affect their shape. Exogenous Wnt enforces transitions from branched into a cyst-like growth pattern; known to occur spontaneously during long term organoid expansion. Based on our simulation results, we predict that the cyst-like pattern is associated with biomechanical changes of the cells which assign them a growth advantage. The results suggest ongoing stem cell adaptation to in vitro conditions during long term expansion by stabilizing Wnt-activity. Our study exemplifies the potential of individual cell-based modeling in unraveling links between molecular stem cell regulation and 3D growth of tissues. This kind of modeling combines experimental results in the fields of stem cell biology and cell biomechanics constituting a prerequisite for a better understanding of tissue regeneration as well as developmental processes. Copyright © 2017 Elsevier Inc. All rights reserved.
Targeted cellular ablation based on the morphology of malignant cells

NASA Astrophysics Data System (ADS)

Ivey, Jill W.; Latouche, Eduardo L.; Sano, Michael B.; Rossmeisl, John H.; Davalos, Rafael V.; Verbridge, Scott S.

2015-11-01

Treatment of glioblastoma multiforme (GBM) is especially challenging due to a shortage of methods to preferentially target diffuse infiltrative cells, and therapy-resistant glioma stem cell populations. Here we report a physical treatment method based on electrical disruption of cells, whose action depends strongly on cellular morphology. Interestingly, numerical modeling suggests that while outer lipid bilayer disruption induced by long pulses (~100 μs) is enhanced for larger cells, short pulses (~1 μs) preferentially result in high fields within the cell interior, which scale in magnitude with nucleus size. Because enlarged nuclei represent a reliable indicator of malignancy, this suggested a means of preferentially targeting malignant cells. While we demonstrate killing of both normal and malignant cells using pulsed electric fields (PEFs) to treat spontaneous canine GBM, we proposed that properly tuned PEFs might provide targeted ablation based on nuclear size. Using 3D hydrogel models of normal and malignant brain tissues, which permit high-resolution interrogation during treatment testing, we confirmed that PEFs could be tuned to preferentially kill cancerous cells. Finally, we estimated the nuclear envelope electric potential disruption needed for cell death from PEFs. Our results may be useful in safely targeting the therapy-resistant cell niches that cause recurrence of GBM tumors.
Targeted cellular ablation based on the morphology of malignant cells

PubMed Central

Ivey, Jill W.; Latouche, Eduardo L.; Sano, Michael B.; Rossmeisl, John H.; Davalos, Rafael V.; Verbridge, Scott S.

2015-01-01

Treatment of glioblastoma multiforme (GBM) is especially challenging due to a shortage of methods to preferentially target diffuse infiltrative cells, and therapy-resistant glioma stem cell populations. Here we report a physical treatment method based on electrical disruption of cells, whose action depends strongly on cellular morphology. Interestingly, numerical modeling suggests that while outer lipid bilayer disruption induced by long pulses (~100 μs) is enhanced for larger cells, short pulses (~1 μs) preferentially result in high fields within the cell interior, which scale in magnitude with nucleus size. Because enlarged nuclei represent a reliable indicator of malignancy, this suggested a means of preferentially targeting malignant cells. While we demonstrate killing of both normal and malignant cells using pulsed electric fields (PEFs) to treat spontaneous canine GBM, we proposed that properly tuned PEFs might provide targeted ablation based on nuclear size. Using 3D hydrogel models of normal and malignant brain tissues, which permit high-resolution interrogation during treatment testing, we confirmed that PEFs could be tuned to preferentially kill cancerous cells. Finally, we estimated the nuclear envelope electric potential disruption needed for cell death from PEFs. Our results may be useful in safely targeting the therapy-resistant cell niches that cause recurrence of GBM tumors. PMID:26596248

A Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays

DTIC Science & Technology

2012-01-31

of the “expected relative Kullback - Leibler distance” ( information loss) when a model is used to describe a data set [23...deconvolution of the data into cell numbers, it cannot be used to accurately assess the number of cells in a particular generation. This information could be...notation is meant to emphasize the dependence of the estimate on the particular data set used to fit the model. It should be noted that, rather
Heat transfer and vascular cambium necrosis in the boles of trees during surface fires

Treesearch

M. B. Dickinson

2002-01-01

Heat-transfer and cell-survival models are used to link surface fire behavior with vascular cambium necrosis from heating by flames. Vascular cambium cell survival was predicted with a numerical model based on the kinetics of protein denaturation and parameterized with data from the literature. Cell survival was predicted for vascular cambium temperature regimes...
Origin of the biomechanical properties of wood related to the fine structure of the multi-layered cell wall.

PubMed

Yamamoto, H; Kojima, Y; Okuyama, T; Abasolo, W P; Gril, J

2002-08-01

In this study, a basic model is introduced to describe the biomechanical properties of the wood from the viewpoint of the composite structure of its cell wall. First, the mechanical interaction between the cellulose microfibril (CMF) as a bundle framework and the lignin-hemicellulose as a matrix (MT) skeleton in the secondary wall is formulated based on "the two phase approximation." Thereafter, the origins of (1) tree growth stress, (2) shrinkage or swelling anisotropy of the wood, and (3) moisture dependency of the Young's modulus of wood along the grain were simulated using the newly introduced model. Through the model formulation; (1) the behavior of the cellulose microfibril (CMF) and the matrix substance (MT) during cell wall maturation was estimated; (2) the moisture reactivity of each cell wall constituent was investigated; and (3) a realistic model of the fine composite structure of the matured cell wall was proposed. Thus, it is expected that the fine structure and internal property of each cell wall constituent can be estimated through the analyses of the macroscopic behaviors of wood based on the two phase approximation.
Application of induced pluripotent stem cells to understand neurobiological basis of bipolar disorder and schizophrenia.

PubMed

Liu, Yao-Nan; Lu, Si-Yao; Yao, Jun

2017-09-01

The etiology of neuropsychiatric disorders, such as schizophrenia and bipolar disorder, usually involves complex combinations of genetic defects/variations and environmental impacts, which hindered, for a long time, research efforts based on animal models and patients' non-neuronal cells or post-mortem tissues. However, the development of human induced pluripotent stem cell (iPSC) technology by the Yamanaka group was immediately applied to establish cell research models for neuronal disorders. Since then, techniques to achieve highly efficient differentiation of different types of neural cells following iPSC modeling have made much progress. The fast-growing iPSC and neural differentiation techniques have brought valuable insights into the pathology and neurobiology of neuropsychiatric disorders. In this article, we first review the application of iPSC technology in modeling neuronal disorders and discuss the progress in the accompanying neural differentiation. Then, we summarize the progress in iPSC-based research that has been accomplished so far regarding schizophrenia and bipolar disorder. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.
Multi-Scale Spatio-Temporal Modeling: Lifelines of Microorganisms in Bioreactors and Tracking Molecules in Cells

NASA Astrophysics Data System (ADS)

Lapin, Alexei; Klann, Michael; Reuss, Matthias

Agent-based models are rigorous tools for simulating the interactions of individual entities, such as organisms or molecules within cells and assessing their effects on the dynamic behavior of the system as a whole. In context with bioprocess and biosystems engineering there are several interesting and important applications. This contribution aims at introducing this strategy with the aid of two examples characterized by striking distinctions in the scale of the individual entities and the mode of their interactions. In the first example a structured-segregated model is applied to travel along the lifelines of single cells in the environment of a three-dimensional turbulent field of a stirred bioreactor. The modeling approach is based on an Euler-Lagrange formulation of the system. The strategy permits one to account for the heterogeneity present in real reactors in both the fluid and cellular phases, respectively. The individual response of the cells to local variations in the extracellular concentrations is pictured by a dynamically structured model of the key reactions of the central metabolism. The approach permits analysis of the lifelines of individual cells in space and time.
An Îto stochastic differential equations model for the dynamics of the MCF-7 breast cancer cell line treated by radiotherapy.

PubMed

Oroji, Amin; Omar, Mohd; Yarahmadian, Shantia

2016-10-21

In this paper, a new mathematical model is proposed for studying the population dynamics of breast cancer cells treated by radiotherapy by using a system of stochastic differential equations. The novelty of the model is essentially in capturing the concept of the cell cycle in the modeling to be able to evaluate the tumor lifespan. According to the cell cycle, each cell belongs to one of three subpopulations G, S, or M, representing gap, synthesis and mitosis subpopulations. Cells in the M subpopulation are highly radio-sensitive, whereas cells in the S subpopulation are highly radio-resistant. Therefore, in the process of radiotherapy, cell death rates of different subpopulations are not equal. In addition, since flow cytometry is unable to detect apoptotic cells accurately, the small changes in cell death rate in each subpopulation during treatment are considered. Subsequently, the proposed model is calibrated using experimental data from previous experiments involving the MCF-7 breast cancer cell line. Consequently, the proposed model is able to predict tumor lifespan based on the number of initial carcinoma cells. The results show the effectiveness of the radiation under the condition of stability, which describes the decreasing trend of the tumor cells population. Copyright © 2016 Elsevier Ltd. All rights reserved.
Stem Cell Technology for (Epi)genetic Brain Disorders.

PubMed

Riemens, Renzo J M; Soares, Edilene S; Esteller, Manel; Delgado-Morales, Raul

2017-01-01

Despite the enormous efforts of the scientific community over the years, effective therapeutics for many (epi)genetic brain disorders remain unidentified. The common and persistent failures to translate preclinical findings into clinical success are partially attributed to the limited efficiency of current disease models. Although animal and cellular models have substantially improved our knowledge of the pathological processes involved in these disorders, human brain research has generally been hampered by a lack of satisfactory humanized model systems. This, together with our incomplete knowledge of the multifactorial causes in the majority of these disorders, as well as a thorough understanding of associated (epi)genetic alterations, has been impeding progress in gaining more mechanistic insights from translational studies. Over the last years, however, stem cell technology has been offering an alternative approach to study and treat human brain disorders. Owing to this technology, we are now able to obtain a theoretically inexhaustible source of human neural cells and precursors in vitro that offer a platform for disease modeling and the establishment of therapeutic interventions. In addition to the potential to increase our general understanding of how (epi)genetic alterations contribute to the pathology of brain disorders, stem cells and derivatives allow for high-throughput drugs and toxicity testing, and provide a cell source for transplant therapies in regenerative medicine. In the current chapter, we will demonstrate the validity of human stem cell-based models and address the utility of other stem cell-based applications for several human brain disorders with multifactorial and (epi)genetic bases, including Parkinson's disease (PD), Alzheimer's disease (AD), fragile X syndrome (FXS), Angelman syndrome (AS), Prader-Willi syndrome (PWS), and Rett syndrome (RTT).
Power conversion and control methods for renewable energy sources

NASA Astrophysics Data System (ADS)

Yu, Dachuan

2005-07-01

In recent years, there has been an increase in the use of renewable energy due to the growing concern over the pollution caused by fossil-fuel-based energy. Renewable energy sources, such as photovoltaic (PV) and fuel cell, can be used to enhance the safety, reliability, sustainability, and transmission efficiency of a power system. This dissertation focuses on the power conversion and control for two major renewable-energy sources: PV and fuel cell. Firstly, a current-based, maximum power-point tracking (MPPT) algorithm is proposed for PV energy. An economical converter system using the above scheme for converting the output from PV panels into 60 Hz AC voltage is developed and built. Secondly, a novel circuit model for the Proton Exchange Membrane (PEM) fuel-cell stack that is useful in the design and analysis of fuel-cell-based power systems is proposed. This Pspice-based model uses elements available in the Pspice library with some modifications to represent both the static and dynamic responses of a PEM fuel-cell module. The accuracy of the model is verified by comparing the simulation and experimental results. Thirdly, a DSP-controlled three-phase induction-motor drive using constant voltage over frequency is built and can be used in a fuel-cell automobile. A hydrogen sensor is used in the drive to both sound an alarm and shut down the inverter trigger pulses through the DSP. Finally, a hybrid power system consisting of PV panels and fuel cell is proposed and built. In the proposed system, PV panels can supply most of the power when the sunlight is available, and the excess power required by the load is supplied by a fuel cell. Load sharing between a fuel cell (FC) and the PV panel is investigated by both simulation and experiments.
Influence of nanotopography on periodontal ligament stem cell functions and cell sheet based periodontal regeneration

PubMed Central

Gao, Hui; Li, Bei; Zhao, Lingzhou; Jin, Yan

2015-01-01

Periodontal regeneration is an important part of regenerative medicine, with great clinical significance; however, the effects of nanotopography on the functions of periodontal ligament (PDL) stem cells (PDLSCs) and on PDLSC sheet based periodontal regeneration have never been explored. Titania nanotubes (NTs) layered on titanium (Ti) provide a good platform to study this. In the current study, the influence of NTs of different tube size on the functions of PDLSCs was observed. Afterward, an ectopic implantation model using a Ti/cell sheets/hydroxyapatite (HA) complex was applied to study the effect of the NTs on cell sheet based periodontal regeneration. The NTs were able to enhance the initial PDLSC adhesion and spread, as well as collagen secretion. With the Ti/cell sheets/HA complex model, it was demonstrated that the PDLSC sheets were capable of regenerating the PDL tissue, when combined with bone marrow mesenchymal stem cell (BMSC) sheets and HA, without the need for extra soluble chemical cues. Simultaneously, the NTs improved the periodontal regeneration result of the ectopically implanted Ti/cell sheets/HA complex, giving rise to functionally aligned collagen fiber bundles. Specifically, much denser collagen fibers, with abundant blood vessels as well as cementum-like tissue on the Ti surface, which well-resembled the structure of natural PDL, were observed in the NT5 and NT10 sample groups. Our study provides the first evidence that the nanotopographical cues obviously influence the functions of PDLSCs and improve the PDLSC sheet based periodontal regeneration size dependently, which provides new insight to the periodontal regeneration. The Ti/cell sheets/HA complex may constitute a good model to predict the effect of biomaterials on periodontal regeneration. PMID:26150714
Influence of nanotopography on periodontal ligament stem cell functions and cell sheet based periodontal regeneration.

PubMed

Gao, Hui; Li, Bei; Zhao, Lingzhou; Jin, Yan

2015-01-01

Periodontal regeneration is an important part of regenerative medicine, with great clinical significance; however, the effects of nanotopography on the functions of periodontal ligament (PDL) stem cells (PDLSCs) and on PDLSC sheet based periodontal regeneration have never been explored. Titania nanotubes (NTs) layered on titanium (Ti) provide a good platform to study this. In the current study, the influence of NTs of different tube size on the functions of PDLSCs was observed. Afterward, an ectopic implantation model using a Ti/cell sheets/hydroxyapatite (HA) complex was applied to study the effect of the NTs on cell sheet based periodontal regeneration. The NTs were able to enhance the initial PDLSC adhesion and spread, as well as collagen secretion. With the Ti/cell sheets/HA complex model, it was demonstrated that the PDLSC sheets were capable of regenerating the PDL tissue, when combined with bone marrow mesenchymal stem cell (BMSC) sheets and HA, without the need for extra soluble chemical cues. Simultaneously, the NTs improved the periodontal regeneration result of the ectopically implanted Ti/cell sheets/HA complex, giving rise to functionally aligned collagen fiber bundles. Specifically, much denser collagen fibers, with abundant blood vessels as well as cementum-like tissue on the Ti surface, which well-resembled the structure of natural PDL, were observed in the NT5 and NT10 sample groups. Our study provides the first evidence that the nanotopographical cues obviously influence the functions of PDLSCs and improve the PDLSC sheet based periodontal regeneration size dependently, which provides new insight to the periodontal regeneration. The Ti/cell sheets/HA complex may constitute a good model to predict the effect of biomaterials on periodontal regeneration.
Part I. Development of a model system for studying nitric oxide in tumors: high nitric oxide-adapted head and neck squamous cell carcinoma cell lines.

PubMed

Yarmolyuk, Yaroslav R; Vesper, Benjamin J; Paradise, William A; Elseth, Kim M; Tarjan, Gabor; Haines, G Kenneth; Radosevich, James A

2011-02-01

The free radical nitric oxide (NO) is over-expressed in many tumors, including head and neck squamous cell carcinomas (HNSCC); however, the role NO plays in tumor pathophysiology is still not well understood. We, herein, report the development of an in vitro model system which can be used to probe the role of NO in the carcinogenesis of HNSCC. Five HNSCC cell lines were adapted to a high NO (HNO) environment by gradually introducing increasing concentrations of DETA-NONOate, a nitrogen-based NO donor, to cell media. The adaptation process was carried out until a sufficiently high enough donor concentration was reached which enabled the HNO cells to survive and grow, but which was lethal to the original, unadapted ("parent") cells. The adapted HNO cells exhibited analogous morphology to the parent cells, but grew better than their corresponding parent cells in normal media, on soft agar, and in the presence of hydrogen peroxide, an oxygen-based free radical donor. These results indicate that the HNO cell lines are unique and possess biologically different properties than the parent cell lines from which they originated. The HNO/parent cell lines developed herein may be used as a model system to better understand the role NO plays in HNSCC carcinogenesis.
In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model

PubMed Central

Kolokotroni, Eleni; Dionysiou, Dimitra; Veith, Christian; Kim, Yoo-Jin; Franz, Astrid; Grgic, Aleksandar; Bohle, Rainer M.; Stamatakos, Georgios

2016-01-01

The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions. PMID:27657742
Target specific delivery of anticancer drug in silk fibroin based 3D distribution model of bone-breast cancer cells.

PubMed

Subia, Bano; Dey, Tuli; Sharma, Shaily; Kundu, Subhas C

2015-02-04

To avoid the indiscriminating action of anticancer drugs, the cancer cell specific targeting of drug molecule becomes a preferred choice for the treatment. The successful screening of the drug molecules in 2D culture system requires further validation. The failure of target specific drug in animal model raises the issue of creating a platform in between the in vitro (2D) and in vivo animal testing. The metastatic breast cancer cells migrate and settle at different sites such as bone tissue. This work evaluates the in vitro 3D model of the breast cancer and bone cells to understand the cellular interactions in the presence of a targeted anticancer drug delivery system. The silk fibroin based cytocompatible 3D scaffold is used as in vitro 3D distribution model. Human breast adenocarcinoma and osteoblast like cells are cocultured to evaluate the efficiency of doxorubicin loaded folic acid conjugated silk fibroin nanoparticle as drug delivery system. Decreasing population of the cancer cells, which lower the levels of vascular endothelial growth factors, glucose consumption, and lactate production are observed in the drug treated coculture constructs. The drug treated constructs do not show any major impact on bone mineralization. The diminished expression of osteogenic markers such as osteocalcein and alkaline phosphatase are recorded. The result indicates that this type of silk based 3D in vitro coculture model may be utilized as a bridge between the traditional 2D and animal model system to evaluate the new drug molecule (s) or to reassay the known drug molecules or to develop target specific drug in cancer research.
Cellular aging (the Hayflick limit) and species longevity: a unification model based on clonal succession.

PubMed

Juckett, D A

1987-03-01

A model is presented which proposes a specific cause-and-effect relationship between a limited cell division potential and the maximum lifespan of humans and other mammals. It is based on the clonal succession hypothesis of Kay which states that continually replicating cell beds (e.g. bone marrow, intestinal crypts, epidermis) could be composed of cells with short, well-defined division potentials. In this model, the cells of these beds are proposed to exist in an ordered hierarchy which establishes a specific sequence for cell divisions throughout the organism's lifespan. The depletion of division potential at all hierarchical levels leads to a loss of bed function and sets an intrinsic limit to species longevity. A specific hierarchy for cell proliferation is defined which allows the calculation of time to bed depletion and, ultimately, to organism mortality. The model allows the existence of a small number (n) of critical cell beds within the organism and defines organism death as the inability of any one of these beds to produce cells. The model is consistent with all major observations related to cellular and organismic aging. In particular, it links the PDLs (population doubling limit) observed for various species to their mean lifespan; it explains the slow decline in PDL as a function of age of the donor; it establishes a thermodynamically stable maximum lifespan for a disease-free population; and it can explain why tissue transplants outlive donors or hosts.
A recursive vesicle-based model protocell with a primitive model cell cycle

PubMed Central

Kurihara, Kensuke; Okura, Yusaku; Matsuo, Muneyuki; Toyota, Taro; Suzuki, Kentaro; Sugawara, Tadashi

2015-01-01

Self-organized lipid structures (protocells) have been proposed as an intermediate between nonliving material and cellular life. Synthetic production of model protocells can demonstrate the potential processes by which living cells first arose. While we have previously described a giant vesicle (GV)-based model protocell in which amplification of DNA was linked to self-reproduction, the ability of a protocell to recursively self-proliferate for multiple generations has not been demonstrated. Here we show that newborn daughter GVs can be restored to the status of their parental GVs by pH-induced vesicular fusion of daughter GVs with conveyer GVs filled with depleted substrates. We describe a primitive model cell cycle comprising four discrete phases (ingestion, replication, maturity and division), each of which is selectively activated by a specific external stimulus. The production of recursive self-proliferating model protocells represents a step towards eventual production of model protocells that are able to mimic evolution. PMID:26418735
Simple Electrolyzer Model Development for High-Temperature Electrolysis System Analysis Using Solid Oxide Electrolysis Cell

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

JaeHwa Koh; DuckJoo Yoon; Chang H. Oh

2010-07-01

An electrolyzer model for the analysis of a hydrogen-production system using a solid oxide electrolysis cell (SOEC) has been developed, and the effects for principal parameters have been estimated by sensitivity studies based on the developed model. The main parameters considered are current density, area specific resistance, temperature, pressure, and molar fraction and flow rates in the inlet and outlet. Finally, a simple model for a high-temperature hydrogen-production system using the solid oxide electrolysis cell integrated with very high temperature reactors is estimated.
A chemo-mechanical free-energy-based approach to model durotaxis and extracellular stiffness-dependent contraction and polarization of cells.

PubMed

Shenoy, Vivek B; Wang, Hailong; Wang, Xiao

2016-02-06

We propose a chemo-mechanical model based on stress-dependent recruitment of myosin motors to describe how the contractility, polarization and strain in cells vary with the stiffness of their surroundings and their shape. A contractility tensor, which depends on the distribution of myosin motors, is introduced to describe the chemical free energy of the cell due to myosin recruitment. We explicitly include the contributions to the free energy that arise from mechanosensitive signalling pathways (such as the SFX, Rho-Rock and MLCK pathways) through chemo-mechanical coupling parameters. Taking the variations of the total free energy, which consists of the chemical and mechanical components, in accordance with the second law of thermodynamics provides equations for the temporal evolution of the active stress and the contractility tensor. Following this approach, we are able to recover the well-known Hill relation for active stresses, based on the fundamental principles of irreversible thermodynamics rather than phenomenology. We have numerically implemented our free energy-based approach to model spatial distribution of strain and contractility in (i) cells supported by flexible microposts, (ii) cells on two-dimensional substrates, and (iii) cells in three-dimensional matrices. We demonstrate how the polarization of the cells and the orientation of stress fibres can be deduced from the eigenvalues and eigenvectors of the contractility tensor. Our calculations suggest that the chemical free energy of the cell decreases with the stiffness of the extracellular environment as the cytoskeleton polarizes in response to stress-dependent recruitment of molecular motors. The mechanical energy, which includes the strain energy and motor potential energy, however, increases with stiffness, but the overall energy is lower for cells in stiffer environments. This provides a thermodynamic basis for durotaxis, whereby cells preferentially migrate towards stiffer regions of the extracellular environment. Our models also explain, from an energetic perspective, why the shape of the cells can change in response to stiffness of the surroundings. The effect of the stiffness of the nucleus on its shape and the orientation of the stress fibres is also studied for all the above geometries. Along with making testable predictions, we have estimated the magnitudes of the chemo-mechanical coupling parameters for myofibroblasts based on data reported in the literature.
A chemo-mechanical free-energy-based approach to model durotaxis and extracellular stiffness-dependent contraction and polarization of cells

PubMed Central

Shenoy, Vivek B.; Wang, Hailong; Wang, Xiao

2016-01-01

We propose a chemo-mechanical model based on stress-dependent recruitment of myosin motors to describe how the contractility, polarization and strain in cells vary with the stiffness of their surroundings and their shape. A contractility tensor, which depends on the distribution of myosin motors, is introduced to describe the chemical free energy of the cell due to myosin recruitment. We explicitly include the contributions to the free energy that arise from mechanosensitive signalling pathways (such as the SFX, Rho-Rock and MLCK pathways) through chemo-mechanical coupling parameters. Taking the variations of the total free energy, which consists of the chemical and mechanical components, in accordance with the second law of thermodynamics provides equations for the temporal evolution of the active stress and the contractility tensor. Following this approach, we are able to recover the well-known Hill relation for active stresses, based on the fundamental principles of irreversible thermodynamics rather than phenomenology. We have numerically implemented our free energy-based approach to model spatial distribution of strain and contractility in (i) cells supported by flexible microposts, (ii) cells on two-dimensional substrates, and (iii) cells in three-dimensional matrices. We demonstrate how the polarization of the cells and the orientation of stress fibres can be deduced from the eigenvalues and eigenvectors of the contractility tensor. Our calculations suggest that the chemical free energy of the cell decreases with the stiffness of the extracellular environment as the cytoskeleton polarizes in response to stress-dependent recruitment of molecular motors. The mechanical energy, which includes the strain energy and motor potential energy, however, increases with stiffness, but the overall energy is lower for cells in stiffer environments. This provides a thermodynamic basis for durotaxis, whereby cells preferentially migrate towards stiffer regions of the extracellular environment. Our models also explain, from an energetic perspective, why the shape of the cells can change in response to stiffness of the surroundings. The effect of the stiffness of the nucleus on its shape and the orientation of the stress fibres is also studied for all the above geometries. Along with making testable predictions, we have estimated the magnitudes of the chemo-mechanical coupling parameters for myofibroblasts based on data reported in the literature. PMID:26855753
Extended Latanoprost Release from Commercial Contact Lenses: In Vitro Studies Using Corneal Models

PubMed Central

Mohammadi, Saman; Jones, Lyndon; Gorbet, Maud

2014-01-01

In this study, we compared, for the first time, the release of a 432 kDa prostaglandin analogue drug, Latanoprost, from commercially available contact lenses using in vitro models with corneal epithelial cells. Conventional polyHEMA-based and silicone hydrogel soft contact lenses were soaked in drug solution ( solution in phosphate buffered saline). The drug release from the contact lens material and its diffusion through three in vitro models was studied. The three in vitro models consisted of a polyethylene terephthalate (PET) membrane without corneal epithelial cells, a PET membrane with a monolayer of human corneal epithelial cells (HCEC), and a PET membrane with stratified HCEC. In the cell-based in vitro corneal epithelium models, a zero order release was obtained with the silicone hydrogel materials (linear for the duration of the experiment) whereby, after 48 hours, between 4 to 6 of latanoprost (an amount well within the range of the prescribed daily dose for glaucoma patients) was released. In the absence of cells, a significantly lower amount of drug, between 0.3 to 0.5 , was released, (). The difference observed in release from the hydrogel lens materials in the presence and absence of cells emphasizes the importance of using an in vitro corneal model that is more representative of the physiological conditions in the eye to more adequately characterize ophthalmic drug delivery materials. Our results demonstrate how in vitro models with corneal epithelial cells may allow better prediction of in vivo release. It also highlights the potential of drug-soaked silicone hydrogel contact lens materials for drug delivery purposes. PMID:25207851
Plasmonic Light Trapping in Thin-Film Solar Cells: Impact of Modeling on Performance Prediction

PubMed Central

Micco, Alberto; Pisco, Marco; Ricciardi, Armando; Mercaldo, Lucia V.; Usatii, Iurie; La Ferrara, Vera; Delli Veneri, Paola; Cutolo, Antonello; Cusano, Andrea

2015-01-01

We present a comparative study on numerical models used to predict the absorption enhancement in thin-film solar cells due to the presence of structured back-reflectors exciting, at specific wavelengths, hybrid plasmonic-photonic resonances. To evaluate the effectiveness of the analyzed models, they have been applied in a case study: starting from a U-shaped textured glass thin-film, µc-Si:H solar cells have been successfully fabricated. The fabricated cells, with different intrinsic layer thicknesses, have been morphologically, optically and electrically characterized. The experimental results have been successively compared with the numerical predictions. We have found that, in contrast to basic models based on the underlying schematics of the cell, numerical models taking into account the real morphology of the fabricated device, are able to effectively predict the cells performances in terms of both optical absorption and short-circuit current values.

Modelling cell motility and chemotaxis with evolving surface finite elements

PubMed Central

Elliott, Charles M.; Stinner, Björn; Venkataraman, Chandrasekhar

2012-01-01

We present a mathematical and a computational framework for the modelling of cell motility. The cell membrane is represented by an evolving surface, with the movement of the cell determined by the interaction of various forces that act normal to the surface. We consider external forces such as those that may arise owing to inhomogeneities in the medium and a pressure that constrains the enclosed volume, as well as internal forces that arise from the reaction of the cells' surface to stretching and bending. We also consider a protrusive force associated with a reaction–diffusion system (RDS) posed on the cell membrane, with cell polarization modelled by this surface RDS. The computational method is based on an evolving surface finite-element method. The general method can account for the large deformations that arise in cell motility and allows the simulation of cell migration in three dimensions. We illustrate applications of the proposed modelling framework and numerical method by reporting on numerical simulations of a model for eukaryotic chemotaxis and a model for the persistent movement of keratocytes in two and three space dimensions. Movies of the simulated cells can be obtained from http://homepages.warwick.ac.uk/∼maskae/CV_Warwick/Chemotaxis.html. PMID:22675164
Empirical correlations of the performance of vapor-anode PX-series AMTEC cells

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

Huang, L.; Merrill, J.M.; Mayberry, C.

Power systems based on AMTEC technology will be used for future NASA missions, including a Pluto-Express (PX) or Europa mission planned for approximately year 2004. AMTEC technology may also be used as an alternative to photovoltaic based power systems for future Air Force missions. An extensive development program of Alkali-Metal Thermal-to-Electric Conversion (AMTEC) technology has been underway at the Vehicle Technologies Branch of the Air Force Research Laboratory (AFRL) in Albuquerque, New Mexico since 1992. Under this program, numerical modeling and experimental investigations of the performance of the various multi-BASE tube, vapor-anode AMTEC cells have been and are being performed.more » Vacuum testing of AMTEC cells at AFRL determines the effects of changing the hot and cold end temperatures, T{sub hot} and T{sub cold}, and applied external load, R{sub ext}, on the cell electric power output, current-voltage characteristics, and conversion efficiency. Test results have traditionally been used to provide feedback to cell designers, and to validate numerical models. The current work utilizes the test data to develop empirical correlations for cell output performance under various working conditions. Because the empirical correlations are developed directly from the experimental data, uncertainties arising from material properties that must be used in numerical modeling can be avoided. Empirical correlations of recent vapor-anode PX-series AMTEC cells have been developed. Based on AMTEC theory and the experimental data, the cell output power (as well as voltage and current) was correlated as a function of three parameters (T{sub hot}, T{sub cold}, and R{sub ext}) for a given cell. Correlations were developed for different cells (PX-3C, PX-3A, PX-G3, and PX-5A), and were in good agreement with experimental data for these cells. Use of these correlations can greatly reduce the testing required to determine electrical performance of a given type of AMTEC cell over a wide range of operating conditions.« less
Model-based analysis of keratin intermediate filament assembly

NASA Astrophysics Data System (ADS)

Martin, Ines; Leitner, Anke; Walther, Paul; Herrmann, Harald; Marti, Othmar

2015-09-01

The cytoskeleton of epithelial cells consists of three types of filament systems: microtubules, actin filaments and intermediate filaments (IFs). Here, we took a closer look at type I and type II IF proteins, i.e. keratins. They are hallmark constituents of epithelial cells and are responsible for the generation of stiffness, the cellular response to mechanical stimuli and the integrity of entire cell layers. Thereby, keratin networks constitute an important instrument for cells to adapt to their environment. In particular, we applied models to characterize the assembly of keratin K8 and K18 into elongated filaments as a means for network formation. For this purpose, we measured the length of in vitro assembled keratin K8/K18 filaments by transmission electron microscopy at different time points. We evaluated the experimental data of the longitudinal annealing reaction using two models from polymer chemistry: the Schulz-Zimm model and the condensation polymerization model. In both scenarios one has to make assumptions about the reaction process. We compare how well the models fit the measured data and thus determine which assumptions fit best. Based on mathematical modelling of experimental filament assembly data we define basic mechanistic properties of the elongation reaction process.
Cell-based dose responses from open-well microchambers.

PubMed

Hamon, Morgan; Jambovane, Sachin; Bradley, Lauren; Khademhosseini, Ali; Hong, Jong Wook

2013-05-21

Cell-based assays play a critical role in discovery of new drugs and facilitating research in cancer, immunology, and stem cells. Conventionally, they are performed in Petri dishes, tubes, or well plates, using milliliters of reagents and thousands of cells to obtain one data point. Here, we are introducing a new platform to realize cell-based assay capable of increased throughput and greater sensitivity with a limited number of cells. We integrated an array of open-well microchambers into a gradient generation system. Consequently, cell-based dose responses were examined with a single device. We measured IC50 values of three cytotoxic chemicals, Triton X-100, H2O2, and cadmium chloride, as model compounds. The present system is highly suitable for the discovery of new drugs and studying the effect of chemicals on cell viability or mortality with limited samples and cells.
PEA and luteolin synergistically reduce mast cell-mediated toxicity and elicit neuroprotection in cell-based models of brain ischemia.

PubMed

Parrella, Edoardo; Porrini, Vanessa; Iorio, Rosa; Benarese, Marina; Lanzillotta, Annamaria; Mota, Mariana; Fusco, Mariella; Tonin, Paolo; Spano, PierFranco; Pizzi, Marina

2016-10-01

The combination of palmitoylethanolamide (PEA), an endogenous fatty acid amide belonging to the family of the N-acylethanolamines, and the flavonoid luteolin has been found to exert neuroprotective activities in a variety of mouse models of neurological disorders, including brain ischemia. Indirect findings suggest that the two molecules can reduce the activation of mastocytes in brain ischemia, thus modulating crucial cells that trigger the inflammatory cascade. Though, no evidence exists about a direct effect of PEA and luteolin on mast cells in experimental models of brain ischemia, either used separately or in combination. In order to fill this gap, we developed a novel cell-based model of severe brain ischemia consisting of primary mouse cortical neurons and cloned mast cells derived from mouse fetal liver (MC/9 cells) subjected to oxygen and glucose deprivation (OGD). OGD exposure promoted both mast cell degranulation and the release of lactate dehydrogenase (LDH) in a time-dependent fashion. MC/9 cells exacerbated neuronal damage in neuron-mast cells co-cultures exposed to OGD. Likewise, the conditioned medium derived from OGD-exposed MC/9 cells induced significant neurotoxicity in control primary neurons. PEA and luteolin pre-treatment synergistically prevented the OGD-induced degranulation of mast cells and reduced the neurotoxic potential of MC/9 cells conditioned medium. Finally, the association of the two drugs promoted a direct synergistic neuroprotection even in pure cortical neurons exposed to OGD. In summary, our results indicate that mast cells release neurotoxic factors upon OGD-induced activation. The association PEA-luteolin actively reduces mast cell-mediated neurotoxicity as well as pure neurons susceptibility to OGD. Copyright © 2016 Elsevier B.V. All rights reserved.
A novel, long-lived, and highly engraftable immunodeficient mouse model of mucopolysaccharidosis type I.

PubMed

Mendez, Daniel C; Stover, Alexander E; Rangel, Anthony D; Brick, David J; Nethercott, Hubert E; Torres, Marissa A; Khalid, Omar; Wong, Andrew Ms; Cooper, Jonathan D; Jester, James V; Monuki, Edwin S; McGuire, Cian; Le, Steven Q; Kan, Shih-Hsin; Dickson, Patricia I; Schwartz, Philip H

2015-01-01

Mucopolysaccharidosis type I (MPS I) is an inherited α-L-iduronidase (IDUA, I) deficiency in which glycosaminoglycan (GAG) accumulation causes progressive multisystem organ dysfunction, neurological impairment, and death. Current MPS I mouse models, based on a NOD/SCID (NS) background, are short-lived, providing a very narrow window to assess the long-term efficacy of therapeutic interventions. They also develop thymic lymphomas, making the assessment of potential tumorigenicity of human stem cell transplantation problematic. We therefore developed a new MPS I model based on a NOD/SCID/Il2rγ (NSG) background. This model lives longer than 1 year and is tumor-free during that time. NSG MPS I (NSGI) mice exhibit the typical phenotypic features of MPS I including coarsened fur and facial features, reduced/abnormal gait, kyphosis, and corneal clouding. IDUA is undetectable in all tissues examined while GAG levels are dramatically higher in most tissues. NSGI brain shows a significant inflammatory response and prominent gliosis. Neurological MPS I manifestations are evidenced by impaired performance in behavioral tests. Human neural and hematopoietic stem cells were found to readily engraft, with human cells detectable for at least 1 year posttransplantation. This new MPS I model is thus suitable for preclinical testing of novel pluripotent stem cell-based therapy approaches.
A novel, long-lived, and highly engraftable immunodeficient mouse model of mucopolysaccharidosis type I

PubMed Central

Mendez, Daniel C; Stover, Alexander E; Rangel, Anthony D; Brick, David J; Nethercott, Hubert E; Torres, Marissa A; Khalid, Omar; Wong, Andrew MS; Cooper, Jonathan D; Jester, James V; Monuki, Edwin S; McGuire, Cian; Le, Steven Q; Kan, Shih-hsin; Dickson, Patricia I; Schwartz, Philip H

2015-01-01

Mucopolysaccharidosis type I (MPS I) is an inherited α-L-iduronidase (IDUA, I) deficiency in which glycosaminoglycan (GAG) accumulation causes progressive multisystem organ dysfunction, neurological impairment, and death. Current MPS I mouse models, based on a NOD/SCID (NS) background, are short-lived, providing a very narrow window to assess the long-term efficacy of therapeutic interventions. They also develop thymic lymphomas, making the assessment of potential tumorigenicity of human stem cell transplantation problematic. We therefore developed a new MPS I model based on a NOD/SCID/Il2rγ (NSG) background. This model lives longer than 1 year and is tumor-free during that time. NSG MPS I (NSGI) mice exhibit the typical phenotypic features of MPS I including coarsened fur and facial features, reduced/abnormal gait, kyphosis, and corneal clouding. IDUA is undetectable in all tissues examined while GAG levels are dramatically higher in most tissues. NSGI brain shows a significant inflammatory response and prominent gliosis. Neurological MPS I manifestations are evidenced by impaired performance in behavioral tests. Human neural and hematopoietic stem cells were found to readily engraft, with human cells detectable for at least 1 year posttransplantation. This new MPS I model is thus suitable for preclinical testing of novel pluripotent stem cell-based therapy approaches. PMID:26052536
A noise model for the evaluation of defect states in solar cells

PubMed Central

Landi, G.; Barone, C.; Mauro, C.; Neitzert, H. C.; Pagano, S.

2016-01-01

A theoretical model, combining trapping/detrapping and recombination mechanisms, is formulated to explain the origin of random current fluctuations in silicon-based solar cells. In this framework, the comparison between dark and photo-induced noise allows the determination of important electronic parameters of the defect states. A detailed analysis of the electric noise, at different temperatures and for different illumination levels, is reported for crystalline silicon-based solar cells, in the pristine form and after artificial degradation with high energy protons. The evolution of the dominating defect properties is studied through noise spectroscopy. PMID:27412097
Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.
Remaining useful life assessment of lithium-ion batteries in implantable medical devices

NASA Astrophysics Data System (ADS)

Hu, Chao; Ye, Hui; Jain, Gaurav; Schmidt, Craig

2018-01-01

This paper presents a prognostic study on lithium-ion batteries in implantable medical devices, in which a hybrid data-driven/model-based method is employed for remaining useful life assessment. The method is developed on and evaluated against data from two sets of lithium-ion prismatic cells used in implantable applications exhibiting distinct fade performance: 1) eight cells from Medtronic, PLC whose rates of capacity fade appear to be stable and gradually decrease over a 10-year test duration; and 2) eight cells from Manufacturer X whose rates appear to be greater and show sharp increase after some period over a 1.8-year test duration. The hybrid method enables online prediction of remaining useful life for predictive maintenance/control. It consists of two modules: 1) a sparse Bayesian learning module (data-driven) for inferring capacity from charge-related features; and 2) a recursive Bayesian filtering module (model-based) for updating empirical capacity fade models and predicting remaining useful life. A generic particle filter is adopted to implement recursive Bayesian filtering for the cells from the first set, whose capacity fade behavior can be represented by a single fade model; a multiple model particle filter with fixed-lag smoothing is proposed for the cells from the second data set, whose capacity fade behavior switches between multiple fade models.
Supraphysiologic control over HIV-1 replication mediated by CD8 T cells expressing a re-engineered CD4-based chimeric antigen receptor

PubMed Central

Richardson, Max W.; Ellebrecht, Christoph T.; Glover, Joshua A.; Secreto, Anthony J.; Kulikovskaya, Irina; Yi, Yanjie; Wang, Jianbin; Dufendach, Keith A.; Holmes, Michael C.; Collman, Ronald G.

2017-01-01

HIV is adept at avoiding naturally generated T cell responses; therefore, there is a need to develop HIV-specific T cells with greater potency for use in HIV cure strategies. Starting with a CD4-based chimeric antigen receptor (CAR) that was previously used without toxicity in clinical trials, we optimized the vector backbone, promoter, HIV targeting moiety, and transmembrane and signaling domains to determine which components augmented the ability of T cells to control HIV replication. This re-engineered CAR was at least 50-fold more potent in vitro at controlling HIV replication than the original CD4 CAR, or a TCR-based approach, and substantially better than broadly neutralizing antibody-based CARs. A humanized mouse model of HIV infection demonstrated that T cells expressing optimized CARs were superior at expanding in response to antigen, protecting CD4 T cells from infection, and reducing viral loads compared to T cells expressing the original, clinical trial CAR. Moreover, in a humanized mouse model of HIV treatment, CD4 CAR T cells containing the 4-1BB costimulatory domain controlled HIV spread after ART removal better than analogous CAR T cells containing the CD28 costimulatory domain. Together, these data indicate that potent HIV-specific T cells can be generated using improved CAR design and that CAR T cells could be important components of an HIV cure strategy. PMID:29023549
A pressure relaxation closure model for one-dimensional, two-material Lagrangian hydrodynamics based on the Riemann problem

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

Kamm, James R; Shashkov, Mikhail J

2009-01-01

Despite decades of development, Lagrangian hydrodynamics of strengthfree materials presents numerous open issues, even in one dimension. We focus on the problem of closing a system of equations for a two-material cell under the assumption of a single velocity model. There are several existing models and approaches, each possessing different levels of fidelity to the underlying physics and each exhibiting unique features in the computed solutions. We consider the case in which the change in heat in the constituent materials in the mixed cell is assumed equal. An instantaneous pressure equilibration model for a mixed cell can be cast asmore » four equations in four unknowns, comprised of the updated values of the specific internal energy and the specific volume for each of the two materials in the mixed cell. The unique contribution of our approach is a physics-inspired, geometry-based model in which the updated values of the sub-cell, relaxing-toward-equilibrium constituent pressures are related to a local Riemann problem through an optimization principle. This approach couples the modeling problem of assigning sub-cell pressures to the physics associated with the local, dynamic evolution. We package our approach in the framework of a standard predictor-corrector time integration scheme. We evaluate our model using idealized, two material problems using either ideal-gas or stiffened-gas equations of state and compare these results to those computed with the method of Tipton and with corresponding pure-material calculations.« less
Persistence of Rift Valley fever virus in East Africa

NASA Astrophysics Data System (ADS)

Gachohi, J.; Hansen, F.; Bett, B.; Kitala, P.

2012-04-01

Rift Valley fever virus (RVFv) is a mosquito-borne pathogen of livestock, wildlife and humans that causes severe outbreaks in intervals of several years. One of the open questions is how the virus persists between outbreaks. We developed a spatially-explicit, individual-based simulation model of the RVFv transmission dynamics to investigate this question. The model, is based on livestock and mosquito population dynamics. Spatial aspects are explicitly represented by a set of grid cells that represent mosquito breeding sites. A grid cell measures 500 by 500m and the model considers a grid of 100 by 100 grid cells; the model thus operates on the regional scale of 2500km2. Livestock herds move between grid cells, and provide connectivity between the cells. The model is used to explore the spatio-temporal dynamics of RVFv persistence in absence of a wildlife reservoir in an east African semi-arid context. Specifically, the model assesses the importance of local virus persistence in mosquito breeding sites relative to global virus persistence mitigated by movement of hosts. Local persistence is determined by the length of time the virus remains in a mosquito breeding site once introduced. In the model, this is a function of the number of mosquitoes that emerge infected and their lifespan. Global persistence is determined by the level of connectivity between isolated grid cells. Our work gives insights into the ecological and epidemiological conditions under which RVFv persists. The implication for disease surveillance and management are discussed.
Stochastic Petri Net extension of a yeast cell cycle model.

PubMed

Mura, Ivan; Csikász-Nagy, Attila

2008-10-21

This paper presents the definition, solution and validation of a stochastic model of the budding yeast cell cycle, based on Stochastic Petri Nets (SPN). A specific family of SPNs is selected for building a stochastic version of a well-established deterministic model. We describe the procedure followed in defining the SPN model from the deterministic ODE model, a procedure that can be largely automated. The validation of the SPN model is conducted with respect to both the results provided by the deterministic one and the experimental results available from literature. The SPN model catches the behavior of the wild type budding yeast cells and a variety of mutants. We show that the stochastic model matches some characteristics of budding yeast cells that cannot be found with the deterministic model. The SPN model fine-tunes the simulation results, enriching the breadth and the quality of its outcome.
Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

PubMed

Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

2017-02-01

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.
A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion.

PubMed

Anderson, Alexander R A

2005-06-01

In this paper we present a hybrid mathematical model of the invasion of healthy tissue by a solid tumour. In particular we consider early vascular growth, just after angiogenesis has occurred. We examine how the geometry of the growing tumour is affected by tumour cell heterogeneity caused by genetic mutations. As the tumour grows, mutations occur leading to a heterogeneous tumour cell population with some cells having a greater ability to migrate, proliferate or degrade the surrounding tissue. All of these cell properties are closely controlled by cell-cell and cell-matrix interactions and as such the physical geometry of the whole tumour will be dependent on these individual cell interactions. The hybrid model we develop focuses on four key variables implicated in the invasion process: tumour cells, host tissue (extracellular matrix), matrix-degradative enzymes and oxygen. The model is considered to be hybrid since the latter three variables are continuous (i.e. concentrations) and the tumour cells are discrete (i.e. individuals). With this hybrid model we examine how individual-based cell interactions (with one another and the matrix) can affect the tumour shape and discuss which of these interactions is perhaps most crucial in influencing the tumour's final structure.
Calculation of optical parameters for covalent binary alloys used in optical memories/solar cells: a modified approach

NASA Astrophysics Data System (ADS)

Bhatnagar, Promod K.; Gupta, Poonam; Singh, Laxman

2001-06-01

Chalcogenide based alloys find applications in a number of devices like optical memories, IR detectors, optical switches, photovoltaics, compound semiconductor heterosrtuctures etc. We have modified the Gurman's statistical thermodynamic model (STM) of binary covalent alloys. In the Gurman's model, entropy calculations are based on the number of structural units present. The need to modify this model arose due to the fact that it gives equal probability for all the tetrahedra present in the alloy. We have modified the Gurman's model by introducing the concept that the entropy is based on the bond arrangement rather than that on the structural units present. In the present work calculation based on this modification have been presented for optical properties, which find application in optical switching/memories, solar cells and other optical devices. It has been shown that the calculated optical parameters (for a typical case of GaxSe1-x) based on modified model are closer to the available experimental results. These parameters include refractive index, extinction coefficient, dielectric functions, optical band gap etc. GaxSe1-x has been found to be suitable for reversible optical memories also, where phase change (a yields c and vice versa) takes place at specified physical conditions. DTA/DSC studies also suggest the suitability of this material for optical switching/memory applications. We have also suggested possible use of GaxSe1-x (x = 0.4) in place of oxide layer in a Metal - Oxide - Semiconductor type solar cells. The new structure is Metal - Ga2Se3 - GaAs. The I-V characteristics and other parameters calculated for this structure are found to be much better than that for Si based solar cells. Maximum output power is obtained at the intermediate layer thickness approximately 40 angstroms for this typical solar cell.
Assessing commercial opportunities for autologous and allogeneic cell-based products.

PubMed

Smith, Devyn M

2012-09-01

The two primary cell sources used to produce cell-based therapies are autologous (self-derived) and allogeneic (derived from a donor). This analysis attempts to compare and contrast the two approaches in order to understand whether there is an emerging preference in the market. While the current clinical trials underway are slightly biased to autologous approaches, it is clear that both cell-based approaches are being aggressively pursued. This analysis also breaks down the commercial advantages of each cell-based approach, comparing both cost of goods and the ideal indication type for each. While allogeneic therapies have considerable advantages over autologous therapies, they do have a distinct disadvantage regarding potential immunogenicity. The introduction of the hybrid autologous business model provides the ability for autologous-based therapies to mitigate some of the advantages that allogeneic cell-based therapies enjoy, including cost of goods. Finally, two case studies are presented that demonstrate that there is sufficient space for both autologous and allogeneic cell-based therapies within a single disease area.
Entropy-based separation of yeast cells using a microfluidic system of conjoined spheres

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

Huang, Kai-Jian; Qin, S.-J., E-mail: shuijie.qin@gmail.com; Bai, Zhong-Chen

2013-11-21

A physical model is derived to create a biological cell separator that is based on controlling the entropy in a microfluidic system having conjoined spherical structures. A one-dimensional simplified model of this three-dimensional problem in terms of the corresponding effects of entropy on the Brownian motion of particles is presented. This dynamic mechanism is based on the Langevin equation from statistical thermodynamics and takes advantage of the characteristics of the Fokker-Planck equation. This mechanism can be applied to manipulate biological particles inside a microfluidic system with identical, conjoined, spherical compartments. This theoretical analysis is verified by performing a rapid andmore » a simple technique for separating yeast cells in these conjoined, spherical microfluidic structures. The experimental results basically match with our theoretical model and we further analyze the parameters which can be used to control this separation mechanism. Both numerical simulations and experimental results show that the motion of the particles depends on the geometrical boundary conditions of the microfluidic system and the initial concentration of the diffusing material. This theoretical model can be implemented in future biophysics devices for the optimized design of passive cell sorters.« less
Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

Three-Dimensional Imaging of the Mouse Organ of Corti Cytoarchitecture for Mechanical Modeling

NASA Astrophysics Data System (ADS)

Puria, Sunil; Hartman, Byron; Kim, Jichul; Oghalai, John S.; Ricci, Anthony J.; Liberman, M. Charles

2011-11-01

Cochlear models typically use continuous anatomical descriptions and homogenized parameters based on two-dimensional images for describing the organ of Corti. To produce refined models based more closely on the actual cochlear cytoarchitecture, three-dimensional morphometric parameters of key mechanical structures are required. Towards this goal, we developed and compared three different imaging methods: (1) A fixed cochlear whole-mount preparation using the fluorescent dye Cellmask®, which is a molecule taken up by cell membranes and clearly delineates Deiters' cells, outer hair cells, and the phalangeal process, imaged using confocal microscopy; (2) An in situ fixed preparation with hair cells labeled using anti-prestin and supporting structures labeled using phalloidin, imaged using two-photon microscopy; and (3) A membrane-tomato (mT) mouse with fluorescent proteins expressed in all cell membranes, which enables two-photon imaging of an in situ live preparation with excellent visualization of the organ of Corti. Morphometric parameters including lengths, diameters, and angles, were extracted from 3D cellular surface reconstructions of the resulting images. Preliminary results indicate that the length of the phalangeal processes decreases from the first (inner most) to third (outer most) row of outer hair cells, and that their length also likely varies from base to apex and across species.
Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

NASA Astrophysics Data System (ADS)

Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

2016-03-01

Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787
Modeling neurodegenerative diseases with patient-derived induced pluripotent cells: Possibilities and challenges.

PubMed

Poon, Anna; Zhang, Yu; Chandrasekaran, Abinaya; Phanthong, Phetcharat; Schmid, Benjamin; Nielsen, Troels T; Freude, Kristine K

2017-10-25

The rising prevalence of progressive neurodegenerative diseases coupled with increasing longevity poses an economic burden at individual and societal levels. There is currently no effective cure for the majority of neurodegenerative diseases and disease-affected tissues from patients have been difficult to obtain for research and drug discovery in pre-clinical settings. While the use of animal models has contributed invaluable mechanistic insights and potential therapeutic targets, the translational value of animal models could be further enhanced when combined with in vitro models derived from patient-specific induced pluripotent stem cells (iPSCs) and isogenic controls generated using CRISPR-Cas9 mediated genome editing. The iPSCs are self-renewable and capable of being differentiated into the cell types affected by the diseases. These in vitro models based on patient-derived iPSCs provide the opportunity to model disease development, uncover novel mechanisms and test potential therapeutics. Here we review findings from iPSC-based modeling of selected neurodegenerative diseases, including Alzheimer's disease, frontotemporal dementia and spinocerebellar ataxia. Furthermore, we discuss the possibilities of generating three-dimensional (3D) models using the iPSCs-derived cells and compare their advantages and disadvantages to conventional two-dimensional (2D) models. Copyright © 2017 Elsevier B.V. All rights reserved.
Induced pluripotent stem cell technology for modelling and therapy of cerebellar ataxia

PubMed Central

Watson, Lauren M.; Wong, Maggie M. K.; Becker, Esther B. E.

2015-01-01

Induced pluripotent stem cell (iPSC) technology has emerged as an important tool in understanding, and potentially reversing, disease pathology. This is particularly true in the case of neurodegenerative diseases, in which the affected cell types are not readily accessible for study. Since the first descriptions of iPSC-based disease modelling, considerable advances have been made in understanding the aetiology and progression of a diverse array of neurodegenerative conditions, including Parkinson's disease and Alzheimer's disease. To date, however, relatively few studies have succeeded in using iPSCs to model the neurodegeneration observed in cerebellar ataxia. Given the distinct neurodevelopmental phenotypes associated with certain types of ataxia, iPSC-based models are likely to provide significant insights, not only into disease progression, but also to the development of early-intervention therapies. In this review, we describe the existing iPSC-based disease models of this heterogeneous group of conditions and explore the challenges associated with generating cerebellar neurons from iPSCs, which have thus far hindered the expansion of this research. PMID:26136256
Strategies to improve homing of mesenchymal stem cells for greater efficacy in stem cell therapy.

PubMed

Naderi-Meshkin, Hojjat; Bahrami, Ahmad Reza; Bidkhori, Hamid Reza; Mirahmadi, Mahdi; Ahmadiankia, Naghmeh

2015-01-01

Stem/progenitor cell-based therapeutic approach in clinical practice has been an elusive dream in medical sciences, and improvement of stem cell homing is one of major challenges in cell therapy programs. Stem/progenitor cells have a homing response to injured tissues/organs, mediated by interactions of chemokine receptors expressed on the cells and chemokines secreted by the injured tissue. For improvement of directed homing of the cells, many techniques have been developed either to engineer stem/progenitor cells with higher amount of chemokine receptors (stem cell-based strategies) or to modulate the target tissues to release higher level of the corresponding chemokines (target tissue-based strategies). This review discusses both of these strategies involved in the improvement of stem cell homing focusing on mesenchymal stem cells as most frequent studied model in cellular therapies. © 2014 International Federation for Cell Biology.
Peripheral T cell lymphoma, not otherwise specified (PTCL-NOS). A new prognostic model developed by the International T cell Project Network.

PubMed

Federico, Massimo; Bellei, Monica; Marcheselli, Luigi; Schwartz, Marc; Manni, Martina; Tarantino, Vittoria; Pileri, Stefano; Ko, Young-Hyeh; Cabrera, Maria E; Horwitz, Steven; Kim, Won S; Shustov, Andrei; Foss, Francine M; Nagler, Arnon; Carson, Kenneth; Pinter-Brown, Lauren C; Montoto, Silvia; Spina, Michele; Feldman, Tatyana A; Lechowicz, Mary J; Smith, Sonali M; Lansigan, Frederick; Gabus, Raul; Vose, Julie M; Advani, Ranjana H

2018-06-01

Different models to investigate the prognosis of peripheral T cell lymphoma not otherwise specified (PTCL-NOS) have been developed by means of retrospective analyses. Here we report on a new model designed on data from the prospective T Cell Project. Twelve covariates collected by the T Cell Project were analysed and a new model (T cell score), based on four covariates (serum albumin, performance status, stage and absolute neutrophil count) that maintained their prognostic value in multiple Cox proportional hazards regression analysis was proposed. Among patients registered in the T Cell Project, 311 PTCL-NOS were retained for study. At a median follow-up of 46 months, the median overall survival (OS) and progression-free survival (PFS) was 20 and 10 months, respectively. Three groups were identified at low risk (LR, 48 patients, 15%, score 0), intermediate risk (IR, 189 patients, 61%, score 1-2), and high risk (HiR, 74 patients, 24%, score 3-4), having a 3-year OS of 76% [95% confidence interval 61-88], 43% [35-51], and 11% [4-21], respectively (P < 0·001). Comparing the performance of the T cell score on OS to that of each of the previously developed models, it emerged that the new score had the best discriminant power. The new T cell score, based on clinical variables, identifies a group with very unfavourable outcomes. © 2018 The Authors. British Journal of Haematology published by John Wiley & Sons Ltd.
A plant cell division algorithm based on cell biomechanics and ellipse-fitting.

PubMed

Abera, Metadel K; Verboven, Pieter; Defraeye, Thijs; Fanta, Solomon Workneh; Hertog, Maarten L A T M; Carmeliet, Jan; Nicolai, Bart M

2014-09-01

The importance of cell division models in cellular pattern studies has been acknowledged since the 19th century. Most of the available models developed to date are limited to symmetric cell division with isotropic growth. Often, the actual growth of the cell wall is either not considered or is updated intermittently on a separate time scale to the mechanics. This study presents a generic algorithm that accounts for both symmetrically and asymmetrically dividing cells with isotropic and anisotropic growth. Actual growth of the cell wall is simulated simultaneously with the mechanics. The cell is considered as a closed, thin-walled structure, maintained in tension by turgor pressure. The cell walls are represented as linear elastic elements that obey Hooke's law. Cell expansion is induced by turgor pressure acting on the yielding cell-wall material. A system of differential equations for the positions and velocities of the cell vertices as well as for the actual growth of the cell wall is established. Readiness to divide is determined based on cell size. An ellipse-fitting algorithm is used to determine the position and orientation of the dividing wall. The cell vertices, walls and cell connectivity are then updated and cell expansion resumes. Comparisons are made with experimental data from the literature. The generic plant cell division algorithm has been implemented successfully. It can handle both symmetrically and asymmetrically dividing cells coupled with isotropic and anisotropic growth modes. Development of the algorithm highlighted the importance of ellipse-fitting to produce randomness (biological variability) even in symmetrically dividing cells. Unlike previous models, a differential equation is formulated for the resting length of the cell wall to simulate actual biological growth and is solved simultaneously with the position and velocity of the vertices. The algorithm presented can produce different tissues varying in topological and geometrical properties. This flexibility to produce different tissue types gives the model great potential for use in investigations of plant cell division and growth in silico.
Models for Microbial Fuel Cells: A critical review

NASA Astrophysics Data System (ADS)

Xia, Chengshuo; Zhang, Daxing; Pedrycz, Witold; Zhu, Yingmin; Guo, Yongxian

2018-01-01

Microbial fuel cells (MFCs) have been widely viewed as one of the most promising alternative sources of renewable energy. A recognition of needs of efficient development methods based on multidisciplinary research becomes crucial for the optimization of MFCs. Modeling of MFCs is an effective way for not only gaining a thorough understanding of the effects of operation conditions on the performance of power generation but also becomes of essential interest to the successful implementation of MFCs. The MFC models encompass the underlying reaction process and limiting factors of the MFC. The models come in various forms, such as the mathematical equations or the equivalent circuits. Different modeling focuses and approaches of the MFC have emerged. In this study, we present a state of the art of MFCs modeling; the past modeling methods are reviewed as well. Models and modeling methods are elaborated on based on the classification provided by Mechanism-based models and Application-based models. Mechanisms, advantages, drawbacks, and application fields of different models are illustrated as well. We exhibit a complete and comprehensive exposition of the different models for MFCs and offer further guidance to promote the performance of MFCs.
Spontaneous emulsification and self-propulsion of oil droplets induced by the synthesis of amino acid-based surfactants.

PubMed

Nagasaka, Yuriko; Tanaka, Shinpei; Nehira, Tatsuo; Amimoto, Tomoko

2017-09-27

It is well known that oil droplets in or on water exhibit spontaneous movement induced by surfactants, and this self-propulsion is regarded as an important factor in droplet-based models for a living cell. We report here an oil-droplet system spontaneously producing amino acid-based surfactants, which are then utilized for the droplets' self-propulsion. Thus this system is an active system capable of producing the fuel for the propulsion by itself, which can be used as a conceptual model for cell metabolism.
Regulation, cell differentiation and protein-based inheritance.

PubMed

Malagnac, Fabienne; Silar, Philippe

2006-11-01

Recent research using fungi as models provide new insight into the ability of regulatory networks to generate cellular states that are sufficiently stable to be faithfully transmitted to daughter cells, thereby generating epigenetic inheritance. Such protein-based inheritance is driven by infectious factors endowed with properties usually displayed by prions. We emphasize the contribution of regulatory networks to the emerging properties displayed by cells.
Preparation of a Three-Dimensional Full Thickness Skin Equivalent.

PubMed

Reuter, Christian; Walles, Heike; Groeber, Florian

2017-01-01

In vitro test systems are a promising alternative to animal models. Due to the use of human cells in a three-dimensional arrangement that allows cell-cell or cell-matrix interactions these models may be more predictive for the human situation compared to animal models or two-dimensional cell culture systems. Especially for dermatological research, skin models such as epidermal or full-thickness skin equivalents (FTSE) are used for different applications. Although epidermal models provide highly standardized conditions for risk assessment, FTSE facilitate a cellular crosstalk between the dermal and epidermal layer and thus can be used as more complex models for the investigation of processes such as wound healing, skin development, or infectious diseases. In this chapter, we describe the generation and culture of an FTSE, based on a collagen type I matrix and provide troubleshooting tips for commonly encountered technical problems.
Demystifying the cytokine network: Mathematical models point the way.

PubMed

Morel, Penelope A; Lee, Robin E C; Faeder, James R

2017-10-01

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.
Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity.

PubMed

Kaya, Mine; Hajimirza, Shima

2018-05-25

This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.
Modeling of single event transients with dual double-exponential current sources: Implications for logic cell characterization

DOE PAGES

Black, Dolores Archuleta; Robinson, William H.; Wilcox, Ian Zachary; ...

2015-08-07

Single event effects (SEE) are a reliability concern for modern microelectronics. Bit corruptions can be caused by single event upsets (SEUs) in the storage cells or by sampling single event transients (SETs) from a logic path. Likewise, an accurate prediction of soft error susceptibility from SETs requires good models to convert collected charge into compact descriptions of the current injection process. This paper describes a simple, yet effective, method to model the current waveform resulting from a charge collection event for SET circuit simulations. The model uses two double-exponential current sources in parallel, and the results illustrate why a conventionalmore » model based on one double-exponential source can be incomplete. Furthermore, a small set of logic cells with varying input conditions, drive strength, and output loading are simulated to extract the parameters for the dual double-exponential current sources. As a result, the parameters are based upon both the node capacitance and the restoring current (i.e., drive strength) of the logic cell.« less
Spin glass model for cell reprogramming

NASA Astrophysics Data System (ADS)

Pusuluri, Sai Teja; Castillo, Horacio E.

2014-03-01

Recent experiments show that differentiated cells can be reprogrammed to become pluripotent stem cells. The possible cell fates can be modeled as attractors in a dynamical system, the ``epigenetic landscape.'' Both cellular differentiation and reprogramming can be described in the landscape picture as motion from one attractor state to another attractor state. We use a simple model based on spin glass theory that can construct a simulated epigenetic landscape starting from the experimental genomic data. We modify the model to incorporate experimental reprogramming protocols. Our simulations successfully reproduce several reprogramming experiments. We probe the robustness of the results against random changes in the model, explore the importance of asymmetric interactions between transcription factors and study the importance of histone modification errors in reprogramming.
Mound-Interface Kinetics in Dictyostelium Aggregation

NASA Astrophysics Data System (ADS)

Tutu, Hiroki

2002-09-01

The mound development of the cellular slime mold amoebae Dictyostelium discoideum is studied with an interface kinetic model for the height of cell layers. As a competitive role for the chemotaxis, we compare two types of curvature relaxations; the surface relaxation induced by cell-substrate affinity (model A), and that comes from a cell-cell adhesive effect (model B). It is found that both models are characterized by the growth law for the maximum mound height. Based on a self-similarity scaling hypothesis for the spatial structure of streaming pattern, we suggest a scaling law for the growth of mound-height hmax ˜ t1-1/α+β/α with α = 2 (4) for the model A (B) and a number 0 ≤ β < 1.
The fractal dimension of cell membrane correlates with its capacitance: A new fractal single-shell model

PubMed Central

Wang, Xujing; Becker, Frederick F.; Gascoyne, Peter R. C.

2010-01-01

The scale-invariant property of the cytoplasmic membrane of biological cells is examined by applying the Minkowski–Bouligand method to digitized scanning electron microscopy images of the cell surface. The membrane is found to exhibit fractal behavior, and the derived fractal dimension gives a good description of its morphological complexity. Furthermore, we found that this fractal dimension correlates well with the specific membrane dielectric capacitance derived from the electrorotation measurements. Based on these findings, we propose a new fractal single-shell model to describe the dielectrics of mammalian cells, and compare it with the conventional single-shell model (SSM). We found that while both models fit with experimental data well, the new model is able to eliminate the discrepancy between the measured dielectric property of cells and that predicted by the SSM. PMID:21198103
An approach to collective behavior in cell cultures: modeling and analysis of ECIS data

NASA Astrophysics Data System (ADS)

Rabson, David; Lafalce, Evan; Lovelady, Douglas; Lo, Chun-Min

2011-03-01

We review recent results in which statistical measures of noise in ECIS data distinguished healthy cell cultures from cancerous or poisoned ones: after subtracting the ``signal,'' the 1 /fα noise in the healthy cultures shows longer short-time and long-time correlations. We discuss application of an artificial neural network to detect the cancer signal, and we demonstrate a computational model of cell-cell communication that produces signals similar to those of the experimental data. The simulation is based on the q -state Potts model with inspiration from the Bak-Tang-Wiesenfeld sand-pile model. We view the level of organization larger than cells but smaller than organs or tissues as a kind of ``mesoscopic'' biological physics, in which few-body interactions dominate, and the experiments and computational model as ways of exploring this regime.
Use of genome editing tools in human stem cell-based disease modeling and precision medicine.

PubMed

Wei, Yu-da; Li, Shuang; Liu, Gai-gai; Zhang, Yong-xian; Ding, Qiu-rong

2015-10-01

Precision medicine emerges as a new approach that takes into account individual variability. The successful conduct of precision medicine requires the use of precise disease models. Human pluripotent stem cells (hPSCs), as well as adult stem cells, can be differentiated into a variety of human somatic cell types that can be used for research and drug screening. The development of genome editing technology over the past few years, especially the CRISPR/Cas system, has made it feasible to precisely and efficiently edit the genetic background. Therefore, disease modeling by using a combination of human stem cells and genome editing technology has offered a new platform to generate " personalized " disease models, which allow the study of the contribution of individual genetic variabilities to disease progression and the development of precise treatments. In this review, recent advances in the use of genome editing in human stem cells and the generation of stem cell models for rare diseases and cancers are discussed.

Development of tyrosinase-based reporter genes for preclinical photoacoustic imaging of mesenchymal stem cells

NASA Astrophysics Data System (ADS)

Märk, Julia; Ruschke, Karen; Dortay, Hakan; Schreiber, Isabelle; Sass, Andrea; Qazi, Taimoor; Pumberger, Matthias; Laufer, Jan

2014-03-01

The capability to image stem cells in vivo in small animal models over extended periods of time is important to furthering our understanding of the processes involved in tissue regeneration. Photoacoustic imaging is suited to this application as it can provide high resolution (tens of microns) absorption-based images of superficial tissues (cm depths). However, stem cells are rare, highly migratory, and can divide into more specialised cells. Genetic labelling strategies are therefore advantageous for their visualisation. In this study, methods for the transfection and viral transduction of mesenchymal stem cells with reporter genes for the co-expression of tyrosinase and a fluorescent protein (mCherry). Initial photoacoustic imaging experiments of tyrosinase expressing cells in small animal models of tissue regeneration were also conducted. Lentiviral transduction methods were shown to result in stable expression of tyrosinase and mCherry in mesenchymal stem cells. The results suggest that photoacoustic imaging using reporter genes is suitable for the study of stem cell driven tissue regeneration in small animals.
Derivation of rigorous conditions for high cell-type diversity by algebraic approach.

PubMed

Yoshida, Hiroshi; Anai, Hirokazu; Horimoto, Katsuhisa

2007-01-01

The development of a multicellular organism is a dynamic process. Starting with one or a few cells, the organism develops into different types of cells with distinct functions. We have constructed a simple model by considering the cell number increase and the cell-type order conservation, and have assessed conditions for cell-type diversity. This model is based on a stochastic Lindenmayer system with cell-to-cell interactions for three types of cells. In the present model, we have successfully derived complex but rigorous algebraic relations between the proliferation and transition rates for cell-type diversity by using a symbolic method: quantifier elimination (QE). Surprisingly, three modes for the proliferation and transition rates have emerged for large ratios of the initial cells to the developed cells. The three modes have revealed that the equality between the development rates for the highest cell-type diversity is reduced during the development process of multicellular organisms. Furthermore, we have found that the highest cell-type diversity originates from order conservation.
The Use of Multidimensional Image-Based Analysis to Accurately Monitor Cell Growth in 3D Bioreactor Culture

PubMed Central

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells. PMID:22028809
The use of multidimensional image-based analysis to accurately monitor cell growth in 3D bioreactor culture.

PubMed

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells.
Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states

NASA Astrophysics Data System (ADS)

García-Morales, Vladimir; Manzanares, José A.; Mafe, Salvador

2017-04-01

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according to local rules that are modulated by a parameter κ . This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatiotemporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis, where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.
Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states.

PubMed

García-Morales, Vladimir; Manzanares, José A; Mafe, Salvador

2017-04-01

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according to local rules that are modulated by a parameter κ. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatiotemporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis, where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.
Finite element method (FEM) model of the mechanical stress on phospholipid membranes from shock waves produced in nanosecond electric pulses (nsEP)

NASA Astrophysics Data System (ADS)

Barnes, Ronald; Roth, Caleb C.; Shadaram, Mehdi; Beier, Hope; Ibey, Bennett L.

2015-03-01

The underlying mechanism(s) responsible for nanoporation of phospholipid membranes by nanosecond pulsed electric fields (nsEP) remains unknown. The passage of a high electric field through a conductive medium creates two primary contributing factors that may induce poration: the electric field interaction at the membrane and the shockwave produced from electrostriction of a polar submersion medium exposed to an electric field. Previous work has focused on the electric field interaction at the cell membrane, through such models as the transport lattice method. Our objective is to model the shock wave cell membrane interaction induced from the density perturbation formed at the rising edge of a high voltage pulse in a polar liquid resulting in a shock wave propagating away from the electrode toward the cell membrane. Utilizing previous data from cell membrane mechanical parameters, and nsEP generated shockwave parameters, an acoustic shock wave model based on the Helmholtz equation for sound pressure was developed and coupled to a cell membrane model with finite-element modeling in COMSOL. The acoustic structure interaction model was developed to illustrate the harmonic membrane displacements and stresses resulting from shockwave and membrane interaction based on Hooke's law. Poration is predicted by utilizing membrane mechanical breakdown parameters including cortical stress limits and hydrostatic pressure gradients.
Formation of rarefaction waves in origami-based metamaterials

DOE PAGES

Yasuda, H.; Chong, C.; Charalampidis, E. G.; ...

2016-04-15

Here, we investigate the nonlinear wave dynamics of origami-based metamaterials composed of Tachi-Miura polyhedron (TMP) unit cells. These cells exhibit strain softening behavior under compression, which can be tuned by modifying their geometrical configurations or initial folded conditions. We assemble these TMP cells into a cluster of origami-based metamaterials, and we theoretically model and numerically analyze their wave transmission mechanism under external impact. Numerical simulations show that origami-based metamaterials can provide a prototypical platform for the formation of nonlinear coherent structures in the form of rarefaction waves, which feature a tensile wavefront upon the application of compression to the system.more » We also demonstrate the existence of numerically exact traveling rarefaction waves in an effective lumped-mass model. Origami-based metamaterials can be highly useful for mitigating shock waves, potentially enabling a wide variety of engineering applications.« less
CelOWS: an ontology based framework for the provision of semantic web services related to biological models.

PubMed

Matos, Ely Edison; Campos, Fernanda; Braga, Regina; Palazzi, Daniele

2010-02-01

The amount of information generated by biological research has lead to an intensive use of models. Mathematical and computational modeling needs accurate description to share, reuse and simulate models as formulated by original authors. In this paper, we introduce the Cell Component Ontology (CelO), expressed in OWL-DL. This ontology captures both the structure of a cell model and the properties of functional components. We use this ontology in a Web project (CelOWS) to describe, query and compose CellML models, using semantic web services. It aims to improve reuse and composition of existent components and allow semantic validation of new models.
Coaction of intercellular adhesion and cortical tension specifies tissue surface tension

PubMed Central

Manning, M. Lisa; Foty, Ramsey A.; Steinberg, Malcolm S.; Schoetz, Eva-Maria

2010-01-01

In the course of animal morphogenesis, large-scale cell movements occur, which involve the rearrangement, mutual spreading, and compartmentalization of cell populations in specific configurations. Morphogenetic cell rearrangements such as cell sorting and mutual tissue spreading have been compared with the behaviors of immiscible liquids, which they closely resemble. Based on this similarity, it has been proposed that tissues behave as liquids and possess a characteristic surface tension, which arises as a collective, macroscopic property of groups of mobile, cohering cells. But how are tissue surface tensions generated? Different theories have been proposed to explain how mesoscopic cell properties such as cell–cell adhesion and contractility of cell interfaces may underlie tissue surface tensions. Although recent work suggests that both may be contributors, an explicit model for the dependence of tissue surface tension on these mesoscopic parameters has been missing. Here we show explicitly that the ratio of adhesion to cortical tension determines tissue surface tension. Our minimal model successfully explains the available experimental data and makes predictions, based on the feedback between mechanical energy and geometry, about the shapes of aggregate surface cells, which we verify experimentally. This model indicates that there is a crossover from adhesion dominated to cortical-tension dominated behavior as a function of the ratio between these two quantities. PMID:20616053
Establishment and characterization of a differentiated epithelial cell culture model derived from the porcine cervix uteri

PubMed Central

2012-01-01

Background Cervical uterine epithelial cells maintain a physiological and pathogen-free milieu in the female mammalian reproductive tract and are involved in sperm-epithelium interaction. Easily accessible, differentiated model systems of the cervical epithelium are not yet available to elucidate the underlying molecular mechanisms within these highly specialized cells. Therefore, the aim of the study was to establish a cell culture of the porcine cervical epithelium representing in vivo-like properties of the tissue. Results We tested different isolation methods and culture conditions and validated purity of the cultured cells by immunohistochemistry against keratins. We could reproducibly culture pure epithelial cells from cervical tissue explants. Based on a morphology score and the WST-1 Proliferation Assay, we optimized the growth medium composition. Primary porcine cervical cells performed best in conditioned Ham's F-12, containing 10% FCS, EGF and insulin. After cultivation in an air-liquid interface for three weeks, the cells showed a discontinuously multilayered phenotype. Finally, differentiation was validated via immunohistochemistry against beta catenin. Mucopolysaccharide production could be shown via alcian blue staining. Conclusions We provide the first suitable protocol to establish a differentiated porcine epithelial model of the cervix uteri, based on easily accessible cells using slaughterhouse material. PMID:22429795
Model-based approach for design verification and co-optimization of catastrophic and parametric-related defects due to systematic manufacturing variations

NASA Astrophysics Data System (ADS)

Perry, Dan; Nakamoto, Mark; Verghese, Nishath; Hurat, Philippe; Rouse, Rich

2007-03-01

Model-based hotspot detection and silicon-aware parametric analysis help designers optimize their chips for yield, area and performance without the high cost of applying foundries' recommended design rules. This set of DFM/ recommended rules is primarily litho-driven, but cannot guarantee a manufacturable design without imposing overly restrictive design requirements. This rule-based methodology of making design decisions based on idealized polygons that no longer represent what is on silicon needs to be replaced. Using model-based simulation of the lithography, OPC, RET and etch effects, followed by electrical evaluation of the resulting shapes, leads to a more realistic and accurate analysis. This analysis can be used to evaluate intelligent design trade-offs and identify potential failures due to systematic manufacturing defects during the design phase. The successful DFM design methodology consists of three parts: 1. Achieve a more aggressive layout through limited usage of litho-related recommended design rules. A 10% to 15% area reduction is achieved by using more aggressive design rules. DFM/recommended design rules are used only if there is no impact on cell size. 2. Identify and fix hotspots using a model-based layout printability checker. Model-based litho and etch simulation are done at the cell level to identify hotspots. Violations of recommended rules may cause additional hotspots, which are then fixed. The resulting design is ready for step 3. 3. Improve timing accuracy with a process-aware parametric analysis tool for transistors and interconnect. Contours of diffusion, poly and metal layers are used for parametric analysis. In this paper, we show the results of this physical and electrical DFM methodology at Qualcomm. We describe how Qualcomm was able to develop more aggressive cell designs that yielded a 10% to 15% area reduction using this methodology. Model-based shape simulation was employed during library development to validate architecture choices and to optimize cell layout. At the physical verification stage, the shape simulator was run at full-chip level to identify and fix residual hotspots on interconnect layers, on poly or metal 1 due to interaction between adjacent cells, or on metal 1 due to interaction between routing (via and via cover) and cell geometry. To determine an appropriate electrical DFM solution, Qualcomm developed an experiment to examine various electrical effects. After reporting the silicon results of this experiment, which showed sizeable delay variations due to lithography-related systematic effects, we also explain how contours of diffusion, poly and metal can be used for silicon-aware parametric analysis of transistors and interconnect at the cell-, block- and chip-level.
Light scattering from normal and cervical cancer cells.

PubMed

Lin, Xiaogang; Wan, Nan; Weng, Lingdong; Zhou, Yong

2017-04-20

The light scattering characteristic plays a very important role in optic imaging and diagnostic applications. For optical detection of the cell, cell scattering characteristics have an extremely vital role. In this paper, we use the finite-difference time-domain (FDTD) algorithm to simulate the propagation and scattering of light in biological cells. The two-dimensional scattering cell models were set up based on the FDTD algorithm. The cell models of normal cells and cancerous cells were established, and the shapes of organelles, such as mitochondria, were elliptical. Based on these models, three aspects of the scattering characteristics were studied. First, the radar cross section (RCS) distribution curves of the corresponding cell models were calculated, then corresponding relationships between the size and the refractive index of the nucleus and light scattering information were analyzed in the three periods of cell canceration. The values of RCS increase positively with the increase of the nucleo-cytoplasmic ratio in the cancerous process when the scattering angle ranges from 0° to 20°. Second, the effect of organelles in the scattering was analyzed. The peak value of the RCS of cells with mitochondria is higher than the cells without mitochondria when the scattering angle ranges from 20° to 180°. Third, we demonstrated that the influence of cell shape is important, and the impact was revealed by the two typical ideal cells: round cells and oval cells. When the scattering angle ranges from 0° to 80°, the peak values and the frequencies of the appearance of the peaks from the two models are roughly similar. It can be concluded that: (1) the size of the nuclei and the change of the refractive index of cells have a certain impact on light scattering information of the whole cell; (2) mitochondria and other small organelles contribute to the cell light scattering characteristics in the larger scattering angle area; and (3) the change of the cell shape significantly influences the value of scattering peak and the deviation of scattering peak position. The results of the numerical simulation will guide subsequent experiments and early diagnosis of cervical cancer.
Cell refractive index for cell biology and disease diagnosis: past, present and future.

PubMed

Liu, P Y; Chin, L K; Ser, W; Chen, H F; Hsieh, C-M; Lee, C-H; Sung, K-B; Ayi, T C; Yap, P H; Liedberg, B; Wang, K; Bourouina, T; Leprince-Wang, Y

2016-02-21

Cell refractive index is a key biophysical parameter, which has been extensively studied. It is correlated with other cell biophysical properties including mechanical, electrical and optical properties, and not only represents the intracellular mass and concentration of a cell, but also provides important insight for various biological models. Measurement techniques developed earlier only measure the effective refractive index of a cell or a cell suspension, providing only limited information on cell refractive index and hence hindering its in-depth analysis and correlation. Recently, the emergence of microfluidic, photonic and imaging technologies has enabled the manipulation of a single cell and the 3D refractive index of a single cell down to sub-micron resolution, providing powerful tools to study cells based on refractive index. In this review, we provide an overview of cell refractive index models and measurement techniques including microfluidic chip-based techniques for the last 50 years, present the applications and significance of cell refractive index in cell biology, hematology, and pathology, and discuss future research trends in the field, including 3D imaging methods, integration with microfluidics and potential applications in new and breakthrough research areas.
Mathematical modeling of the malignancy of cancer using graph evolution.

PubMed

Gunduz-Demir, Cigdem

2007-10-01

We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and demonstrate strong relation between the malignancy of cancer and the phase of its graph. From the photomicrographs of tissues, which are diagnosed as normal, low-grade cancerous and high-grade cancerous, we construct cell-graphs based on the locations of cells; we probabilistically generate an edge between every pair of cells depending on the Euclidean distance between them. For a cell-graph, we extract connectivity information including the properties of its connected components in order to analyze the phase of the cell-graph. Working with brain tissue samples surgically removed from 12 patients, we demonstrate that cell-graphs generated for different tissue types evolve differently and that they exhibit different phase properties, which distinguish a tissue type from another.
Human neuron-astrocyte 3D co-culture-based assay for evaluation of neuroprotective compounds.

PubMed

Terrasso, Ana Paula; Silva, Ana Carina; Filipe, Augusto; Pedroso, Pedro; Ferreira, Ana Lúcia; Alves, Paula Marques; Brito, Catarina

Central nervous system drug development has registered high attrition rates, mainly due to the lack of efficacy of drug candidates, highlighting the low reliability of the models used in early-stage drug development and the need for new in vitro human cell-based models and assays to accurately identify and validate drug candidates. 3D human cell models can include different tissue cell types and represent the spatiotemporal context of the original tissue (co-cultures), allowing the establishment of biologically-relevant cell-cell and cell-extracellular matrix interactions. Nevertheless, exploitation of these 3D models for neuroprotection assessment has been limited due to the lack of data to validate such 3D co-culture approaches. In this work we combined a 3D human neuron-astrocyte co-culture with a cell viability endpoint for the implementation of a novel in vitro neuroprotection assay, over an oxidative insult. Neuroprotection assay robustness and specificity, and the applicability of Presto Blue, MTT and CytoTox-Glo viability assays to the 3D co-culture were evaluated. Presto Blue was the adequate endpoint as it is non-destructive and is a simpler and reliable assay. Semi-automation of the cell viability endpoint was performed, indicating that the assay setup is amenable to be transferred to automated screening platforms. Finally, the neuroprotection assay setup was applied to a series of 36 test compounds and several candidates with higher neuroprotective effect than the positive control, Idebenone, were identified. The robustness and simplicity of the implemented neuroprotection assay with the cell viability endpoint enables the use of more complex and reliable 3D in vitro cell models to identify and validate drug candidates. Copyright © 2016 Elsevier Inc. All rights reserved.
Model-based analysis of N-glycosylation in Chinese hamster ovary cells

PubMed Central

Krambeck, Frederick J.; Bennun, Sandra V.; Betenbaugh, Michael J.

2017-01-01

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products. PMID:28486471
Algorithm for cellular reprogramming.

PubMed

Ronquist, Scott; Patterson, Geoff; Muir, Lindsey A; Lindsly, Stephen; Chen, Haiming; Brown, Markus; Wicha, Max S; Bloch, Anthony; Brockett, Roger; Rajapakse, Indika

2017-11-07

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle-synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Copyright © 2017 the Author(s). Published by PNAS.
Mathematical modeling of planar cell polarity signaling in the Drosophila melanogaster wing

NASA Astrophysics Data System (ADS)

Amonlirdviman, Keith

Planar cell polarity (PCP) signaling refers to the coordinated polarization of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. For example, in the Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. Through a poorly understood mechanism, cell clones mutant for some PCP signaling components, including some, but not all alleles of the receptor frizzled, cause polarity disruptions of neighboring, wild-type cells, a phenomenon referred to as domineering nonautonomy. Previous models have proposed diffusible factors to explain nonautonomy, but no such factors have yet been found. This dissertation describes the mathematical modeling of PCP in the Drosophila wing, based on a contact dependent signaling hypothesis derived from experimental results. Intuition alone is insufficient to deduce that this hypothesis, which relies on a local feedback loop acting at the cell membrane, underlies the complex patterns observed in large fields of cells containing mutant clones, and others have argued that it cannot account for observed phenotypes. Through reaction-diffusion, partial differential equation modeling and simulation, the feedback loop is shown to fully reproduce PCP phenotypes, including domineering nonautonomy. The sufficiency of this model and the experimental validation of model predictions argue that previously proposed diffusible factors need not be invoked to explain PCP signaling and reveal how specific protein-protein interactions lead to autonomy or domineering nonautonomy. Based on these results, an ordinary differential equation model is derived to study the relationship of the feedback loop with upstream signaling components. The cadherin Fat transduces a cue to the local feedback loop, biasing the polarity direction of each cell toward the wing tip. The feedback loop then amplifies and propagates PCP across the pupal wing, but polarity information does not always propagate correctly across cells lacking Fat function. Using the simplified model, the presence and severity of polarity defects in fat clones is shown to be an inherent consequence of the feedback loop when confronted with irregular variations in cell geometry.
An electrical model of vapor-anode, multitube AMTEC cells[Alkali Metal Thermal to Electric Conversion

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

Tournier, J.M.; El-Genk, M.S.

1998-07-01

A two-dimensional electrical model of vapor-anode, multi-tube AMTEC cells was developed, which included four options of current collector configurations. Simulation results of several cells tested at AFRL showed that electrical losses in the current collector networks and the connecting leads were negligible. The polarization/concentration losses in the TiN electrodes were significant, amounting to 25%--50% of the cell theoretical power, while the contact and BASE ionic losses amounted to less than 16% of the cell theoretical power.

Multidisciplinary insight into clonal expansion of HTLV-1-infected cells in adult T-cell leukemia via modeling by deterministic finite automata coupled with high-throughput sequencing.

PubMed

Farmanbar, Amir; Firouzi, Sanaz; Park, Sung-Joon; Nakai, Kenta; Uchimaru, Kaoru; Watanabe, Toshiki

2017-01-31

Clonal expansion of leukemic cells leads to onset of adult T-cell leukemia (ATL), an aggressive lymphoid malignancy with a very poor prognosis. Infection with human T-cell leukemia virus type-1 (HTLV-1) is the direct cause of ATL onset, and integration of HTLV-1 into the human genome is essential for clonal expansion of leukemic cells. Therefore, monitoring clonal expansion of HTLV-1-infected cells via isolation of integration sites assists in analyzing infected individuals from early infection to the final stage of ATL development. However, because of the complex nature of clonal expansion, the underlying mechanisms have yet to be clarified. Combining computational/mathematical modeling with experimental and clinical data of integration site-based clonality analysis derived from next generation sequencing technologies provides an appropriate strategy to achieve a better understanding of ATL development. As a comprehensively interdisciplinary project, this study combined three main aspects: wet laboratory experiments, in silico analysis and empirical modeling. We analyzed clinical samples from HTLV-1-infected individuals with a broad range of proviral loads using a high-throughput methodology that enables isolation of HTLV-1 integration sites and accurate measurement of the size of infected clones. We categorized clones into four size groups, "very small", "small", "big", and "very big", based on the patterns of clonal growth and observed clone sizes. We propose an empirical formal model based on deterministic finite state automata (DFA) analysis of real clinical samples to illustrate patterns of clonal expansion. Through the developed model, we have translated biological data of clonal expansion into the formal language of mathematics and represented the observed clonality data with DFA. Our data suggest that combining experimental data (absolute size of clones) with DFA can describe the clonality status of patients. This kind of modeling provides a basic understanding as well as a unique perspective for clarifying the mechanisms of clonal expansion in ATL.
A Multistate Toggle Switch Defines Fungal Cell Fates and Is Regulated by Synergistic Genetic Cues

PubMed Central

Anderson, Matthew Z.; Porman, Allison M.; Wang, Na; Mancera, Eugenio; Bennett, Richard J.

2016-01-01

Heritable epigenetic changes underlie the ability of cells to differentiate into distinct cell types. Here, we demonstrate that the fungal pathogen Candida tropicalis exhibits multipotency, undergoing stochastic and reversible switching between three cellular states. The three cell states exhibit unique cellular morphologies, growth rates, and global gene expression profiles. Genetic analysis identified six transcription factors that play key roles in regulating cell differentiation. In particular, we show that forced expression of Wor1 or Efg1 transcription factors can be used to manipulate transitions between all three cell states. A model for tristability is proposed in which Wor1 and Efg1 are self-activating but mutually antagonistic transcription factors, thereby forming a symmetrical self-activating toggle switch. We explicitly test this model and show that ectopic expression of WOR1 can induce white-to-hybrid-to-opaque switching, whereas ectopic expression of EFG1 drives switching in the opposite direction, from opaque-to-hybrid-to-white cell states. We also address the stability of induced cell states and demonstrate that stable differentiation events require ectopic gene expression in combination with chromatin-based cues. These studies therefore experimentally test a model of multistate stability and demonstrate that transcriptional circuits act synergistically with chromatin-based changes to drive cell state transitions. We also establish close mechanistic parallels between phenotypic switching in unicellular fungi and cell fate decisions during stem cell reprogramming. PMID:27711197
Systematic optimization of fed-batch simultaneous saccharification and fermentation at high-solid loading based on enzymatic hydrolysis and dynamic metabolic modeling of Saccharomyces cerevisiae.

PubMed

Unrean, Pornkamol; Khajeeram, Sutamat; Laoteng, Kobkul

2016-03-01

An integrative simultaneous saccharification and fermentation (SSF) modeling is a useful guiding tool for rapid process optimization to meet the techno-economic requirement of industrial-scale lignocellulosic ethanol production. In this work, we have developed the SSF model composing of a metabolic network of a Saccharomyces cerevisiae cell associated with fermentation kinetics and enzyme hydrolysis model to quantitatively capture dynamic responses of yeast cell growth and fermentation during SSF. By using model-based design of feeding profiles for substrate and yeast cell in the fed-batch SSF process, an efficient ethanol production with high titer of up to 65 g/L and high yield of 85 % of theoretical yield was accomplished. The ethanol titer and productivity was increased by 47 and 41 %, correspondingly, in optimized fed-batch SSF as compared to batch process. The developed integrative SSF model is, therefore, considered as a promising approach for systematic design of economical and sustainable SSF bioprocessing of lignocellulose.
Assessing the toxicity of Pb- and Sn-based perovskite solar cells in model organism Danio rerio

NASA Astrophysics Data System (ADS)

Babayigit, Aslihan; Duy Thanh, Dinh; Ethirajan, Anitha; Manca, Jean; Muller, Marc; Boyen, Hans-Gerd; Conings, Bert

2016-01-01

Intensive development of organometal halide perovskite solar cells has lead to a dramatic surge in power conversion efficiency up to 20%. Unfortunately, the most efficient perovskite solar cells all contain lead (Pb), which is an unsettling flaw that leads to severe environmental concerns and is therefore a stumbling block envisioning their large-scale application. Aiming for the retention of favorable electro-optical properties, tin (Sn) has been considered the most likely substitute. Preliminary studies have however shown that Sn-based perovskites are highly unstable and, moreover, Sn is also enlisted as a harmful chemical, with similar concerns regarding environment and health. To bring more clarity into the appropriateness of both metals in perovskite solar cells, we provide a case study with systematic comparison regarding the environmental impact of Pb- and Sn-based perovskites, using zebrafish (Danio Rerio) as model organism. Uncovering an unexpected route of intoxication in the form of acidification, it is shown that Sn based perovskite may not be the ideal Pb surrogate.
Assessing the toxicity of Pb- and Sn-based perovskite solar cells in model organism Danio rerio

PubMed Central

Babayigit, Aslihan; Duy Thanh, Dinh; Ethirajan, Anitha; Manca, Jean; Muller, Marc; Boyen, Hans-Gerd; Conings, Bert

2016-01-01

Intensive development of organometal halide perovskite solar cells has lead to a dramatic surge in power conversion efficiency up to 20%. Unfortunately, the most efficient perovskite solar cells all contain lead (Pb), which is an unsettling flaw that leads to severe environmental concerns and is therefore a stumbling block envisioning their large-scale application. Aiming for the retention of favorable electro-optical properties, tin (Sn) has been considered the most likely substitute. Preliminary studies have however shown that Sn-based perovskites are highly unstable and, moreover, Sn is also enlisted as a harmful chemical, with similar concerns regarding environment and health. To bring more clarity into the appropriateness of both metals in perovskite solar cells, we provide a case study with systematic comparison regarding the environmental impact of Pb- and Sn-based perovskites, using zebrafish (Danio Rerio) as model organism. Uncovering an unexpected route of intoxication in the form of acidification, it is shown that Sn based perovskite may not be the ideal Pb surrogate. PMID:26759068
Spatiotemporal modelling of viral infection dynamics

NASA Astrophysics Data System (ADS)

Beauchemin, Catherine

Viral kinetics have been studied extensively in the past through the use of ordinary differential equations describing the time evolution of the diseased state in a spatially well-mixed medium. However, emerging spatial structures such as localized populations of dead cells might affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In the first phase of the project, a simple two-dimensional cellular automaton model of viral infections was developed. It was validated against clinical immunological data for uncomplicated influenza A infections and shown to be accurate enough to adequately model them. In the second phase of the project, the simple two-dimensional cellular automaton model was used to investigate the effects of relaxing the well-mixed assumption on viral infection dynamics. It was shown that grouping the initially infected cells into patches rather than distributing them uniformly on the grid reduced the infection rate as only cells on the perimeter of the patch have healthy neighbours to infect. Use of a local epithelial cell regeneration rule where dead cells are replaced by healthy cells when an immediate neighbour divides was found to result in more extensive damage of the epithelium and yielded a better fit to experimental influenza A infection data than a global regeneration rule based on division rate of healthy cell. Finally, the addition of immune cell at the site of infection was found to be a better strategy at low infection levels, while addition at random locations on the grid was the better strategy at high infection level. In the last project, the movement of T cells within lymph nodes in the absence of antigen, was investigated. Based on individual T cell track data captured by two-photon microscopy experiments in vivo, a simple model was proposed for the motion of T cells. This is the first step towards the implementation of a more realistic spatiotemporal model of HIV than those proposed thus far.
Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

PubMed Central

Sen, Partho; Kemppainen, Esko; Orešič, Matej

2018-01-01

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models. PMID:29376056
Grade 12 Students' Conceptual Understanding and Mental Models of Galvanic Cells before and after Learning by Using Small-Scale Experiments in Conjunction with a Model Kit

ERIC Educational Resources Information Center

Supasorn, Saksri

2015-01-01

This study aimed to develop the small-scale experiments involving electrochemistry and the galvanic cell model kit featuring the sub-microscopic level. The small-scale experiments in conjunction with the model kit were implemented based on the 5E inquiry learning approach to enhance students' conceptual understanding of electrochemistry. The…
Micromechanical models for textile structural composites

NASA Technical Reports Server (NTRS)

Marrey, Ramesh V.; Sankar, Bhavani V.

1995-01-01

The objective is to develop micromechanical models for predicting the stiffness and strength properties of textile composite materials. Two models are presented to predict the homogeneous elastic constants and coefficients of thermal expansion of a textile composite. The first model is based on rigorous finite element analysis of the textile composite unit-cell. Periodic boundary conditions are enforced between opposite faces of the unit-cell to simulate deformations accurately. The second model implements the selective averaging method (SAM), which is based on a judicious combination of stiffness and compliance averaging. For thin textile composites, both models can predict the plate stiffness coefficients and plate thermal coefficients. The finite element procedure is extended to compute the thermal residual microstresses, and to estimate the initial failure envelope for textile composites.
Biocompatibility effects of indirect exposure of base-metal dental casting alloys to a human-derived three-dimensional oral mucosal model.

PubMed

McGinley, Emma Louise; Moran, Gary P; Fleming, Garry J P

2013-11-01

The study employed a three-dimensional (3D) human-derived oral mucosal model to assess the biocompatibility of base-metal dental casting alloys ubiquitous in fixed prosthodontic and orthodontic dentistry. Oral mucosal models were generated using primary human oral keratinocyte and gingival fibroblast cells seeded onto human de-epidermidised dermal scaffolds. Nickel-chromium (Ni-Cr) and cobalt-chromium (Co-Cr) base-metal alloy immersion solutions were exposed to oral mucosal models for increasing time periods (2-72h). Analysis methodologies (histology, viable cell counts, oxidative stress, cytokine expression and toxicity) were performed following exposure. Ni-based alloy immersion solutions elicited significantly decreased cell viability (P<0.0004) with increased oxidative stress (P<0.0053), inflammatory cytokine expression (P<0.0077) and cellular toxicity levels (P<0.0001) compared with the controls. However, the Ni-free Co-Cr-based alloy immersion solutions did not elicit adverse oxidative stress (P>0.4755) or cellular toxicity (P<0.2339) responses compared with controls. Although the multiple analyses highlighted Ni-Cr base-metal alloy immersion solutions elicited significantly detrimental effects to the oral mucosal models, it was possible to distinguish between Ni-Cr alloys using the approach employed. The study employed a 3D human-derived full-thickness differentiated oral mucosal model suitable for biocompatibility assessment of base-metal dental casting alloys through discriminatory experimental parameters. Increasing incidences of Ni hypersensitivity in the general population warrants serious consideration from dental practitioners and patients alike where fixed prosthodontic/orthodontic dental treatments are the treatment modality involved. The novel and analytical oral mucosal model has the potential to significantly contribute to the advancement of reproducible dental medical device and dental material appraisals. Copyright © 2013 Elsevier Ltd. All rights reserved.
Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

PubMed

Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

2015-12-01

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.
Effect of the depth base along the vertical on the electrical parameters of a vertical parallel silicon solar cell in open and short circuit

NASA Astrophysics Data System (ADS)

Sahin, Gokhan; Kerimli, Genber

2018-03-01

This article presented a modeling study of effect of the depth base initiating on vertical parallel silicon solar cell's photovoltaic conversion efficiency. After the resolution of the continuity equation of excess minority carriers, we calculated the electrical parameters such as the photocurrent density, the photovoltage, series resistance and shunt resistances, diffusion capacitance, electric power, fill factor and the photovoltaic conversion efficiency. We determined the maximum electric power, the operating point of the solar cell and photovoltaic conversion efficiency according to the depth z in the base. We showed that the photocurrent density decreases with the depth z. The photovoltage decreased when the depth base increases. Series and shunt resistances were deduced from electrical model and were influenced and the applied the depth base. The capacity decreased with the depth z of the base. We had studied the influence of the variation of the depth z on the electrical parameters in the base.
Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling.

PubMed

Kornecki, Martin; Strube, Jochen

2018-03-16

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network-either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.
Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

PubMed Central

Kornecki, Martin; Strube, Jochen

2018-01-01

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream. PMID:29547557
Systems Analysis Of Advanced Coal-Based Power Plants

NASA Technical Reports Server (NTRS)

Ferrall, Joseph F.; Jennings, Charles N.; Pappano, Alfred W.

1988-01-01

Report presents appraisal of integrated coal-gasification/fuel-cell power plants. Based on study comparing fuel-cell technologies with each other and with coal-based alternatives and recommends most promising ones for research and development. Evaluates capital cost, cost of electricity, fuel consumption, and conformance with environmental standards. Analyzes sensitivity of cost of electricity to changes in fuel cost, to economic assumptions, and to level of technology. Recommends further evaluation of integrated coal-gasification/fuel-cell integrated coal-gasification/combined-cycle, and pulverized-coal-fired plants. Concludes with appendixes detailing plant-performance models, subsystem-performance parameters, performance goals, cost bases, plant-cost data sheets, and plant sensitivity to fuel-cell performance.
CELDA – an ontology for the comprehensive representation of cells in complex systems

PubMed Central

2013-01-01

Background The need for detailed description and modeling of cells drives the continuous generation of large and diverse datasets. Unfortunately, there exists no systematic and comprehensive way to organize these datasets and their information. CELDA (Cell: Expression, Localization, Development, Anatomy) is a novel ontology for the association of primary experimental data and derived knowledge to various types of cells of organisms. Results CELDA is a structure that can help to categorize cell types based on species, anatomical localization, subcellular structures, developmental stages and origin. It targets cells in vitro as well as in vivo. Instead of developing a novel ontology from scratch, we carefully designed CELDA in such a way that existing ontologies were integrated as much as possible, and only minimal extensions were performed to cover those classes and areas not present in any existing model. Currently, ten existing ontologies and models are linked to CELDA through the top-level ontology BioTop. Together with 15.439 newly created classes, CELDA contains more than 196.000 classes and 233.670 relationship axioms. CELDA is primarily used as a representational framework for modeling, analyzing and comparing cells within and across species in CellFinder, a web based data repository on cells (http://cellfinder.org). Conclusions CELDA can semantically link diverse types of information about cell types. It has been integrated within the research platform CellFinder, where it exemplarily relates cell types from liver and kidney during development on the one hand and anatomical locations in humans on the other, integrating information on all spatial and temporal stages. CELDA is available from the CellFinder website: http://cellfinder.org/about/ontology. PMID:23865855
New Model of Wood Cell Wall Microfibril and Its Implications

Treesearch

Umesh P. Agarwal; Sally A. Ralph; Rick S. Reiner; Carlos Baez

2015-01-01

Traditionally it has been accepted that the cell walls are made up of microfibrils which are partly crystalline. However, based on the recently obtained Raman evidence that showed that the interior of the microfibril was significantly disordered and water accessible, a new model is proposed. In this model, the molecular chains of cellulose are still organized along the...
Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

PubMed

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.
Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

PubMed Central

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797
Organic electrochemical transistors for cell-based impedance sensing

NASA Astrophysics Data System (ADS)

Rivnay, Jonathan; Ramuz, Marc; Leleux, Pierre; Hama, Adel; Huerta, Miriam; Owens, Roisin M.

2015-01-01

Electrical impedance sensing of biological systems, especially cultured epithelial cell layers, is now a common technique to monitor cell motion, morphology, and cell layer/tissue integrity for high throughput toxicology screening. Existing methods to measure electrical impedance most often rely on a two electrode configuration, where low frequency signals are challenging to obtain for small devices and for tissues with high resistance, due to low current. Organic electrochemical transistors (OECTs) are conducting polymer-based devices, which have been shown to efficiently transduce and amplify low-level ionic fluxes in biological systems into electronic output signals. In this work, we combine OECT-based drain current measurements with simultaneous measurement of more traditional impedance sensing using the gate current to produce complex impedance traces, which show low error at both low and high frequencies. We apply this technique in vitro to a model epithelial tissue layer and show that the data can be fit to an equivalent circuit model yielding trans-epithelial resistance and cell layer capacitance values in agreement with literature. Importantly, the combined measurement allows for low biases across the cell layer, while still maintaining good broadband signal.

Pentastatin-1, a collagen IV derived 20-mer peptide, suppresses tumor growth in a small cell lung cancer xenograft model.

PubMed

Koskimaki, Jacob E; Karagiannis, Emmanouil D; Tang, Benjamin C; Hammers, Hans; Watkins, D Neil; Pili, Roberto; Popel, Aleksander S

2010-02-01

Angiogenesis is the formation of neovasculature from a pre-existing vascular network. Progression of solid tumors including lung cancer is angiogenesis-dependent. We previously introduced a bioinformatics-based methodology to identify endogenous anti-angiogenic peptide sequences, and validated these predictions in vitro in human umbilical vein endothelial cell (HUVEC) proliferation and migration assays. One family of peptides with high activity is derived from the alpha-fibrils of type IV collagen. Based on the results from the in vitro screening, we have evaluated the ability of a 20 amino acid peptide derived from the alpha5 fibril of type IV collagen, pentastatin-1, to suppress vessel growth in an angioreactor-based directed in vivo angiogenesis assay (DIVAA). In addition, pentastatin-1 suppressed tumor growth with intraperitoneal peptide administration in a small cell lung cancer (SCLC) xenograft model in nude mice using the NCI-H82 human cancer cell line. Pentastatin-1 decreased the invasion of vessels into angioreactors in vivo in a dose dependent manner. The peptide also decreased the rate of tumor growth and microvascular density in vivo in a small cell lung cancer xenograft model. The peptide treatment significantly decreased the invasion of microvessels in angioreactors and the rate of tumor growth in the xenograft model, indicating potential treatment for angiogenesis-dependent disease, and for translational development as a therapeutic agent for lung cancer.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Yasuda, H.; Chong, C.; Charalampidis, E. G.

Here, we investigate the nonlinear wave dynamics of origami-based metamaterials composed of Tachi-Miura polyhedron (TMP) unit cells. These cells exhibit strain softening behavior under compression, which can be tuned by modifying their geometrical configurations or initial folded conditions. We assemble these TMP cells into a cluster of origami-based metamaterials, and we theoretically model and numerically analyze their wave transmission mechanism under external impact. Numerical simulations show that origami-based metamaterials can provide a prototypical platform for the formation of nonlinear coherent structures in the form of rarefaction waves, which feature a tensile wavefront upon the application of compression to the system.more » We also demonstrate the existence of numerically exact traveling rarefaction waves in an effective lumped-mass model. Origami-based metamaterials can be highly useful for mitigating shock waves, potentially enabling a wide variety of engineering applications.« less
An individual based computational model of intestinal crypt fission and its application to predicting unrestrictive growth of the intestinal epithelium.

PubMed

Pin, Carmen; Parker, Aimee; Gunning, A Patrick; Ohta, Yuki; Johnson, Ian T; Carding, Simon R; Sato, Toshiro

2015-02-01

Intestinal crypt fission is a homeostatic phenomenon, observable in healthy adult mucosa, but which also plays a pathological role as the main mode of growth of some intestinal polyps. Building on our previous individual based model for the small intestinal crypt and on in vitro cultured intestinal organoids, we here model crypt fission as a budding process based on fluid mechanics at the individual cell level and extrapolated predictions for growth of the intestinal epithelium. Budding was always observed in regions of organoids with abundant Paneth cells. Our data support a model in which buds are biomechanically initiated by single stem cells surrounded by Paneth cells which exhibit greater resistance to viscoelastic deformation, a hypothesis supported by atomic force measurements of single cells. Time intervals between consecutive budding events, as simulated by the model and observed in vitro, were 2.84 and 2.62 days, respectively. Predicted cell dynamics was unaffected within the original crypt which retained its full capability of providing cells to the epithelium throughout fission. Mitotic pressure in simulated primary crypts forced upward migration of buds, which simultaneously grew into new protruding crypts at a rate equal to 1.03 days(-1) in simulations and 0.99 days(-1) in cultured organoids. Simulated crypts reached their final size in 4.6 days, and required 6.2 days to migrate to the top of the primary crypt. The growth of the secondary crypt is independent of its migration along the original crypt. Assuming unrestricted crypt fission and multiple budding events, a maximal growth rate of the intestinal epithelium of 0.10 days(-1) is predicted and thus approximately 22 days are required for a 10-fold increase of polyp size. These predictions are in agreement with the time reported to develop macroscopic adenomas in mice after loss of Apc in intestinal stem cells.
Incorporating photon recycling into the analytical drift-diffusion model of high efficiency solar cells

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

Lumb, Matthew P.; Naval Research Laboratory, Washington, DC 20375; Steiner, Myles A.

The analytical drift-diffusion formalism is able to accurately simulate a wide range of solar cell architectures and was recently extended to include those with back surface reflectors. However, as solar cells approach the limits of material quality, photon recycling effects become increasingly important in predicting the behavior of these cells. In particular, the minority carrier diffusion length is significantly affected by the photon recycling, with consequences for the solar cell performance. In this paper, we outline an approach to account for photon recycling in the analytical Hovel model and compare analytical model predictions to GaAs-based experimental devices operating close tomore » the fundamental efficiency limit.« less
In vitro models of the blood–brain barrier: An overview of commonly used brain endothelial cell culture models and guidelines for their use

PubMed Central

Helms, Hans C; Abbott, N Joan; Burek, Malgorzata; Cecchelli, Romeo; Couraud, Pierre-Olivier; Deli, Maria A; Förster, Carola; Galla, Hans J; Romero, Ignacio A; Shusta, Eric V; Stebbins, Matthew J; Vandenhaute, Elodie; Weksler, Babette

2016-01-01

The endothelial cells lining the brain capillaries separate the blood from the brain parenchyma. The endothelial monolayer of the brain capillaries serves both as a crucial interface for exchange of nutrients, gases, and metabolites between blood and brain, and as a barrier for neurotoxic components of plasma and xenobiotics. This “blood-brain barrier” function is a major hindrance for drug uptake into the brain parenchyma. Cell culture models, based on either primary cells or immortalized brain endothelial cell lines, have been developed, in order to facilitate in vitro studies of drug transport to the brain and studies of endothelial cell biology and pathophysiology. In this review, we aim to give an overview of established in vitro blood–brain barrier models with a focus on their validation regarding a set of well-established blood–brain barrier characteristics. As an ideal cell culture model of the blood–brain barrier is yet to be developed, we also aim to give an overview of the advantages and drawbacks of the different models described. PMID:26868179
Current State-of-the-Art 3D Tissue Models and Their Compatibility with Live Cell Imaging.

PubMed

Bardsley, Katie; Deegan, Anthony J; El Haj, Alicia; Yang, Ying

2017-01-01

Mammalian cells grow within a complex three-dimensional (3D) microenvironment where multiple cells are organized and surrounded by extracellular matrix (ECM). The quantity and types of ECM components, alongside cell-to-cell and cell-to-matrix interactions dictate cellular differentiation, proliferation and function in vivo. To mimic natural cellular activities, various 3D tissue culture models have been established to replace conventional two dimensional (2D) culture environments. Allowing for both characterization and visualization of cellular activities within possibly bulky 3D tissue models presents considerable challenges due to the increased thickness and subsequent light scattering features of such 3D models. In this chapter, state-of-the-art methodologies used to establish 3D tissue models are discussed, first with a focus on both scaffold-free and scaffold-based 3D tissue model formation. Following on, multiple 3D live cell imaging systems, mainly optical imaging modalities, are introduced. Their advantages and disadvantages are discussed, with the aim of stimulating more research in this highly demanding research area.
Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology.

PubMed

Lei, Chon Lok; Wang, Ken; Clerx, Michael; Johnstone, Ross H; Hortigon-Vinagre, Maria P; Zamora, Victor; Allan, Andrew; Smith, Godfrey L; Gavaghan, David J; Mirams, Gary R; Polonchuk, Liudmila

2017-01-01

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.
Intravitreal silicon-based quantum dots as neuroprotective factors in a model of retinal photoreceptor degeneration.

PubMed

Olson, Jeffrey L; Velez-Montoya, Raul; Mandava, Naresh; Stoldt, Conrad R

2012-08-17

To study the intravitreal application of silicon quantum dots (QDs) and their capabilities to deliver electrical stimulation to the retinal cells and to assess the potential effect on retinal electrophysiology and anatomy. A Royal College of Surgeon rat model of retinal degeneration was used in this study. A total of 32 eyes were used, divided in four groups of 8 eyes each; the first group received the silicon-based QD, the second group received an inactive gold-based QD, the third group received a sham injection, and the fourth group was used as a control. An electroretinogram (ERG) was done at baseline and thereafter every week for 9 weeks. At the end of the follow-up, eyes were collected for further pathologic analysis and nuclei cell counts. Eyes within the silicon-based QD group showed a definite but transient increase in the waves of the ERG, especially in the rod response compared with the sham and control groups (P < 0.05). The pathologic examination demonstrated a higher nuclei count in the QD group, consistent with a higher cell survival rate than that in the sham and control groups in which cells degenerated as expected. Intravitreal injection of silicon-based QD seems to be safe and well tolerated, with no evident toxic reaction and demonstrates a beneficial effect by prolonging cell survival rate and improving ERG patterns in a well-established model of retinal degeneration. (ClinicalTrials.gov numbers NCT00407602, NCT01490827.).
A Finite Element Study of Micropipette Aspiration of Single Cells: Effect of Compressibility

PubMed Central

Jafari Bidhendi, Amirhossein; Korhonen, Rami K.

2012-01-01

Micropipette aspiration (MA) technique has been widely used to measure the viscoelastic properties of different cell types. Cells experience nonlinear large deformations during the aspiration procedure. Neo-Hookean viscohyperelastic (NHVH) incompressible and compressible models were used to simulate the creep behavior of cells in MA, particularly accounting for the effect of compressibility, bulk relaxation, and hardening phenomena under large strain. In order to find optimal material parameters, the models were fitted to the experimental data available for mesenchymal stem cells. Finally, through Neo-Hookean porohyperelastic (NHPH) material model for the cell, the influence of fluid flow on the aspiration length of the cell was studied. Based on the results, we suggest that the compressibility and bulk relaxation/fluid flow play a significant role in the deformation behavior of single cells and should be taken into account in the analysis of the mechanics of cells. PMID:22400045
Fast determination of the current loss mechanisms in textured crystalline Si-based solar cells

NASA Astrophysics Data System (ADS)

Nakane, Akihiro; Fujimoto, Shohei; Fujiwara, Hiroyuki

2017-11-01

A quite general device analysis method that allows the direct evaluation of optical and recombination losses in crystalline silicon (c-Si)-based solar cells has been developed. By applying this technique, the current loss mechanisms of the state-of-the-art solar cells with ˜20% efficiencies have been revealed. In the established method, the optical and electrical losses are characterized from the analysis of an experimental external quantum efficiency (EQE) spectrum with very low computational cost. In particular, we have performed the EQE analyses of textured c-Si solar cells by employing the experimental reflectance spectra obtained directly from the actual devices while using flat optical models without any fitting parameters. We find that the developed method provides almost perfect fitting to EQE spectra reported for various textured c-Si solar cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell with a MoOx layer, and an n-type passivated emitter with rear locally diffused solar cell. The modeling of the recombination loss further allows the extraction of the minority carrier diffusion length and surface recombination velocity from the EQE analysis. Based on the EQE analysis results, the current loss mechanisms in different types of c-Si solar cells are discussed.
Combining Adoptive Cell Therapy with Cytomegalovirus-Based Vaccine Is Protective against Solid Skin Tumors.

PubMed

Grenier, Jeremy M; Yeung, Stephen T; Qiu, Zhijuan; Jellison, Evan R; Khanna, Kamal M

2017-01-01

Despite many years of research, cancer vaccines have largely been ineffective in the treatment of established cancers. Many barriers to immune-mediated destruction of malignant cells exist, and these likely limit the efficacy of cancer vaccines. In this study, we sought to enhance the efficacy of a cytomegalovirus (CMV)-based vaccine targeting melanoma by combining vaccination with other forms of immunotherapy. Adoptive cell therapy in humans and in animal models has been shown to be effective for tumor regression. Thus, in this study, we assessed whether CMV-based vaccines in combination with adoptively transferred antitumor T cells could provide greater antitumor protection than either therapy alone. Our results show that adoptive cell therapy greatly enhanced the antitumor effects of CMV-based vaccines targeting the foreign model antigen, OVA, or the melanoma differentiation antigen, gp100. Combination adoptive cell therapy and vaccination induced the upregulation of the inhibitory ligands, PD-L1, and Qa-1 b , on B16 tumor cells. This expression paralleled the infiltration of tumors by vaccine-stimulated T cells which also expressed high levels of the receptors PD-1 and NKG2A/C/E, suggesting a potential mechanism of tumor immune evasion. Surprisingly, therapeutic blockade of the PD-1/PD-L1 and NKG2A/Qa-1 b axes did not delay tumor growth following vaccination, suggesting that the presence of inhibitory ligands within malignant tissue may not be an effective biomarker for successful combination therapy with CMV-based vaccines. Overall, our studies show that therapeutic CMV-based vaccines in combination with adoptive T cell transfer alone are effective for tumor rejection.
Combining Adoptive Cell Therapy with Cytomegalovirus-Based Vaccine Is Protective against Solid Skin Tumors

PubMed Central

Grenier, Jeremy M.; Yeung, Stephen T.; Qiu, Zhijuan; Jellison, Evan R.; Khanna, Kamal M.

2018-01-01

Despite many years of research, cancer vaccines have largely been ineffective in the treatment of established cancers. Many barriers to immune-mediated destruction of malignant cells exist, and these likely limit the efficacy of cancer vaccines. In this study, we sought to enhance the efficacy of a cytomegalovirus (CMV)-based vaccine targeting melanoma by combining vaccination with other forms of immunotherapy. Adoptive cell therapy in humans and in animal models has been shown to be effective for tumor regression. Thus, in this study, we assessed whether CMV-based vaccines in combination with adoptively transferred antitumor T cells could provide greater antitumor protection than either therapy alone. Our results show that adoptive cell therapy greatly enhanced the antitumor effects of CMV-based vaccines targeting the foreign model antigen, OVA, or the melanoma differentiation antigen, gp100. Combination adoptive cell therapy and vaccination induced the upregulation of the inhibitory ligands, PD-L1, and Qa-1b, on B16 tumor cells. This expression paralleled the infiltration of tumors by vaccine-stimulated T cells which also expressed high levels of the receptors PD-1 and NKG2A/C/E, suggesting a potential mechanism of tumor immune evasion. Surprisingly, therapeutic blockade of the PD-1/PD-L1 and NKG2A/Qa-1b axes did not delay tumor growth following vaccination, suggesting that the presence of inhibitory ligands within malignant tissue may not be an effective biomarker for successful combination therapy with CMV-based vaccines. Overall, our studies show that therapeutic CMV-based vaccines in combination with adoptive T cell transfer alone are effective for tumor rejection. PMID:29387061
PomBase: The Scientific Resource for Fission Yeast.

PubMed

Lock, Antonia; Rutherford, Kim; Harris, Midori A; Wood, Valerie

2018-01-01

The fission yeast Schizosaccharomyces pombe has become well established as a model species for studying conserved cell-level biological processes, especially the mechanics and regulation of cell division. PomBase integrates the S. pombe genome sequence with traditional genetic, molecular, and cell biological experimental data as well as the growing body of large datasets generated by emerging high-throughput methods. This chapter provides insight into the curation philosophy and data organization at PomBase, and provides a guide to using PomBase for infrequent visitors and anyone considering exploring S. pombe in their research.
Modeling and Simulation of the Direct Methanol Fuel Cell

NASA Technical Reports Server (NTRS)

Wohr, M.; Narayanan, S. R.; Halpert, G.

1996-01-01

From intro.: The direct methanol liquid feed fuel cell uses aqueous solutions of methanol as fuel and oxygen or air as the oxidant and uses an ionically conducting polymer membrane such as Nafion(sup r)117 and the electrolyte. This type of direct oxidation cell is fuel versatile and offers significant advantages in terms of simplicity of design and operation...The present study focuses on the results of a phenomenological model based on current understanding of the various processed operating in these cells.
Direct 3D cell-printing of human skin with functional transwell system.

PubMed

Kim, Byoung Soo; Lee, Jung-Seob; Gao, Ge; Cho, Dong-Woo

2017-06-06

Three-dimensional (3D) cell-printing has been emerging as a promising technology with which to build up human skin models by enabling rapid and versatile design. Despite the technological advances, challenges remain in the development of fully functional models that recapitulate complexities in the native tissue. Moreover, although several approaches have been explored for the development of biomimetic human skin models, the present skin models based on multistep fabrication methods using polydimethylsiloxane chips and commercial transwell inserts could be tackled by leveraging 3D cell-printing technology. In this paper, we present a new 3D cell-printing strategy for engineering a 3D human skin model with a functional transwell system in a single-step process. A hybrid 3D cell-printing system was developed, allowing for the use of extrusion and inkjet modules at the same time. We began by revealing the significance of each module in engineering human skin models; by using the extrusion-dispensing module, we engineered a collagen-based construct with polycaprolactone (PCL) mesh that prevented the contraction of collagen during tissue maturation; the inkjet-based dispensing module was used to uniformly distribute keratinocytes. Taking these features together, we engineered a human skin model with a functional transwell system; the transwell system and fibroblast-populated dermis were consecutively fabricated by using the extrusion modules. Following this process, keratinocytes were uniformly distributed onto the engineered dermis by the inkjet module. Our transwell system indicates a supportive 3D construct composed of PCL, enabling the maturation of a skin model without the aid of commercial transwell inserts. This skin model revealed favorable biological characteristics that included a stabilized fibroblast-stretched dermis and stratified epidermis layers after 14 days. It was also observed that a 50 times reduction in cost was achieved and 10 times less medium was used than in a conventional culture. Collectively, because this single-step process opens up chances for versatile designs, we envision that our cell-printing strategy could provide an attractive platform in engineering various human skin models.
A neural network based computational model to predict the output power of different types of photovoltaic cells.

PubMed

Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

2017-01-01

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.
Design, Modeling, Fabrication & Characterization of Industrial Si Solar Cells

NASA Astrophysics Data System (ADS)

Chowdhury, Ahrar Ahmed

Photovoltaic is a viable solution towards meeting the energy demand in an ecofriendly environment. To ensure the mass access in photovoltaic electricity, cost effective approach needs to be adapted. This thesis aims towards substrate independent fabrication process in order to achieve high efficiency cost effective industrial Silicon (Si) solar cells. Most cost-effective structures, such as, Al-BSF (Aluminum Back Surface Field), FSF (Front Surface Field) and bifacial cells are investigated in detail to exploit the efficiency potentials. First off, we introduced two-dimensional simulation model to design and modeling of most commonly used Si solar cells in today's PV arena. Best modelled results of high efficiency Al-BSF, FSF and bifacial cells are 20.50%, 22% and 21.68% respectively. Special attentions are given on the metallization design on all the structures in order to reduce the Ag cost. Furthermore, detail design and modeling were performed on FSF and bifacial cells. The FSF cells has potentials to gain 0.42%abs efficiency by combining the emitter design and front surface passivation. The prospects of bifacial cells can be revealed with the optimization of gridline widths and gridline numbers. Since, bifacial cells have metallization on both sides, a double fold cost saving is possible via innovative metallization design. Following modeling an effort is undertaken to reach the modelled result in fabrication the process. We proposed substrate independent fabrication process aiming towards establishing simultaneous processing sequences for both monofacial and bifacial cells. Subsequently, for the contact formation cost effective screen-printed technology is utilized throughout this thesis. The best Al-BSF cell attained efficiency ˜19.40%. Detail characterization was carried out to find a roadmap of achieving >20.50% efficiency Al-BSF cell. Since, n-type cell is free from Light Induced degradation (LID), recently there is a growing interest on FSF cell. Our best fabricated result of FSF cell achieved ˜18.40% efficiency. Characterizations on such cells provide that, cell performance can be further improved by utilizing high lifetime base wafer. We showed a step by step improvement on the device parameters to achieve ˜22% efficiency FSF cell. Finally, bifacial cells were fabricated with 13.32% front and 9.65% rear efficiency. The efficiency limitation is due to the quality of base wafer. Detail resistance breakdown was conducted on these cells to analyze parasitic resistance losses. It was found that base and gridline resistances dominated the FF loss. However, very low contact resistance of 20 mO-cm 2 at front side and 2 mO-cm2 at the rear side was observed by utilizing same Ag paste for front and rear contact formation. This might provide a pathway towards the search of an optimized Ag paste to attain high efficiency screen-printed bifacial cell. Detail investigations needs to be carried out to unveil the property of this Ag paste. In future work, more focus will be given on the metallization design to incorporate further reduction in Ag cost. Al2O3 passivation layer will be incorporated as a means to attain ˜23% screen-printed bifacial cell.
Neurovascular Cell Sheet Transplantation in a Canine Model of Intracranial Hemorrhage

PubMed Central

Lee, Woo-Jin; Lee, Jong Young; Jung, Keun-Hwa; Lee, Soon-Tae; Kim, Hyo Yeol; Park, Dong-Kyu; Yu, Jung-Suk; Kim, So-Yun; Jeon, Daejong; Kim, Manho; Lee, Sang Kun; Roh, Jae-Kyu; Chu, Kon

2017-01-01

Cell-based therapy for intracerebral hemorrhage (ICH) has a great therapeutic potential. However, methods to effectively induce direct regeneration of the damaged neural tissue after cell transplantation have not been established, which, if done, would improve the efficacy of cell-based therapy. In this study, we aimed to develop a cell sheet with neurovasculogenic potential and evaluate its usefulness in a canine ICH model. We designed a composite cell sheet made of neural progenitors derived from human olfactory neuroepithelium and vascular progenitors from human adipose tissue-derived stromal cells. We also generated a physiologic canine ICH model by manually injecting and then infusing autologous blood under arterial pressure. We transplanted the sheet cells (cell sheet group) or saline (control group) at the cortex over the hematoma at subacute stages (2 weeks from ICH induction). At 4 weeks from the cell transplantation, cell survival, migration, and differentiation were evaluated. Hemispheric atrophy and neurobehavioral recovery were also compared between the groups. As a result, the cell sheet was rich in extracellular matrices and expressed neurotrophic factors as well as the markers for neuronal development. After transplantation, the cells successfully survived for 4 weeks, and a large portion of those migrated to the perihematomal site and differentiated into neurons and pericytes (20% and 30% of migrated stem cells, respectively). Transplantation of cell sheets alleviated hemorrhage-related hemispheric atrophy (p = 0.042) and showed tendency for improving functional recovery (p = 0.062). Therefore, we concluded that the cell sheet transplantation technique might induce direct regeneration of neural tissue and might improve outcomes of intracerebral hemorrhage. PMID:28713638
A statistical model describing combined irreversible electroporation and electroporation-induced blood-brain barrier disruption.

PubMed

Sharabi, Shirley; Kos, Bor; Last, David; Guez, David; Daniels, Dianne; Harnof, Sagi; Mardor, Yael; Miklavcic, Damijan

2016-03-01

Electroporation-based therapies such as electrochemotherapy (ECT) and irreversible electroporation (IRE) are emerging as promising tools for treatment of tumors. When applied to the brain, electroporation can also induce transient blood-brain-barrier (BBB) disruption in volumes extending beyond IRE, thus enabling efficient drug penetration. The main objective of this study was to develop a statistical model predicting cell death and BBB disruption induced by electroporation. This model can be used for individual treatment planning. Cell death and BBB disruption models were developed based on the Peleg-Fermi model in combination with numerical models of the electric field. The model calculates the electric field thresholds for cell kill and BBB disruption and describes the dependence on the number of treatment pulses. The model was validated using in vivo experimental data consisting of rats brains MRIs post electroporation treatments. Linear regression analysis confirmed that the model described the IRE and BBB disruption volumes as a function of treatment pulses number (r(2) = 0.79; p < 0.008, r(2) = 0.91; p < 0.001). The results presented a strong plateau effect as the pulse number increased. The ratio between complete cell death and no cell death thresholds was relatively narrow (between 0.88-0.91) even for small numbers of pulses and depended weakly on the number of pulses. For BBB disruption, the ratio increased with the number of pulses. BBB disruption radii were on average 67% ± 11% larger than IRE volumes. The statistical model can be used to describe the dependence of treatment-effects on the number of pulses independent of the experimental setup.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Espinoza, I; Peschke, P; Karger, C

Purpose: In radiotherapy, it is important to predict the response of tumour to irradiation prior to the treatment. Mathematical modelling of tumour control probability (TCP) based on the dose distribution, medical imaging and other biological information may help to improve this prediction and to optimize the treatment plan. The aim of this work is to develop an image based 3D multiscale radiobiological model, which describes the growth and the response to radiotherapy of hypoxic tumors. Methods: The computer model is based on voxels, containing tumour, normal (including capillary) and dead cells. Killing of tumour cells due to irradiation is calculatedmore » by the Linear Quadratic Model (extended for hypoxia), and the proliferation and resorption of cells are modelled by exponential laws. The initial shape of the tumours is taken from CT images and the initial vascular and cell density information from PET and/or MR images. Including the fractionation regime and the physical dose distribution of the radiation treatment, the model simulates the spatial-temporal evolution of the tumor. Additionally, the dose distribution may be biologically optimized. Results: The model describes the appearance of hypoxia during tumour growth and the reoxygenation processes during radiotherapy. Among other parameters, the TCP is calculated for different dose distributions. The results are in accordance with published results. Conclusion: The simulation model may contribute to the understanding of the influence of biological parameters on tumor response during treatment, and specifically on TCP. It may be used to implement dose-painting approaches. Experimental and clinical validation is needed. This study is supported by a grant from the Ministry of Education of Chile, Programa Mece Educacion Superior (2)« less

Learning LM Specificity for Ganglion Cells

NASA Technical Reports Server (NTRS)

Ahumada, Albert J.

2015-01-01

Unsupervised learning models have been proposed based on experience (Ahumada and Mulligan, 1990;Wachtler, Doi, Lee and Sejnowski, 2007) that allow the cortex to develop units with LM specific color opponent receptive fields like the blob cells reported by Hubel and Wiesel on the basis of visual experience. These models used ganglion cells with LM indiscriminate wiring as inputs to the learning mechanism, which was presumed to occur at the cortical level.
Diffusion lengths in irradiated N/P InP-on-Si solar cells

NASA Technical Reports Server (NTRS)

Wojtczuk, Steven; Colerico, Claudia; Summers, Geoffrey P.; Walters, Robert J.; Burke, Edward A.

1996-01-01

Indium phosphide (InP) solar cells were made on silicon (Si) wafers (InP/Si) by to take advantage of both the radiation-hardness properties of the InP solar cell and the light weight and low cost of Si wafers. The InP/Si cell application is for long duration and/or high radiation orbit space missions. Spire has made N/P InP/Si cells of sizes up to 2 cm by 4 cm with beginning-of-life (BOL) AM0 efficiencies over 13% (one-sun, 28C). These InP/Si cells have higher absolute efficiency and power density after a high radiation dose than gallium arsenide (GaAs) or silicon (Si) solar cells after a fluence of about 2e15 1 MeV electrons/sq. cm. In this work, we investigate the minority carrier (electron) base diffusion lengths in the N/P InP/Si cells. A quantum efficiency model was constructed for a 12% BOL AM0 N/P InP/Si cell which agreed well with the absolutely measured quantum efficiency and the sun-simulator measured AM0 photocurrent (30.1 mA/sq. cm). This model was then used to generate a table of AM0 photocurrents for a range of base diffusion lengths. AM0 photocurrents were then measured for irradiations up to 7.7e16 1 MeV electrons/sq. cm (the 12% BOL cell was 8% after the final irradiation). By comparing the measured photocurrents with the predicted photocurrents, base diffusion lengths were assigned at each fluence level. A damage coefficient K of 4e-8 and a starting (unirradiated) base electron diffusion length of 0.8 microns fits the data well. The quantum efficiency was measured again at the end of the experiment to verify that the photocurrent predicted by the model (25.5 mA/sq. cm) agreed with the simulator-measured photocurrent after irradiation (25.7 mA/sq. cm).
Perspectives on Non-Animal Alternatives for Assessing Sensitization Potential in Allergic Contact Dermatitis

PubMed Central

Sharma, Nripen S.; Jindal, Rohit; Mitra, Bhaskar; Lee, Serom; Li, Lulu; Maguire, Tim J.; Schloss, Rene; Yarmush, Martin L.

2014-01-01

Skin sensitization remains a major environmental and occupational health hazard. Animal models have been used as the gold standard method of choice for estimating chemical sensitization potential. However, a growing international drive and consensus for minimizing animal usage have prompted the development of in vitro methods to assess chemical sensitivity. In this paper, we examine existing approaches including in silico models, cell and tissue based assays for distinguishing between sensitizers and irritants. The in silico approaches that have been discussed include Quantitative Structure Activity Relationships (QSAR) and QSAR based expert models that correlate chemical molecular structure with biological activity and mechanism based read-across models that incorporate compound electrophilicity. The cell and tissue based assays rely on an assortment of mono and co-culture cell systems in conjunction with 3D skin models. Given the complexity of allergen induced immune responses, and the limited ability of existing systems to capture the entire gamut of cellular and molecular events associated with these responses, we also introduce a microfabricated platform that can capture all the key steps involved in allergic contact sensitivity. Finally, we describe the development of an integrated testing strategy comprised of two or three tier systems for evaluating sensitization potential of chemicals. PMID:24741377
An alternative low-loss stack topology for vanadium redox flow battery: Comparative assessment

NASA Astrophysics Data System (ADS)

Moro, Federico; Trovò, Andrea; Bortolin, Stefano; Del, Davide, , Col; Guarnieri, Massimo

2017-02-01

Two vanadium redox flow battery topologies have been compared. In the conventional series stack, bipolar plates connect cells electrically in series and hydraulically in parallel. The alternative topology consists of cells connected in parallel inside stacks by means of monopolar plates in order to reduce shunt currents along channels and manifolds. Channelled and flat current collectors interposed between cells were considered in both topologies. In order to compute the stack losses, an equivalent circuit model of a VRFB cell was built from a 2D FEM multiphysics numerical model based on Comsol®, accounting for coupled electrical, electrochemical, and charge and mass transport phenomena. Shunt currents were computed inside the cells with 3D FEM models and in the piping and manifolds by means of equivalent circuits solved with Matlab®. Hydraulic losses were computed with analytical models in piping and manifolds and with 3D numerical analyses based on ANSYS Fluent® in the cell porous electrodes. Total losses in the alternative topology resulted one order of magnitude lower than in an equivalent conventional battery. The alternative topology with channelled current collectors exhibits the lowest shunt currents and hydraulic losses, with round-trip efficiency higher by about 10%, as compared to the conventional topology.
Genome Editing of Erythroid Cell Culture Model Systems.

PubMed

Yik, Jinfen J; Crossley, Merlin; Quinlan, Kate G R

2018-01-01

Genome editing to introduce specific mutations or to knock out genes in model cell systems has become an efficient platform for research in the fields of molecular biology, genetics, and cell biology. With recent rapid improvements in genome editing techniques, bench-top manipulation of the genome in cell culture has become progressively easier. The application of this knowledge to erythroid cell culture systems now allows the rapid analysis of the downstream effects of virtually any engineered gene disruption or modification in cell systems. Here, we describe a CRISPR/Cas9-based approach to making genomic modifications in erythroid lineage cells which we have successfully used in both murine (MEL) and human (K562) erythroleukaemia immortalized cell lines.
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.
Modeling the hydrodynamic and electrochemical efficiency of semi-solid flow batteries

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

Brunini, VE; Chiang, YM; Carter, WC

2012-05-01

A mathematical model of flow cell operation incorporating hydrodynamic and electrochemical effects in three dimensions is developed. The model and resulting simulations apply to recently demonstrated high energy-density semi-solid flow cells. In particular, state of charge gradients that develop during low flow rate operation and their effects on the spatial non-uniformity of current density within flow cells are quantified. A one-dimensional scaling model is also developed and compared to the full three-dimensional simulation. The models are used to demonstrate the impact of the choice of electrochemical couple on flow cell performance. For semi-solid flow electrodes, which can use solid activemore » materials with a wide variety of voltage-capacity responses, we find that cell efficiency is maximized for electrochemical couples that have a relatively flat voltage vs. capacity curve, operated under slow flow conditions. For example, in flow electrodes limited by macroscopic charge transport, an LiFePO4-based system requires one-third the polarization to reach the same cycling rate as an LiCoO2-based system, all else being equal. Our conclusions are generally applicable to high energy density flow battery systems, in which flow rates can be comparatively low for a given required power. (C) 2012 Elsevier Ltd. All rights reserved.« less
Mechanistic equivalent circuit modelling of a commercial polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Giner-Sanz, J. J.; Ortega, E. M.; Pérez-Herranz, V.

2018-03-01

Electrochemical impedance spectroscopy (EIS) has been widely used in the fuel cell field since it allows deconvolving the different physic-chemical processes that affect the fuel cell performance. Typically, EIS spectra are modelled using electric equivalent circuits. In this work, EIS spectra of an individual cell of a commercial PEM fuel cell stack were obtained experimentally. The goal was to obtain a mechanistic electric equivalent circuit in order to model the experimental EIS spectra. A mechanistic electric equivalent circuit is a semiempirical modelling technique which is based on obtaining an equivalent circuit that does not only correctly fit the experimental spectra, but which elements have a mechanistic physical meaning. In order to obtain the aforementioned electric equivalent circuit, 12 different models with defined physical meanings were proposed. These equivalent circuits were fitted to the obtained EIS spectra. A 2 step selection process was performed. In the first step, a group of 4 circuits were preselected out of the initial list of 12, based on general fitting indicators as the determination coefficient and the fitted parameter uncertainty. In the second step, one of the 4 preselected circuits was selected on account of the consistency of the fitted parameter values with the physical meaning of each parameter.
Toward the reconstitution of synthetic cell motility

PubMed Central

Siton-Mendelson, Orit; Bernheim-Groswasser, Anne

2016-01-01

ABSTRACT Cellular motility is a fundamental process essential for embryonic development, wound healing, immune responses, and tissues development. Cells are mostly moving by crawling on external, or inside, substrates which can differ in their surface composition, geometry, and dimensionality. Cells can adopt different migration phenotypes, e.g., bleb-based and protrusion-based, depending on myosin contractility, surface adhesion, and cell confinement. In the few past decades, research on cell motility has focused on uncovering the major molecular players and their order of events. Despite major progresses, our ability to infer on the collective behavior from the molecular properties remains a major challenge, especially because cell migration integrates numerous chemical and mechanical processes that are coupled via feedbacks that span over large range of time and length scales. For this reason, reconstituted model systems were developed. These systems allow for full control of the molecular constituents and various system parameters, thereby providing insight into their individual roles and functions. In this review we describe the various reconstituted model systems that were developed in the past decades. Because of the multiple steps involved in cell motility and the complexity of the overall process, most of the model systems focus on very specific aspects of the individual steps of cell motility. Here we describe the main advancement in cell motility reconstitution and discuss the main challenges toward the realization of a synthetic motile cell. PMID:27019160
A 3-D model of tumor progression based on complex automata driven by particle dynamics.

PubMed

Wcisło, Rafał; Dzwinel, Witold; Yuen, David A; Dudek, Arkadiusz Z

2009-12-01

The dynamics of a growing tumor involving mechanical remodeling of healthy tissue and vasculature is neglected in most of the existing tumor models. This is due to the lack of efficient computational framework allowing for simulation of mechanical interactions. Meanwhile, just these interactions trigger critical changes in tumor growth dynamics and are responsible for its volumetric and directional progression. We describe here a novel 3-D model of tumor growth, which combines particle dynamics with cellular automata concept. The particles represent both tissue cells and fragments of the vascular network. They interact with their closest neighbors via semi-harmonic central forces simulating mechanical resistance of the cell walls. The particle dynamics is governed by both the Newtonian laws of motion and the cellular automata rules. These rules can represent cell life-cycle and other biological interactions involving smaller spatio-temporal scales. We show that our complex automata, particle based model can reproduce realistic 3-D dynamics of the entire system consisting of the tumor, normal tissue cells, blood vessels and blood flow. It can explain phenomena such as the inward cell motion in avascular tumor, stabilization of tumor growth by the external pressure, tumor vascularization due to the process of angiogenesis, trapping of healthy cells by invading tumor, and influence of external (boundary) conditions on the direction of tumor progression. We conclude that the particle model can serve as a general framework for designing advanced multiscale models of tumor dynamics and it is very competitive to the modeling approaches presented before.
Hydrologic impacts of climate change and urbanization in Las Vegas Wash Watershed, Nevada

EPA Science Inventory

In this study, a cell-based model for the Las Vegas Wash (LVW) Watershed in Clark County, Nevada, was developed by combining the traditional hydrologic modeling methods (Thornthwaite’s water balance model and the Soil Conservation Survey’s Curve Number method) with the pixel-base...
Probability based models for estimation of wildfire risk

Treesearch

Haiganoush Preisler; D. R. Brillinger; R. E. Burgan; John Benoit

2004-01-01

We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 kmÂ²-day cell level. We fit a spatially and temporally explicit non-...
Induced Pluripotent Stem Cells in Huntington's Disease: Disease Modeling and the Potential for Cell-Based Therapy.

PubMed

Liu, Ling; Huang, Jin-Sha; Han, Chao; Zhang, Guo-Xin; Xu, Xiao-Yun; Shen, Yan; Li, Jie; Jiang, Hai-Yang; Lin, Zhi-Cheng; Xiong, Nian; Wang, Tao

2016-12-01

Huntington's disease (HD) is an incurable neurodegenerative disorder that is characterized by motor dysfunction, cognitive impairment, and behavioral abnormalities. It is an autosomal dominant disorder caused by a CAG repeat expansion in the huntingtin gene, resulting in progressive neuronal loss predominately in the striatum and cortex. Despite the discovery of the causative gene in 1993, the exact mechanisms underlying HD pathogenesis have yet to be elucidated. Treatments that slow or halt the disease process are currently unavailable. Recent advances in induced pluripotent stem cell (iPSC) technologies have transformed our ability to study disease in human neural cells. Here, we firstly review the progress made to model HD in vitro using patient-derived iPSCs, which reveal unique insights into illuminating molecular mechanisms and provide a novel human cell-based platform for drug discovery. We then highlight the promises and challenges for pluripotent stem cells that might be used as a therapeutic source for cell replacement therapy of the lost neurons in HD brains.
Stomatal Opening: The Role of Cell-Wall Mechanical Anisotropy and Its Analytical Relations to the Bio-composite Characteristics

PubMed Central

Marom, Ziv; Shtein, Ilana; Bar-On, Benny

2017-01-01

Stomata are pores on the leaf surface, which are formed by a pair of curved, tubular guard cells; an increase in turgor pressure deforms the guard cells, resulting in the opening of the stomata. Recent studies employed numerical simulations, based on experimental data, to analyze the effects of various structural, chemical, and mechanical features of the guard cells on the stomatal opening characteristics; these studies all support the well-known qualitative observation that the mechanical anisotropy of the guard cells plays a critical role in stomatal opening. Here, we propose a computationally based analytical model that quantitatively establishes the relations between the degree of anisotropy of the guard cell, the bio-composite constituents of the cell wall, and the aperture and area of stomatal opening. The model introduces two non-dimensional key parameters that dominate the guard cell deformations—the inflation driving force and the anisotropy ratio—and it serves as a generic framework that is not limited to specific plant species. The modeling predictions are in line with a wide range of previous experimental studies, and its analytical formulation sheds new light on the relations between the structure, mechanics, and function of stomata. Moreover, the model provides an analytical tool to back-calculate the elastic characteristics of the matrix that composes the guard cell walls, which, to the best of our knowledge, cannot be probed by direct nano-mechanical experiments; indeed, the estimations of our model are in good agreement with recently published results of independent numerical optimization schemes. The emerging insights from the stomatal structure-mechanics “design guidelines” may promote the development of miniature, yet complex, multiscale composite actuation mechanisms for future engineering platforms. PMID:29312365
Autologous mesenchymal stem cells or meniscal cells: what is the best cell source for regenerative meniscus treatment in an early osteoarthritis situation?

PubMed

Zellner, Johannes; Pattappa, Girish; Koch, Matthias; Lang, Siegmund; Weber, Johannes; Pfeifer, Christian G; Mueller, Michael B; Kujat, Richard; Nerlich, Michael; Angele, Peter

2017-10-10

Treatment of meniscus tears within the avascular region represents a significant challenge, particularly in a situation of early osteoarthritis. Cell-based tissue engineering approaches have shown promising results. However, studies have not found a consensus on the appropriate autologous cell source in a clinical situation, specifically in a challenging degenerative environment. The present study sought to evaluate the appropriate cell source for autologous meniscal repair in a demanding setting of early osteoarthritis. A rabbit model was used to test autologous meniscal repair. Bone marrow and medial menisci were harvested 4 weeks prior to surgery. Bone marrow-derived mesenchymal stem cells (MSCs) and meniscal cells were isolated, expanded, and seeded onto collagen-hyaluronan scaffolds before implantation. A punch defect model was performed on the lateral meniscus and then a cell-seeded scaffold was press-fit into the defect. Following 6 or 12 weeks, gross joint morphology and OARSI grade were assessed, and menisci were harvested for macroscopic, histological, and immunohistochemical evaluation using a validated meniscus scoring system. In conjunction, human meniscal cells isolated from non-repairable bucket handle tears and human MSCs were expanded and, using the pellet culture model, assessed for their meniscus-like potential in a translational setting through collagen type I and II immunostaining, collagen type II enzyme-linked immunosorbent assay (ELISA), and gene expression analysis. After resections of the medial menisci, all knees showed early osteoarthritic changes (average OARSI grade 3.1). However, successful repair of meniscus punch defects was performed using either meniscal cells or MSCs. Gross joint assessment demonstrated donor site morbidity for meniscal cell treatment. Furthermore, human MSCs had significantly increased collagen type II gene expression and production compared to meniscal cells (p < 0.05). The regenerative potential of the meniscus by an autologous cell-based tissue engineering approach was shown even in a challenging setting of early osteoarthritis. Autologous MSCs and meniscal cells were found to have improved meniscal healing in an animal model, thus demonstrating their feasibility in a clinical setting. However, donor site morbidity, reduced availability, and reduced chondrogenic differentiation of human meniscal cells from debris of meniscal tears favors autologous MSCs for clinical use for cell-based meniscus regeneration.
A multi scale multi-dimensional thermo electrochemical modelling of high capacity lithium-ion cells

NASA Astrophysics Data System (ADS)

Tourani, Abbas; White, Peter; Ivey, Paul

2014-06-01

Lithium iron phosphate (LFP) and lithium manganese oxide (LMO) are competitive and complementary to each other as cathode materials for lithium-ion batteries, especially for use in electric vehicles. A multi scale multi-dimensional physic-based model is proposed in this paper to study the thermal behaviour of the two lithium-ion chemistries. The model consists of two sub models, a one dimensional (1D) electrochemical sub model and a two dimensional (2D) thermo-electric sub model, which are coupled and solved concurrently. The 1D model predicts the heat generation rate (Qh) and voltage (V) of the battery cell through different load cycles. The 2D model of the battery cell accounts for temperature distribution and current distribution across the surface of the battery cell. The two cells are examined experimentally through 90 h load cycles including high/low charge/discharge rates. The experimental results are compared with the model results and they are in good agreement. The presented results in this paper verify the cells temperature behaviour at different operating conditions which will lead to the design of a cost effective thermal management system for the battery pack.
The Cell Collective: Toward an open and collaborative approach to systems biology

PubMed Central

2012-01-01

Background Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. Results The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. Conclusions The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction. PMID:22871178
Preconditioning of mesenchymal stromal cells toward nucleus pulposus-like cells by microcryogels-based 3D cell culture and syringe-based pressure loading system.

PubMed

Zeng, Yang; Feng, Siyu; Liu, Wei; Fu, Qinyouen; Li, Yaqian; Li, Xiaokang; Chen, Chun; Huang, Chenyu; Ge, Zigang; Du, Yanan

2017-04-01

To precondition mesenchymal stromal/stem cells (MSCs) with mechanical stimulation may enhance cell survival and functions following implantation in load bearing environment such as nucleus pulposus (NP) in intervertebral disc (IVD). In this study, preconditioning of MSCs toward NP-like cells was achieved in previously developed poly (ethylene glycol) diacrylate (PEGDA) microcryogels (PMs) within a syringe-based three-dimensional (3D) culture system which provided a facile and cost-effective pressure loading approach. PMs loaded with alginate and MSCs could be incubated in a sealable syringe which could be air-compressed to apply pressure loading through a programmable syringe pump. Expression levels of chondrogenic marker genes SOX9, COL II, and ACAN were significantly upregulated in MSCs when pressure loading of 0.2 MPa or 0.8 MPa was implemented. Expression levels of COL I and COL X were downregulated when pressure loading was applied. In a nude mouse model, MSCs loaded in PMs mechanically stimulated for three days were subcutaneously injected using the same culture syringe. Three weeks postinjection, more proteoglycans (PGs) were deposited and more SOX9 and COL II but less COL I and COL X were stained in 0.2 MPa group. Furthermore, injectable MSCs-loaded PMs were utilized in an ex vivo rabbit IVD organ culture model that demonstrated the leak-proof function and enhanced cell retention of PMs assisted cell delivery to a load bearing environment for potential NP regeneration. This microcryogels-based 3D cell culture and syringe-based pressure loading system represents a novel method for 3D cell culture with mechanical stimulation for better function. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 507-520, 2017. © 2015 Wiley Periodicals, Inc.
An egalitarian network model for the emergence of simple and complex cells in visual cortex

PubMed Central

Tao, Louis; Shelley, Michael; McLaughlin, David; Shapley, Robert

2004-01-01

We explain how simple and complex cells arise in a large-scale neuronal network model of the primary visual cortex of the macaque. Our model consists of ≈4,000 integrate-and-fire, conductance-based point neurons, representing the cells in a small, 1-mm2 patch of an input layer of the primary visual cortex. In the model the local connections are isotropic and nonspecific, and convergent input from the lateral geniculate nucleus confers cortical cells with orientation and spatial phase preference. The balance between lateral connections and lateral geniculate nucleus drive determines whether individual neurons in this recurrent circuit are simple or complex. The model reproduces qualitatively the experimentally observed distributions of both extracellular and intracellular measures of simple and complex response. PMID:14695891
Improved CORF model of simple cell combined with non-classical receptive field and its application on edge detection

NASA Astrophysics Data System (ADS)

Sun, Xiao; Chai, Guobei; Liu, Wei; Bao, Wenzhuo; Zhao, Xiaoning; Ming, Delie

2018-02-01

Simple cells in primary visual cortex are believed to extract local edge information from a visual scene. In this paper, inspired by different receptive field properties and visual information flow paths of neurons, an improved Combination of Receptive Fields (CORF) model combined with non-classical receptive fields was proposed to simulate the responses of simple cell's receptive fields. Compared to the classical model, the proposed model is able to better imitate simple cell's physiologic structure with consideration of facilitation and suppression of non-classical receptive fields. And on this base, an edge detection algorithm as an application of the improved CORF model was proposed. Experimental results validate the robustness of the proposed algorithm to noise and background interference.

Stem cells in pharmaceutical biotechnology.

PubMed

Zuba-Surma, Ewa K; Józkowicz, Alicja; Dulak, Józef

2011-11-01

Multiple populations of stem cells have been indicated to potentially participate in regeneration of injured organs. Especially, embryonic stem cells (ESC) and recently inducible pluripotent stem cells (iPS) receive a marked attention from scientists and clinicians for regenerative medicine because of their high proliferative and differentiation capacities. Despite that ESC and iPS cells are expected to give rise into multiple regenerative applications when their side effects are overcame during appropriate preparation procedures, in fact their most recent application of human ESC may, however, reside in their use as a tool in drug development and disease modeling. This review focuses on the applications of stem cells in pharmaceutical biotechnology. We discuss possible relevance of pluripotent cell stem populations in developing physiological models for any human tissue cell type useful for pharmacological, metabolic and toxicity evaluation necessary in the earliest steps of drug development. The present models applied for preclinical drug testing consist of primary cells or immortalized cell lines that show limitations in terms of accessibility or relevance to their in vivo counterparts. The availability of renewable human cells with functional similarities to their in vivo counterparts is the first landmark for a new generation of cell-based assays. We discuss the approaches for using stem cells as valuable physiological targets of drug activity which may increase the strength of target validation and efficacy potentially resulting in introducing new safer remedies into clinical trials and the marketplace. Moreover, we discuss the possible applications of stem cells for elucidating mechanisms of disease pathogenesis. The knowledge about the mechanisms governing the development and progression of multitude disorders which would come from the cellular models established based on stem cells, may give rise to new therapeutical strategies for such diseases. All together, the applications of various cell types derived from patient specific pluripotent stem cells may lead to targeted drug and cellular therapies for certain individuals.
Laser-based direct-write techniques for cell printing

PubMed Central

Schiele, Nathan R; Corr, David T; Huang, Yong; Raof, Nurazhani Abdul; Xie, Yubing; Chrisey, Douglas B

2016-01-01

Fabrication of cellular constructs with spatial control of cell location (±5 μm) is essential to the advancement of a wide range of applications including tissue engineering, stem cell and cancer research. Precise cell placement, especially of multiple cell types in co- or multi-cultures and in three dimensions, can enable research possibilities otherwise impossible, such as the cell-by-cell assembly of complex cellular constructs. Laser-based direct writing, a printing technique first utilized in electronics applications, has been adapted to transfer living cells and other biological materials (e.g., enzymes, proteins and bioceramics). Many different cell types have been printed using laser-based direct writing, and this technique offers significant improvements when compared to conventional cell patterning techniques. The predominance of work to date has not been in application of the technique, but rather focused on demonstrating the ability of direct writing to pattern living cells, in a spatially precise manner, while maintaining cellular viability. This paper reviews laser-based additive direct-write techniques for cell printing, and the various cell types successfully laser direct-written that have applications in tissue engineering, stem cell and cancer research are highlighted. A particular focus is paid to process dynamics modeling and process-induced cell injury during laser-based cell direct writing. PMID:20814088
Sheldon spectrum and the plankton paradox: two sides of the same coin-a trait-based plankton size-spectrum model.

PubMed

Cuesta, José A; Delius, Gustav W; Law, Richard

2018-01-01

The Sheldon spectrum describes a remarkable regularity in aquatic ecosystems: the biomass density as a function of logarithmic body mass is approximately constant over many orders of magnitude. While size-spectrum models have explained this phenomenon for assemblages of multicellular organisms, this paper introduces a species-resolved size-spectrum model to explain the phenomenon in unicellular plankton. A Sheldon spectrum spanning the cell-size range of unicellular plankton necessarily consists of a large number of coexisting species covering a wide range of characteristic sizes. The coexistence of many phytoplankton species feeding on a small number of resources is known as the Paradox of the Plankton. Our model resolves the paradox by showing that coexistence is facilitated by the allometric scaling of four physiological rates. Two of the allometries have empirical support, the remaining two emerge from predator-prey interactions exactly when the abundances follow a Sheldon spectrum. Our plankton model is a scale-invariant trait-based size-spectrum model: it describes the abundance of phyto- and zooplankton cells as a function of both size and species trait (the maximal size before cell division). It incorporates growth due to resource consumption and predation on smaller cells, death due to predation, and a flexible cell division process. We give analytic solutions at steady state for both the within-species size distributions and the relative abundances across species.
A new biologic prognostic model based on immunohistochemistry predicts survival in patients with diffuse large B-cell lymphoma.

PubMed

Perry, Anamarija M; Cardesa-Salzmann, Teresa M; Meyer, Paul N; Colomo, Luis; Smith, Lynette M; Fu, Kai; Greiner, Timothy C; Delabie, Jan; Gascoyne, Randy D; Rimsza, Lisa; Jaffe, Elaine S; Ott, German; Rosenwald, Andreas; Braziel, Rita M; Tubbs, Raymond; Cook, James R; Staudt, Louis M; Connors, Joseph M; Sehn, Laurie H; Vose, Julie M; López-Guillermo, Armando; Campo, Elias; Chan, Wing C; Weisenburger, Dennis D

2012-09-13

Biologic factors that predict the survival of patients with a diffuse large B-cell lymphoma, such as cell of origin and stromal signatures, have been discovered by gene expression profiling. We attempted to simulate these gene expression profiling findings and create a new biologic prognostic model based on immunohistochemistry. We studied 199 patients (125 in the training set, 74 in the validation set) with de novo diffuse large B-cell lymphoma treated with rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or CHOP-like therapies, and immunohistochemical stains were performed on paraffin-embedded tissue microarrays. In the model, 1 point was awarded for each adverse prognostic factor: nongerminal center B cell-like subtype, SPARC (secreted protein, acidic, and rich in cysteine) < 5%, and microvascular density quartile 4. The model using these 3 biologic markers was highly predictive of overall survival and event-free survival in multivariate analysis after adjusting for the International Prognostic Index in both the training and validation sets. This new model delineates 2 groups of patients, 1 with a low biologic score (0-1) and good survival and the other with a high score (2-3) and poor survival. This new biologic prognostic model could be used with the International Prognostic Index to stratify patients for novel or risk-adapted therapies.
Optimized Delivery System Achieves Enhanced Endomyocardial Stem Cell Retention

PubMed Central

Behfar, Atta; Latere, Jean-Pierre; Bartunek, Jozef; Homsy, Christian; Daro, Dorothee; Crespo-Diaz, Ruben J.; Stalboerger, Paul G.; Steenwinckel, Valerie; Seron, Aymeric; Redfield, Margaret M.; Terzic, Andre

2014-01-01

Background Regenerative cell-based therapies are associated with limited myocardial retention of delivered stem cells. The objective of this study is to develop an endocardial delivery system for enhanced cell retention. Methods and Results Stem cell retention was simulated in silico using one and three-dimensional models of tissue distortion and compliance associated with delivery. Needle designs, predicted to be optimal, were accordingly engineered using nitinol – a nickel and titanium alloy displaying shape memory and super-elasticity. Biocompatibility was tested with human mesenchymal stem cells. Experimental validation was performed with species-matched cells directly delivered into Langendorff-perfused porcine hearts or administered percutaneously into the endocardium of infarcted pigs. Cell retention was quantified by flow cytometry and real time quantitative polymerase chain reaction methodology. Models, computing optimal distribution of distortion calibrated to favor tissue compliance, predicted that a 75°-curved needle featuring small-to-large graded side holes would ensure the highest cell retention profile. In isolated hearts, the nitinol curved needle catheter (C-Cath) design ensured 3-fold superior stem cell retention compared to a standard needle. In the setting of chronic infarction, percutaneous delivery of stem cells with C-Cath yielded a 37.7±7.1% versus 10.0±2.8% retention achieved with a traditional needle, without impact on biocompatibility or safety. Conclusions Modeling guided development of a nitinol-based curved needle delivery system with incremental side holes achieved enhanced myocardial stem cell retention. PMID:24326777
Predicting neuroblastoma using developmental signals and a logic-based model.

PubMed

Kasemeier-Kulesa, Jennifer C; Schnell, Santiago; Woolley, Thomas; Spengler, Jennifer A; Morrison, Jason A; McKinney, Mary C; Pushel, Irina; Wolfe, Lauren A; Kulesa, Paul M

2018-07-01

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression. Copyright © 2018. Published by Elsevier B.V.
A 3D Monte Carlo model of radiation affecting cells, and its application to neuronal cells and GCR irradiation

NASA Astrophysics Data System (ADS)

Ponomarev, Artem; Sundaresan, Alamelu; Kim, Angela; Vazquez, Marcelo E.; Guida, Peter; Kim, Myung-Hee; Cucinotta, Francis A.

A 3D Monte Carlo model of radiation transport in matter is applied to study the effect of heavy ion radiation on human neuronal cells. Central nervous system effects, including cognitive impairment, are suspected from the heavy ion component of galactic cosmic radiation (GCR) during space missions. The model can count, for instance, the number of direct hits from ions, which will have the most affect on the cells. For comparison, the remote hits, which are received through δ-rays from the projectile traversing space outside the volume of the cell, are also simulated and their contribution is estimated. To simulate tissue effects from irradiation, cellular matrices of neuronal cells, which were derived from confocal microscopy, were simulated in our model. To produce this realistic model of the brain tissue, image segmentation was used to identify cells in the images of cells cultures. The segmented cells were inserted pixel by pixel into the modeled physical space, which represents a volume of interacting cells with periodic boundary conditions (PBCs). PBCs were used to extrapolate the model results to the macroscopic tissue structures. Specific spatial patterns for cell apoptosis are expected from GCR, as heavy ions produce concentrated damage along their trajectories. The apoptotic cell patterns were modeled based on the action cross sections for apoptosis, which were estimated from the available experimental data. The cell patterns were characterized with an autocorrelation function, which values are higher for non-random cell patterns, and the values of the autocorrelation function were compared for X rays and Fe ion irradiations. The autocorrelation function indicates the directionality effects present in apoptotic neuronal cells from GCR.
Asynchronous adaptive time step in quantitative cellular automata modeling

PubMed Central

Zhu, Hao; Pang, Peter YH; Sun, Yan; Dhar, Pawan

2004-01-01

Background The behaviors of cells in metazoans are context dependent, thus large-scale multi-cellular modeling is often necessary, for which cellular automata are natural candidates. Two related issues are involved in cellular automata based multi-cellular modeling: how to introduce differential equation based quantitative computing to precisely describe cellular activity, and upon it, how to solve the heavy time consumption issue in simulation. Results Based on a modified, language based cellular automata system we extended that allows ordinary differential equations in models, we introduce a method implementing asynchronous adaptive time step in simulation that can considerably improve efficiency yet without a significant sacrifice of accuracy. An average speedup rate of 4–5 is achieved in the given example. Conclusions Strategies for reducing time consumption in simulation are indispensable for large-scale, quantitative multi-cellular models, because even a small 100 × 100 × 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment. PMID:15222901
On the Finite Element Implementation of the Generalized Method of Cells Micromechanics Constitutive Model

NASA Technical Reports Server (NTRS)

Wilt, T. E.

1995-01-01

The Generalized Method of Cells (GMC), a micromechanics based constitutive model, is implemented into the finite element code MARC using the user subroutine HYPELA. Comparisons in terms of transverse deformation response, micro stress and strain distributions, and required CPU time are presented for GMC and finite element models of fiber/matrix unit cell. GMC is shown to provide comparable predictions of the composite behavior and requires significantly less CPU time as compared to a finite element analysis of the unit cell. Details as to the organization of the HYPELA code are provided with the actual HYPELA code included in the appendix.
Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

PubMed

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Encapsulated Optically Responsive Cell Systems: Toward Smart Implants in Biomedicine.

PubMed

Boss, Christophe; Bouche, Nicolas; De Marchi, Umberto

2018-04-01

Managing increasingly prevalent chronic diseases will require close continuous monitoring of patients. Cell-based biosensors may be used for implantable diagnostic systems to monitor health status. Cells are indeed natural sensors in the body. Functional cellular systems can be maintained in the body for long-term implantation using cell encapsulation technology. By taking advantage of recent progress in miniaturized optoelectronic systems, the genetic engineering of optically responsive cells may be combined with cell encapsulation to generate smart implantable cell-based sensing systems. In biomedical research, cell-based biosensors may be used to study cell signaling, therapeutic effects, and dosing of bioactive molecules in preclinical models. Today, a wide variety of genetically encoded fluorescent sensors have been developed for real-time imaging of living cells. Here, recent developments in genetically encoded sensors, cell encapsulation, and ultrasmall optical systems are highlighted. The integration of these components in a new generation of biosensors is creating innovative smart in vivo cell-based systems, bringing novel perspectives for biomedical research and ultimately allowing unique health monitoring applications. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Multiplex CRISPR/Cas9-Based Genome Editing in Human Hematopoietic Stem Cells Models Clonal Hematopoiesis and Myeloid Neoplasia.

PubMed

Tothova, Zuzana; Krill-Burger, John M; Popova, Katerina D; Landers, Catherine C; Sievers, Quinlan L; Yudovich, David; Belizaire, Roger; Aster, Jon C; Morgan, Elizabeth A; Tsherniak, Aviad; Ebert, Benjamin L

2017-10-05

Hematologic malignancies are driven by combinations of genetic lesions that have been difficult to model in human cells. We used CRISPR/Cas9 genome engineering of primary adult and umbilical cord blood CD34 + human hematopoietic stem and progenitor cells (HSPCs), the cells of origin for myeloid pre-malignant and malignant diseases, followed by transplantation into immunodeficient mice to generate genetic models of clonal hematopoiesis and neoplasia. Human hematopoietic cells bearing mutations in combinations of genes, including cohesin complex genes, observed in myeloid malignancies generated immunophenotypically defined neoplastic clones capable of long-term, multi-lineage reconstitution and serial transplantation. Employing these models to investigate therapeutic efficacy, we found that TET2 and cohesin-mutated hematopoietic cells were sensitive to azacitidine treatment. These findings demonstrate the potential for generating genetically defined models of human myeloid diseases, and they are suitable for examining the biological consequences of somatic mutations and the testing of therapeutic agents. Copyright © 2017 Elsevier Inc. All rights reserved.
Distributed Multi-Cell Resource Allocation with Price Based ICI Coordination in Downlink OFDMA Networks

NASA Astrophysics Data System (ADS)

Lv, Gangming; Zhu, Shihua; Hui, Hui

Multi-cell resource allocation under minimum rate request for each user in OFDMA networks is addressed in this paper. Based on Lagrange dual decomposition theory, the joint multi-cell resource allocation problem is decomposed and modeled as a limited-cooperative game, and a distributed multi-cell resource allocation algorithm is thus proposed. Analysis and simulation results show that, compared with non-cooperative iterative water-filling algorithm, the proposed algorithm can remarkably reduce the ICI level and improve overall system performances.
Three-phase boundary length in solid-oxide fuel cells: A mathematical model

NASA Astrophysics Data System (ADS)

Janardhanan, Vinod M.; Heuveline, Vincent; Deutschmann, Olaf

A mathematical model to calculate the volume specific three-phase boundary length in the porous composite electrodes of solid-oxide fuel cell is presented. The model is exclusively based on geometrical considerations accounting for porosity, particle diameter, particle size distribution, and solids phase distribution. Results are presented for uniform particle size distribution as well as for non-uniform particle size distribution.
Preliminary model of fluid and solute distribution and transport during hemorrhage.

PubMed

Gyenge, C C; Bowen, B D; Reed, R K; Bert, J L

2003-01-01

The distribution and transport of fluid, ions, and other solutes (plasma proteins and glucose) are described in a mathematical model of unresuscitated hemorrhage. The model is based on balances of each material in both the circulation and its red blood cells, as well as in a whole-body tissue compartment along with its cells. Exchange between these four compartments occurs by a number of different mechanisms. The hemorrhage model has as its basis a validated model, due to Gyenge et al., of fluid and solute exchange in the whole body of a standard human. Hypothetical but physiologically based features such as glucose and small ion releases along with cell membrane changes are incorporated into the hemorrhage model to describe the system behavior, particularly during larger hemorrhages. Moderate (10%-30% blood volume loss) and large (> 30% blood loss) hemorrhage dynamics are simulated and compared with available data. The model predictions compare well with the available information for both types of hemorrhages and provide a reasonable description of the progression of a large hemorrhage from the compensatory phase through vascular collapse.
An analytical model with flexible accuracy for deep submicron DCVSL cells

NASA Astrophysics Data System (ADS)

Valiollahi, Sepideh; Ardeshir, Gholamreza

2018-07-01

Differential cascoded voltage switch logic (DCVSL) cells are among the best candidates of circuit designers for a wide range of applications due to advantages such as low input capacitance, high switching speed, small area and noise-immunity; nevertheless, a proper model has not yet been developed to analyse them. This paper analyses deep submicron DCVSL cells based on a flexible accuracy-simplicity trade-off including the following key features: (1) the model is capable of producing closed-form expressions with an acceptable accuracy; (2) model equations can be solved numerically to offer higher accuracy; (3) the short-circuit currents occurring in high-low/low-high transitions are accounted in analysis and (4) the changes in the operating modes of transistors during transitions together with an efficient submicron I-V model, which incorporates the most important non-ideal short-channel effects, are considered. The accuracy of the proposed model is validated in IBM 0.13 µm CMOS technology through comparisons with the accurate physically based BSIM3 model. The maximum error caused by analytical solutions is below 10%, while this amount is below 7% for numerical solutions.
Biomarker Discovery by Modeling Behçet's Disease with Patient-Specific Human Induced Pluripotent Stem Cells.

PubMed

Son, Mi-Young; Kim, Young-Dae; Seol, Binna; Lee, Mi-Ok; Na, Hee-Jun; Yoo, Bin; Chang, Jae-Suk; Cho, Yee Sook

2017-01-15

Behçet's disease (BD) is a chronic inflammatory and multisystemic autoimmune disease of unknown etiology. Due to the lack of a specific test for BD, its diagnosis is very difficult and therapeutic options are limited. Induced pluripotent stem cell (iPSC) technology, which provides inaccessible disease-relevant cell types, opens a new era for disease treatment. In this study, we generated BD iPSCs from patient somatic cells and differentiated them into hematopoietic precursor cells (BD iPSC-HPCs) as BD model cells. Based on comparative transcriptome analysis using our BD model cells, we identified eight novel BD-specific genes, AGTR2, CA9, CD44, CXCL1, HTN3, IL-2, PTGER4, and TSLP, which were differentially expressed in BD patients compared with healthy controls or patients with other immune diseases. The use of CXCL1 as a BD biomarker was further validated at the protein level using both a BD iPSC-HPC-based assay system and BD patient serum samples. Furthermore, we show that our BD iPSC-HPC-based drug screening system is highly effective for testing CXCL1 BD biomarkers, as determined by monitoring the efficacy of existing anti-inflammatory drugs. Our results shed new light on the usefulness of patient-specific iPSC technology in the development of a benchmarking platform for disease-specific biomarkers, phenotype- or target-driven drug discovery, and patient-tailored therapies.
Preclinical mouse model to monitor live Muc5b-producing conjunctival goblet cell density under pharmacological treatments.

PubMed

Portal, Céline; Gouyer, Valérie; Gottrand, Frédéric; Desseyn, Jean-Luc

2017-01-01

Modification of mucous cell density and gel-forming mucin production are established hallmarks of mucosal diseases. Our aim was to develop and validate a mouse model to study live goblet cell density in pathological situations and under pharmacological treatments. We created a reporter mouse for the gel-forming mucin gene Muc5b. Muc5b-positive goblet cells were studied in the eye conjunctiva by immunohistochemistry and probe-based confocal laser endomicroscopy (pCLE) in living mice. Dry eye syndrome (DES) model was induced by topical application of benzalkonium chloride (BAK) and recombinant interleukine (rIL) 13 was administered to reverse the goblet cell loss in the DES model. Almost 50% of the total of conjunctival goblet cells are Muc5b+ in unchallenged mice. The decrease density of Muc5b+ conjunctival goblet cell population in the DES model reflects the whole conjunctival goblet cell loss. Ten days of BAK in one eye followed by 4 days without any treatment induced a -18.3% decrease in conjunctival goblet cell density. A four days of rIL13 application in the DES model restored the normal goblet cell density. Muc5b is a biological marker of DES mouse models. We bring the proof of concept that our model is unique and allows a better understanding of the mechanisms that regulate gel-forming mucin production/secretion and mucous cell differentiation in the conjunctiva of living mice and can be used to test treatment compounds in mucosal disease models.
Mechanism of cell alignment in groups of Myxococcus xanthus bacteria

NASA Astrophysics Data System (ADS)

Balgam, Rajesh; Igoshin, Oleg

2015-03-01

Myxococcus xanthus is a model for studying self-organization in bacteria. These flexible cylindrical bacteria move along. In groups, M. xanthus cells align themselves into dynamic cell clusters but the mechanism underlying their formation is unknown. It has been shown that steric interactions can cause alignment in self-propelled hard rods but it is not clear how flexibility and reversals affect the alignment and cluster formation. We have investigated cell alignment process using our biophysical model of M. xanthus cell in an agent-based simulation framework under realistic cell flexibility values. We observed that flexible model cells can form aligned cell clusters when reversals are suppressed but these clusters disappeared when reversals frequency becomes similar to the observed value. However, M. xanthus cells follow slime (polysaccharide gel like material) trails left by other cells and we show that implementing this into our model rescues cell clustering for reversing cells. Our results show that slime following along with periodic cell reversals act as positive feedback to reinforce existing slime trails and recruit more cells. Furthermore, we have observed that mechanical cell alignment combined with slime following is sufficient to explain the distinct clustering patterns of reversing and non-reversing cells as observed in recent experiments. This work is supported by NSF MCB 0845919 and 1411780.
Chip Based Magnetic Imager for Molecular Profiling of Ovarian Cancer Cells

DTIC Science & Technology

2016-12-01

2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246-1260. PMC4380877, PMID:25748654. Acknowledgement of...Weissleder R, Lee H, Zhang F, Sharp PA (2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246-1260. 5. Im H, Shao H...Lett 32(10):1229–1231. 6 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1501815112 Im et al. Resource Genome-wide CRISPR Screen in a Mouse Model of Tumor

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