Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity

PubMed Central

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand “complex behavior” and complexity theory, and from which important biological insight can be gained. PMID:24999297

Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity.

PubMed

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand "complex behavior" and complexity theory, and from which important biological insight can be gained.

Dynamic Simulation of 1D Cellular Automata in the Active aTAM.

PubMed

Jonoska, Nataša; Karpenko, Daria; Seki, Shinnosuke

2015-07-01

The Active aTAM is a tile based model for self-assembly where tiles are able to transfer signals and change identities according to the signals received. We extend Active aTAM to include deactivation signals and thereby allow detachment of tiles. We show that the model allows a dynamic simulation of cellular automata with assemblies that do not record the entire computational history but only the current updates of the states, and thus provide a way for (a) algorithmic dynamical structural changes in the assembly and (b) reusable space in self-assembly. The simulation is such that at a given location the sequence of tiles that attach and detach corresponds precisely to the sequence of states the synchronous cellular automaton generates at that location.

Dynamic Simulation of 1D Cellular Automata in the Active aTAM

PubMed Central

Jonoska, Nataša; Karpenko, Daria; Seki, Shinnosuke

2016-01-01

The Active aTAM is a tile based model for self-assembly where tiles are able to transfer signals and change identities according to the signals received. We extend Active aTAM to include deactivation signals and thereby allow detachment of tiles. We show that the model allows a dynamic simulation of cellular automata with assemblies that do not record the entire computational history but only the current updates of the states, and thus provide a way for (a) algorithmic dynamical structural changes in the assembly and (b) reusable space in self-assembly. The simulation is such that at a given location the sequence of tiles that attach and detach corresponds precisely to the sequence of states the synchronous cellular automaton generates at that location. PMID:27789918

Cellular Automata

NASA Astrophysics Data System (ADS)

Gutowitz, Howard

1991-08-01

Cellular automata, dynamic systems in which space and time are discrete, are yielding interesting applications in both the physical and natural sciences. The thirty four contributions in this book cover many aspects of contemporary studies on cellular automata and include reviews, research reports, and guides to recent literature and available software. Chapters cover mathematical analysis, the structure of the space of cellular automata, learning rules with specified properties: cellular automata in biology, physics, chemistry, and computation theory; and generalizations of cellular automata in neural nets, Boolean nets, and coupled map lattices. Current work on cellular automata may be viewed as revolving around two central and closely related problems: the forward problem and the inverse problem. The forward problem concerns the description of properties of given cellular automata. Properties considered include reversibility, invariants, criticality, fractal dimension, and computational power. The role of cellular automata in computation theory is seen as a particularly exciting venue for exploring parallel computers as theoretical and practical tools in mathematical physics. The inverse problem, an area of study gaining prominence particularly in the natural sciences, involves designing rules that possess specified properties or perform specified task. A long-term goal is to develop a set of techniques that can find a rule or set of rules that can reproduce quantitative observations of a physical system. Studies of the inverse problem take up the organization and structure of the set of automata, in particular the parameterization of the space of cellular automata. Optimization and learning techniques, like the genetic algorithm and adaptive stochastic cellular automata are applied to find cellular automaton rules that model such physical phenomena as crystal growth or perform such adaptive-learning tasks as balancing an inverted pole. Howard Gutowitz is Collaborateur in the Service de Physique du Solide et Résonance Magnetique, Commissariat a I'Energie Atomique, Saclay, France.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

PubMed

Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

2016-01-01

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

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.

Discrepancy between mRNA and protein abundance: Insight from information retrieval process in computers

PubMed Central

Wang, Degeng

2008-01-01

Discrepancy between the abundance of cognate protein and RNA molecules is frequently observed. A theoretical understanding of this discrepancy remains elusive, and it is frequently described as surprises and/or technical difficulties in the literature. Protein and RNA represent different steps of the multi-stepped cellular genetic information flow process, in which they are dynamically produced and degraded. This paper explores a comparison with a similar process in computers - multi-step information flow from storage level to the execution level. Functional similarities can be found in almost every facet of the retrieval process. Firstly, common architecture is shared, as the ribonome (RNA space) and the proteome (protein space) are functionally similar to the computer primary memory and the computer cache memory respectively. Secondly, the retrieval process functions, in both systems, to support the operation of dynamic networks – biochemical regulatory networks in cells and, in computers, the virtual networks (of CPU instructions) that the CPU travels through while executing computer programs. Moreover, many regulatory techniques are implemented in computers at each step of the information retrieval process, with a goal of optimizing system performance. Cellular counterparts can be easily identified for these regulatory techniques. In other words, this comparative study attempted to utilize theoretical insight from computer system design principles as catalysis to sketch an integrative view of the gene expression process, that is, how it functions to ensure efficient operation of the overall cellular regulatory network. In context of this bird’s-eye view, discrepancy between protein and RNA abundance became a logical observation one would expect. It was suggested that this discrepancy, when interpreted in the context of system operation, serves as a potential source of information to decipher regulatory logics underneath biochemical network operation. PMID:18757239

Discrepancy between mRNA and protein abundance: insight from information retrieval process in computers.

PubMed

Wang, Degeng

2008-12-01

Discrepancy between the abundance of cognate protein and RNA molecules is frequently observed. A theoretical understanding of this discrepancy remains elusive, and it is frequently described as surprises and/or technical difficulties in the literature. Protein and RNA represent different steps of the multi-stepped cellular genetic information flow process, in which they are dynamically produced and degraded. This paper explores a comparison with a similar process in computers-multi-step information flow from storage level to the execution level. Functional similarities can be found in almost every facet of the retrieval process. Firstly, common architecture is shared, as the ribonome (RNA space) and the proteome (protein space) are functionally similar to the computer primary memory and the computer cache memory, respectively. Secondly, the retrieval process functions, in both systems, to support the operation of dynamic networks-biochemical regulatory networks in cells and, in computers, the virtual networks (of CPU instructions) that the CPU travels through while executing computer programs. Moreover, many regulatory techniques are implemented in computers at each step of the information retrieval process, with a goal of optimizing system performance. Cellular counterparts can be easily identified for these regulatory techniques. In other words, this comparative study attempted to utilize theoretical insight from computer system design principles as catalysis to sketch an integrative view of the gene expression process, that is, how it functions to ensure efficient operation of the overall cellular regulatory network. In context of this bird's-eye view, discrepancy between protein and RNA abundance became a logical observation one would expect. It was suggested that this discrepancy, when interpreted in the context of system operation, serves as a potential source of information to decipher regulatory logics underneath biochemical network operation.

Systematic Computation of Nonlinear Cellular and Molecular Dynamics with Low-Power CytoMimetic Circuits: A Simulation Study

PubMed Central

Papadimitriou, Konstantinos I.; Stan, Guy-Bart V.; Drakakis, Emmanuel M.

2013-01-01

This paper presents a novel method for the systematic implementation of low-power microelectronic circuits aimed at computing nonlinear cellular and molecular dynamics. The method proposed is based on the Nonlinear Bernoulli Cell Formalism (NBCF), an advanced mathematical framework stemming from the Bernoulli Cell Formalism (BCF) originally exploited for the modular synthesis and analysis of linear, time-invariant, high dynamic range, logarithmic filters. Our approach identifies and exploits the striking similarities existing between the NBCF and coupled nonlinear ordinary differential equations (ODEs) typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating fast and with good accuracy cellular and molecular dynamics. The application of the method is illustrated by synthesising for the first time microelectronic CytoMimetic topologies which simulate successfully: 1) a nonlinear intracellular calcium oscillations model for several Hill coefficient values and 2) a gene-protein regulatory system model. The dynamic behaviours generated by the proposed CytoMimetic circuits are compared and found to be in very good agreement with their biological counterparts. The circuits exploit the exponential law codifying the low-power subthreshold operation regime and have been simulated with realistic parameters from a commercially available CMOS process. They occupy an area of a fraction of a square-millimetre, while consuming between 1 and 12 microwatts of power. Simulations of fabrication-related variability results are also presented. PMID:23393550

Modeling mechanical interactions in growing populations of rod-shaped bacteria

NASA Astrophysics Data System (ADS)

Winkle, James J.; Igoshin, Oleg A.; Bennett, Matthew R.; Josić, Krešimir; Ott, William

2017-10-01

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell’s environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Kinetic Monte Carlo and cellular particle dynamics simulations of multicellular systems

NASA Astrophysics Data System (ADS)

Flenner, Elijah; Janosi, Lorant; Barz, Bogdan; Neagu, Adrian; Forgacs, Gabor; Kosztin, Ioan

2012-03-01

Computer modeling of multicellular systems has been a valuable tool for interpreting and guiding in vitro experiments relevant to embryonic morphogenesis, tumor growth, angiogenesis and, lately, structure formation following the printing of cell aggregates as bioink particles. Here we formulate two computer simulation methods: (1) a kinetic Monte Carlo (KMC) and (2) a cellular particle dynamics (CPD) method, which are capable of describing and predicting the shape evolution in time of three-dimensional multicellular systems during their biomechanical relaxation. Our work is motivated by the need of developing quantitative methods for optimizing postprinting structure formation in bioprinting-assisted tissue engineering. The KMC and CPD model parameters are determined and calibrated by using an original computational-theoretical-experimental framework applied to the fusion of two spherical cell aggregates. The two methods are used to predict the (1) formation of a toroidal structure through fusion of spherical aggregates and (2) cell sorting within an aggregate formed by two types of cells with different adhesivities.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

DOE PAGES

Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe; ...

2017-04-07

In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

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

Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe

In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures

PubMed Central

de la Fuente, Ildefonso Martínez

2010-01-01

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life. PMID:20957111

Systems microscopy: an emerging strategy for the life sciences.

PubMed

Lock, John G; Strömblad, Staffan

2010-05-01

Dynamic cellular processes occurring in time and space are fundamental to all physiology and disease. To understand complex and dynamic cellular processes therefore demands the capacity to record and integrate quantitative multiparametric data from the four spatiotemporal dimensions within which living cells self-organize, and to subsequently use these data for the mathematical modeling of cellular systems. To this end, a raft of complementary developments in automated fluorescence microscopy, cell microarray platforms, quantitative image analysis and data mining, combined with multivariate statistics and computational modeling, now coalesce to produce a new research strategy, "systems microscopy", which facilitates systems biology analyses of living cells. Systems microscopy provides the crucial capacities to simultaneously extract and interrogate multiparametric quantitative data at resolution levels ranging from the molecular to the cellular, thereby elucidating a more comprehensive and richly integrated understanding of complex and dynamic cellular systems. The unique capacities of systems microscopy suggest that it will become a vital cornerstone of systems biology, and here we describe the current status and future prospects of this emerging field, as well as outlining some of the key challenges that remain to be overcome. Copyright 2010 Elsevier Inc. All rights reserved.

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

PubMed Central

Kaul, Himanshu; Cui, Zhanfeng; Ventikos, Yiannis

2013-01-01

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

Time, space, and disorder in the expanding proteome universe.

PubMed

Minde, David-Paul; Dunker, A Keith; Lilley, Kathryn S

2017-04-01

Proteins are highly dynamic entities. Their myriad functions require specific structures, but proteins' dynamic nature ranges all the way from the local mobility of their amino acid constituents to mobility within and well beyond single cells. A truly comprehensive view of the dynamic structural proteome includes: (i) alternative sequences, (ii) alternative conformations, (iii) alternative interactions with a range of biomolecules, (iv) cellular localizations, (v) alternative behaviors in different cell types. While these aspects have traditionally been explored one protein at a time, we highlight recently emerging global approaches that accelerate comprehensive insights into these facets of the dynamic nature of protein structure. Computational tools that integrate and expand on multiple orthogonal data types promise to enable the transition from a disjointed list of static snapshots to a structurally explicit understanding of the dynamics of cellular mechanisms. © 2017 The Authors. Proteomics Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Symbolic Computation Using Cellular Automata-Based Hyperdimensional Computing.

PubMed

Yilmaz, Ozgur

2015-12-01

This letter introduces a novel framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation. A cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells, and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is shown to be capable of long-term memory, and it requires orders of magnitude less computation compared to echo state networks. As the focus of the letter, we suggest that binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing. To demonstrate the capability of the proposed system, we make analogies directly on image data by asking, What is the automobile of air?

Computation Modeling of Limb-bud Dysmorphogenesis: Predicting Cellular Dynamics and Key Events in Developmental Toxicity with a Multicellular Systems Model (FutureToxII)

EPA Science Inventory

Congenital limb malformations are among the most frequent malformation occurs in humans, with a frequency of about 1 in 500 to 1 in 1000 human live births. ToxCast is profiling the bioactivity of thousands of chemicals based on high-throughput (HTS) and computational methods that...

Mathematical modeling and computational prediction of cancer drug resistance.

PubMed

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mapping the dynamics of force transduction at cell–cell junctions of epithelial clusters

PubMed Central

Ng, Mei Rosa; Besser, Achim; Brugge, Joan S; Danuser, Gaudenz

2014-01-01

Force transduction at cell-cell adhesions regulates tissue development, maintenance and adaptation. We developed computational and experimental approaches to quantify, with both sub-cellular and multi-cellular resolution, the dynamics of force transmission in cell clusters. Applying this technology to spontaneously-forming adherent epithelial cell clusters, we found that basal force fluctuations were coupled to E-cadherin localization at the level of individual cell-cell junctions. At the multi-cellular scale, cell-cell force exchange depended on the cell position within a cluster, and was adaptive to reconfigurations due to cell divisions or positional rearrangements. Importantly, force transmission through a cell required coordinated modulation of cell-matrix adhesion and actomyosin contractility in the cell and its neighbors. These data provide insights into mechanisms that could control mechanical stress homeostasis in dynamic epithelial tissues, and highlight our methods as a resource for the study of mechanotransduction in cell-cell adhesions. DOI: http://dx.doi.org/10.7554/eLife.03282.001 PMID:25479385

Estimating phase synchronization in dynamical systems using cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Sowa, Robert; Chernihovskyi, Anton; Mormann, Florian; Lehnertz, Klaus

2005-06-01

We propose a method for estimating phase synchronization between time series using the parallel computing architecture of cellular nonlinear networks (CNN’s). Applying this method to time series of coupled nonlinear model systems and to electroencephalographic time series from epilepsy patients, we show that an accurate approximation of the mean phase coherence R —a bivariate measure for phase synchronization—can be achieved with CNN’s using polynomial-type templates.

Computer Modeling of the Earliest Cellular Structures and Functions

NASA Technical Reports Server (NTRS)

Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl

2000-01-01

In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membrane< We will discuss a series of large-scale, molecular-level computer simulations which demonstrate (a) how small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.

Dynamics of biological systems: role of systems biology in medical research.

PubMed

Assmus, Heike E; Herwig, Ralf; Cho, Kwang-Hyun; Wolkenhauer, Olaf

2006-11-01

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

Wavefront cellular learning automata.

PubMed

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Wavefront cellular learning automata

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

On Patterns in Affective Media

NASA Astrophysics Data System (ADS)

ADAMATZKY, ANDREW

In computational experiments with cellular automaton models of affective solutions, where chemical species represent happiness, anger, fear, confusion and sadness, we study phenomena of space time dynamic of emotions. We demonstrate feasibility of the affective solution paradigm in example of emotional abuse therapy. Results outlined in the present paper offer unconventional but promising technique to design, analyze and interpret spatio-temporal dynamic of mass moods in crowds.

Discrete Dynamics Lab

NASA Astrophysics Data System (ADS)

Wuensche, Andrew

DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Networks, and Discrete Dynamical Networks in general and studying their behavior, both from the time-series perspective — space-time patterns, and from the state-space perspective — attractor basins. DDLab is relevant to research, applications, and education in the fields of complexity, self-organization, emergent phenomena, chaos, collision-based computing, neural networks, content addressable memory, genetic regulatory networks, dynamical encryption, generative art and music, and the study of the abstract mathematical/physical/dynamical phenomena in their own right.

Computer simulation studies on passive recruitment dynamics of lipids induced by the adsorption of charged nanoparticles.

PubMed

Li, Yang

2014-07-07

The recruitment dynamics of lipids in the biomembrane is believed to play an important role in a variety of cellular processes. In this work, we investigate the nanoparticle-induced recruitment dynamics of lipids in the heterogeneous phospholipid bilayers of distearoyl-phosphatidylcholine (DSPC) and dioleoyl-phosphatidylglycerol (DOPG) via coarse-grained molecular dynamics simulations. Three dynamic modes of individual charged DOPG lipid molecules have been taken into account in the recruitment process: lateral diffusion, protrusions, and flip-flops. Based on analysis of the mobility pattern of lipids, structural variations in the membrane as well as activation energy of the structure of lipid eyelids characterized by the potential of mean force, we have concluded that the electrostatic attraction of nanoparticles plays a crucial role in the recruitment process of lipids in phospholipid bilayers. These studies are consistent with experimental observations and to some extent give insight into the origin of some cellular processes such as signaling, formation of lipid rafts, and endocytosis.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

CAM: A high-performance cellular-automaton machine

NASA Astrophysics Data System (ADS)

Toffoli, Tommaso

1984-01-01

CAM is a high-performance machine dedicated to the simulation of cellular automata and other distributed dynamical systems. Its speed is about one-thousand times greater than that of a general-purpose computer programmed to do the same task; in practical terms, this means that CAM can show the evolution of cellular automata on a color monitor with an update rate, dynamic range, and spatial resolution comparable to those of a Super-8 movie, thus permitting intensive interactive experimentation. Machines of this kind can open up novel fields of research, and in this context it is important that results be easy to obtain, reproduce, and transmit. For these reasons, in designing CAM it was important to achieve functional simplicity, high flexibility, and moderate production cost. We expect that many research groups will be able to own their own copy of the machine to do research with.

A comprehensive model of the spatio-temporal stem cell and tissue organisation in the intestinal crypt.

PubMed

Buske, Peter; Galle, Jörg; Barker, Nick; Aust, Gabriela; Clevers, Hans; Loeffler, Markus

2011-01-06

We introduce a novel dynamic model of stem cell and tissue organisation in murine intestinal crypts. Integrating the molecular, cellular and tissue level of description, this model links a broad spectrum of experimental observations encompassing spatially confined cell proliferation, directed cell migration, multiple cell lineage decisions and clonal competition.Using computational simulations we demonstrate that the model is capable of quantitatively describing and predicting the dynamic behaviour of the intestinal tissue during steady state as well as after cell damage and following selective gain or loss of gene function manipulations affecting Wnt- and Notch-signalling. Our simulation results suggest that reversibility and flexibility of cellular decisions are key elements of robust tissue organisation of the intestine. We predict that the tissue should be able to fully recover after complete elimination of cellular subpopulations including subpopulations deemed to be functional stem cells. This challenges current views of tissue stem cell organisation.

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

PubMed Central

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-01-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3. PMID:25644994

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

NASA Astrophysics Data System (ADS)

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-02-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.

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

Experimental design for dynamics identification of cellular processes.

PubMed

Dinh, Vu; Rundell, Ann E; Buzzard, Gregery T

2014-03-01

We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in W. Dong et al. (PLoS ONE 3(8):e3105, 2008) and independently in M.M. Donahue et al. (IET Syst. Biol. 4:249-262, 2010). In this approach, existing data is used to define a probability distribution on the parameters; the next measurement point is the one that yields the largest model output variance with this distribution. Building upon this approach, we introduce the Expected Dynamics Estimator (EDE), which is the expected value using this distribution of the output as a function of time. We prove the consistency of this estimator (uniform convergence to true dynamics) even when the chosen experiments cluster in a finite set of points. We extend this proof of consistency to various practical assumptions on noisy data and moderate levels of model mismatch. Through the derivation and proof, we develop a relaxed version of MINE that is more computationally tractable and robust than the original formulation. The results are illustrated with numerical examples on two nonlinear ordinary differential equation models of biomolecular and cellular processes.

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.

Building New Bridges between In Vitro and In Vivo in Early Drug Discovery: Where Molecular Modeling Meets Systems Biology.

PubMed

Pearlstein, Robert A; McKay, Daniel J J; Hornak, Viktor; Dickson, Callum; Golosov, Andrei; Harrison, Tyler; Velez-Vega, Camilo; Duca, José

2017-01-01

Cellular drug targets exist within networked function-generating systems whose constituent molecular species undergo dynamic interdependent non-equilibrium state transitions in response to specific perturbations (i.e.. inputs). Cellular phenotypic behaviors are manifested through the integrated behaviors of such networks. However, in vitro data are frequently measured and/or interpreted with empirical equilibrium or steady state models (e.g. Hill, Michaelis-Menten, Briggs-Haldane) relevant to isolated target populations. We propose that cells act as analog computers, "solving" sets of coupled "molecular differential equations" (i.e. represented by populations of interacting species)via "integration" of the dynamic state probability distributions among those populations. Disconnects between biochemical and functional/phenotypic assays (cellular/in vivo) may arise with targetcontaining systems that operate far from equilibrium, and/or when coupled contributions (including target-cognate partner binding and drug pharmacokinetics) are neglected in the analysis of biochemical results. The transformation of drug discovery from a trial-and-error endeavor to one based on reliable design criteria depends on improved understanding of the dynamic mechanisms powering cellular function/dysfunction at the systems level. Here, we address the general mechanisms of molecular and cellular function and pharmacological modulation thereof. We outline a first principles theory on the mechanisms by which free energy is stored and transduced into biological function, and by which biological function is modulated by drug-target binding. We propose that cellular function depends on dynamic counter-balanced molecular systems necessitated by the exponential behavior of molecular state transitions under non-equilibrium conditions, including positive versus negative mass action kinetics and solute-induced perturbations to the hydrogen bonds of solvating water versus kT. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

PubMed

Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E; Conolly, Rory B

2010-02-01

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

Dynamic NF-κB and E2F interactions control the priority and timing of inflammatory signalling and cell proliferation

PubMed Central

Ankers, John M; Awais, Raheela; Jones, Nicholas A; Boyd, James; Ryan, Sheila; Adamson, Antony D; Harper, Claire V; Bridge, Lloyd; Spiller, David G; Jackson, Dean A; Paszek, Pawel; Sée, Violaine; White, Michael RH

2016-01-01

Dynamic cellular systems reprogram gene expression to ensure appropriate cellular fate responses to specific extracellular cues. Here we demonstrate that the dynamics of Nuclear Factor kappa B (NF-κB) signalling and the cell cycle are prioritised differently depending on the timing of an inflammatory signal. Using iterative experimental and computational analyses, we show physical and functional interactions between NF-κB and the E2 Factor 1 (E2F-1) and E2 Factor 4 (E2F-4) cell cycle regulators. These interactions modulate the NF-κB response. In S-phase, the NF-κB response was delayed or repressed, while cell cycle progression was unimpeded. By contrast, activation of NF-κB at the G1/S boundary resulted in a longer cell cycle and more synchronous initial NF-κB responses between cells. These data identify new mechanisms by which the cellular response to stress is differentially controlled at different stages of the cell cycle. DOI: http://dx.doi.org/10.7554/eLife.10473.001 PMID:27185527

Metabolic dynamics in skeletal muscle during acute reduction in blood flow and oxygen supply to mitochondria: in-silico studies using a multi-scale, top-down integrated model.

PubMed

Dash, Ranjan K; Li, Yanjun; Kim, Jaeyeon; Beard, Daniel A; Saidel, Gerald M; Cabrera, Marco E

2008-09-09

Control mechanisms of cellular metabolism and energetics in skeletal muscle that may become evident in response to physiological stresses such as reduction in blood flow and oxygen supply to mitochondria can be quantitatively understood using a multi-scale computational model. The analysis of dynamic responses from such a model can provide insights into mechanisms of metabolic regulation that may not be evident from experimental studies. For the purpose, a physiologically-based, multi-scale computational model of skeletal muscle cellular metabolism and energetics was developed to describe dynamic responses of key chemical species and reaction fluxes to muscle ischemia. The model, which incorporates key transport and metabolic processes and subcellular compartmentalization, is based on dynamic mass balances of 30 chemical species in both capillary blood and tissue cells (cytosol and mitochondria) domains. The reaction fluxes in cytosol and mitochondria are expressed in terms of a general phenomenological Michaelis-Menten equation involving the compartmentalized energy controller ratios ATP/ADP and NADH/NAD(+). The unknown transport and reaction parameters in the model are estimated simultaneously by minimizing the differences between available in vivo experimental data on muscle ischemia and corresponding model outputs in coupled with the resting linear flux balance constraints using a robust, nonlinear, constrained-based, reduced gradient optimization algorithm. With the optimal parameter values, the model is able to simulate dynamic responses to reduced blood flow and oxygen supply to mitochondria associated with muscle ischemia of several key metabolite concentrations and metabolic fluxes in the subcellular cytosolic and mitochondrial compartments, some that can be measured and others that can not be measured with the current experimental techniques. The model can be applied to test complex hypotheses involving dynamic regulation of cellular metabolism and energetics in skeletal muscle during physiological stresses such as ischemia, hypoxia, and exercise.

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

PubMed Central

Rieffel, John A.; Valero-Cuevas, Francisco J.; Lipson, Hod

2010-01-01

Traditional engineering approaches strive to avoid, or actively suppress, nonlinear dynamic coupling among components. Biological systems, in contrast, are often rife with these dynamics. Could there be, in some cases, a benefit to high degrees of dynamical coupling? Here we present a distributed robotic control scheme inspired by the biological phenomenon of tensegrity-based mechanotransduction. This emergence of morphology-as-information-conduit or ‘morphological communication’, enabled by time-sensitive spiking neural networks, presents a new paradigm for the decentralized control of large, coupled, modular systems. These results significantly bolster, both in magnitude and in form, the idea of morphological computation in robotic control. Furthermore, they lend further credence to ideas of embodied anatomical computation in biological systems, on scales ranging from cellular structures up to the tendinous networks of the human hand. PMID:19776146

Mammalian synthetic biology for studying the cell

PubMed Central

Mathur, Melina; Xiang, Joy S.

2017-01-01

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. PMID:27932576

Effect of alternate energy substrates on mammalian brain metabolism during ischemic events.

PubMed

Koppaka, S S; Puchowicz; LaManna, J C; Gatica, J E

2008-01-01

Regulation of brain metabolism and cerebral blood flow involves complex control systems with several interacting variables at both cellular and organ levels. Quantitative understanding of the spatially and temporally heterogeneous brain control mechanisms during internal and external stimuli requires the development and validation of a computational (mathematical) model of metabolic processes in brain. This paper describes a computational model of cellular metabolism in blood-perfused brain tissue, which considers the astrocyte-neuron lactate-shuttle (ANLS) hypothesis. The model structure consists of neurons, astrocytes, extra-cellular space, and a surrounding capillary network. Each cell is further compartmentalized into cytosol and mitochondria. Inter-compartment interaction is accounted in the form of passive and carrier-mediated transport. Our model was validated against experimental data reported by Crumrine and LaManna, who studied the effect of ischemia and its recovery on various intra-cellular tissue substrates under standard diet conditions. The effect of ketone bodies on brain metabolism was also examined under ischemic conditions following cardiac resuscitation through our model simulations. The influence of ketone bodies on lactate dynamics on mammalian brain following ischemia is studied incorporating experimental data.

Hybrid stochastic and deterministic simulations of calcium blips.

PubMed

Rüdiger, S; Shuai, J W; Huisinga, W; Nagaiah, C; Warnecke, G; Parker, I; Falcke, M

2007-09-15

Intracellular calcium release is a prime example for the role of stochastic effects in cellular systems. Recent models consist of deterministic reaction-diffusion equations coupled to stochastic transitions of calcium channels. The resulting dynamics is of multiple time and spatial scales, which complicates far-reaching computer simulations. In this article, we introduce a novel hybrid scheme that is especially tailored to accurately trace events with essential stochastic variations, while deterministic concentration variables are efficiently and accurately traced at the same time. We use finite elements to efficiently resolve the extreme spatial gradients of concentration variables close to a channel. We describe the algorithmic approach and we demonstrate its efficiency compared to conventional methods. Our single-channel model matches experimental data and results in intriguing dynamics if calcium is used as charge carrier. Random openings of the channel accumulate in bursts of calcium blips that may be central for the understanding of cellular calcium dynamics.

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.

PubMed

Held, Michael; Schmitz, Michael H A; Fischer, Bernd; Walter, Thomas; Neumann, Beate; Olma, Michael H; Peter, Matthias; Ellenberg, Jan; Gerlich, Daniel W

2010-09-01

Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. Incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions and confusion between different functional states with similar morphology. We demonstrate generic applicability in different assays and perturbation conditions, including a candidate-based RNA interference screen for regulators of mitotic exit in human cells. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.

Cellular automaton model for molecular traffic jams

NASA Astrophysics Data System (ADS)

Belitsky, V.; Schütz, G. M.

2011-07-01

We consider the time evolution of an exactly solvable cellular automaton with random initial conditions both in the large-scale hydrodynamic limit and on the microscopic level. This model is a version of the totally asymmetric simple exclusion process with sublattice parallel update and thus may serve as a model for studying traffic jams in systems of self-driven particles. We study the emergence of shocks from the microscopic dynamics of the model. In particular, we introduce shock measures whose time evolution we can compute explicitly, both in the thermodynamic limit and for open boundaries where a boundary-induced phase transition driven by the motion of a shock occurs. The motion of the shock, which results from the collective dynamics of the exclusion particles, is a random walk with an internal degree of freedom that determines the jump direction. This type of hopping dynamics is reminiscent of some transport phenomena in biological systems.

Cellular-based modeling of oscillatory dynamics in brain networks.

PubMed

Skinner, Frances K

2012-08-01

Oscillatory, population activities have long been known to occur in our brains during different behavioral states. We know that many different cell types exist and that they contribute in distinct ways to the generation of these activities. I review recent papers that involve cellular-based models of brain networks, most of which include theta, gamma and sharp wave-ripple activities. To help organize the modeling work, I present it from a perspective of three different types of cellular-based modeling: 'Generic', 'Biophysical' and 'Linking'. Cellular-based modeling is taken to encompass the four features of experiment, model development, theory/analyses, and model usage/computation. The three modeling types are shown to include these features and interactions in different ways. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dynamic Clamp in Cardiac and Neuronal Systems Using RTXI

PubMed Central

Ortega, Francis A.; Butera, Robert J.; Christini, David J.; White, John A.; Dorval, Alan D.

2016-01-01

The injection of computer-simulated conductances through the dynamic clamp technique has allowed researchers to probe the intercellular and intracellular dynamics of cardiac and neuronal systems with great precision. By coupling computational models to biological systems, dynamic clamp has become a proven tool in electrophysiology with many applications, such as generating hybrid networks in neurons or simulating channelopathies in cardiomyocytes. While its applications are broad, the approach is straightforward: synthesizing traditional patch clamp, computational modeling, and closed-loop feedback control to simulate a cellular conductance. Here, we present two example applications: artificial blocking of the inward rectifier potassium current in a cardiomyocyte and coupling of a biological neuron to a virtual neuron through a virtual synapse. The design and implementation of the necessary software to administer these dynamic clamp experiments can be difficult. In this chapter, we provide an overview of designing and implementing a dynamic clamp experiment using the Real-Time eXperiment Interface (RTXI), an open- source software system tailored for real-time biological experiments. We present two ways to achieve this using RTXI’s modular format, through the creation of a custom user-made module and through existing modules found in RTXI’s online library. PMID:25023319

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.

Challenges in structural approaches to cell modeling

PubMed Central

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A.

2016-01-01

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863

Mammalian synthetic biology for studying the cell.

PubMed

Mathur, Melina; Xiang, Joy S; Smolke, Christina D

2017-01-02

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. © 2017 Mathur et al.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

PubMed

Feng, Song; Ollivier, Julien F; Swain, Peter S; Soyer, Orkun S

2015-10-30

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

EPA Pesticide Factsheets

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

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

EPA Pesticide Factsheets

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

Network representations of immune system complexity

PubMed Central

Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

2015-01-01

The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.

PubMed

Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M; Queisser, Gillian

2014-01-01

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator-which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics.

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

PubMed Central

Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M.; Queisser, Gillian

2014-01-01

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator—which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics. PMID:25120463

Cellular burdens and biological effects on tissue level caused by inhaled radon progenies.

PubMed

Madas, B G; Balásházy, I; Farkas, Á; Szoke, I

2011-02-01

In the case of radon exposure, the spatial distribution of deposited radioactive particles is highly inhomogeneous in the central airways. The object of this research is to investigate the consequences of this heterogeneity regarding cellular burdens in the bronchial epithelium and to study the possible biological effects at tissue level. Applying computational fluid and particle dynamics techniques, the deposition distribution of inhaled radon daughters has been determined in a bronchial airway model for 23 min of work in the New Mexico uranium mine corresponding to 0.0129 WLM exposure. A numerical epithelium model based on experimental data has been utilised in order to quantify cellular hits and doses. Finally, a carcinogenesis model considering cell death-induced cell-cycle shortening has been applied to assess the biological responses. Present computations reveal that cellular dose may reach 1.5 Gy, which is several orders of magnitude higher than tissue dose. The results are in agreement with the histological finding that the uneven deposition distribution of radon progenies may lead to inhomogeneous spatial distribution of tumours in the bronchial airways. In addition, at the macroscopic level, the relationship between cancer risk and radiation burden seems to be non-linear.

Challenges in structural approaches to cell modeling.

PubMed

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A

2016-07-31

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impact of time delay on the dynamics of SEIR epidemic model using cellular automata

NASA Astrophysics Data System (ADS)

Sharma, Natasha; Gupta, Arvind Kumar

2017-04-01

The delay of an infectious disease is significant when aiming to predict its strength and spreading patterns. In this paper the SEIR ​(susceptible-exposed-infected-recovered) epidemic spread with time delay is analyzed through a two-dimensional cellular automata model. The time delay corresponding to the infectious span, predominantly, includes death during the latency period in due course of infection. The advancement of whole system is described by SEIR transition function complemented with crucial factors like inhomogeneous population distribution, birth and disease independent mortality. Moreover, to reflect more realistic population dynamics some stochastic parameters like population movement and connections at local level are also considered. The existence and stability of disease free equilibrium is investigated. Two prime behavioral patterns of disease dynamics is found depending on delay. The critical value of delay, beyond which there are notable variations in spread patterns, is computed. The influence of important parameters affecting the disease dynamics on basic reproduction number is also examined. The results obtained show that delay plays an affirmative role to control disease progression in an infected host.

Image processing for navigation on a mobile embedded platform

NASA Astrophysics Data System (ADS)

Preuss, Thomas; Gentsch, Lars; Rambow, Mark

2006-02-01

Mobile computing devices such as PDAs or cellular phones may act as "Personal Multimedia Exchanges", but they are limited in their processing power as well as in their connectivity. Sensors as well as cellular phones and PDAs are able to gather multimedia data, e. g. images, but leak computing power to process that data on their own. Therefore, it is necessary, that these devices connect to devices with more performance, which provide e.g. image processing services. In this paper, a generic approach is presented that connects different kinds of clients with each other and allows them to interact with more powerful devices. This architecture, called BOSPORUS, represents a communication framework for dynamic peer-to-peer computing. Each peer offers and uses services in this network and communicates loosely coupled and asynchronously with the others. These features make BOSPORUS a service oriented network architecture (SONA). A mobile embedded system, which uses external services for image processing based on the BOSPORUS Framework is shown as an application of the BOSPORUS framework.

Revealing the vectors of cellular identity with single-cell genomics

PubMed Central

Wagner, Allon; Regev, Aviv; Yosef, Nir

2017-01-01

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell’s identity and type and of the ways in which they are regulated by the cell’s molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell’s identity, from a taxonomy of discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of ‘basis vectors’, each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges—from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities. PMID:27824854

Octree-based, GPU implementation of a continuous cellular automaton for the simulation of complex, evolving surfaces

NASA Astrophysics Data System (ADS)

Ferrando, N.; Gosálvez, M. A.; Cerdá, J.; Gadea, R.; Sato, K.

2011-03-01

Presently, dynamic surface-based models are required to contain increasingly larger numbers of points and to propagate them over longer time periods. For large numbers of surface points, the octree data structure can be used as a balance between low memory occupation and relatively rapid access to the stored data. For evolution rules that depend on neighborhood states, extended simulation periods can be obtained by using simplified atomistic propagation models, such as the Cellular Automata (CA). This method, however, has an intrinsic parallel updating nature and the corresponding simulations are highly inefficient when performed on classical Central Processing Units (CPUs), which are designed for the sequential execution of tasks. In this paper, a series of guidelines is presented for the efficient adaptation of octree-based, CA simulations of complex, evolving surfaces into massively parallel computing hardware. A Graphics Processing Unit (GPU) is used as a cost-efficient example of the parallel architectures. For the actual simulations, we consider the surface propagation during anisotropic wet chemical etching of silicon as a computationally challenging process with a wide-spread use in microengineering applications. A continuous CA model that is intrinsically parallel in nature is used for the time evolution. Our study strongly indicates that parallel computations of dynamically evolving surfaces simulated using CA methods are significantly benefited by the incorporation of octrees as support data structures, substantially decreasing the overall computational time and memory usage.

A DNA network as an information processing system.

PubMed

Santini, Cristina Costa; Bath, Jonathan; Turberfield, Andrew J; Tyrrell, Andy M

2012-01-01

Biomolecular systems that can process information are sought for computational applications, because of their potential for parallelism and miniaturization and because their biocompatibility also makes them suitable for future biomedical applications. DNA has been used to design machines, motors, finite automata, logic gates, reaction networks and logic programs, amongst many other structures and dynamic behaviours. Here we design and program a synthetic DNA network to implement computational paradigms abstracted from cellular regulatory networks. These show information processing properties that are desirable in artificial, engineered molecular systems, including robustness of the output in relation to different sources of variation. We show the results of numerical simulations of the dynamic behaviour of the network and preliminary experimental analysis of its main components.

Metabolic gene regulation in a dynamically changing environment.

PubMed

Bennett, Matthew R; Pang, Wyming Lee; Ostroff, Natalie A; Baumgartner, Bridget L; Nayak, Sujata; Tsimring, Lev S; Hasty, Jeff

2008-08-28

Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation, and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat. Here we monitor the response of Saccharomyces cerevisiae metabolic gene regulation to periodic changes in the external carbon source by using a microfluidic platform that allows precise, dynamic control over environmental conditions. We show that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fast fluctuations. The sensitive low-frequency response was significantly faster than in predictions arising from our computational modelling, and this discrepancy was resolved by the discovery that two key galactose transcripts possess half-lives that depend on the carbon source. Finally, to explore how induction characteristics affect frequency response, we compare two S. cerevisiae strains and show that they have the same frequency response despite having markedly different induction properties. This suggests that although certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment.

Iterons, fractals and computations of automata

NASA Astrophysics Data System (ADS)

Siwak, Paweł

1999-03-01

Processing of strings by some automata, when viewed on space-time (ST) diagrams, reveals characteristic soliton-like coherent periodic objects. They are inherently associated with iterations of automata mappings thus we call them the iterons. In the paper we present two classes of one-dimensional iterons: particles and filtrons. The particles are typical for parallel (cellular) processing, while filtrons, introduced in (32) are specific for serial processing of strings. In general, the images of iterated automata mappings exhibit not only coherent entities but also the fractals, and quasi-periodic and chaotic dynamics. We show typical images of such computations: fractals, multiplication by a number, and addition of binary numbers defined by a Turing machine. Then, the particles are presented as iterons generated by cellular automata in three computations: B/U code conversion (13, 29), majority classification (9), and in discrete version of the FPU (Fermi-Pasta-Ulam) dynamics (7, 23). We disclose particles by a technique of combinational recoding of ST diagrams (as opposed to sequential recoding). Subsequently, we recall the recursive filters based on FCA (filter cellular automata) window operators, and considered by Park (26), Ablowitz (1), Fokas (11), Fuchssteiner (12), Bruschi (5) and Jiang (20). We present the automata equivalents to these filters (33). Some of them belong to the class of filter automata introduced in (30). We also define and illustrate some properties of filtrons. Contrary to particles, the filtrons interact nonlocally in the sense that distant symbols may influence one another. Thus their interactions are very unusual. Some examples have been given in (32). Here we show new examples of filtron phenomena: multifiltron solitonic collisions, attracting and repelling filtrons, trapped bouncing filtrons (which behave like a resonance cavity) and quasi filtrons.

Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

2016-03-01

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics.

PubMed

de Vargas Roditi, Laura; Claassen, Manfred

2015-08-01

Novel technological developments enable single cell population profiling with respect to their spatial and molecular setup. These include single cell sequencing, flow cytometry and multiparametric imaging approaches and open unprecedented possibilities to learn about the heterogeneity, dynamics and interplay of the different cell types which constitute tissues and multicellular organisms. Statistical and dynamic systems theory approaches have been applied to quantitatively describe a variety of cellular processes, such as transcription and cell signaling. Machine learning approaches have been developed to define cell types, their mutual relationships, and differentiation hierarchies shaping heterogeneous cell populations, yielding insights into topics such as, for example, immune cell differentiation and tumor cell type composition. This combination of experimental and computational advances has opened perspectives towards learning predictive multi-scale models of heterogeneous cell populations. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Dynamical analysis of cellular ageing by modeling of gene regulatory network based attractor landscape.

PubMed

Chong, Ket Hing; Zhang, Xiaomeng; Zheng, Jie

2018-01-01

Ageing is a natural phenomenon that is inherently complex and remains a mystery. Conceptual model of cellular ageing landscape was proposed for computational studies of ageing. However, there is a lack of quantitative model of cellular ageing landscape. This study aims to investigate the mechanism of cellular ageing in a theoretical model using the framework of Waddington's epigenetic landscape. We construct an ageing gene regulatory network (GRN) consisting of the core cell cycle regulatory genes (including p53). A model parameter (activation rate) is used as a measure of the accumulation of DNA damage. Using the bifurcation diagrams to estimate the parameter values that lead to multi-stability, we obtained a conceptual model for capturing three distinct stable steady states (or attractors) corresponding to homeostasis, cell cycle arrest, and senescence or apoptosis. In addition, we applied a Monte Carlo computational method to quantify the potential landscape, which displays: I) one homeostasis attractor for low accumulation of DNA damage; II) two attractors for cell cycle arrest and senescence (or apoptosis) in response to high accumulation of DNA damage. Using the Waddington's epigenetic landscape framework, the process of ageing can be characterized by state transitions from landscape I to II. By in silico perturbations, we identified the potential landscape of a perturbed network (inactivation of p53), and thereby demonstrated the emergence of a cancer attractor. The simulated dynamics of the perturbed network displays a landscape with four basins of attraction: homeostasis, cell cycle arrest, senescence (or apoptosis) and cancer. Our analysis also showed that for the same perturbed network with low DNA damage, the landscape displays only the homeostasis attractor. The mechanistic model offers theoretical insights that can facilitate discovery of potential strategies for network medicine of ageing-related diseases such as cancer.

Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas[OPEN

PubMed Central

Montenegro-Johnson, Thomas D.; Stamm, Petra; Strauss, Soeren; Topham, Alexander T.; Tsagris, Michail; Wood, Andrew T.A.; Smith, Richard S.; Bassel, George W.

2015-01-01

Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach, we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of diverse regulatory networks to be integrated with 3D organ growth. PMID:25901089

A computational study of liposome logic: towards cellular computing from the bottom up

PubMed Central

Smaldon, James; Romero-Campero, Francisco J.; Fernández Trillo, Francisco; Gheorghe, Marian; Alexander, Cameron

2010-01-01

In this paper we propose a new bottom-up approach to cellular computing, in which computational chemical processes are encapsulated within liposomes. This “liposome logic” approach (also called vesicle computing) makes use of supra-molecular chemistry constructs, e.g. protocells, chells, etc. as minimal cellular platforms to which logical functionality can be added. Modeling and simulations feature prominently in “top-down” synthetic biology, particularly in the specification, design and implementation of logic circuits through bacterial genome reengineering. The second contribution in this paper is the demonstration of a novel set of tools for the specification, modelling and analysis of “bottom-up” liposome logic. In particular, simulation and modelling techniques are used to analyse some example liposome logic designs, ranging from relatively simple NOT gates and NAND gates to SR-Latches, D Flip-Flops all the way to 3 bit ripple counters. The approach we propose consists of specifying, by means of P systems, gene regulatory network-like systems operating inside proto-membranes. This P systems specification can be automatically translated and executed through a multiscaled pipeline composed of dissipative particle dynamics (DPD) simulator and Gillespie’s stochastic simulation algorithm (SSA). Finally, model selection and analysis can be performed through a model checking phase. This is the first paper we are aware of that brings to bear formal specifications, DPD, SSA and model checking to the problem of modeling target computational functionality in protocells. Potential chemical routes for the laboratory implementation of these simulations are also discussed thus for the first time suggesting a potentially realistic physiochemical implementation for membrane computing from the bottom-up. PMID:21886681

Computational complexity of symbolic dynamics at the onset of chaos

NASA Astrophysics Data System (ADS)

Lakdawala, Porus

1996-05-01

In a variety of studies of dynamical systems, the edge of order and chaos has been singled out as a region of complexity. It was suggested by Wolfram, on the basis of qualitative behavior of cellular automata, that the computational basis for modeling this region is the universal Turing machine. In this paper, following a suggestion of Crutchfield, we try to show that the Turing machine model may often be too powerful as a computational model to describe the boundary of order and chaos. In particular we study the region of the first accumulation of period doubling in unimodal and bimodal maps of the interval, from the point of view of language theory. We show that in relation to the ``extended'' Chomsky hierarchy, the relevant computational model in the unimodal case is the nested stack automaton or the related indexed languages, while the bimodal case is modeled by the linear bounded automaton or the related context-sensitive languages.

A novel grid-based mesoscopic model for evacuation dynamics

NASA Astrophysics Data System (ADS)

Shi, Meng; Lee, Eric Wai Ming; Ma, Yi

2018-05-01

This study presents a novel grid-based mesoscopic model for evacuation dynamics. In this model, the evacuation space is discretised into larger cells than those used in microscopic models. This approach directly computes the dynamic changes crowd densities in cells over the course of an evacuation. The density flow is driven by the density-speed correlation. The computation is faster than in traditional cellular automata evacuation models which determine density by computing the movements of each pedestrian. To demonstrate the feasibility of this model, we apply it to a series of practical scenarios and conduct a parameter sensitivity study of the effect of changes in time step δ. The simulation results show that within the valid range of δ, changing δ has only a minor impact on the simulation. The model also makes it possible to directly acquire key information such as bottleneck areas from a time-varied dynamic density map, even when a relatively large time step is adopted. We use the commercial software AnyLogic to evaluate the model. The result shows that the mesoscopic model is more efficient than the microscopic model and provides more in-situ details (e.g., pedestrian movement pattern) than the macroscopic models.

Cellular automata-based modelling and simulation of biofilm structure on multi-core computers.

PubMed

Skoneczny, Szymon

2015-01-01

The article presents a mathematical model of biofilm growth for aerobic biodegradation of a toxic carbonaceous substrate. Modelling of biofilm growth has fundamental significance in numerous processes of biotechnology and mathematical modelling of bioreactors. The process following double-substrate kinetics with substrate inhibition proceeding in a biofilm has not been modelled so far by means of cellular automata. Each process in the model proposed, i.e. diffusion of substrates, uptake of substrates, growth and decay of microorganisms and biofilm detachment, is simulated in a discrete manner. It was shown that for flat biofilm of constant thickness, the results of the presented model agree with those of a continuous model. The primary outcome of the study was to propose a mathematical model of biofilm growth; however a considerable amount of focus was also placed on the development of efficient algorithms for its solution. Two parallel algorithms were created, differing in the way computations are distributed. Computer programs were created using OpenMP Application Programming Interface for C++ programming language. Simulations of biofilm growth were performed on three high-performance computers. Speed-up coefficients of computer programs were compared. Both algorithms enabled a significant reduction of computation time. It is important, inter alia, in modelling and simulation of bioreactor dynamics.

ReaDDy - A Software for Particle-Based Reaction-Diffusion Dynamics in Crowded Cellular Environments

PubMed Central

Schöneberg, Johannes; Noé, Frank

2013-01-01

We introduce the software package ReaDDy for simulation of detailed spatiotemporal mechanisms of dynamical processes in the cell, based on reaction-diffusion dynamics with particle resolution. In contrast to other particle-based reaction kinetics programs, ReaDDy supports particle interaction potentials. This permits effects such as space exclusion, molecular crowding and aggregation to be modeled. The biomolecules simulated can be represented as a sphere, or as a more complex geometry such as a domain structure or polymer chain. ReaDDy bridges the gap between small-scale but highly detailed molecular dynamics or Brownian dynamics simulations and large-scale but little-detailed reaction kinetics simulations. ReaDDy has a modular design that enables the exchange of the computing core by efficient platform-specific implementations or dynamical models that are different from Brownian dynamics. PMID:24040218

Photochromic molecular implementations of universal computation.

PubMed

Chaplin, Jack C; Krasnogor, Natalio; Russell, Noah A

2014-12-01

Unconventional computing is an area of research in which novel materials and paradigms are utilised to implement computation. Previously we have demonstrated how registers, logic gates and logic circuits can be implemented, unconventionally, with a biocompatible molecular switch, NitroBIPS, embedded in a polymer matrix. NitroBIPS and related molecules have been shown elsewhere to be capable of modifying many biological processes in a manner that is dependent on its molecular form. Thus, one possible application of this type of unconventional computing is to embed computational processes into biological systems. Here we expand on our earlier proof-of-principle work and demonstrate that universal computation can be implemented using NitroBIPS. We have previously shown that spatially localised computational elements, including registers and logic gates, can be produced. We explain how parallel registers can be implemented, then demonstrate an application of parallel registers in the form of Turing machine tapes, and demonstrate both parallel registers and logic circuits in the form of elementary cellular automata. The Turing machines and elementary cellular automata utilise the same samples and same hardware to implement their registers, logic gates and logic circuits; and both represent examples of universal computing paradigms. This shows that homogenous photochromic computational devices can be dynamically repurposed without invasive reconfiguration. The result represents an important, necessary step towards demonstrating the general feasibility of interfacial computation embedded in biological systems or other unconventional materials and environments. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES).

PubMed

Campos, Marcelino; Llorens, Carlos; Sempere, José M; Futami, Ricardo; Rodriguez, Irene; Carrasco, Purificación; Capilla, Rafael; Latorre, Amparo; Coque, Teresa M; Moya, Andres; Baquero, Fernando

2015-08-05

Antibiotic resistance is a major biomedical problem upon which public health systems demand solutions to construe the dynamics and epidemiological risk of resistant bacteria in anthropogenically-altered environments. The implementation of computable models with reciprocity within and between levels of biological organization (i.e. essential nesting) is central for studying antibiotic resistances. Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes. Such a complex background motivated us to explore the P-system standards of membrane computing an innovative natural computing formalism that abstracts the notion of movement across membranes to simulate antibiotic resistance evolution processes across nested levels of micro- and macro-environmental organization in a given ecosystem. In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysis. The stochastic nature of the P-system model implemented in ARES explicitly links within and between host dynamics into a simulation, with feedback reciprocity among the different units of selection influenced by antibiotic exposure at various ecological levels. ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached. ARES can be accessed at http://gydb.org/ares.

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics. PMID:27812323

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

PubMed Central

Thomas-Vaslin, Véronique; Six, Adrien; Ganascia, Jean-Gabriel; Bersini, Hugues

2013-01-01

Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of “state-transitions” of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared. PMID:24101919

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.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

PubMed

Di Paola, Vieri; Marijuán, Pedro C; Lahoz-Beltra, Rafael

2004-01-01

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.

Supporting High School Student Accomplishment of Biology Content Using Interactive Computer-Based Curricular Case Studies

NASA Astrophysics Data System (ADS)

Oliver, Joseph Steve; Hodges, Georgia W.; Moore, James N.; Cohen, Allan; Jang, Yoonsun; Brown, Scott A.; Kwon, Kyung A.; Jeong, Sophia; Raven, Sara P.; Jurkiewicz, Melissa; Robertson, Tom P.

2017-11-01

Research into the efficacy of modules featuring dynamic visualizations, case studies, and interactive learning environments is reported here. This quasi-experimental 2-year study examined the implementation of three interactive computer-based instructional modules within a curricular unit covering cellular biology concepts in an introductory high school biology course. The modules featured dynamic visualizations and focused on three processes that underlie much of cellular biology: diffusion, osmosis, and filtration. Pre-tests and post-tests were used to assess knowledge growth across the unit. A mixture Rasch model analysis of the post-test data revealed two groups of students. In both years of the study, a large proportion of the students were classified as low-achieving based on their pre-test scores. The use of the modules in the Cell Unit in year 2 was associated with a much larger proportion of the students having transitioned to the high-achieving group than in year 1. In year 2, the same teachers taught the same concepts as year 1 but incorporated the interactive computer-based modules into the cell biology unit of the curriculum. In year 2, 67% of students initially classified as low-achieving were classified as high-achieving at the end of the unit. Examination of responses to assessments embedded within the modules as well as post-test items linked transition to the high-achieving group with correct responses to items that both referenced the visualization and the contextualization of that visualization within the module. This study points to the importance of dynamic visualization within contextualized case studies as a means to support student knowledge acquisition in biology.

Evolution of cellular automata with memory: The Density Classification Task.

PubMed

Stone, Christopher; Bull, Larry

2009-08-01

The Density Classification Task is a well known test problem for two-state discrete dynamical systems. For many years researchers have used a variety of evolutionary computation approaches to evolve solutions to this problem. In this paper, we investigate the evolvability of solutions when the underlying Cellular Automaton is augmented with a type of memory based on the Least Mean Square algorithm. To obtain high performance solutions using a simple non-hybrid genetic algorithm, we design a novel representation based on the ternary representation used for Learning Classifier Systems. The new representation is found able to produce superior performance to the bit string traditionally used for representing Cellular automata. Moreover, memory is shown to improve evolvability of solutions and appropriate memory settings are able to be evolved as a component part of these solutions.

[Study of the influence of cellular phones and personal computers on schoolchildren's health: hygienic aspects].

PubMed

Chernenkov, Iu V; Gumeniuk, O I

2009-01-01

The paper presents the results of studying the impact of using cellular phones and personal computers on the health status of 277 Saratov schoolchildren (mean age 13.2 +/- 2.3 years). About 80% of the adolescents have been ascertained to use cellular phones and computers mainly for game purposes. The active users of cellular phones and computers show a high aggressiveness, anxiety, hostility, and social stress, low stress resistance, and susceptibility to arterial hypotension. The negative influence of cellular phones and computers on the schoolchildren's health increases with the increased duration and frequency of their use.

Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer.

PubMed

Aono, Masashi; Gunji, Yukio-Pegio

2003-10-01

The emergence derived from errors is the key importance for both novel computing and novel usage of the computer. In this paper, we propose an implementable experimental plan for the biological computing so as to elicit the emergent property of complex systems. An individual plasmodium of the true slime mold Physarum polycephalum acts in the slime mold computer. Modifying the Elementary Cellular Automaton as it entails the global synchronization problem upon the parallel computing provides the NP-complete problem solved by the slime mold computer. The possibility to solve the problem by giving neither all possible results nor explicit prescription of solution-seeking is discussed. In slime mold computing, the distributivity in the local computing logic can change dynamically, and its parallel non-distributed computing cannot be reduced into the spatial addition of multiple serial computings. The computing system based on exhaustive absence of the super-system may produce, something more than filling the vacancy.

Computational Study of the Genomic and Epigenomic Phenomena

NASA Astrophysics Data System (ADS)

Yang, Wenjing

Biological systems are perhaps the ultimate complex systems, uniquely capable of processing and communicating information, reproducing in their lifetimes, and adapting in evolutionary time scales. My dissertation research focuses on using computational approaches to understand the biocomplexity manifested in the multitude of length scales and time scales. At the molecular and cellular level, central to the complex behavior of a biological system is the regulatory network. My research study focused on epigenetics, which is essential for multicellular organisms to establish cellular identity during development or in response to intracellular and environmental stimuli. My computational study of epigenomics is greatly facilitated by recent advances in high-throughput sequencing technology, which enables high-resolution snapshots of epigenomes and transcriptomes. Using human CD4+ T cell as a model system, the dynamical changes in epigenome and transcriptome pertinent to T cell activation were investigated at the genome scale. Going beyond traditional focus on transcriptional regulation, I provided evidences that post-transcriptional regulation may serve as a major component of the regulatory network. In addition, I explored alternative polyadenylation, another novel aspect of gene regulation, and how it cross-talks with the local chromatin structure. As the renowned theoretical biologist Theodosius Dobzhansky said eloquently, "Nothing in biology makes sense except in the light of evolution''. To better understand this ubiquitous driving force in the biological world, I went beyond molecular events in a single organism, and investigated the dynamical changes of population structure along the evolutionary time scale. To this end, we used HIV virus population dynamics in the host immune system as a model system. The evolution of HIV viral population plays a key role in AIDS immunopathogenesis with its exceptionally high mutation rate. However, the theoretical studies of the effect of recombination have been rather limited. Given the phylogenetic and experimental evidences for the high recombination rate and its important role in HIV evolution and epidemics, I established a mathematical model to study the effect of recombination, and explored the complex behavior of this dynamics system.

The ECM moves during primitive streak formation--computation of ECM versus cellular motion.

PubMed

Zamir, Evan A; Rongish, Brenda J; Little, Charles D

2008-10-14

Galileo described the concept of motion relativity--motion with respect to a reference frame--in 1632. He noted that a person below deck would be unable to discern whether the boat was moving. Embryologists, while recognizing that embryonic tissues undergo large-scale deformations, have failed to account for relative motion when analyzing cell motility data. A century of scientific articles has advanced the concept that embryonic cells move ("migrate") in an autonomous fashion such that, as time progresses, the cells and their progeny assemble an embryo. In sharp contrast, the motion of the surrounding extracellular matrix scaffold has been largely ignored/overlooked. We developed computational/optical methods that measure the extent embryonic cells move relative to the extracellular matrix. Our time-lapse data show that epiblastic cells largely move in concert with a sub-epiblastic extracellular matrix during stages 2 and 3 in primitive streak quail embryos. In other words, there is little cellular motion relative to the extracellular matrix scaffold--both components move together as a tissue. The extracellular matrix displacements exhibit bilateral vortical motion, convergence to the midline, and extension along the presumptive vertebral axis--all patterns previously attributed solely to cellular "migration." Our time-resolved data pose new challenges for understanding how extracellular chemical (morphogen) gradients, widely hypothesized to guide cellular trajectories at early gastrulation stages, are maintained in this dynamic extracellular environment. We conclude that models describing primitive streak cellular guidance mechanisms must be able to account for sub-epiblastic extracellular matrix displacements.

Update schemes of multi-velocity floor field cellular automaton for pedestrian dynamics

NASA Astrophysics Data System (ADS)

Luo, Lin; Fu, Zhijian; Cheng, Han; Yang, Lizhong

2018-02-01

Modeling pedestrian movement is an interesting problem both in statistical physics and in computational physics. Update schemes of cellular automaton (CA) models for pedestrian dynamics govern the schedule of pedestrian movement. Usually, different update schemes make the models behave in different ways, which should be carefully recalibrated. Thus, in this paper, we investigated the influence of four different update schemes, namely parallel/synchronous scheme, random scheme, order-sequential scheme and shuffled scheme, on pedestrian dynamics. The multi-velocity floor field cellular automaton (FFCA) considering the changes of pedestrians' moving properties along walking paths and heterogeneity of pedestrians' walking abilities was used. As for parallel scheme only, the collisions detection and resolution should be considered, resulting in a great difference from any other update schemes. For pedestrian evacuation, the evacuation time is enlarged, and the difference in pedestrians' walking abilities is better reflected, under parallel scheme. In face of a bottleneck, for example a exit, using a parallel scheme leads to a longer congestion period and a more dispersive density distribution. The exit flow and the space-time distribution of density and velocity have significant discrepancies under four different update schemes when we simulate pedestrian flow with high desired velocity. Update schemes may have no influence on pedestrians in simulation to create tendency to follow others, but sequential and shuffled update scheme may enhance the effect of pedestrians' familiarity with environments.

Time-dependent computational studies of flames in microgravity

NASA Technical Reports Server (NTRS)

Oran, Elaine S.; Kailasanath, K.

1989-01-01

The research performed at the Center for Reactive Flow and Dynamical Systems in the Laboratory for Computational Physics and Fluid Dynamics, at the Naval Research Laboratory, in support of the NASA Microgravity Science and Applications Program is described. The primary focus was on investigating fundamental questions concerning the propagation and extinction of premixed flames in Earth gravity and in microgravity environments. The approach was to use detailed time-dependent, multispecies, numerical models as tools to simulate flames in different gravity environments. The models include a detailed chemical kinetics mechanism consisting of elementary reactions among the eight reactive species involved in hydrogen combustion, coupled to algorithms for convection, thermal conduction, viscosity, molecular and thermal diffusion, and external forces. The external force, gravity, can be put in any direction relative to flame propagation and can have a range of values. A combination of one-dimensional and two-dimensional simulations was used to investigate the effects of curvature and dilution on ignition and propagation of flames, to help resolve fundamental questions on the existence of flammability limits when there are no external losses or buoyancy forces in the system, to understand the mechanism leading to cellular instability, and to study the effects of gravity on the transition to cellular structure. A flame in a microgravity environment can be extinguished without external losses, and the mechanism leading to cellular structure is not preferential diffusion but a thermo-diffusive instability. The simulations have also lead to a better understanding of the interactions between buoyancy forces and the processes leading to thermo-diffusive instability.

A computational model of amoeboid cell swimming in unbounded medium and through obstacles

NASA Astrophysics Data System (ADS)

Campbell, Eric; Bagchi, Prosenjit

2017-11-01

Pseudopod-driven motility is commonly observed in eukaryotic cells. Pseudopodia are actin-rich protrusions of the cellular membrane which extend, bifurcate, and retract in cycles resulting in amoeboid locomotion. While actin-myosin interactions are responsible for pseudopod generation, cell deformability is crucial concerning pseudopod dynamics. Because pseudopodia are highly dynamic, cells are capable of deforming into complex shapes over time. Pseudopod-driven motility represents a multiscale and complex process, coupling cell deformation, protein biochemistry, and cytoplasmic and extracellular fluid motion. In this work, we present a 3D computational model of amoeboid cell swimming in an extracellular medium (ECM). The ECM is represented as a fluid medium with or without obstacles. The model integrates full cell deformation, a coarse-grain reaction-diffusion system for protein dynamics, and fluid interaction. Our model generates pseudopodia which bifurcate and retract, showing remarkable similarity to experimental observations. Influence of cell deformation, protein diffusivity and cytoplasmic viscosity on the swimming speed is analyzed in terms of altered pseudopod dynamics. Insights into the role of matrix porosity and obstacle size on cell motility are also provided. Funded by NSF CBET 1438255.

Modelling the aggregation process of cellular slime mold by the chemical attraction.

PubMed

Atangana, Abdon; Vermeulen, P D

2014-01-01

We put into exercise a comparatively innovative analytical modus operandi, the homotopy decomposition method (HDM), for solving a system of nonlinear partial differential equations arising in an attractor one-dimensional Keller-Segel dynamics system. Numerical solutions are given and some properties show evidence of biologically practical reliance on the parameter values. The reliability of HDM and the reduction in computations give HDM a wider applicability.

Synchronous versus asynchronous modeling of gene regulatory networks.

PubMed

Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

2008-09-01

In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Extracellular matrix motion and early morphogenesis

PubMed Central

Loganathan, Rajprasad; Rongish, Brenda J.; Smith, Christopher M.; Filla, Michael B.; Czirok, Andras; Bénazéraf, Bertrand

2016-01-01

For over a century, embryologists who studied cellular motion in early amniotes generally assumed that morphogenetic movement reflected migration relative to a static extracellular matrix (ECM) scaffold. However, as we discuss in this Review, recent investigations reveal that the ECM is also moving during morphogenesis. Time-lapse studies show how convective tissue displacement patterns, as visualized by ECM markers, contribute to morphogenesis and organogenesis. Computational image analysis distinguishes between cell-autonomous (active) displacements and convection caused by large-scale (composite) tissue movements. Modern quantification of large-scale ‘total’ cellular motion and the accompanying ECM motion in the embryo demonstrates that a dynamic ECM is required for generation of the emergent motion patterns that drive amniote morphogenesis. PMID:27302396

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time.

PubMed

Harder, Nathalie; Mora-Bermúdez, Felipe; Godinez, William J; Wünsche, Annelie; Eils, Roland; Ellenberg, Jan; Rohr, Karl

2009-11-01

Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.

Cellular automata simulation of topological effects on the dynamics of feed-forward motifs

PubMed Central

Apte, Advait A; Cain, John W; Bonchev, Danail G; Fong, Stephen S

2008-01-01

Background Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops. The search for the most efficient network architecture could be of particular interest for regulatory or signaling pathways in biology, as well as in computational and communication systems. Results Feed-forward motif dynamics were studied using cellular automata and compared with differential equation modeling. The number of cellular automata iterations needed for a 100% conversion of a substrate into a target product was used as an inverse measure of the transformation rate. Several basic topological patterns were identified that order the specific feed-forward constructions according to the rate of dynamics they enable. At the same number of network nodes and constant other parameters, the bi-parallel and tri-parallel motifs provide higher network efficacy than single feed-forward motifs. Additionally, a topological property of isodynamicity was identified for feed-forward motifs where different network architectures resulted in the same overall rate of the target production. Conclusion It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. These fundamental results can be used as a basis for simulating larger networks as combinations of smaller network modules with implications on studying synthetic gene circuits, small regulatory systems, and eventually dynamic whole-cell models. PMID:18304325

Discovering Coherent Structures Using Local Causal States

NASA Astrophysics Data System (ADS)

Rupe, Adam; Crutchfield, James P.; Kashinath, Karthik; Prabhat, Mr.

2017-11-01

Coherent structures were introduced in the study of fluid dynamics and were initially defined as regions characterized by high levels of coherent vorticity, i.e. regions where instantaneously space and phase correlated vorticity are high. In a more general spatiotemporal setting, coherent structures can be seen as localized broken symmetries which persist in time. Building off the computational mechanics framework, which integrates tools from computation and information theory to capture pattern and structure in nonlinear dynamical systems, we introduce a theory of coherent structures, in the more general sense. Central to computational mechanics is the causal equivalence relation, and a local spatiotemporal generalization of it is used to construct the local causal states, which are utilized to uncover a system's spatiotemporal symmetries. Coherent structures are then identified as persistent, localized deviations from these symmetries. We illustrate how novel patterns and structures can be discovered in cellular automata and outline the path from them to laminar, transitional and turbulent flows. Funded by Intel through the Big Data Center at LBNL and the IPCC at UC Davis.

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.

Using ToxCast data to reconstruct dynamic cell state ...

EPA Pesticide Factsheets

AbstractBackground. High-throughput in vitro screening is an important tool for evaluating the potential biological activity of the thousands of existing chemicals in commerce and the hundreds more introduced each year. Among the assay technologies available, high-content imaging (HCI) allows multiplexed measurements of cellular phenotypic changes induced by chemical exposures. For a large chemical inventory having limited concentration-time series data, the deconvolution of cellular response profiles into transitive or irrevocable state trajectories is an important consideration. Objectives. Our goal was to analyze temporal and concentration-related cellular changes measured using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods. The effects of 976 chemicals (ToxCast Phase I and II) were evaluated using HCI as a function of concentration and time in HepG2 cells over a 72-hr exposure period to concentrations ranging from 0.4- to 200 µM. The cellular endpoints included nuclear p53 accumulation, JNK, markers of oxidative stress, cytoskeletal changes, mitochondrial energization and density, cell viability and cell cycle progression. A novel computational model was developed to interpret dynamic multidimensional system responses as cell-state trajectories. Results. Analysis of cell-state trajectories showed that HepG2 cells were resilient to the effects of 178 chemicals up to the highest co

The Role of Protein Loops and Linkers in Conformational Dynamics and Allostery.

PubMed

Papaleo, Elena; Saladino, Giorgio; Lambrughi, Matteo; Lindorff-Larsen, Kresten; Gervasio, Francesco Luigi; Nussinov, Ruth

2016-06-08

Proteins are dynamic entities that undergo a plethora of conformational changes that may take place on a wide range of time scales. These changes can be as small as the rotation of one or a few side-chain dihedral angles or involve concerted motions in larger portions of the three-dimensional structure; both kinds of motions can be important for biological function and allostery. It is becoming increasingly evident that "connector regions" are important components of the dynamic personality of protein structures. These regions may be either disordered loops, i.e., poorly structured regions connecting secondary structural elements, or linkers that connect entire protein domains. Experimental and computational studies have, however, revealed that these regions are not mere connectors, and their role in allostery and conformational changes has been emerging in the last few decades. Here we provide a detailed overview of the structural properties and classification of loops and linkers, as well as a discussion of the main computational methods employed to investigate their function and dynamical properties. We also describe their importance for protein dynamics and allostery using as examples key proteins in cellular biology and human diseases such as kinases, ubiquitinating enzymes, and transcription factors.

EYE MOVEMENT RECORDING AND NONLINEAR DYNAMICS ANALYSIS – THE CASE OF SACCADES#

PubMed Central

Aştefănoaei, Corina; Pretegiani, Elena; Optican, L.M.; Creangă, Dorina; Rufa, Alessandra

2015-01-01

Evidence of a chaotic behavioral trend in eye movement dynamics was examined in the case of a saccadic temporal series collected from a healthy human subject. Saccades are highvelocity eye movements of very short duration, their recording being relatively accessible, so that the resulting data series could be studied computationally for understanding the neural processing in a motor system. The aim of this study was to assess the complexity degree in the eye movement dynamics. To do this we analyzed the saccadic temporal series recorded with an infrared camera eye tracker from a healthy human subject in a special experimental arrangement which provides continuous records of eye position, both saccades (eye shifting movements) and fixations (focusing over regions of interest, with rapid, small fluctuations). The semi-quantitative approach used in this paper in studying the eye functioning from the viewpoint of non-linear dynamics was accomplished by some computational tests (power spectrum, portrait in the state space and its fractal dimension, Hurst exponent and largest Lyapunov exponent) derived from chaos theory. A high complexity dynamical trend was found. Lyapunov largest exponent test suggested bi-stability of cellular membrane resting potential during saccadic experiment. PMID:25698889

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

Simulation of miniature endplate potentials in neuromuscular junctions by using a cellular automaton

NASA Astrophysics Data System (ADS)

Avella, Oscar Javier; Muñoz, José Daniel; Fayad, Ramón

2008-01-01

Miniature endplate potentials are recorded in the neuromuscular junction when the acetylcholine contents of one or a few synaptic vesicles are spontaneously released into the synaptic cleft. Since their discovery by Fatt and Katz in 1952, they have been among the paradigms in neuroscience. Those potentials are usually simulated by means of numerical approaches, such as Brownian dynamics, finite differences and finite element methods. Hereby we propose that diffusion cellular automata can be a useful alternative for investigating them. To illustrate this point, we simulate a miniature endplate potential by using experimental parameters. Our model reproduces the potential shape, amplitude and time course. Since our automaton is able to track the history and interactions of each single particle, it is very easy to introduce non-linear effects with little computational effort. This makes cellular automata excellent candidates for simulating biological reaction-diffusion processes, where no other external forces are involved.

Adsorption of charged protein residues on an inorganic nanosheet: Computer simulation of LDH interaction with ion channel

NASA Astrophysics Data System (ADS)

Tsukanov, Alexey A.; Psakhie, Sergey G.

2016-08-01

Quasi-two-dimensional and hybrid nanomaterials based on layered double hydroxides (LDH), cationic clays, layered oxyhydroxides and hydroxides of metals possess large specific surface area and strong electrostatic properties with permanent or pH-dependent electric charge. Such nanomaterials may impact cellular electrostatics, changing the ion balance, pH and membrane potential. Selective ion adsorption/exchange may alter the transmembrane electrochemical gradient, disrupting potential-dependent cellular processes. Cellular proteins as a rule have charged residues which can be effectively adsorbed on the surface of layered hydroxide based nanomaterials. The aim of this study is to attempt to shed some light on the possibility and mechanisms of protein "adhesion" an LDH nanosheet and to propose a new direction in anticancer medicine, based on physical impact and strong electrostatics. An unbiased molecular dynamics simulation was performed and the combined process free energy estimation (COPFEE) approach was used.

Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

PubMed

Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

2013-01-01

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models.

Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

PubMed Central

Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

2013-01-01

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models. PMID:24244124

A reconfigurable visual-programming library for real-time closed-loop cellular electrophysiology

PubMed Central

Biró, István; Giugliano, Michele

2015-01-01

Most of the software platforms for cellular electrophysiology are limited in terms of flexibility, hardware support, ease of use, or re-configuration and adaptation for non-expert users. Moreover, advanced experimental protocols requiring real-time closed-loop operation to investigate excitability, plasticity, dynamics, are largely inaccessible to users without moderate to substantial computer proficiency. Here we present an approach based on MATLAB/Simulink, exploiting the benefits of LEGO-like visual programming and configuration, combined to a small, but easily extendible library of functional software components. We provide and validate several examples, implementing conventional and more sophisticated experimental protocols such as dynamic-clamp or the combined use of intracellular and extracellular methods, involving closed-loop real-time control. The functionality of each of these examples is demonstrated with relevant experiments. These can be used as a starting point to create and support a larger variety of electrophysiological tools and methods, hopefully extending the range of default techniques and protocols currently employed in experimental labs across the world. PMID:26157385

Imaging with organic indicators and high-speed charge-coupled device cameras in neurons: some applications where these classic techniques have advantages.

PubMed

Ross, William N; Miyazaki, Kenichi; Popovic, Marko A; Zecevic, Dejan

2015-04-01

Dynamic calcium and voltage imaging is a major tool in modern cellular neuroscience. Since the beginning of their use over 40 years ago, there have been major improvements in indicators, microscopes, imaging systems, and computers. While cutting edge research has trended toward the use of genetically encoded calcium or voltage indicators, two-photon microscopes, and in vivo preparations, it is worth noting that some questions still may be best approached using more classical methodologies and preparations. In this review, we highlight a few examples in neurons where the combination of charge-coupled device (CCD) imaging and classical organic indicators has revealed information that has so far been more informative than results using the more modern systems. These experiments take advantage of the high frame rates, sensitivity, and spatial integration of the best CCD cameras. These cameras can respond to the faster kinetics of organic voltage and calcium indicators, which closely reflect the fast dynamics of the underlying cellular events.

Robust optimization with transiently chaotic dynamical systems

NASA Astrophysics Data System (ADS)

Sumi, R.; Molnár, B.; Ercsey-Ravasz, M.

2014-05-01

Efficiently solving hard optimization problems has been a strong motivation for progress in analog computing. In a recent study we presented a continuous-time dynamical system for solving the NP-complete Boolean satisfiability (SAT) problem, with a one-to-one correspondence between its stable attractors and the SAT solutions. While physical implementations could offer great efficiency, the transiently chaotic dynamics raises the question of operability in the presence of noise, unavoidable on analog devices. Here we show that the probability of finding solutions is robust to noise intensities well above those present on real hardware. We also developed a cellular neural network model realizable with analog circuits, which tolerates even larger noise intensities. These methods represent an opportunity for robust and efficient physical implementations.

Time-Lapse Video Microscopy for Assessment of EYFP-Parkin Aggregation as a Marker for Cellular Mitophagy

PubMed Central

Di Sante, Gabriele; Casimiro, Mathew C.; Pestell, Timothy G.; Pestell, Richard G.

2016-01-01

Time-lapse video microscopy can be defined as the real time imaging of living cells. This technique relies on the collection of images at different time points. Time intervals can be set through a computer interface that controls the microscope-integrated camera. This kind of microscopy requires both the ability to acquire very rapid events and the signal generated by the observed cellular structure during these events. After the images have been collected, a movie of the entire experiment is assembled to show the dynamic of the molecular events of interest. Time-lapse video microscopy has a broad range of applications in the biomedical research field and is a powerful and unique tool for following the dynamics of the cellular events in real time. Through this technique, we can assess cellular events such as migration, division, signal transduction, growth, and death. Moreover, using fluorescent molecular probes we are able to mark specific molecules, such as DNA, RNA or proteins and follow them through their molecular pathways and functions. Time-lapse video microscopy has multiple advantages, the major one being the ability to collect data at the single-cell level, that make it a unique technology for investigation in the field of cell biology. However, time-lapse video microscopy has limitations that can interfere with the acquisition of high quality images. Images can be compromised by both external factors; temperature fluctuations, vibrations, humidity and internal factors; pH, cell motility. Herein, we describe a protocol for the dynamic acquisition of a specific protein, Parkin, fused with the enhanced yellow fluorescent protein (EYFP) in order to track the selective removal of damaged mitochondria, using a time-lapse video microscopy approach. PMID:27168174

Time-Lapse Video Microscopy for Assessment of EYFP-Parkin Aggregation as a Marker for Cellular Mitophagy.

PubMed

Di Sante, Gabriele; Casimiro, Mathew C; Pestell, Timothy G; Pestell, Richard G

2016-05-04

Time-lapse video microscopy can be defined as the real time imaging of living cells. This technique relies on the collection of images at different time points. Time intervals can be set through a computer interface that controls the microscope-integrated camera. This kind of microscopy requires both the ability to acquire very rapid events and the signal generated by the observed cellular structure during these events. After the images have been collected, a movie of the entire experiment is assembled to show the dynamic of the molecular events of interest. Time-lapse video microscopy has a broad range of applications in the biomedical research field and is a powerful and unique tool for following the dynamics of the cellular events in real time. Through this technique, we can assess cellular events such as migration, division, signal transduction, growth, and death. Moreover, using fluorescent molecular probes we are able to mark specific molecules, such as DNA, RNA or proteins and follow them through their molecular pathways and functions. Time-lapse video microscopy has multiple advantages, the major one being the ability to collect data at the single-cell level, that make it a unique technology for investigation in the field of cell biology. However, time-lapse video microscopy has limitations that can interfere with the acquisition of high quality images. Images can be compromised by both external factors; temperature fluctuations, vibrations, humidity and internal factors; pH, cell motility. Herein, we describe a protocol for the dynamic acquisition of a specific protein, Parkin, fused with the enhanced yellow fluorescent protein (EYFP) in order to track the selective removal of damaged mitochondria, using a time-lapse video microscopy approach.

[A Cellular Automata Model for a Community Comprising Two Plant Species of Different Growth Forms].

PubMed

Frolov, P V; Zubkova, E V; Komarov, A S

2015-01-01

A cellular automata computer model for the interactions between two plant species of different growth forms--the lime hairgrass Deschampsia caespitosa (L.) P. Beauv., a sod cereal, and the moneywort Lysimachia nummularia L., a ground creeping perennial herb--is considered. Computer experiments on the self-maintenance of the populations of each species against the background of a gradual increase in the share of randomly eliminated individuals, coexistence of the populations of two species, and the effect of the phytogenous field have been conducted. As has been shown, all the studied factors determine the number of individuals and self-sustainability of the simulated populations by the degree of their impact. The limits of action have been determined for individual factors; within these limits, the specific features in plant reproduction and dispersal provide sustainable coexistence of the simulated populations. It has been demonstrated that the constructed model allows for studying the long-term developmental dynamics of the plants belonging to the selected growth forms.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity.

PubMed

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G; Helland, Aslaug; Rye, Inga H; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-02-13

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of cellular diversity for genetic and phenotypic features

PubMed Central

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G.; Helland, Åslaug; Rye, Inga H.; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L.; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-01-01

SUMMARY Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor subtype-specific and it did not change during treatment in tumors with partial or no response. However, lower pre-treatment genetic diversity was significantly associated with complete pathologic response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. PMID:24462293

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

DOE PAGES

Almendro, Vanessa; Cheng, Yu -Kang; Randles, Amanda; ...

2014-02-01

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatialmore » distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.« less

Weak temporal signals can synchronize and accelerate the transition dynamics of biopolymers under tension.

PubMed

Kim, Won Kyu; Hyeon, Changbong; Sung, Wokyung

2012-09-04

In addition to thermal noise, which is essential to promote conformational transitions in biopolymers, the cellular environment is replete with a spectrum of athermal fluctuations that are produced from a plethora of active processes. To understand the effect of athermal noise on biological processes, we studied how a small oscillatory force affects the thermally induced folding and unfolding transition of an RNA hairpin, whose response to constant tension had been investigated extensively in both theory and experiments. Strikingly, our molecular simulations performed under overdamped condition show that even at a high (low) tension that renders the hairpin (un)folding improbable, a weak external oscillatory force at a certain frequency can synchronously enhance the transition dynamics of RNA hairpin and increase the mean transition rate. Furthermore, the RNA dynamics can still discriminate a signal with resonance frequency even when the signal is mixed among other signals with nonresonant frequencies. In fact, our computational demonstration of thermally induced resonance in RNA hairpin dynamics is a direct realization of the phenomena called stochastic resonance and resonant activation. Our study, amenable to experimental tests using optical tweezers, is of great significance to the folding of biopolymers in vivo that are subject to the broad spectrum of cellular noises.

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

PubMed Central

Li, Min; Chen, Weijie; Wang, Jianxin; Pan, Yi

2014-01-01

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures. PMID:24963481

Nonlinear dynamics of the cellular-automaton ``game of Life''

NASA Astrophysics Data System (ADS)

Garcia, J. B. C.; Gomes, M. A. F.; Jyh, T. I.; Ren, T. I.; Sales, T. R. M.

1993-11-01

A statistical analysis of the ``game of Life'' due to Conway [Berlekamp, Conway, and Guy, Winning Ways for Your Mathematical Plays (Academic, New York, 1982), Vol. 2] is reported. The results are based on extensive computer simulations starting with uncorrelated distributions of live sites at t=0. The number n(s,t) of clusters of s live sites at time t, the mean cluster size s¯(t), and the diversity of sizes among other statistical functions are obtained. The dependence of the statistical functions with the initial density of live sites is examined. Several scaling relations as well as static and dynamic critical exponents are found.

Cellular Level Brain Imaging in Behaving Mammals: An Engineering Approach

PubMed Central

Hamel, Elizabeth J.O.; Grewe, Benjamin F.; Parker, Jones G.; Schnitzer, Mark J.

2017-01-01

Fluorescence imaging offers expanding capabilities for recording neural dynamics in behaving mammals, including the means to monitor hundreds of cells targeted by genetic type or connectivity, track cells over weeks, densely sample neurons within local microcircuits, study cells too inactive to isolate in extracellular electrical recordings, and visualize activity in dendrites, axons, or dendritic spines. We discuss recent progress and future directions for imaging in behaving mammals from a systems engineering perspective, which seeks holistic consideration of fluorescent indicators, optical instrumentation, and computational analyses. Today, genetically encoded indicators of neural Ca2+ dynamics are widely used, and those of trans-membrane voltage are rapidly improving. Two complementary imaging paradigms involve conventional microscopes for studying head-restrained animals and head-mounted miniature microscopes for imaging in freely behaving animals. Overall, the field has attained sufficient sophistication that increased cooperation between those designing new indicators, light sources, microscopes, and computational analyses would greatly benefit future progress. PMID:25856491

Synchrotron-based X-ray computed tomography during compression loading of cellular materials

DOE PAGES

Cordes, Nikolaus L.; Henderson, Kevin; Stannard, Tyler; ...

2015-04-29

Three-dimensional X-ray computed tomography (CT) of in situ dynamic processes provides internal snapshot images as a function of time. Tomograms are mathematically reconstructed from a series of radiographs taken in rapid succession as the specimen is rotated in small angular increments. In addition to spatial resolution, temporal resolution is important. Thus temporal resolution indicates how close together in time two distinct tomograms can be acquired. Tomograms taken in rapid succession allow detailed analyses of internal processes that cannot be obtained by other means. This article describes the state-of-the-art for such measurements acquired using synchrotron radiation as the X-ray source.

Modeling oscillatory dynamics in brain microcircuits as a way to help uncover neurological disease mechanisms: A proposal

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

Skinner, F. K.; Department of Medicine; Department of Physiology, University of Toronto Medical Sciences Building, 3rd Floor, 1 King's College Circle, Toronto, Ontario M5S 1A8

There is an undisputed need and requirement for theoretical and computational studies in Neuroscience today. Furthermore, it is clear that oscillatory dynamical output from brain networks is representative of various behavioural states, and it is becoming clear that one could consider these outputs as measures of normal and pathological brain states. Although mathematical modeling of oscillatory dynamics in the context of neurological disease exists, it is a highly challenging endeavour because of the many levels of organization in the nervous system. This challenge is coupled with the increasing knowledge of cellular specificity and network dysfunction that is associated with disease.more » Recently, whole hippocampus in vitro preparations from control animals have been shown to spontaneously express oscillatory activities. In addition, when using preparations derived from animal models of disease, these activities show particular alterations. These preparations present an opportunity to address challenges involved with using models to gain insight because of easier access to simultaneous cellular and network measurements, and pharmacological modulations. We propose that by developing and using models with direct links to experiment at multiple levels, which at least include cellular and microcircuit, a cycling can be set up and used to help us determine critical mechanisms underlying neurological disease. We illustrate our proposal using our previously developed inhibitory network models in the context of these whole hippocampus preparations and show the importance of having direct links at multiple levels.« less

Integrative, Dynamic Structural Biology at Atomic Resolution—It’s About Time

PubMed Central

van den Bedem, Henry; Fraser, James S.

2015-01-01

Biomolecules adopt a dynamic ensemble of conformations, each with the potential to interact with binding partners or perform the chemical reactions required for a multitude of cellular functions. Recent advances in X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, and other techniques are helping us realize the dream of seeing—in atomic detail—how different parts of biomolecules exchange between functional sub-states using concerted motions. Integrative structural biology has advanced our understanding of the formation of large macromolecular complexes and how their components interact in assemblies by leveraging data from many low-resolution methods. Here, we review the growing opportunities for integrative, dynamic structural biology at the atomic scale, contending there is increasing synergistic potential between X-ray crystallography, NMR, and computer simulations to reveal a structural basis for protein conformational dynamics at high resolution. PMID:25825836

The concept of self-organization in cellular architecture

PubMed Central

Misteli, Tom

2001-01-01

In vivo microscopy has recently revealed the dynamic nature of many cellular organelles. The dynamic properties of several cellular structures are consistent with a role for self-organization in their formation, maintenance, and function; therefore, self-organization might be a general principle in cellular organization. PMID:11604416

Electoral surveys’ influence on the voting processes: a cellular automata model

NASA Astrophysics Data System (ADS)

Alves, S. G.; Oliveira Neto, N. M.; Martins, M. L.

2002-12-01

Nowadays, in societies threatened by atomization, selfishness, short-term thinking, and alienation from political life, there is a renewed debate about classical questions concerning the quality of democratic decision making. In this work a cellular automata model for the dynamics of free elections, based on the social impact theory is proposed. By using computer simulations, power-law distributions for the size of electoral clusters and decision time have been obtained. The major role of broadcasted electoral surveys in guiding opinion formation and stabilizing the “status quo” was demonstrated. Furthermore, it was shown that in societies where these surveys are manipulated within the universally accepted statistical error bars, even a majoritary opposition could be hindered from reaching power through the electoral path.

Hydrodynamic Contributions to Amoeboid Cell Motility

NASA Astrophysics Data System (ADS)

Lewis, Owen; Guy, Robert

2011-11-01

Understanding the methods by which cells move is a fundamental problem in modern biology. Recent evidence has shown that the fluid dynamics of cytoplasm can play a vital role in cellular motility. The slime mold Physarum polycephalum provides an excellent model organism for the study of amoeboid motion. In this research, we use both analytic and computational models to investigate intracellular fluid flow in a simple model of Physarum. In both models, of we are specifically interested in stresses generated by cytoplasmic flow which act in the direction of cellular motility. In our numerical model, the Immersed Boundary Method is used to account for such stresses. We investigate the relationship between contraction waves, low waves and locomotive forces, and attempt characterize conditions necessary to generate directed motion.

Extracellular matrix motion and early morphogenesis.

PubMed

Loganathan, Rajprasad; Rongish, Brenda J; Smith, Christopher M; Filla, Michael B; Czirok, Andras; Bénazéraf, Bertrand; Little, Charles D

2016-06-15

For over a century, embryologists who studied cellular motion in early amniotes generally assumed that morphogenetic movement reflected migration relative to a static extracellular matrix (ECM) scaffold. However, as we discuss in this Review, recent investigations reveal that the ECM is also moving during morphogenesis. Time-lapse studies show how convective tissue displacement patterns, as visualized by ECM markers, contribute to morphogenesis and organogenesis. Computational image analysis distinguishes between cell-autonomous (active) displacements and convection caused by large-scale (composite) tissue movements. Modern quantification of large-scale 'total' cellular motion and the accompanying ECM motion in the embryo demonstrates that a dynamic ECM is required for generation of the emergent motion patterns that drive amniote morphogenesis. © 2016. Published by The Company of Biologists Ltd.

About the discrete-continuous nature of a hematopoiesis model for Chronic Myeloid Leukemia.

PubMed

Gaudiano, Marcos E; Lenaerts, Tom; Pacheco, Jorge M

2016-12-01

Blood of mammals is composed of a variety of cells suspended in a fluid medium known as plasma. Hematopoiesis is the biological process of birth, replication and differentiation of blood cells. Despite of being essentially a stochastic phenomenon followed by a huge number of discrete entities, blood formation has naturally an associated continuous dynamics, because the cellular populations can - on average - easily be described by (e.g.) differential equations. This deterministic dynamics by no means contemplates some important stochastic aspects related to abnormal hematopoiesis, that are especially significant for studying certain blood cancer deceases. For instance, by mere stochastic competition against the normal cells, leukemic cells sometimes do not reach the population thereshold needed to kill the organism. Of course, a pure discrete model able to follow the stochastic paths of billons of cells is computationally impossible. In order to avoid this difficulty, we seek a trade-off between the computationally feasible and the biologically realistic, deriving an equation able to size conveniently both the discrete and continuous parts of a model for hematopoiesis in terrestrial mammals, in the context of Chronic Myeloid Leukemia. Assuming the cancer is originated from a single stem cell inside of the bone marrow, we also deduce a theoretical formula for the probability of non-diagnosis as a function of the mammal average adult mass. In addition, this work cellular dynamics analysis may shed light on understanding Peto's paradox, which is shown here as an emergent property of the discrete-continuous nature of the system. Copyright © 2016 Elsevier Inc. All rights reserved.

Steady-state kinetic modeling constrains cellular resting states and dynamic behavior.

PubMed

Purvis, Jeremy E; Radhakrishnan, Ravi; Diamond, Scott L

2009-03-01

A defining characteristic of living cells is the ability to respond dynamically to external stimuli while maintaining homeostasis under resting conditions. Capturing both of these features in a single kinetic model is difficult because the model must be able to reproduce both behaviors using the same set of molecular components. Here, we show how combining small, well-defined steady-state networks provides an efficient means of constructing large-scale kinetic models that exhibit realistic resting and dynamic behaviors. By requiring each kinetic module to be homeostatic (at steady state under resting conditions), the method proceeds by (i) computing steady-state solutions to a system of ordinary differential equations for each module, (ii) applying principal component analysis to each set of solutions to capture the steady-state solution space of each module network, and (iii) combining optimal search directions from all modules to form a global steady-state space that is searched for accurate simulation of the time-dependent behavior of the whole system upon perturbation. Importantly, this stepwise approach retains the nonlinear rate expressions that govern each reaction in the system and enforces constraints on the range of allowable concentration states for the full-scale model. These constraints not only reduce the computational cost of fitting experimental time-series data but can also provide insight into limitations on system concentrations and architecture. To demonstrate application of the method, we show how small kinetic perturbations in a modular model of platelet P2Y(1) signaling can cause widespread compensatory effects on cellular resting states.

Conditional bistability, a generic cellular mnemonic mechanism for robust and flexible working memory computations.

PubMed

Rodriguez, Guillaume; Sarazin, Matthieu; Clemente, Alexandra; Holden, Stephanie; Paz, Jeanne T; Delord, Bruno

2018-04-30

Persistent neural activity, the substrate of working memory, is thought to emerge from synaptic reverberation within recurrent networks. However, reverberation models do not robustly explain fundamental dynamics of persistent activity, including high-spiking irregularity, large intertrial variability, and state transitions. While cellular bistability may contribute to persistent activity, its rigidity appears incompatible with persistent activity labile characteristics. Here, we unravel in a cellular model a form of spike-mediated conditional bistability that is robust, generic and provides a rich repertoire of mnemonic computations. Under asynchronous synaptic inputs of the awakened state, conditional bistability generates spiking/bursting episodes, accounting for the irregularity, variability and state transitions characterizing persistent activity. This mechanism has likely been overlooked because of the sub-threshold input it requires and we predict how to assess it experimentally. Our results suggest a reexamination of the role of intrinsic properties in the collective network dynamics responsible for flexible working memory. SIGNIFICANCE STATEMENT This study unravels a novel form of intrinsic neuronal property, i.e. conditional bistability. We show that, thanks of its conditional character, conditional bistability favors the emergence of flexible and robust forms of persistent activity in PFC neural networks, in opposition to previously studied classical forms of absolute bistability. Specifically, we demonstrate for the first time that conditional bistability 1) is a generic biophysical spike-dependent mechanism of layer V pyramidal neurons in the PFC and that 2) it accounts for essential neurodynamical features for the organisation and flexibility of PFC persistent activity (the large irregularity and intertrial variability of the discharge and its organization under discrete stable states), which remain unexplained in a robust fashion by current models. Copyright © 2018 the authors.

Multiscale modeling of the dynamics of multicellular systems

NASA Astrophysics Data System (ADS)

Kosztin, Ioan

2011-03-01

Describing the biomechanical properties of cellular systems, regarded as complex highly viscoelastic materials, is a difficult problem of great conceptual and practical value. Here we present a novel approach, referred to as the Cellular Particle Dynamics (CPD) method, for: (i) quantitatively relating biomechanical properties at the cell level to those at the multicellular and tissue level, and (ii) describing and predicting the time evolution of multicellular systems that undergo biomechanical relaxations. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. Cell and multicellular level biomechanical properties (e.g., viscosity, surface tension and shear modulus) are determined through the combined use of experiments and theory of continuum viscoelastic media. The same biomechanical properties are also ``measured'' computationally by employing the CPD method, the results being expressed in terms of CPD parameters. Once these parameters have been calibrated experimentally, the formalism provides a systematic framework to predict the time evolution of complex multicellular systems during shape-changing biomechanical transformations. By design, the CPD method is rather flexible and most suitable for multiscale modeling of multicellular system. The spatial level of detail of the system can be easily tuned by changing the number of CPs in a cell. Thus, CPD can be used equally well to describe both cell level processes (e.g., the adhesion of two cells) and tissue level processes (e.g., the formation of 3D constructs of millions of cells through bioprinting). Work supported by NSF [FIBR-0526854 and PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

Cellular network entropy as the energy potential in Waddington's differentiation landscape

PubMed Central

Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.

2013-01-01

Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. PMID:24154593

Determination of cellular strains by combined atomic force microscopy and finite element modeling.

PubMed Central

Charras, Guillaume T; Horton, Mike A

2002-01-01

Many organs adapt to their mechanical environment as a result of physiological change or disease. Cells are both the detectors and effectors of this process. Though many studies have been performed in vitro to investigate the mechanisms of detection and adaptation to mechanical strains, the cellular strains remain unknown and results from different stimulation techniques cannot be compared. By combining experimental determination of cell profiles and elasticities by atomic force microscopy with finite element modeling and computational fluid dynamics, we report the cellular strain distributions exerted by common whole-cell straining techniques and from micromanipulation techniques, hence enabling their comparison. Using data from our own analyses and experiments performed by others, we examine the threshold of activation for different signal transduction processes and the strain components that they may detect. We show that modulating cell elasticity, by increasing the F-actin content of the cytoskeleton, or cellular Poisson ratio are good strategies to resist fluid shear or hydrostatic pressure. We report that stray fluid flow in some substrate-stretch systems elicits significant cellular strains. In conclusion, this technique shows promise in furthering our understanding of the interplay among mechanical forces, strain detection, gene expression, and cellular adaptation in physiology and disease. PMID:12124270

Detailed Multidimensional Simulations of the Structure and Dynamics of Flames

NASA Technical Reports Server (NTRS)

Patnaik, G.; Kailasanath, K.

1999-01-01

Numerical simulations in which the various physical and chemical processes can be independently controlled can significantly advance our understanding of the structure, stability, dynamics and extinction of flames. Therefore, our approach has been to use detailed time-dependent, multidimensional, multispecies numerical models to perform carefully designed computational experiments of flames on Earth and in microgravity environments. Some of these computational experiments are complementary to physical experiments performed under the Microgravity Program while others provide a fundamental understanding that cannot be obtained from physical experiments alone. In this report, we provide a brief summary of our recent research highlighting the contributions since the previous microgravity combustion workshop. There are a number of mechanisms that can cause flame instabilities and result in the formation of dynamic multidimensional structures. In the past, we have used numerical simulations to show that it is the thermo-diffusive instability rather than an instability due to preferential diffusion that is the dominant mechanism for the formation of cellular flames in lean hydrogen-air mixtures. Other studies have explored the role of gravity on flame dynamics and extinguishment, multi-step kinetics and radiative losses on flame instabilities in rich hydrogen-air flames, and heat losses on burner-stabilized flames in microgravity. The recent emphasis of our work has been on exploring flame-vortex interactions and further investigating the structure and dynamics of lean hydrogen-air flames in microgravity. These topics are briefly discussed after a brief discussion of our computational approach for solving these problems.

Genome-wide coexpression dynamics: Theory and application

PubMed Central

Li, Ker-Chau

2002-01-01

High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics. PMID:12486219

A Real Space Cellular Automaton Laboratory

NASA Astrophysics Data System (ADS)

Rozier, O.; Narteau, C.

2013-12-01

Investigations in geomorphology may benefit from computer modelling approaches that rely entirely on self-organization principles. In the vast majority of numerical models, instead, points in space are characterised by a variety of physical variables (e.g. sediment transport rate, velocity, temperature) recalculated over time according to some predetermined set of laws. However, there is not always a satisfactory theoretical framework from which we can quantify the overall dynamics of the system. For these reasons, we prefer to concentrate on interaction patterns using a basic cellular automaton modelling framework, the Real Space Cellular Automaton Laboratory (ReSCAL), a powerful and versatile generator of 3D stochastic models. The objective of this software suite released under a GNU license is to develop interdisciplinary research collaboration to investigate the dynamics of complex systems. The models in ReSCAL are essentially constructed from a small number of discrete states distributed on a cellular grid. An elementary cell is a real-space representation of the physical environment and pairs of nearest neighbour cells are called doublets. Each individual physical process is associated with a set of doublet transitions and characteristic transition rates. Using a modular approach, we can simulate and combine a wide range of physical, chemical and/or anthropological processes. Here, we present different ingredients of ReSCAL leading to applications in geomorphology: dune morphodynamics and landscape evolution. We also discuss how ReSCAL can be applied and developed across many disciplines in natural and human sciences.

Complexity in Dynamical Systems

NASA Astrophysics Data System (ADS)

Moore, Cristopher David

The study of chaos has shown us that deterministic systems can have a kind of unpredictability, based on a limited knowledge of their initial conditions; after a finite time, the motion appears essentially random. This observation has inspired a general interest in the subject of unpredictability, and more generally, complexity; how can we characterize how "complex" a dynamical system is?. In this thesis, we attempt to answer this question with a paradigm of complexity that comes from computer science, we extract sets of symbol sequences, or languages, from a dynamical system using standard methods of symbolic dynamics; we then ask what kinds of grammars or automata are needed a generate these languages. This places them in the Chomsky heirarchy, which in turn tells us something about how subtle and complex the dynamical system's behavior is. This gives us insight into the question of unpredictability, since these automata can also be thought of as computers attempting to predict the system. In the culmination of the thesis, we find a class of smooth, two-dimensional maps which are equivalent to the highest class in the Chomsky heirarchy, the turning machine; they are capable of universal computation. Therefore, these systems possess a kind of unpredictability qualitatively different from the usual "chaos": even if the initial conditions are known exactly, questions about the system's long-term dynamics are undecidable. No algorithm exists to answer them. Although this kind of unpredictability has been discussed in the context of distributed, many-degree-of -freedom systems (for instance, cellular automata) we believe this is the first example of such phenomena in a smooth, finite-degree-of-freedom system.

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.

Computer Simulation of Embryonic Systems: What can a ...

EPA Pesticide Factsheets

(1) Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative pr

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative predic

Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations.

PubMed

Liao, David; Tlsty, Thea D

2014-08-06

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

Dynamics of relaxation to a stationary state for interacting molecular motors

NASA Astrophysics Data System (ADS)

Gomes, Luiza V. F.; Kolomeisky, Anatoly B.

2018-01-01

Motor proteins are active enzymatic molecules that drive a variety of biological processes, including transfer of genetic information, cellular transport, cell motility and muscle contraction. It is known that these biological molecular motors usually perform their cellular tasks by acting collectively, and there are interactions between individual motors that specify the overall collective behavior. One of the fundamental issues related to the collective dynamics of motor proteins is the question if they function at stationary-state conditions. To investigate this problem, we analyze a relaxation to the stationary state for the system of interacting molecular motors. Our approach utilizes a recently developed theoretical framework, which views the collective dynamics of motor proteins as a totally asymmetric simple exclusion process of interacting particles, where interactions are taken into account via a thermodynamically consistent approach. The dynamics of relaxation to the stationary state is analyzed using a domain-wall method that relies on a mean-field description, which takes into account some correlations. It is found that the system quickly relaxes for repulsive interactions, while attractive interactions always slow down reaching the stationary state. It is also predicted that for some range of parameters the fastest relaxation might be achieved for a weak repulsive interaction. Our theoretical predictions are tested with Monte Carlo computer simulations. The implications of our findings for biological systems are briefly discussed.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Programming mRNA decay to modulate synthetic circuit resource allocation

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

Venturelli, Ophelia S.; Tei, Mika; Bauer, Stefan

Synthetic circuits embedded in host cells compete with cellular processes for limited intracellular resources. Here we show how funnelling of cellular resources, after global transcriptome degradation by the sequence-dependent endoribonuclease MazF, to a synthetic circuit can increase production. Target genes are protected from MazF activity by recoding the gene sequence to eliminate recognition sites, while preserving the amino acid sequence. The expression of a protected fluorescent reporter and flux of a high-value metabolite are significantly enhanced using this genome-scale control strategy. Proteomics measurements discover a host factor in need of protection to improve resource redistribution activity. A computational model demonstratesmore » that the MazF mRNA-decay feedback loop enables proportional control of MazF in an optimal operating regime. Transcriptional profiling of MazF-induced cells elucidates the dynamic shifts in transcript abundance and discovers regulatory design elements. Altogether, our results suggest that manipulation of cellular resource allocation is a key control parameter for synthetic circuit design.« less

Programming mRNA decay to modulate synthetic circuit resource allocation

DOE PAGES

Venturelli, Ophelia S.; Tei, Mika; Bauer, Stefan; ...

2017-04-26

Synthetic circuits embedded in host cells compete with cellular processes for limited intracellular resources. Here we show how funnelling of cellular resources, after global transcriptome degradation by the sequence-dependent endoribonuclease MazF, to a synthetic circuit can increase production. Target genes are protected from MazF activity by recoding the gene sequence to eliminate recognition sites, while preserving the amino acid sequence. The expression of a protected fluorescent reporter and flux of a high-value metabolite are significantly enhanced using this genome-scale control strategy. Proteomics measurements discover a host factor in need of protection to improve resource redistribution activity. A computational model demonstratesmore » that the MazF mRNA-decay feedback loop enables proportional control of MazF in an optimal operating regime. Transcriptional profiling of MazF-induced cells elucidates the dynamic shifts in transcript abundance and discovers regulatory design elements. Altogether, our results suggest that manipulation of cellular resource allocation is a key control parameter for synthetic circuit design.« less

Microscopic Imaging and Spectroscopy with Scattered Light

PubMed Central

Boustany, Nada N.; Boppart, Stephen A.; Backman, Vadim

2012-01-01

Optical contrast based on elastic scattering interactions between light and matter can be used to probe cellular structure and dynamics, and image tissue architecture. The quantitative nature and high sensitivity of light scattering signals to subtle alterations in tissue morphology, as well as the ability to visualize unstained tissue in vivo, has recently generated significant interest in optical scatter based biosensing and imaging. Here we review the fundamental methodologies used to acquire and interpret optical scatter data. We report on recent findings in this field and present current advances in optical scatter techniques and computational methods. Cellular and tissue data enabled by current advances in optical scatter spectroscopy and imaging stand to impact a variety of biomedical applications including clinical tissue diagnosis, in vivo imaging, drug discovery and basic cell biology. PMID:20617940

Hydrodynamic Contributions to Amoeboid Cell Motility

NASA Astrophysics Data System (ADS)

Lewis, Owen; Guy, Robert

2012-11-01

Understanding the methods by which cells move is a fundamental problem in modern biology. Recent evidence has shown that the fluid dynamics of cytoplasm can play a vital role in cellular motility. The slime mold Physarum polycephalum provides an excellent model organism for the study of amoeboid motion. In this research, we use a simply analytic model in conjuction with computational experiments to investigate intracellular fluid flow in a simple model of Physarum. Of particlar interest are stresses generated by cytoplasmic flow which may be used to aid in cellular motility. In our numerical model, the Immersed Boundary Method is used to account for such stresses. We investigate the relationship between contraction waves, flow waves, adhesion, and locomotive forces in an attempt to characterize conditions necessary to generate directed motion.

Quantitation of Cellular Dynamics in Growing Arabidopsis Roots with Light Sheet Microscopy

PubMed Central

Birnbaum, Kenneth D.; Leibler, Stanislas

2011-01-01

To understand dynamic developmental processes, living tissues have to be imaged frequently and for extended periods of time. Root development is extensively studied at cellular resolution to understand basic mechanisms underlying pattern formation and maintenance in plants. Unfortunately, ensuring continuous specimen access, while preserving physiological conditions and preventing photo-damage, poses major barriers to measurements of cellular dynamics in growing organs such as plant roots. We present a system that integrates optical sectioning through light sheet fluorescence microscopy with hydroponic culture that enables us to image, at cellular resolution, a vertically growing Arabidopsis root every few minutes and for several consecutive days. We describe novel automated routines to track the root tip as it grows, to track cellular nuclei and to identify cell divisions. We demonstrate the system's capabilities by collecting data on divisions and nuclear dynamics. PMID:21731697

A Compact Synchronous Cellular Model of Nonlinear Calcium Dynamics: Simulation and FPGA Synthesis Results.

PubMed

Soleimani, Hamid; Drakakis, Emmanuel M

2017-06-01

Recent studies have demonstrated that calcium is a widespread intracellular ion that controls a wide range of temporal dynamics in the mammalian body. The simulation and validation of such studies using experimental data would benefit from a fast large scale simulation and modelling tool. This paper presents a compact and fully reconfigurable cellular calcium model capable of mimicking Hopf bifurcation phenomenon and various nonlinear responses of the biological calcium dynamics. The proposed cellular model is synthesized on a digital platform for a single unit and a network model. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed cellular model can mimic the biological calcium behaviors with considerably low hardware overhead. The approach has the potential to speed up large-scale simulations of slow intracellular dynamics by sharing more cellular units in real-time. To this end, various networks constructed by pipelining 10 k to 40 k cellular calcium units are compared with an equivalent simulation run on a standard PC workstation. Results show that the cellular hardware model is, on average, 83 times faster than the CPU version.

Vertex Models of Epithelial Morphogenesis

PubMed Central

Fletcher, Alexander G.; Osterfield, Miriam; Baker, Ruth E.; Shvartsman, Stanislav Y.

2014-01-01

The dynamic behavior of epithelial cell sheets plays a central role during numerous developmental processes. Genetic and imaging studies of epithelial morphogenesis in a wide range of organisms have led to increasingly detailed mechanisms of cell sheet dynamics. Computational models offer a useful means by which to investigate and test these mechanisms, and have played a key role in the study of cell-cell interactions. A variety of modeling approaches can be used to simulate the balance of forces within an epithelial sheet. Vertex models are a class of such models that consider cells as individual objects, approximated by two-dimensional polygons representing cellular interfaces, in which each vertex moves in response to forces due to growth, interfacial tension, and pressure within each cell. Vertex models are used to study cellular processes within epithelia, including cell motility, adhesion, mitosis, and delamination. This review summarizes how vertex models have been used to provide insight into developmental processes and highlights current challenges in this area, including progressing these models from two to three dimensions and developing new tools for model validation. PMID:24896108

Computational Modeling of Fluctuations in Energy and Metabolic Pathways of Methanogenic Archaea

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

Luthey-Schulten, Zaida

The methanogenic archaea, anaerobic microbes that convert CO2 and H2 and/or other small organic fermentation products into methane, play an unusually large role in the global carbon cycle. As they perform the final step in the anaerobic breakdown of biomass, methanogens are a biogenic source of an estimated one billion tons methane each year. Depending on the location, produced methane can be considered as either a greenhouse gas (agricultural byproduct), sequestered carbon storage (methane hydrate deposits), or a potential energy source (organic wastewater treatment). These microbes therefore represent an important target for biotechnology applications. Computational models of methanogens with predictivemore » power are useful aids in the adaptation of methanogenic systems, but need to connect processes of wide-ranging time and length scales. In this project, we developed several computational methodologies for modeling the dynamic behavior of entire cells that connects stochastic reaction-diffusion dynamics of individual biochemical pathways with genome-scale modeling of metabolic networks. While each of these techniques were in the realm of well-defined computational methods, here we integrated them to develop several entirely new approaches to systems biology. The first scientific aim of the project was to model how noise in a biochemical pathway propagates into cellular phenotypes. Genetic circuits have been optimized by evolution to regulate molecular processes despite stochastic noise, but the effect of such noise on a cellular biochemical networks is currently unknown. An integrated stochastic/systems model of Escherichia coli species was created to analyze how noise in protein expression gives—and therefore noise in metabolic fluxes—gives rise to multiple cellular phenotype in isogenic population. After the initial work developing and validating methods that allow characterization of the heterogeneity in the model organism E. coli, the project shifted toward investigations of the methanogen Methanosarcina acetivorans. By integrating an unprecedented transcriptomics dataset for growth of the methanogen on many substrates with an in silico model, heterogeneity in metabolic pathway usage and methane production were examined. This lent insight into the physiological requirements of the organism under different environmental conditions and uncovered the unique regulatory role that mRNA half-life has in shaping metabolic flux distributions in this organism.« less

Molecular dynamics simulations on interaction between bacterial proteins: Implication on pathogenic activities.

PubMed

Mondal, Manas; Chakrabarti, Jaydeb; Ghosh, Mahua

2018-03-01

We perform molecular dynamics simulation studies on interaction between bacterial proteins: an outer-membrane protein STY3179 and a yfdX protein STY3178 of Salmonella Typhi. STY3179 has been found to be involved in bacterial adhesion and invasion. STY3178 is recently biophysically characterized. It is a soluble protein having antibiotic binding and chaperon activity capabilities. These two proteins co-occur and are from neighboring gene in Salmonella Typhi-occurrence of homologs of both STY3178 and STY3179 are identified in many Gram-negative bacteria. We show using homology modeling, docking followed by molecular dynamics simulation that they can form a stable complex. STY3178 belongs to aqueous phase, while the beta barrel portion of STY3179 remains buried in DPPC bilayer with extra-cellular loops exposed to water. To understand the molecular basis of interaction between STY3178 and STY3179, we compute the conformational thermodynamics which indicate that these two proteins interact through polar and acidic residues belonging to their interfacial region. Conformational thermodynamics results further reveal instability of certain residues in extra-cellular loops of STY3179 upon complexation with STY3178 which is an indication for binding with host cell protein laminin. © 2017 Wiley Periodicals, Inc.

Real-time monitoring of metabolic function in liver-on-chip microdevices tracks the dynamics of mitochondrial dysfunction

PubMed Central

Bavli, Danny; Prill, Sebastian; Ezra, Elishai; Levy, Gahl; Cohen, Merav; Vinken, Mathieu; Vanfleteren, Jan; Jaeger, Magnus; Nahmias, Yaakov

2016-01-01

Microfluidic organ-on-a-chip technology aims to replace animal toxicity testing, but thus far has demonstrated few advantages over traditional methods. Mitochondrial dysfunction plays a critical role in the development of chemical and pharmaceutical toxicity, as well as pluripotency and disease processes. However, current methods to evaluate mitochondrial activity still rely on end-point assays, resulting in limited kinetic and prognostic information. Here, we present a liver-on-chip device capable of maintaining human tissue for over a month in vitro under physiological conditions. Mitochondrial respiration was monitored in real time using two-frequency phase modulation of tissue-embedded phosphorescent microprobes. A computer-controlled microfluidic switchboard allowed contiguous electrochemical measurements of glucose and lactate, providing real-time analysis of minute shifts from oxidative phosphorylation to anaerobic glycolysis, an early indication of mitochondrial stress. We quantify the dynamics of cellular adaptation to mitochondrial damage and the resulting redistribution of ATP production during rotenone-induced mitochondrial dysfunction and troglitazone (Rezulin)-induced mitochondrial stress. We show troglitazone shifts metabolic fluxes at concentrations previously regarded as safe, suggesting a mechanism for its observed idiosyncratic effect. Our microfluidic platform reveals the dynamics and strategies of cellular adaptation to mitochondrial damage, a unique advantage of organ-on-chip technology. PMID:27044092

Patient-specific modeling and analysis of dynamic behavior of individual sickle red blood cells under hypoxic conditions

NASA Astrophysics Data System (ADS)

Li, Xuejin; Du, E.; Li, Zhen; Tang, Yu-Hang; Lu, Lu; Dao, Ming; Karniadakis, George

2015-11-01

Sickle cell anemia is an inherited blood disorder exhibiting heterogeneous morphology and abnormal dynamics under hypoxic conditions. We developed a time-dependent cell model that is able to simulate the dynamic processes of repeated sickling and unsickling of red blood cells (RBCs) under physiological conditions. By using the kinetic cell model with parameters derived from patient-specific data, we present a mesoscopic computational study of the dynamic behavior of individual sickle RBCs flowing in a microfluidic channel with multiple microgates. We investigate how individual sickle RBCs behave differently from healthy ones in channel flow, and analyze the alteration of cellular behavior and response to single-cell capillary obstruction induced by cell rheologic rigidification and morphological change due to cell sickling under hypoxic conditions. We also simulate the flow dynamics of sickle RBCs treated with hydroxyurea (HU) and quantify the relative enhancement of hemodynamic performance of HU. This work was supported by the National Institutes of Health (NIH) Grant U01HL114476.

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

PubMed Central

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-01-01

Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as w, Δt, z, α, μ, α1, and α2, play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method. PMID:26158101

Coarse-grained modeling of RNA 3D structure.

PubMed

Dawson, Wayne K; Maciejczyk, Maciej; Jankowska, Elzbieta J; Bujnicki, Janusz M

2016-07-01

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A large dataset of protein dynamics in the mammalian heart proteome.

PubMed

Lau, Edward; Cao, Quan; Ng, Dominic C M; Bleakley, Brian J; Dincer, T Umut; Bot, Brian M; Wang, Ding; Liem, David A; Lam, Maggie P Y; Ge, Junbo; Ping, Peipei

2016-03-15

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems.

Real-time Visualization of Tissue Dynamics during Embryonic Development and Malignant Transformation

NASA Astrophysics Data System (ADS)

Yamada, Kenneth

Tissues undergo dramatic changes in organization during embryonic development, as well as during cancer progression and invasion. Recent advances in microscopy now allow us to visualize and track directly the dynamic movements of tissues, their constituent cells, and cellular substructures. This behavior can now be visualized not only in regular tissue culture on flat surfaces (`2D' environments), but also in a variety of 3D environments that may provide physiological cues relevant to understanding dynamics within living organisms. Acquisition of imaging data using various microscopy modalities will provide rich opportunities for determining the roles of physical factors and for computational modeling of complex processes in living tissues. Direct visualization of real-time motility is providing insight into biology spanning multiple spatio-temporal scales. Many cells in our body are known to be in contact with connective tissue and other forms of extracellular matrix. They do so through microscopic cellular adhesions that bind to matrix proteins. In particular, fluorescence microscopy has revealed that cells dynamically probe and bend the matrix at the sites of cell adhesions, and that 3D matrix architecture, stiffness, and elasticity can each regulate migration of the cells. Conversely, cells remodel their local matrix as organs form or tumors invade. Cancer cells can invade tissues using microscopic protrusions that degrade the surrounding matrix; in this case, the local matrix protein concentration is more important for inducing the micro-invasive protrusions than stiffness. On the length scales of tissues, transiently high rates of individual cell movement appear to help establish organ architecture. In fact, isolated cells can self-organize to form tissue structures. In all of these cases, in-depth real-time visualization will ultimately provide the extensive data needed for computer modeling and for testing hypotheses in which physical forces interact closely with cell signaling to form organs or promote tumor invasion.

Physics Computing '92: Proceedings of the 4th International Conference

NASA Astrophysics Data System (ADS)

de Groot, Robert A.; Nadrchal, Jaroslav

1993-04-01

The Table of Contents for the book is as follows: * Preface * INVITED PAPERS * Ab Initio Theoretical Approaches to the Structural, Electronic and Vibrational Properties of Small Clusters and Fullerenes: The State of the Art * Neural Multigrid Methods for Gauge Theories and Other Disordered Systems * Multicanonical Monte Carlo Simulations * On the Use of the Symbolic Language Maple in Physics and Chemistry: Several Examples * Nonequilibrium Phase Transitions in Catalysis and Population Models * Computer Algebra, Symmetry Analysis and Integrability of Nonlinear Evolution Equations * The Path-Integral Quantum Simulation of Hydrogen in Metals * Digital Optical Computing: A New Approach of Systolic Arrays Based on Coherence Modulation of Light and Integrated Optics Technology * Molecular Dynamics Simulations of Granular Materials * Numerical Implementation of a K.A.M. Algorithm * Quasi-Monte Carlo, Quasi-Random Numbers and Quasi-Error Estimates * What Can We Learn from QMC Simulations * Physics of Fluctuating Membranes * Plato, Apollonius, and Klein: Playing with Spheres * Steady States in Nonequilibrium Lattice Systems * CONVODE: A REDUCE Package for Differential Equations * Chaos in Coupled Rotators * Symplectic Numerical Methods for Hamiltonian Problems * Computer Simulations of Surfactant Self Assembly * High-dimensional and Very Large Cellular Automata for Immunological Shape Space * A Review of the Lattice Boltzmann Method * Electronic Structure of Solids in the Self-interaction Corrected Local-spin-density Approximation * Dedicated Computers for Lattice Gauge Theory Simulations * Physics Education: A Survey of Problems and Possible Solutions * Parallel Computing and Electronic-Structure Theory * High Precision Simulation Techniques for Lattice Field Theory * CONTRIBUTED PAPERS * Case Study of Microscale Hydrodynamics Using Molecular Dynamics and Lattice Gas Methods * Computer Modelling of the Structural and Electronic Properties of the Supported Metal Catalysis * Ordered Particle Simulations for Serial and MIMD Parallel Computers * "NOLP" -- Program Package for Laser Plasma Nonlinear Optics * Algorithms to Solve Nonlinear Least Square Problems * Distribution of Hydrogen Atoms in Pd-H Computed by Molecular Dynamics * A Ray Tracing of Optical System for Protein Crystallography Beamline at Storage Ring-SIBERIA-2 * Vibrational Properties of a Pseudobinary Linear Chain with Correlated Substitutional Disorder * Application of the Software Package Mathematica in Generalized Master Equation Method * Linelist: An Interactive Program for Analysing Beam-foil Spectra * GROMACS: A Parallel Computer for Molecular Dynamics Simulations * GROMACS Method of Virial Calculation Using a Single Sum * The Interactive Program for the Solution of the Laplace Equation with the Elimination of Singularities for Boundary Functions * Random-Number Generators: Testing Procedures and Comparison of RNG Algorithms * Micro-TOPIC: A Tokamak Plasma Impurities Code * Rotational Molecular Scattering Calculations * Orthonormal Polynomial Method for Calibrating of Cryogenic Temperature Sensors * Frame-based System Representing Basis of Physics * The Role of Massively Data-parallel Computers in Large Scale Molecular Dynamics Simulations * Short-range Molecular Dynamics on a Network of Processors and Workstations * An Algorithm for Higher-order Perturbation Theory in Radiative Transfer Computations * Hydrostochastics: The Master Equation Formulation of Fluid Dynamics * HPP Lattice Gas on Transputers and Networked Workstations * Study on the Hysteresis Cycle Simulation Using Modeling with Different Functions on Intervals * Refined Pruning Techniques for Feed-forward Neural Networks * Random Walk Simulation of the Motion of Transient Charges in Photoconductors * The Optical Hysteresis in Hydrogenated Amorphous Silicon * Diffusion Monte Carlo Analysis of Modern Interatomic Potentials for He * A Parallel Strategy for Molecular Dynamics Simulations of Polar Liquids on Transputer Arrays * Distribution of Ions Reflected on Rough Surfaces * The Study of Step Density Distribution During Molecular Beam Epitaxy Growth: Monte Carlo Computer Simulation * Towards a Formal Approach to the Construction of Large-scale Scientific Applications Software * Correlated Random Walk and Discrete Modelling of Propagation through Inhomogeneous Media * Teaching Plasma Physics Simulation * A Theoretical Determination of the Au-Ni Phase Diagram * Boson and Fermion Kinetics in One-dimensional Lattices * Computational Physics Course on the Technical University * Symbolic Computations in Simulation Code Development and Femtosecond-pulse Laser-plasma Interaction Studies * Computer Algebra and Integrated Computing Systems in Education of Physical Sciences * Coordinated System of Programs for Undergraduate Physics Instruction * Program Package MIRIAM and Atomic Physics of Extreme Systems * High Energy Physics Simulation on the T_Node * The Chapman-Kolmogorov Equation as Representation of Huygens' Principle and the Monolithic Self-consistent Numerical Modelling of Lasers * Authoring System for Simulation Developments * Molecular Dynamics Study of Ion Charge Effects in the Structure of Ionic Crystals * A Computational Physics Introductory Course * Computer Calculation of Substrate Temperature Field in MBE System * Multimagnetical Simulation of the Ising Model in Two and Three Dimensions * Failure of the CTRW Treatment of the Quasicoherent Excitation Transfer * Implementation of a Parallel Conjugate Gradient Method for Simulation of Elastic Light Scattering * Algorithms for Study of Thin Film Growth * Algorithms and Programs for Physics Teaching in Romanian Technical Universities * Multicanonical Simulation of 1st order Transitions: Interface Tension of the 2D 7-State Potts Model * Two Numerical Methods for the Calculation of Periodic Orbits in Hamiltonian Systems * Chaotic Behavior in a Probabilistic Cellular Automata? * Wave Optics Computing by a Networked-based Vector Wave Automaton * Tensor Manipulation Package in REDUCE * Propagation of Electromagnetic Pulses in Stratified Media * The Simple Molecular Dynamics Model for the Study of Thermalization of the Hot Nucleon Gas * Electron Spin Polarization in PdCo Alloys Calculated by KKR-CPA-LSD Method * Simulation Studies of Microscopic Droplet Spreading * A Vectorizable Algorithm for the Multicolor Successive Overrelaxation Method * Tetragonality of the CuAu I Lattice and Its Relation to Electronic Specific Heat and Spin Susceptibility * Computer Simulation of the Formation of Metallic Aggregates Produced by Chemical Reactions in Aqueous Solution * Scaling in Growth Models with Diffusion: A Monte Carlo Study * The Nucleus as the Mesoscopic System * Neural Network Computation as Dynamic System Simulation * First-principles Theory of Surface Segregation in Binary Alloys * Data Smooth Approximation Algorithm for Estimating the Temperature Dependence of the Ice Nucleation Rate * Genetic Algorithms in Optical Design * Application of 2D-FFT in the Study of Molecular Exchange Processes by NMR * Advanced Mobility Model for Electron Transport in P-Si Inversion Layers * Computer Simulation for Film Surfaces and its Fractal Dimension * Parallel Computation Techniques and the Structure of Catalyst Surfaces * Educational SW to Teach Digital Electronics and the Corresponding Text Book * Primitive Trinomials (Mod 2) Whose Degree is a Mersenne Exponent * Stochastic Modelisation and Parallel Computing * Remarks on the Hybrid Monte Carlo Algorithm for the ∫4 Model * An Experimental Computer Assisted Workbench for Physics Teaching * A Fully Implicit Code to Model Tokamak Plasma Edge Transport * EXPFIT: An Interactive Program for Automatic Beam-foil Decay Curve Analysis * Mapping Technique for Solving General, 1-D Hamiltonian Systems * Freeway Traffic, Cellular Automata, and Some (Self-Organizing) Criticality * Photonuclear Yield Analysis by Dynamic Programming * Incremental Representation of the Simply Connected Planar Curves * Self-convergence in Monte Carlo Methods * Adaptive Mesh Technique for Shock Wave Propagation * Simulation of Supersonic Coronal Streams and Their Interaction with the Solar Wind * The Nature of Chaos in Two Systems of Ordinary Nonlinear Differential Equations * Considerations of a Window-shopper * Interpretation of Data Obtained by RTP 4-Channel Pulsed Radar Reflectometer Using a Multi Layer Perceptron * Statistics of Lattice Bosons for Finite Systems * Fractal Based Image Compression with Affine Transformations * Algorithmic Studies on Simulation Codes for Heavy-ion Reactions * An Energy-Wise Computer Simulation of DNA-Ion-Water Interactions Explains the Abnormal Structure of Poly[d(A)]:Poly[d(T)] * Computer Simulation Study of Kosterlitz-Thouless-Like Transitions * Problem-oriented Software Package GUN-EBT for Computer Simulation of Beam Formation and Transport in Technological Electron-Optical Systems * Parallelization of a Boundary Value Solver and its Application in Nonlinear Dynamics * The Symbolic Classification of Real Four-dimensional Lie Algebras * Short, Singular Pulses Generation by a Dye Laser at Two Wavelengths Simultaneously * Quantum Monte Carlo Simulations of the Apex-Oxygen-Model * Approximation Procedures for the Axial Symmetric Static Einstein-Maxwell-Higgs Theory * Crystallization on a Sphere: Parallel Simulation on a Transputer Network * FAMULUS: A Software Product (also) for Physics Education * MathCAD vs. FAMULUS -- A Brief Comparison * First-principles Dynamics Used to Study Dissociative Chemisorption * A Computer Controlled System for Crystal Growth from Melt * A Time Resolved Spectroscopic Method for Short Pulsed Particle Emission * Green's Function Computation in Radiative Transfer Theory * Random Search Optimization Technique for One-criteria and Multi-criteria Problems * Hartley Transform Applications to Thermal Drift Elimination in Scanning Tunneling Microscopy * Algorithms of Measuring, Processing and Interpretation of Experimental Data Obtained with Scanning Tunneling Microscope * Time-dependent Atom-surface Interactions * Local and Global Minima on Molecular Potential Energy Surfaces: An Example of N3 Radical * Computation of Bifurcation Surfaces * Symbolic Computations in Quantum Mechanics: Energies in Next-to-solvable Systems * A Tool for RTP Reactor and Lamp Field Design * Modelling of Particle Spectra for the Analysis of Solid State Surface * List of Participants

Analysis of peristaltic waves and their role in migrating Physarum plasmodia

NASA Astrophysics Data System (ADS)

Lewis, Owen L.; Guy, Robert D.

2017-07-01

The true slime mold Physarum polycephalum exhibits a vast array of sophisticated manipulations of its intracellular cytoplasm. Growing microplasmodia of Physarum have been observed to adopt an elongated tadpole shape, then contract in a rhythmic, traveling wave pattern that resembles peristaltic pumping. This contraction drives a fast flow of non-gelated cytoplasm along the cell longitudinal axis. It has been hypothesized that this flow of cytoplasm is a driving factor in generating motility of the plasmodium. In this work, we use two different mathematical models to investigate how peristaltic pumping within Physarum may be used to drive cellular motility. We compare the relative phase of flow and deformation waves predicted by both models to similar phase data collected from in vivo experiments using Physarum plasmodia. The first is a PDE model based on a dimensional reduction of peristaltic pumping within a finite length chamber. The second is a more sophisticated computational model which accounts for more general shape changes, more complex cellular mechanics, and dynamically modulated adhesion to the underlying substrate. This model allows us to directly compute cell crawling speed. Both models suggest that a mechanical asymmetry in the cell is required to reproduce the experimental observations. Such a mechanical asymmetry is also shown to increase the potential for cellular migration, as measured by both stress generation and migration velocity.

Promise of new imaging technologies for assessing ovarian function.

PubMed

Singh, Jaswant; Adams, Gregg P; Pierson, Roger A

2003-10-15

Advancements in imaging technologies over the last two decades have ushered a quiet revolution in research approaches to the study of ovarian structure and function. The most significant changes in our understanding of the ovary have resulted from the use of ultrasonography which has enabled sequential analyses in live animals. Computer-assisted image analysis and mathematical modeling of the dynamic changes within the ovary has permitted exciting new avenues of research with readily quantifiable endpoints. Spectral, color-flow and power Doppler imaging now facilitate physiologic interpretations of vascular dynamics over time. Similarly, magnetic resonance imaging (MRI) is emerging as a research tool in ovarian imaging. New technologies, such as three-dimensional ultrasonography and MRI, ultrasound-based biomicroscopy and synchrotron-based techniques each have the potential to enhance our real-time picture of ovarian function to the near-cellular level. Collectively, information available in ultrasonography, MRI, computer-assisted image analysis and mathematical modeling heralds a new era in our understanding of the basic processes of female and male reproduction.

Probing RNA Native Conformational Ensembles with Structural Constraints.

PubMed

Fonseca, Rasmus; van den Bedem, Henry; Bernauer, Julie

2016-05-01

Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.

FRET-based genetically-encoded sensors for quantitative monitoring of metabolites.

PubMed

Mohsin, Mohd; Ahmad, Altaf; Iqbal, Muhammad

2015-10-01

Neighboring cells in the same tissue can exist in different states of dynamic activities. After genomics, proteomics and metabolomics, fluxomics is now equally important for generating accurate quantitative information on the cellular and sub-cellular dynamics of ions and metabolite, which is critical for functional understanding of organisms. Various spectrometry techniques are used for monitoring ions and metabolites, although their temporal and spatial resolutions are limited. Discovery of the fluorescent proteins and their variants has revolutionized cell biology. Therefore, novel tools and methods targeting sub-cellular compartments need to be deployed in specific cells and targeted to sub-cellular compartments in order to quantify the target-molecule dynamics directly. We require tools that can measure cellular activities and protein dynamics with sub-cellular resolution. Biosensors based on fluorescence resonance energy transfer (FRET) are genetically encoded and hence can specifically target sub-cellular organelles by fusion to proteins or targetted sequences. Since last decade, FRET-based genetically encoded sensors for molecules involved in energy production, reactive oxygen species and secondary messengers have helped to unravel key aspects of cellular physiology. This review, describing the design and principles of sensors, presents a database of sensors for different analytes/processes, and illustrate examples of application in quantitative live cell imaging.

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Computational and theoretical approaches for studies of a lipid recognition protein on biological membranes

PubMed Central

Yamamoto, Eiji

2017-01-01

Many cellular functions, including cell signaling and related events, are regulated by the association of peripheral membrane proteins (PMPs) with biological membranes containing anionic lipids, e.g., phosphatidylinositol phosphate (PIP). This association is often mediated by lipid recognition modules present in many PMPs. Here, I summarize computational and theoretical approaches to investigate the molecular details of the interactions and dynamics of a lipid recognition module, the pleckstrin homology (PH) domain, on biological membranes. Multiscale molecular dynamics simulations using combinations of atomistic and coarse-grained models yielded results comparable to those of actual experiments and could be used to elucidate the molecular mechanisms of the formation of protein/lipid complexes on membrane surfaces, which are often difficult to obtain using experimental techniques. Simulations revealed some modes of membrane localization and interactions of PH domains with membranes in addition to the canonical binding mode. In the last part of this review, I address the dynamics of PH domains on the membrane surface. Local PIP clusters formed around the proteins exhibit anomalous fluctuations. This dynamic change in protein-lipid interactions cause temporally fluctuating diffusivity of proteins, i.e., the short-term diffusivity of the bound protein changes substantially with time, and may in turn contribute to the formation/dissolution of protein complexes in membranes. PMID:29159013

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

Towards a Quantum Game of Life

NASA Astrophysics Data System (ADS)

Flitney, Adrian P.; Abbott, Derek

Cellular automata provide a means of obtaining complex behaviour from a simple array of cells and a deterministic transition function. They supply a method of computation that dispenses with the need for manipulation of individual cells and they are computationally universal. Classical cellular automata have proved of great interest to computer scientists but the construction of quantum cellular automata pose particular difficulties. We present a version of John Conway's famous two-dimensional classical cellular automata Life that has some quantum-like features, including interference effects. Some basic structures in the new automata are given and comparisons are made with Conway's game.

Impact of jamming on collective cell migration

NASA Astrophysics Data System (ADS)

Nnetu, Kenechukwu David; Knorr, Melanie; Pawlizak, Steve; Fuhs, Thomas; Zink, Mareike; KäS, Josef A.

2012-02-01

Multi-cellular migration plays an important role in physiological processes such as embryogenesis, cancer metastasis and tissue repair. During migration, single cells undergo cycles of extension, adhesion and retraction resulting in morphological changes. In a confluent monolayer, there are inter-cellular interactions and crowding, however, the impact of these interactions on the dynamics and elasticity of the monolayer at the multi-cellular and single cell level is not well understood. Here we study the dynamics of a confluent epithelial monolayer by simultaneously measuring cell motion at the multi-cellular and single cell level for various cell densities and tensile elasticity. At the multi-cellular level, the system exhibited spatial kinetic transitions from isotropic to anisotropic migration on long times and the velocity of the monolayer decreased with increasing cell density. Moreover, the dynamics was spatially and temporally heterogeneous. Interestingly, the dynamics was also heterogeneous in wound-healing assays and the correlation length was fitted by compressed exponential. On the single cell scale, we observed transient caging effects with increasing cage rearrangement times as the system age due to an increase in density. Also, the density dependent elastic modulus of the monolayer scaled as a weak power law. Together, these findings suggest that caging effects at the single cell level initiates a slow and heterogeneous dynamics at the multi-cellular level which is similar to the glassy dynamics of deformable colloidal systems.

Modeling of fracture of protective concrete structures under impact loads

NASA Astrophysics Data System (ADS)

Radchenko, P. A.; Batuev, S. P.; Radchenko, A. V.; Plevkov, V. S.

2015-10-01

This paper presents results of numerical simulation of interaction between a Boeing 747-400 aircraft and the protective shell of a nuclear power plant. The shell is presented as a complex multilayered cellular structure consisting of layers of concrete and fiber concrete bonded with steel trusses. Numerical simulation was performed three-dimensionally using the original algorithm and software taking into account algorithms for building grids of complex geometric objects and parallel computations. Dynamics of the stress-strain state and fracture of the structure were studied. Destruction is described using a two-stage model that allows taking into account anisotropy of elastic and strength properties of concrete and fiber concrete. It is shown that wave processes initiate destruction of the cellular shell structure; cells start to destruct in an unloading wave originating after the compression wave arrival at free cell surfaces.

ENVIRONMENT: a computational platform to stochastically simulate reacting and self-reproducing lipid compartments

NASA Astrophysics Data System (ADS)

Mavelli, Fabio; Ruiz-Mirazo, Kepa

2010-09-01

'ENVIRONMENT' is a computational platform that has been developed in the last few years with the aim to simulate stochastically the dynamics and stability of chemically reacting protocellular systems. Here we present and describe some of its main features, showing how the stochastic kinetics approach can be applied to study the time evolution of reaction networks in heterogeneous conditions, particularly when supramolecular lipid structures (micelles, vesicles, etc) coexist with aqueous domains. These conditions are of special relevance to understand the origins of cellular, self-reproducing compartments, in the context of prebiotic chemistry and evolution. We contrast our simulation results with real lab experiments, with the aim to bring together theoretical and experimental research on protocell and minimal artificial cell systems.

Neuronal avalanches and coherence potentials

NASA Astrophysics Data System (ADS)

Plenz, D.

2012-05-01

The mammalian cortex consists of a vast network of weakly interacting excitable cells called neurons. Neurons must synchronize their activities in order to trigger activity in neighboring neurons. Moreover, interactions must be carefully regulated to remain weak (but not too weak) such that cascades of active neuronal groups avoid explosive growth yet allow for activity propagation over long-distances. Such a balance is robustly realized for neuronal avalanches, which are defined as cortical activity cascades that follow precise power laws. In experiments, scale-invariant neuronal avalanche dynamics have been observed during spontaneous cortical activity in isolated preparations in vitro as well as in the ongoing cortical activity of awake animals and in humans. Theory, models, and experiments suggest that neuronal avalanches are the signature of brain function near criticality at which the cortex optimally responds to inputs and maximizes its information capacity. Importantly, avalanche dynamics allow for the emergence of a subset of avalanches, the coherence potentials. They emerge when the synchronization of a local neuronal group exceeds a local threshold, at which the system spawns replicas of the local group activity at distant network sites. The functional importance of coherence potentials will be discussed in the context of propagating structures, such as gliders in balanced cellular automata. Gliders constitute local population dynamics that replicate in space after a finite number of generations and are thought to provide cellular automata with universal computation. Avalanches and coherence potentials are proposed to constitute a modern framework of cortical synchronization dynamics that underlies brain function.

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-04-23

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

Physarum solver: A biologically inspired method of road-network navigation

NASA Astrophysics Data System (ADS)

Tero, Atsushi; Kobayashi, Ryo; Nakagaki, Toshiyuki

2006-04-01

We have proposed a mathematical model for the adaptive dynamics of the transport network in an amoeba-like organism, the true slime mold Physarum polycephalum. The model is based on physiological observations of this species, but can also be used for path-finding in the complicated networks of mazes and road maps. In this paper, we describe the physiological basis and the formulation of the model, as well as the results of simulations of some complicated networks. The path-finding method used by Physarum is a good example of cellular computation.

Complex cellular logic computation using ribocomputing devices.

PubMed

Green, Alexander A; Kim, Jongmin; Ma, Duo; Silver, Pamela A; Collins, James J; Yin, Peng

2017-08-03

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our 'ribocomputing' systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.

Computational strategies to address chromatin structure problems

NASA Astrophysics Data System (ADS)

Perišić, Ognjen; Schlick, Tamar

2016-06-01

While the genetic information is contained in double helical DNA, gene expression is a complex multilevel process that involves various functional units, from nucleosomes to fully formed chromatin fibers accompanied by a host of various chromatin binding enzymes. The chromatin fiber is a polymer composed of histone protein complexes upon which DNA wraps, like yarn upon many spools. The nature of chromatin structure has been an open question since the beginning of modern molecular biology. Many experiments have shown that the chromatin fiber is a highly dynamic entity with pronounced structural diversity that includes properties of idealized zig-zag and solenoid models, as well as other motifs. This diversity can produce a high packing ratio and thus inhibit access to a majority of the wound DNA. Despite much research, chromatin’s dynamic structure has not yet been fully described. Long stretches of chromatin fibers exhibit puzzling dynamic behavior that requires interpretation in the light of gene expression patterns in various tissue and organisms. The properties of chromatin fiber can be investigated with experimental techniques, like in vitro biochemistry, in vivo imagining, and high-throughput chromosome capture technology. Those techniques provide useful insights into the fiber’s structure and dynamics, but they are limited in resolution and scope, especially regarding compact fibers and chromosomes in the cellular milieu. Complementary but specialized modeling techniques are needed to handle large floppy polymers such as the chromatin fiber. In this review, we discuss current approaches in the chromatin structure field with an emphasis on modeling, such as molecular dynamics and coarse-grained computational approaches. Combinations of these computational techniques complement experiments and address many relevant biological problems, as we will illustrate with special focus on epigenetic modulation of chromatin structure.

Early Experiences Porting the NAMD and VMD Molecular Simulation and Analysis Software to GPU-Accelerated OpenPOWER Platforms

PubMed Central

Stone, John E.; Hynninen, Antti-Pekka; Phillips, James C.; Schulten, Klaus

2017-01-01

All-atom molecular dynamics simulations of biomolecules provide a powerful tool for exploring the structure and dynamics of large protein complexes within realistic cellular environments. Unfortunately, such simulations are extremely demanding in terms of their computational requirements, and they present many challenges in terms of preparation, simulation methodology, and analysis and visualization of results. We describe our early experiences porting the popular molecular dynamics simulation program NAMD and the simulation preparation, analysis, and visualization tool VMD to GPU-accelerated OpenPOWER hardware platforms. We report our experiences with compiler-provided autovectorization and compare with hand-coded vector intrinsics for the POWER8 CPU. We explore the performance benefits obtained from unique POWER8 architectural features such as 8-way SMT and its value for particular molecular modeling tasks. Finally, we evaluate the performance of several GPU-accelerated molecular modeling kernels and relate them to other hardware platforms. PMID:29202130

A large dataset of protein dynamics in the mammalian heart proteome

PubMed Central

Lau, Edward; Cao, Quan; Ng, Dominic C.M.; Bleakley, Brian J.; Dincer, T. Umut; Bot, Brian M.; Wang, Ding; Liem, David A.; Lam, Maggie P.Y.; Ge, Junbo; Ping, Peipei

2016-01-01

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems. PMID:26977904

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY

PubMed Central

Somogyi, Endre; Hagar, Amit; Glazier, James A.

2017-01-01

Living tissues are dynamic, heterogeneous compositions of objects, including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes. Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology (CCOPM) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models. PMID:29282379

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY.

PubMed

Somogyi, Endre; Hagar, Amit; Glazier, James A

2016-12-01

Living tissues are dynamic, heterogeneous compositions of objects , including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes . Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology ( CCOPM ) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.

Calcium dynamics and signaling in vascular regulation: computational models

PubMed Central

Tsoukias, Nikolaos Michael

2013-01-01

Calcium is a universal signaling molecule with a central role in a number of vascular functions including in the regulation of tone and blood flow. Experimentation has provided insights into signaling pathways that lead to or affected by Ca2+ mobilization in the vasculature. Mathematical modeling offers a systematic approach to the analysis of these mechanisms and can serve as a tool for data interpretation and for guiding new experimental studies. Comprehensive models of calcium dynamics are well advanced for some systems such as the heart. This review summarizes the progress that has been made in modeling Ca2+ dynamics and signaling in vascular cells. Model simulations show how Ca2+ signaling emerges as a result of complex, nonlinear interactions that cannot be properly analyzed using only a reductionist's approach. A strategy of integrative modeling in the vasculature is outlined that will allow linking macroscale pathophysiological responses to the underlying cellular mechanisms. PMID:21061306

Population dynamics, information transfer, and spatial organization in a chemical reaction network under spatial confinement and crowding conditions

NASA Astrophysics Data System (ADS)

Bellesia, Giovanni; Bales, Benjamin B.

2016-10-01

We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

PubMed Central

2010-01-01

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics. PMID:21067546

Impact of release dynamics of laser-irradiated polymer micropallets on the viability of selected adherent cells

PubMed Central

Ma, Huan; Mismar, Wael; Wang, Yuli; Small, Donald W.; Ras, Mat; Allbritton, Nancy L.; Sims, Christopher E.; Venugopalan, Vasan

2012-01-01

We use time-resolved interferometry, fluorescence assays and computational fluid dynamics (CFD) simulations to examine the viability of confluent adherent cell monolayers to selection via laser microbeam release of photoresist polymer micropallets. We demonstrate the importance of laser microbeam pulse energy and focal volume position relative to the glass–pallet interface in governing the threshold energies for pallet release as well as the pallet release dynamics. Measurements using time-resolved interferometry show that increases in laser pulse energy result in increasing pallet release velocities that can approach 10 m s−1 through aqueous media. CFD simulations reveal that the pallet motion results in cellular exposure to transient hydrodynamic shear stress amplitudes that can exceed 100 kPa on microsecond timescales, and which produces reduced cell viability. Moreover, CFD simulation results show that the maximum shear stress on the pallet surface varies spatially, with the largest shear stresses occurring on the pallet periphery. Cell viability of confluent cell monolayers on the pallet surface confirms that the use of larger pulse energies results in increased rates of necrosis for those cells situated away from the pallet centre, while cells situated at the pallet centre remain viable. Nevertheless, experiments that examine the viability of these cell monolayers following pallet release show that proper choices for laser microbeam pulse energy and focal volume position lead to the routine achievement of cell viability in excess of 90 per cent. These laser microbeam parameters result in maximum pallet release velocities below 6 m s−1 and cellular exposure of transient hydrodynamic shear stresses below 20 kPa. Collectively, these results provide a mechanistic understanding that relates pallet release dynamics and associated transient shear stresses with subsequent cellular viability. This provides a quantitative, mechanistic basis for determining optimal operating conditions for laser microbeam-based pallet release systems for the isolation and selection of adherent cells. PMID:22158840

Impact of release dynamics of laser-irradiated polymer micropallets on the viability of selected adherent cells.

PubMed

Ma, Huan; Mismar, Wael; Wang, Yuli; Small, Donald W; Ras, Mat; Allbritton, Nancy L; Sims, Christopher E; Venugopalan, Vasan

2012-06-07

We use time-resolved interferometry, fluorescence assays and computational fluid dynamics (CFD) simulations to examine the viability of confluent adherent cell monolayers to selection via laser microbeam release of photoresist polymer micropallets. We demonstrate the importance of laser microbeam pulse energy and focal volume position relative to the glass-pallet interface in governing the threshold energies for pallet release as well as the pallet release dynamics. Measurements using time-resolved interferometry show that increases in laser pulse energy result in increasing pallet release velocities that can approach 10 m s(-1) through aqueous media. CFD simulations reveal that the pallet motion results in cellular exposure to transient hydrodynamic shear stress amplitudes that can exceed 100 kPa on microsecond timescales, and which produces reduced cell viability. Moreover, CFD simulation results show that the maximum shear stress on the pallet surface varies spatially, with the largest shear stresses occurring on the pallet periphery. Cell viability of confluent cell monolayers on the pallet surface confirms that the use of larger pulse energies results in increased rates of necrosis for those cells situated away from the pallet centre, while cells situated at the pallet centre remain viable. Nevertheless, experiments that examine the viability of these cell monolayers following pallet release show that proper choices for laser microbeam pulse energy and focal volume position lead to the routine achievement of cell viability in excess of 90 per cent. These laser microbeam parameters result in maximum pallet release velocities below 6 m s(-1) and cellular exposure of transient hydrodynamic shear stresses below 20 kPa. Collectively, these results provide a mechanistic understanding that relates pallet release dynamics and associated transient shear stresses with subsequent cellular viability. This provides a quantitative, mechanistic basis for determining optimal operating conditions for laser microbeam-based pallet release systems for the isolation and selection of adherent cells.

Modeling the fusion of cylindrical bioink particles in post bioprinting structure formation

NASA Astrophysics Data System (ADS)

McCune, Matt; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

2015-03-01

Cellular Particle Dynamics (CPD) is an effective computational method to describe the shape evolution and biomechanical relaxation processes in multicellular systems. Thus, CPD is a useful tool to predict the outcome of post-printing structure formation in bioprinting. The predictive power of CPD has been demonstrated for multicellular systems composed of spherical bioink units. Experiments and computer simulations were related through an independently developed theoretical formalism based on continuum mechanics. Here we generalize the CPD formalism to (i) include cylindrical bioink particles often used in specific bioprinting applications, (ii) describe the more realistic experimental situation in which both the length and the volume of the cylindrical bioink units decrease during post-printing structure formation, and (iii) directly connect CPD simulations to the corresponding experiments without the need of the intermediate continuum theory inherently based on simplifying assumptions. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

Brownian dynamics simulations of sequence-dependent duplex denaturation in dynamically superhelical DNA

NASA Astrophysics Data System (ADS)

Mielke, Steven P.; Grønbech-Jensen, Niels; Krishnan, V. V.; Fink, William H.; Benham, Craig J.

2005-09-01

The topological state of DNA in vivo is dynamically regulated by a number of processes that involve interactions with bound proteins. In one such process, the tracking of RNA polymerase along the double helix during transcription, restriction of rotational motion of the polymerase and associated structures, generates waves of overtwist downstream and undertwist upstream from the site of transcription. The resulting superhelical stress is often sufficient to drive double-stranded DNA into a denatured state at locations such as promoters and origins of replication, where sequence-specific duplex opening is a prerequisite for biological function. In this way, transcription and other events that actively supercoil the DNA provide a mechanism for dynamically coupling genetic activity with regulatory and other cellular processes. Although computer modeling has provided insight into the equilibrium dynamics of DNA supercoiling, to date no model has appeared for simulating sequence-dependent DNA strand separation under the nonequilibrium conditions imposed by the dynamic introduction of torsional stress. Here, we introduce such a model and present results from an initial set of computer simulations in which the sequences of dynamically superhelical, 147 base pair DNA circles were systematically altered in order to probe the accuracy with which the model can predict location, extent, and time of stress-induced duplex denaturation. The results agree both with well-tested statistical mechanical calculations and with available experimental information. Additionally, we find that sites susceptible to denaturation show a propensity for localizing to supercoil apices, suggesting that base sequence determines locations of strand separation not only through the energetics of interstrand interactions, but also by influencing the geometry of supercoiling.

Brownian dynamics simulations of sequence-dependent duplex denaturation in dynamically superhelical DNA.

PubMed

Mielke, Steven P; Grønbech-Jensen, Niels; Krishnan, V V; Fink, William H; Benham, Craig J

2005-09-22

The topological state of DNA in vivo is dynamically regulated by a number of processes that involve interactions with bound proteins. In one such process, the tracking of RNA polymerase along the double helix during transcription, restriction of rotational motion of the polymerase and associated structures, generates waves of overtwist downstream and undertwist upstream from the site of transcription. The resulting superhelical stress is often sufficient to drive double-stranded DNA into a denatured state at locations such as promoters and origins of replication, where sequence-specific duplex opening is a prerequisite for biological function. In this way, transcription and other events that actively supercoil the DNA provide a mechanism for dynamically coupling genetic activity with regulatory and other cellular processes. Although computer modeling has provided insight into the equilibrium dynamics of DNA supercoiling, to date no model has appeared for simulating sequence-dependent DNA strand separation under the nonequilibrium conditions imposed by the dynamic introduction of torsional stress. Here, we introduce such a model and present results from an initial set of computer simulations in which the sequences of dynamically superhelical, 147 base pair DNA circles were systematically altered in order to probe the accuracy with which the model can predict location, extent, and time of stress-induced duplex denaturation. The results agree both with well-tested statistical mechanical calculations and with available experimental information. Additionally, we find that sites susceptible to denaturation show a propensity for localizing to supercoil apices, suggesting that base sequence determines locations of strand separation not only through the energetics of interstrand interactions, but also by influencing the geometry of supercoiling.

All-atom molecular dynamics of virus capsids as drug targets

DOE PAGES

Perilla, Juan R.; Hadden, Jodi A.; Goh, Boon Chong; ...

2016-04-29

Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways.more » When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Finally, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.« less

All-atom molecular dynamics of virus capsids as drug targets

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

Perilla, Juan R.; Hadden, Jodi A.; Goh, Boon Chong

Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways.more » When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Finally, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.« less

Correlations induced by depressing synapses in critically self-organized networks with quenched dynamics

NASA Astrophysics Data System (ADS)

Campos, João Guilherme Ferreira; Costa, Ariadne de Andrade; Copelli, Mauro; Kinouchi, Osame

2017-04-01

In a recent work, mean-field analysis and computer simulations were employed to analyze critical self-organization in networks of excitable cellular automata where randomly chosen synapses in the network were depressed after each spike (the so-called annealed dynamics). Calculations agree with simulations of the annealed version, showing that the nominal branching ratio σ converges to unity in the thermodynamic limit, as expected of a self-organized critical system. However, the question remains whether the same results apply to the biological case where only the synapses of firing neurons are depressed (the so-called quenched dynamics). We show that simulations of the quenched model yield significant deviations from σ =1 due to spatial correlations. However, the model is shown to be critical, as the largest eigenvalue of the synaptic matrix approaches unity in the thermodynamic limit, that is, λc=1 . We also study the finite size effects near the critical state as a function of the parameters of the synaptic dynamics.

Computational Science in Armenia (Invited Talk)

NASA Astrophysics Data System (ADS)

Marandjian, H.; Shoukourian, Yu.

This survey is devoted to the development of informatics and computer science in Armenia. The results in theoretical computer science (algebraic models, solutions to systems of general form recursive equations, the methods of coding theory, pattern recognition and image processing), constitute the theoretical basis for developing problem-solving-oriented environments. As examples can be mentioned: a synthesizer of optimized distributed recursive programs, software tools for cluster-oriented implementations of two-dimensional cellular automata, a grid-aware web interface with advanced service trading for linear algebra calculations. In the direction of solving scientific problems that require high-performance computing resources, examples of completed projects include the field of physics (parallel computing of complex quantum systems), astrophysics (Armenian virtual laboratory), biology (molecular dynamics study of human red blood cell membrane), meteorology (implementing and evaluating the Weather Research and Forecast Model for the territory of Armenia). The overview also notes that the Institute for Informatics and Automation Problems of the National Academy of Sciences of Armenia has established a scientific and educational infrastructure, uniting computing clusters of scientific and educational institutions of the country and provides the scientific community with access to local and international computational resources, that is a strong support for computational science in Armenia.

Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions.

PubMed

Nehaniv, Chrystopher L; Rhodes, John; Egri-Nagy, Attila; Dini, Paolo; Morris, Eric Rothstein; Horváth, Gábor; Karimi, Fariba; Schreckling, Daniel; Schilstra, Maria J

2015-07-28

Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.

FPGA-based distributed computing microarchitecture for complex physical dynamics investigation.

PubMed

Borgese, Gianluca; Pace, Calogero; Pantano, Pietro; Bilotta, Eleonora

2013-09-01

In this paper, we present a distributed computing system, called DCMARK, aimed at solving partial differential equations at the basis of many investigation fields, such as solid state physics, nuclear physics, and plasma physics. This distributed architecture is based on the cellular neural network paradigm, which allows us to divide the differential equation system solving into many parallel integration operations to be executed by a custom multiprocessor system. We push the number of processors to the limit of one processor for each equation. In order to test the present idea, we choose to implement DCMARK on a single FPGA, designing the single processor in order to minimize its hardware requirements and to obtain a large number of easily interconnected processors. This approach is particularly suited to study the properties of 1-, 2- and 3-D locally interconnected dynamical systems. In order to test the computing platform, we implement a 200 cells, Korteweg-de Vries (KdV) equation solver and perform a comparison between simulations conducted on a high performance PC and on our system. Since our distributed architecture takes a constant computing time to solve the equation system, independently of the number of dynamical elements (cells) of the CNN array, it allows us to reduce the elaboration time more than other similar systems in the literature. To ensure a high level of reconfigurability, we design a compact system on programmable chip managed by a softcore processor, which controls the fast data/control communication between our system and a PC Host. An intuitively graphical user interface allows us to change the calculation parameters and plot the results.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

PubMed Central

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-01-01

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.10056.001 PMID:26705334

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PubMed Central

Baydil, Banu; Daley, William P.; Larsen, Melinda; Yener, Bülent

2012-01-01

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues. PMID:22403724

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets.

PubMed

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-04-01

Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

Does Geophysics Need "A new kind of Science"?

NASA Astrophysics Data System (ADS)

Turcotte, D. L.; Rundle, J. B.

2002-12-01

Stephen Wolfram's book "A New Kind of Science" has received a great deal of attention in the last six months, both positive and negative. The theme of the book is that "cellular automata", which arise from spatial and temporal coarse-graining of equations of motion, provide the foundations for a new nonlinear science of "complexity". The old science is the science of partial differential equations. Some of the major contributions of this old science have been in geophysics, i.e. gravity, magnetics, seismic waves, heat flow. The basis of the new science is the use of massive computing and numerical simulations. The new science is motivated by the observations that many physical systems display a vast multiplicity of space and time scales, and have hidden dynamics that in many cases are impossible to directly observe. An example would be molecular dynamics. Statistical physics derives continuum equations from the discrete interactions between atoms and molecules, in the modern world the continuum equations are then discretized using finite differences, finite elements, etc. in order to obtain numerical solutions. Examples of widely used cellular automata models include diffusion limited aggregation and site percolation. Also the class of models that are said to exhibit self-organized criticality, the sand-pile model, the slider-block model, the forest-fire model. Applications of these models include drainage networks, seismicity, distributions of minerals,and the evolution of landforms and coastlines. Simple cellular automata models generate deterministic chaos, i.e. the logistic map.

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph H.; Koch, Ina; Masoudi-Nejad, Ali

2014-01-01

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics. PMID:24822031

Cellular Ti-6Al-4V structures with interconnected macro porosity for bone implants fabricated by selective electron beam melting.

PubMed

Heinl, Peter; Müller, Lenka; Körner, Carolin; Singer, Robert F; Müller, Frank A

2008-09-01

Selective electron beam melting (SEBM) was successfully used to fabricate novel cellular Ti-6Al-4V structures for orthopaedic applications. Micro computer tomography (microCT) analysis demonstrated the capability to fabricate three-dimensional structures with an interconnected porosity and pore sizes suitable for tissue ingrowth and vascularization. Mechanical properties, such as compressive strength and elastic modulus, of the tested structures were similar to those of human bone. Thus, stress-shielding effects after implantation might be avoided due to a reduced stiffness mismatch between implant and bone. A chemical surface modification using HCl and NaOH induced apatite formation during in vitro bioactivity tests in simulated body fluid under dynamic conditions. The modified bioactive surface is expected to enhance the fixation of the implant in the surrounding bone as well as to improve its long-term stability.

Numerical simulation of deformation and fracture of space protective shell structures from concrete and fiber concrete under pulse loading

NASA Astrophysics Data System (ADS)

Radchenko, P. A.; Batuev, S. P.; Radchenko, A. V.; Plevkov, V. S.

2015-11-01

This paper presents results of numerical simulation of interaction between aircraft Boeing 747-400 and protective shell of nuclear power plant. The shell is presented as complex multilayered cellular structure comprising layers of concrete and fiber concrete bonded with steel trusses. Numerical simulation was held three-dimensionally using the author's algorithm and software taking into account algorithms for building grids of complex geometric objects and parallel computations. The dynamics of stress-strain state and fracture of structure were studied. Destruction is described using two-stage model that allows taking into account anisotropy of elastic and strength properties of concrete and fiber concrete. It is shown that wave processes initiate destruction of shell cellular structure—cells start to destruct in unloading wave, originating after output of compression wave to the free surfaces of cells.

Modeling of fracture of protective concrete structures under impact loads

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

Radchenko, P. A., E-mail: radchenko@live.ru; Batuev, S. P.; Radchenko, A. V.

This paper presents results of numerical simulation of interaction between a Boeing 747-400 aircraft and the protective shell of a nuclear power plant. The shell is presented as a complex multilayered cellular structure consisting of layers of concrete and fiber concrete bonded with steel trusses. Numerical simulation was performed three-dimensionally using the original algorithm and software taking into account algorithms for building grids of complex geometric objects and parallel computations. Dynamics of the stress-strain state and fracture of the structure were studied. Destruction is described using a two-stage model that allows taking into account anisotropy of elastic and strength propertiesmore » of concrete and fiber concrete. It is shown that wave processes initiate destruction of the cellular shell structure; cells start to destruct in an unloading wave originating after the compression wave arrival at free cell surfaces.« less

Computing aggregate properties of preimages for 2D cellular automata.

PubMed

Beer, Randall D

2017-11-01

Computing properties of the set of precursors of a given configuration is a common problem underlying many important questions about cellular automata. Unfortunately, such computations quickly become intractable in dimension greater than one. This paper presents an algorithm-incremental aggregation-that can compute aggregate properties of the set of precursors exponentially faster than naïve approaches. The incremental aggregation algorithm is demonstrated on two problems from the two-dimensional binary Game of Life cellular automaton: precursor count distributions and higher-order mean field theory coefficients. In both cases, incremental aggregation allows us to obtain new results that were previously beyond reach.

Computing aggregate properties of preimages for 2D cellular automata

NASA Astrophysics Data System (ADS)

Beer, Randall D.

2017-11-01

Computing properties of the set of precursors of a given configuration is a common problem underlying many important questions about cellular automata. Unfortunately, such computations quickly become intractable in dimension greater than one. This paper presents an algorithm—incremental aggregation—that can compute aggregate properties of the set of precursors exponentially faster than naïve approaches. The incremental aggregation algorithm is demonstrated on two problems from the two-dimensional binary Game of Life cellular automaton: precursor count distributions and higher-order mean field theory coefficients. In both cases, incremental aggregation allows us to obtain new results that were previously beyond reach.

Exploring GPCR-Lipid Interactions by Molecular Dynamics Simulations: Excitements, Challenges, and the Way Forward.

PubMed

Sengupta, Durba; Prasanna, Xavier; Mohole, Madhura; Chattopadhyay, Amitabha

2018-06-07

Gprotein-coupled receptors (GPCRs) are seven transmembrane receptors that mediate a large number of cellular responses and are important drug targets. One of the current challenges in GPCR biology is to analyze the molecular signatures of receptor-lipid interactions and their subsequent effects on GPCR structure, organization, and function. Molecular dynamics simulation studies have been successful in predicting molecular determinants of receptor-lipid interactions. In particular, predicted cholesterol interaction sites appear to correspond well with experimentally determined binding sites and estimated time scales of association. In spite of several success stories, the methodologies in molecular dynamics simulations are still emerging. In this Feature Article, we provide a comprehensive overview of coarse-grain and atomistic molecular dynamics simulations of GPCR-lipid interaction in the context of experimental observations. In addition, we discuss the effect of secondary and tertiary structural constraints in coarse-grain simulations in the context of functional dynamics and structural plasticity of GPCRs. We envision that this comprehensive overview will help resolve differences in computational studies and provide a way forward.

Japan's Widespread Use of Cellular Telephones To Access the Internet: Implications for Educational Telecommunications.

ERIC Educational Resources Information Center

Scott, Douglass J.

In the 3 years since their introduction, Internet-capable cellular telephones are used by over 47 million Japanese (37% of the population) which nearly equals the number of people using personal computers to access the Internet. If this trend continues, the cellular telephone will overtake the personal computer as the most widely used Internet…

Quantitative fluorescence correlation spectroscopy on DNA in living cells

NASA Astrophysics Data System (ADS)

Hodges, Cameron; Kafle, Rudra P.; Meiners, Jens-Christian

2017-02-01

FCS is a fluorescence technique conventionally used to study the kinetics of fluorescent molecules in a dilute solution. Being a non-invasive technique, it is now drawing increasing interest for the study of more complex systems like the dynamics of DNA or proteins in living cells. Unlike an ordinary dye solution, the dynamics of macromolecules like proteins or entangled DNA in crowded environments is often slow and subdiffusive in nature. This in turn leads to longer residence times of the attached fluorophores in the excitation volume of the microscope and artifacts from photobleaching abound that can easily obscure the signature of the molecular dynamics of interest and make quantitative analysis challenging.We discuss methods and procedures to make FCS applicable to quantitative studies of the dynamics of DNA in live prokaryotic and eukaryotic cells. The intensity autocorrelation is computed function from weighted arrival times of the photons on the detector that maximizes the information content while simultaneously correcting for the effect of photobleaching to yield an autocorrelation function that reflects only the underlying dynamics of the sample. This autocorrelation function in turn is used to calculate the mean square displacement of the fluorophores attached to DNA. The displacement data is more amenable to further quantitative analysis than the raw correlation functions. By using a suitable integral transform of the mean square displacement, we can then determine the viscoelastic moduli of the DNA in its cellular environment. The entire analysis procedure is extensively calibrated and validated using model systems and computational simulations.

Modeling biological pathway dynamics with timed automata.

PubMed

Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N

2014-05-01

Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

Computational studies of Ras and PI3K

NASA Technical Reports Server (NTRS)

Ren, Lei; Cucinotta, Francis A.

2004-01-01

Until recently, experimental techniques in molecular cell biology have been the primary means to investigate biological risk upon space radiation. However, computational modeling provides an alternative theoretical approach, which utilizes various computational tools to simulate proteins, nucleotides, and their interactions. In this study, we are focused on using molecular mechanics (MM) and molecular dynamics (MD) to study the mechanism of protein-protein binding and to estimate the binding free energy between proteins. Ras is a key element in a variety of cell processes, and its activation of phosphoinositide 3-kinase (PI3K) is important for survival of transformed cells. Different computational approaches for this particular study are presented to calculate the solvation energies and binding free energies of H-Ras and PI3K. The goal of this study is to establish computational methods to investigate the roles of different proteins played in the cellular responses to space radiation, including modification of protein function through gene mutation, and to support the studies in molecular cell biology and theoretical kinetics models for our risk assessment project.

A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations

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

Nomura, K; Seymour, R; Wang, W

2009-02-17

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based onmore » hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).« less

Wireless teleradiology and fax using cellular phones and notebook PCs for instant access to consultants.

PubMed

Yamamoto, L G

1995-03-01

The feasibility of wireless portable teleradiology and facsimile (fax) transmission using a pocket cellular phone and a notebook computer to obtain immediate access to consultants at any location was studied. Modems specially designed for data and fax communication via cellular systems were employed to provide a data communication interface between the cellular phone and the notebook computer. Computed tomography (CT) scans, X-rays, and electrocardiograms (ECGs) were transmitted to a wireless unit to measure performance characteristics. Data transmission rates ranged from 520 to 1100 bytes per second. Typical image transmission times ranged from 1 to 10 minutes; however, using joint photographic experts group or fractal image compression methods would shorten typical transmission times to less than one minute. This study showed that wireless teleradiology and fax over cellular communication systems are feasible with current technology. Routine immediate cellular faxing of ECGs to cardiologists may expedite thrombolytic therapy decisions in questionable cases. Routine immediate teleradiology of CT scans may reduce operation room preparation times in severe head trauma.

Canalization and control in automata networks: body segmentation in Drosophila melanogaster.

PubMed

Marques-Pita, Manuel; Rocha, Luis M

2013-01-01

We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). This way, our approach provides a method to link micro- to macro-level dynamics--a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster. We use this model to show that our analysis does not contradict any previous findings. But we also obtain new knowledge about its behaviour: a better understanding of the size of its wild-type attractor basin (larger than previously thought), the identification of novel minimal conditions and critical nodes that control wild-type behaviour, and the resilience of these to stochastic interventions. Our methodology is applicable to any complex network that can be modelled using automata, but we focus on biochemical regulation and signalling, towards a better understanding of the (decentralized) control that orchestrates cellular activity--with the ultimate goal of explaining how do cells and tissues 'compute'.

Mesoscale computational studies of membrane bilayer remodeling by curvature-inducing proteins

PubMed Central

Ramakrishnan, N.; Sunil Kumar, P. B.; Radhakrishnan, Ravi

2014-01-01

Biological membranes constitute boundaries of cells and cell organelles. These membranes are soft fluid interfaces whose thermodynamic states are dictated by bending moduli, induced curvature fields, and thermal fluctuations. Recently, there has been a flood of experimental evidence highlighting active roles for these structures in many cellular processes ranging from trafficking of cargo to cell motility. It is believed that the local membrane curvature, which is continuously altered due to its interactions with myriad proteins and other macromolecules attached to its surface, holds the key to the emergent functionality in these cellular processes. Mechanisms at the atomic scale are dictated by protein-lipid interaction strength, lipid composition, lipid distribution in the vicinity of the protein, shape and amino acid composition of the protein, and its amino acid contents. The specificity of molecular interactions together with the cooperativity of multiple proteins induce and stabilize complex membrane shapes at the mesoscale. These shapes span a wide spectrum ranging from the spherical plasma membrane to the complex cisternae of the Golgi apparatus. Mapping the relation between the protein-induced deformations at the molecular scale and the resulting mesoscale morphologies is key to bridging cellular experiments across the various length scales. In this review, we focus on the theoretical and computational methods used to understand the phenomenology underlying protein-driven membrane remodeling. Interactions at the molecular scale can be computationally probed by all atom and coarse grained molecular dynamics (MD, CGMD), as well as dissipative particle dynamics (DPD) simulations, which we only describe in passing. We choose to focus on several continuum approaches extending the Canham - Helfrich elastic energy model for membranes to include the effect of curvature-inducing proteins and explore the conformational phase space of such systems. In this description, the protein is expressed in the form of a spontaneous curvature field. The approaches include field theoretical methods limited to the small deformation regime, triangulated surfaces and particle-based computational models to investigate the large-deformation regimes observed in the natural state of many biological membranes. Applications of these methods to understand the properties of biological membranes in homogeneous and inhomogeneous environments of proteins, whose underlying curvature fields are either isotropic or anisotropic, are discussed. The diversity in the curvature fields elicits a rich variety of morphological states, including tubes, discs, branched tubes, and caveola. Mapping the thermodynamic stability of these states as a function of tuning parameters such as concentration and strength of curvature induction of the proteins is discussed. The relative stabilities of these self-organized shapes are examined through free-energy calculations. The suite of methods discussed here can be tailored to applications in specific cellular settings such as endocytosis during cargo trafficking and tubulation of filopodial structures in migrating cells, which makes these methods a powerful complement to experimental studies. PMID:25484487

Mesoscale computational studies of membrane bilayer remodeling by curvature-inducing proteins.

PubMed

Ramakrishnan, N; Sunil Kumar, P B; Radhakrishnan, Ravi

2014-10-01

Biological membranes constitute boundaries of cells and cell organelles. These membranes are soft fluid interfaces whose thermodynamic states are dictated by bending moduli, induced curvature fields, and thermal fluctuations. Recently, there has been a flood of experimental evidence highlighting active roles for these structures in many cellular processes ranging from trafficking of cargo to cell motility. It is believed that the local membrane curvature, which is continuously altered due to its interactions with myriad proteins and other macromolecules attached to its surface, holds the key to the emergent functionality in these cellular processes. Mechanisms at the atomic scale are dictated by protein-lipid interaction strength, lipid composition, lipid distribution in the vicinity of the protein, shape and amino acid composition of the protein, and its amino acid contents. The specificity of molecular interactions together with the cooperativity of multiple proteins induce and stabilize complex membrane shapes at the mesoscale. These shapes span a wide spectrum ranging from the spherical plasma membrane to the complex cisternae of the Golgi apparatus. Mapping the relation between the protein-induced deformations at the molecular scale and the resulting mesoscale morphologies is key to bridging cellular experiments across the various length scales. In this review, we focus on the theoretical and computational methods used to understand the phenomenology underlying protein-driven membrane remodeling. Interactions at the molecular scale can be computationally probed by all atom and coarse grained molecular dynamics (MD, CGMD), as well as dissipative particle dynamics (DPD) simulations, which we only describe in passing. We choose to focus on several continuum approaches extending the Canham - Helfrich elastic energy model for membranes to include the effect of curvature-inducing proteins and explore the conformational phase space of such systems. In this description, the protein is expressed in the form of a spontaneous curvature field. The approaches include field theoretical methods limited to the small deformation regime, triangulated surfaces and particle-based computational models to investigate the large-deformation regimes observed in the natural state of many biological membranes. Applications of these methods to understand the properties of biological membranes in homogeneous and inhomogeneous environments of proteins, whose underlying curvature fields are either isotropic or anisotropic, are discussed. The diversity in the curvature fields elicits a rich variety of morphological states, including tubes, discs, branched tubes, and caveola. Mapping the thermodynamic stability of these states as a function of tuning parameters such as concentration and strength of curvature induction of the proteins is discussed. The relative stabilities of these self-organized shapes are examined through free-energy calculations. The suite of methods discussed here can be tailored to applications in specific cellular settings such as endocytosis during cargo trafficking and tubulation of filopodial structures in migrating cells, which makes these methods a powerful complement to experimental studies.

Mesoscale computational studies of membrane bilayer remodeling by curvature-inducing proteins

NASA Astrophysics Data System (ADS)

Ramakrishnan, N.; Sunil Kumar, P. B.; Radhakrishnan, Ravi

2014-10-01

Biological membranes constitute boundaries of cells and cell organelles. These membranes are soft fluid interfaces whose thermodynamic states are dictated by bending moduli, induced curvature fields, and thermal fluctuations. Recently, there has been a flood of experimental evidence highlighting active roles for these structures in many cellular processes ranging from trafficking of cargo to cell motility. It is believed that the local membrane curvature, which is continuously altered due to its interactions with myriad proteins and other macromolecules attached to its surface, holds the key to the emergent functionality in these cellular processes. Mechanisms at the atomic scale are dictated by protein-lipid interaction strength, lipid composition, lipid distribution in the vicinity of the protein, shape and amino acid composition of the protein, and its amino acid contents. The specificity of molecular interactions together with the cooperativity of multiple proteins induce and stabilize complex membrane shapes at the mesoscale. These shapes span a wide spectrum ranging from the spherical plasma membrane to the complex cisternae of the Golgi apparatus. Mapping the relation between the protein-induced deformations at the molecular scale and the resulting mesoscale morphologies is key to bridging cellular experiments across various length scales. In this review, we focus on the theoretical and computational methods used to understand the phenomenology underlying protein-driven membrane remodeling. Interactions at the molecular scale can be computationally probed by all atom and coarse grained molecular dynamics (MD, CGMD), as well as dissipative particle dynamics (DPD) simulations, which we only describe in passing. We choose to focus on several continuum approaches extending the Canham-Helfrich elastic energy model for membranes to include the effect of curvature-inducing proteins and explore the conformational phase space of such systems. In this description, the protein is expressed in the form of a spontaneous curvature field. The approaches include field theoretical methods limited to the small deformation regime, triangulated surfaces and particle-based computational models to investigate the large-deformation regimes observed in the natural state of many biological membranes. Applications of these methods to understand the properties of biological membranes in homogeneous and inhomogeneous environments of proteins, whose underlying curvature fields are either isotropic or anisotropic, are discussed. The diversity in the curvature fields elicits a rich variety of morphological states, including tubes, discs, branched tubes, and caveola. Mapping the thermodynamic stability of these states as a function of tuning parameters such as concentration and strength of curvature induction of the proteins is discussed. The relative stabilities of these self-organized shapes are examined through free-energy calculations. The suite of methods discussed here can be tailored to applications in specific cellular settings such as endocytosis during cargo trafficking and tubulation of filopodial structures in migrating cells, which makes these methods a powerful complement to experimental studies.

The Virtual Cell Animation Collection: Tools for Teaching Molecular and Cellular Biology

PubMed Central

Reindl, Katie M.; White, Alan R.; Johnson, Christina; Vender, Bradley; Slator, Brian M.; McClean, Phillip

2015-01-01

A cell is a minifactory in which structures and molecules are assembled, rearranged, disassembled, packaged, sorted, and transported. Because cellular structures and molecules are invisible to the human eye, students often have difficulty conceptualizing the dynamic nature of cells that function at multiple scales across time and space. To represent these dynamic cellular processes, the Virtual Cell Productions team at North Dakota State University develops freely available multimedia materials to support molecular and cellular biology learning inside and outside the high school and university classroom. PMID:25856580

Theoretical and Computational Studies of Peptides and Receptors of the Insulin Family

PubMed Central

Vashisth, Harish

2015-01-01

Synergistic interactions among peptides and receptors of the insulin family are required for glucose homeostasis, normal cellular growth and development, proliferation, differentiation and other metabolic processes. The peptides of the insulin family are disulfide-linked single or dual-chain proteins, while receptors are ligand-activated transmembrane glycoproteins of the receptor tyrosine kinase (RTK) superfamily. Binding of ligands to the extracellular domains of receptors is known to initiate signaling via activation of intracellular kinase domains. While the structure of insulin has been known since 1969, recent decades have seen remarkable progress on the structural biology of apo and liganded receptor fragments. Here, we review how this useful structural information (on ligands and receptors) has enabled large-scale atomically-resolved simulations to elucidate the conformational dynamics of these biomolecules. Particularly, applications of molecular dynamics (MD) and Monte Carlo (MC) simulation methods are discussed in various contexts, including studies of isolated ligands, apo-receptors, ligand/receptor complexes and intracellular kinase domains. The review concludes with a brief overview and future outlook for modeling and computational studies in this family of proteins. PMID:25680077

The bioelectric code: An ancient computational medium for dynamic control of growth and form.

PubMed

Levin, Michael; Martyniuk, Christopher J

2018-02-01

What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

A SIMPLE CELLULAR AUTOMATON MODEL FOR HIGH-LEVEL VEGETATION DYNAMICS

EPA Science Inventory

We have produced a simple two-dimensional (ground-plan) cellular automata model of vegetation dynamics specifically to investigate high-level community processes. The model is probabilistic, with individual plant behavior determined by physiologically-based rules derived from a w...

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

PubMed Central

2010-01-01

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system. Results In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Conclusions Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics. PMID:21143785

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

PubMed

Ghosh, Preetam; Ghosh, Samik; Basu, Kalyan; Das, Sajal K; Zhang, Chaoyang

2010-12-01

The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.

A Mathematical Model to study the Dynamics of Epithelial Cellular Networks

PubMed Central

Abate, Alessandro; Vincent, Stéphane; Dobbe, Roel; Silletti, Alberto; Master, Neal; Axelrod, Jeffrey D.; Tomlin, Claire J.

2013-01-01

Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction). PMID:23221083

Cholinergic modulation of cognitive processing: insights drawn from computational models

PubMed Central

Newman, Ehren L.; Gupta, Kishan; Climer, Jason R.; Monaghan, Caitlin K.; Hasselmo, Michael E.

2012-01-01

Acetylcholine plays an important role in cognitive function, as shown by pharmacological manipulations that impact working memory, attention, episodic memory, and spatial memory function. Acetylcholine also shows striking modulatory influences on the cellular physiology of hippocampal and cortical neurons. Modeling of neural circuits provides a framework for understanding how the cognitive functions may arise from the influence of acetylcholine on neural and network dynamics. We review the influences of cholinergic manipulations on behavioral performance in working memory, attention, episodic memory, and spatial memory tasks, the physiological effects of acetylcholine on neural and circuit dynamics, and the computational models that provide insight into the functional relationships between the physiology and behavior. Specifically, we discuss the important role of acetylcholine in governing mechanisms of active maintenance in working memory tasks and in regulating network dynamics important for effective processing of stimuli in attention and episodic memory tasks. We also propose that theta rhythm plays a crucial role as an intermediary between the physiological influences of acetylcholine and behavior in episodic and spatial memory tasks. We conclude with a synthesis of the existing modeling work and highlight future directions that are likely to be rewarding given the existing state of the literature for both empiricists and modelers. PMID:22707936

Multiscale Modeling of Cardiac Cellular Energetics

PubMed Central

BASSINGTHWAIGHTE, JAMES B.; CHIZECK, HOWARD J.; ATLAS, LES E.; QIAN, HONG

2010-01-01

Multiscale modeling is essential to integrating knowledge of human physiology starting from genomics, molecular biology, and the environment through the levels of cells, tissues, and organs all the way to integrated systems behavior. The lowest levels concern biophysical and biochemical events. The higher levels of organization in tissues, organs, and organism are complex, representing the dynamically varying behavior of billions of cells interacting together. Models integrating cellular events into tissue and organ behavior are forced to resort to simplifications to minimize computational complexity, thus reducing the model’s ability to respond correctly to dynamic changes in external conditions. Adjustments at protein and gene regulatory levels shortchange the simplified higher-level representations. Our cell primitive is composed of a set of subcellular modules, each defining an intracellular function (action potential, tricarboxylic acid cycle, oxidative phosphorylation, glycolysis, calcium cycling, contraction, etc.), composing what we call the “eternal cell,” which assumes that there is neither proteolysis nor protein synthesis. Within the modules are elements describing each particular component (i.e., enzymatic reactions of assorted types, transporters, ionic channels, binding sites, etc.). Cell subregions are stirred tanks, linked by diffusional or transporter-mediated exchange. The modeling uses ordinary differential equations rather than stochastic or partial differential equations. This basic model is regarded as a primitive upon which to build models encompassing gene regulation, signaling, and long-term adaptations in structure and function. During simulation, simpler forms of the model are used, when possible, to reduce computation. However, when this results in error, the more complex and detailed modules and elements need to be employed to improve model realism. The processes of error recognition and of mapping between different levels of model form complexity are challenging but are essential for successful modeling of large-scale systems in reasonable time. Currently there is to this end no established methodology from computational sciences. PMID:16093514

Using ToxCast™ Data to Reconstruct Dynamic Cell State Trajectories and Estimate Toxicological Points of Departure

PubMed Central

Shah, Imran; Setzer, R. Woodrow; Jack, John; Houck, Keith A.; Judson, Richard S.; Knudsen, Thomas B.; Liu, Jie; Martin, Matthew T.; Reif, David M.; Richard, Ann M.; Thomas, Russell S.; Crofton, Kevin M.; Dix, David J.; Kavlock, Robert J.

2015-01-01

Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 μM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 μM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. Citation: Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB, Liu J, Martin MT, Reif DM, Richard AM, Thomas RS, Crofton KM, Dix DJ, Kavlock RJ. 2016. Using ToxCast™ data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. Environ Health Perspect 124:910–919; http://dx.doi.org/10.1289/ehp.1409029 PMID:26473631

De novo inference of protein function from coarse-grained dynamics.

PubMed

Bhadra, Pratiti; Pal, Debnath

2014-10-01

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 µs coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. © 2014 Wiley Periodicals, Inc.

In silico multi-scale model of transport and dynamic seeding in a bone tissue engineering perfusion bioreactor.

PubMed

Spencer, T J; Hidalgo-Bastida, L A; Cartmell, S H; Halliday, I; Care, C M

2013-04-01

Computer simulations can potentially be used to design, predict, and inform properties for tissue engineering perfusion bioreactors. In this work, we investigate the flow properties that result from a particular poly-L-lactide porous scaffold and a particular choice of perfusion bioreactor vessel design used in bone tissue engineering. We also propose a model to investigate the dynamic seeding properties such as the homogeneity (or lack of) of the cellular distribution within the scaffold of the perfusion bioreactor: a pre-requisite for the subsequent successful uniform growth of a viable bone tissue engineered construct. Flows inside geometrically complex scaffolds have been investigated previously and results shown at these pore scales. Here, it is our aim to show accurately that through the use of modern high performance computers that the bioreactor device scale that encloses a scaffold can affect the flows and stresses within the pores throughout the scaffold which has implications for bioreactor design, control, and use. Central to this work is that the boundary conditions are derived from micro computed tomography scans of both a device chamber and scaffold in order to avoid generalizations and uncertainties. Dynamic seeding methods have also been shown to provide certain advantages over static seeding methods. We propose here a novel coupled model for dynamic seeding accounting for flow, species mass transport and cell advection-diffusion-attachment tuned for bone tissue engineering. The model highlights the timescale differences between different species suggesting that traditional homogeneous porous flow models of transport must be applied with caution to perfusion bioreactors. Our in silico data illustrate the extent to which these experiments have the potential to contribute to future design and development of large-scale bioreactors. Copyright © 2012 Wiley Periodicals, Inc.

Better, Cheaper, Faster Molecular Dynamics

NASA Technical Reports Server (NTRS)

Pohorille, Andrew; DeVincenzi, Donald L. (Technical Monitor)

2001-01-01

Recent, revolutionary progress in genomics and structural, molecular and cellular biology has created new opportunities for molecular-level computer simulations of biological systems by providing vast amounts of data that require interpretation. These opportunities are further enhanced by the increasing availability of massively parallel computers. For many problems, the method of choice is classical molecular dynamics (iterative solving of Newton's equations of motion). It focuses on two main objectives. One is to calculate the relative stability of different states of the system. A typical problem that has' such an objective is computer-aided drug design. Another common objective is to describe evolution of the system towards a low energy (possibly the global minimum energy), "native" state. Perhaps the best example of such a problem is protein folding. Both types of problems share the same difficulty. Often, different states of the system are separated by high energy barriers, which implies that transitions between these states are rare events. This, in turn, can greatly impede exploration of phase space. In some instances this can lead to "quasi non-ergodicity", whereby a part of phase space is inaccessible on time scales of the simulation. To overcome this difficulty and to extend molecular dynamics to "biological" time scales (millisecond or longer) new physical formulations and new algorithmic developments are required. To be efficient they should account for natural limitations of multi-processor computer architecture. I will present work along these lines done in my group. In particular, I will focus on a new approach to calculating the free energies (stability) of different states and to overcoming "the curse of rare events". I will also discuss algorithmic improvements to multiple time step methods and to the treatment of slowly decaying, log-ranged, electrostatic effects.

Physical biology of human brain development.

PubMed

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2015-01-01

Neurodevelopment is a complex, dynamic process that involves a precisely orchestrated sequence of genetic, environmental, biochemical, and physical events. Developmental biology and genetics have shaped our understanding of the molecular and cellular mechanisms during neurodevelopment. Recent studies suggest that physical forces play a central role in translating these cellular mechanisms into the complex surface morphology of the human brain. However, the precise impact of neuronal differentiation, migration, and connection on the physical forces during cortical folding remains unknown. Here we review the cellular mechanisms of neurodevelopment with a view toward surface morphogenesis, pattern selection, and evolution of shape. We revisit cortical folding as the instability problem of constrained differential growth in a multi-layered system. To identify the contributing factors of differential growth, we map out the timeline of neurodevelopment in humans and highlight the cellular events associated with extreme radial and tangential expansion. We demonstrate how computational modeling of differential growth can bridge the scales-from phenomena on the cellular level toward form and function on the organ level-to make quantitative, personalized predictions. Physics-based models can quantify cortical stresses, identify critical folding conditions, rationalize pattern selection, and predict gyral wavelengths and gyrification indices. We illustrate that physical forces can explain cortical malformations as emergent properties of developmental disorders. Combining biology and physics holds promise to advance our understanding of human brain development and enable early diagnostics of cortical malformations with the ultimate goal to improve treatment of neurodevelopmental disorders including epilepsy, autism spectrum disorders, and schizophrenia.

An Efficient and Imperfect Model for Gravel-Bed Braided River Morphodynamics: Numerical Simulations as Exploratory Tools

NASA Astrophysics Data System (ADS)

Kasprak, A.; Brasington, J.; Hafen, K.; Wheaton, J. M.

2015-12-01

Numerical models that predict channel evolution through time are an essential tool for investigating processes that occur over timescales which render field observation intractable. However, available morphodynamic models generally take one of two approaches to the complex problem of computing morphodynamics, resulting in oversimplification of the relevant physics (e.g. cellular models) or faithful, yet computationally intensive, representations of the hydraulic and sediment transport processes at play. The practical implication of these approaches is that river scientists must often choose between unrealistic results, in the case of the former, or computational demands that render modeling realistic spatiotemporal scales of channel evolution impossible. Here we present a new modeling framework that operates at the timescale of individual competent flows (e.g. floods), and uses a highly-simplified sediment transport routine that moves volumes of material according to morphologically-derived characteristic transport distances, or path lengths. Using this framework, we have constructed an open-source morphodynamic model, termed MoRPHED, which is here applied, and its validity investigated, at timescales ranging from a single event to a decade on two braided rivers in the UK and New Zealand. We do not purport that MoRPHED is the best, nor even an adequate, tool for modeling braided river dynamics at this range of timescales. Rather, our goal in this research is to explore the utility, feasibility, and sensitivity of an event-scale, path-length-based modeling framework for predicting braided river dynamics. To that end, we further explore (a) which processes are naturally emergent and which must be explicitly parameterized in the model, (b) the sensitivity of the model to the choice of particle travel distance, and (c) whether an event-scale model timestep is adequate for producing braided channel dynamics. The results of this research may inform techniques for future morphodynamic modeling that seeks to maximize computational resources while modeling fluvial dynamics at the timescales of change.

Dynamic behavior of cellular materials and cellular structures: Experiments and modeling

NASA Astrophysics Data System (ADS)

Gao, Ziyang

Cellular solids, including cellular materials and cellular structures (CMS), have attracted people's great interests because of their low densities and novel physical, mechanical, thermal, electrical and acoustic properties. They offer potential for lightweight structures, energy absorption, thermal management, etc. Therefore, the studies of cellular solids have become one of the hottest research fields nowadays. From energy absorption point of view, any plastically deformed structures can be divided into two types (called type I and type II), and the basic cells of the CMS may take the configurations of these two types of structures. Accordingly, separated discussions are presented in this thesis. First, a modified 1-D model is proposed and numerically solved for a typical type II structure. Good agreement is achieved with the previous experimental data, hence is used to simulate the dynamic behavior of a type II chain. Resulted from different load speeds, interesting collapse modes are observed, and the parameters which govern the cell's post-collapse behavior are identified through a comprehensive non-dimensional analysis on general cellular chains. Secondly, the MHS specimens are chosen as an example of type I foam materials because of their good uniformity of the cell geometry. An extensive experimental study was carried out, where more attention was paid to their responses to dynamic loadings. Great enhancement of the stress-strain curve was observed in dynamic cases, and the energy absorption capacity is found to be several times higher than that of the commercial metal foams. Based on the experimental study, finite elemental simulations and theoretical modeling are also conducted, achieving good agreements and demonstrating the validities of those models. It is believed that the experimental, numerical and analytical results obtained in the present study will certainly deepen the understanding of the unsolved fundamental issues on the mechanical behavior of cellular solids and make substantial contributions to the theoretical advance of impact dynamics.

Coordination of Cellular Dynamics Contributes to Tooth Epithelium Deformations

PubMed Central

Morita, Ritsuko; Kihira, Miho; Nakatsu, Yousuke; Nomoto, Yohei; Ogawa, Miho; Ohashi, Kazumasa; Mizuno, Kensaku; Tachikawa, Tetsuhiko; Ishimoto, Yukitaka; Morishita, Yoshihiro; Tsuji, Takashi

2016-01-01

The morphologies of ectodermal organs are shaped by appropriate combinations of several deformation modes, such as invagination and anisotropic tissue elongation. However, how multicellular dynamics are coordinated during deformation processes remains to be elucidated. Here, we developed a four-dimensional (4D) analysis system for tracking cell movement and division at a single-cell resolution in developing tooth epithelium. The expression patterns of a Fucci probe clarified the region- and stage-specific cell cycle patterns within the tooth germ, which were in good agreement with the pattern of the volume growth rate estimated from tissue-level deformation analysis. Cellular motility was higher in the regions with higher growth rates, while the mitotic orientation was significantly biased along the direction of tissue elongation in the epithelium. Further, these spatio-temporal patterns of cellular dynamics and tissue-level deformation were highly correlated with that of the activity of cofilin, which is an actin depolymerization factor, suggesting that the coordination of cellular dynamics via actin remodeling plays an important role in tooth epithelial morphogenesis. Our system enhances the understanding of how cellular behaviors are coordinated during ectodermal organogenesis, which cannot be observed from histological analyses. PMID:27588418

Cellular dynamics of bovine aortic smooth muscle cells measured using MEMS force sensors

NASA Astrophysics Data System (ADS)

Tsukagoshi, Takuya; Nguyen, Thanh-Vinh; Hirayama Shoji, Kayoko; Takahashi, Hidetoshi; Matsumoto, Kiyoshi; Shimoyama, Isao

2018-04-01

Adhesive cells perceive the mechanical properties of the substrates to which they adhere, adjusting their cellular mechanical forces according to their biological characteristics. This mechanical interaction subsequently affects the growth, locomotion, and differentiation of the cell. However, little is known about the detailed mechanism that underlies this interaction between adherent cells and substrates because dynamically measuring mechanical phenomena is difficult. Here, we utilize microelectromechamical systems force sensors that can measure cellular traction forces with high temporal resolution (~2.5 µs) over long periods (~3 h). We found that the cellular dynamics reflected physical phenomena with time scales from milliseconds to hours, which contradicts the idea that cellular motion is slow. A single focal adhesion (FA) generates an average force of 7 nN, which disappears in ms via the action of trypsin-ethylenediaminetetraacetic acid. The force-changing rate obtained from our measurements suggests that the time required for an FA to decompose was nearly proportional to the force acting on the FA.

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

Bellesia, Giovanni; Bales, Benjamin B.

Here, we investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been “extended” and considered as a prototype reaction-diffusion system. These results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatialmore » stochastic simulation methods for the study of biochemical networks in vivo where the “well-mixed” approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.« less

Mesoscopic modeling for nucleic acid chain dynamics

PubMed Central

Sales-Pardo, M.; Guimerà, R.; Moreira, A. A.; Widom, J.; Amaral, L. A. N.

2007-01-01

To gain a deeper insight into cellular processes such as transcription and translation, one needs to uncover the mechanisms controlling the configurational changes of nucleic acids. As a step toward this aim, we present here a mesoscopic-level computational model that provides a new window into nucleic acid dynamics. We model a single-stranded nucleic as a polymer chain whose monomers are the nucleosides. Each monomer comprises a bead representing the sugar molecule and a pin representing the base. The bead-pin complex can rotate about the backbone of the chain. We consider pairwise stacking and hydrogen-bonding interactions. We use a modified Monte Carlo dynamics that splits the dynamics into translational bead motion and rotational pin motion. By performing a number of tests, we first show that our model is physically sound. We then focus on a study of the kinetics of a DNA hairpin—a single-stranded molecule comprising two complementary segments joined by a noncomplementary loop—studied experimentally. We find that results from our simulations agree with experimental observations, demonstrating that our model is a suitable tool for the investigation of the hybridization of single strands. PMID:16089566

Plate-impact loading of cellular structures formed by selective laser melting

NASA Astrophysics Data System (ADS)

Winter, R. E.; Cotton, M.; Harris, E. J.; Maw, J. R.; Chapman, D. J.; Eakins, D. E.; McShane, G.

2014-03-01

Porous materials are of great interest because of improved energy absorption over their solid counterparts. Their properties, however, have been difficult to optimize. Additive manufacturing has emerged as a potential technique to closely define the structure and properties of porous components, i.e. density, strut width and pore size; however, the behaviour of these materials at very high impact energies remains largely unexplored. We describe an initial study of the dynamic compression response of lattice materials fabricated through additive manufacturing. Lattices consisting of an array of intersecting stainless steel rods were fabricated into discs using selective laser melting. The resulting discs were impacted against solid stainless steel targets at velocities ranging from 300 to 700 m s-1 using a gas gun. Continuum CTH simulations were performed to identify key features in the measured wave profiles, while 3D simulations, in which the individual cells were modelled, revealed details of microscale deformation during collapse of the lattice structure. The validated computer models have been used to provide an understanding of the deformation processes in the cellular samples. The study supports the optimization of cellular structures for application as energy absorbers.

Dirac Cellular Automaton from Split-step Quantum Walk

PubMed Central

Mallick, Arindam; Chandrashekar, C. M.

2016-01-01

Simulations of one quantum system by an other has an implication in realization of quantum machine that can imitate any quantum system and solve problems that are not accessible to classical computers. One of the approach to engineer quantum simulations is to discretize the space-time degree of freedom in quantum dynamics and define the quantum cellular automata (QCA), a local unitary update rule on a lattice. Different models of QCA are constructed using set of conditions which are not unique and are not always in implementable configuration on any other system. Dirac Cellular Automata (DCA) is one such model constructed for Dirac Hamiltonian (DH) in free quantum field theory. Here, starting from a split-step discrete-time quantum walk (QW) which is uniquely defined for experimental implementation, we recover the DCA along with all the fine oscillations in position space and bridge the missing connection between DH-DCA-QW. We will present the contribution of the parameters resulting in the fine oscillations on the Zitterbewegung frequency and entanglement. The tuneability of the evolution parameters demonstrated in experimental implementation of QW will establish it as an efficient tool to design quantum simulator and approach quantum field theory from principles of quantum information theory. PMID:27184159

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity.

PubMed

Papaleo, Elena

2015-01-01

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

PubMed Central

He, Fei; Murabito, Ettore; Westerhoff, Hans V.

2016-01-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. PMID:27075000

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

NASA Astrophysics Data System (ADS)

Tayar, Alexandra M.; Karzbrun, Eyal; Noireaux, Vincent; Bar-Ziv, Roy H.

2015-12-01

Living systems employ front propagation and spatiotemporal patterns encoded in biochemical reactions for communication, self-organization and computation. Emulating such dynamics in minimal systems is important for understanding physical principles in living cells and in vitro. Here, we report a one-dimensional array of DNA compartments in a silicon chip as a coupled system of artificial cells, offering the means to implement reaction-diffusion dynamics by integrated genetic circuits and chip geometry. Using a bistable circuit we programmed a front of protein synthesis propagating in the array as a cascade of signal amplification and short-range diffusion. The front velocity is maximal at a saddle-node bifurcation from a bistable regime with travelling fronts to a monostable regime that is spatially homogeneous. Near the bifurcation the system exhibits large variability between compartments, providing a possible mechanism for population diversity. This demonstrates that on-chip integrated gene circuits are dynamical systems driving spatiotemporal patterns, cellular variability and symmetry breaking.

Population dynamics, information transfer, and spatial organization in a chemical reaction network under spatial confinement and crowding conditions

DOE PAGES

Bellesia, Giovanni; Bales, Benjamin B.

2016-10-10

Here, we investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been “extended” and considered as a prototype reaction-diffusion system. These results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatialmore » stochastic simulation methods for the study of biochemical networks in vivo where the “well-mixed” approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.« less

Structural and Dynamic Insights into the Mechanism of Allosteric Signal Transmission in ERK2-Mediated MKP3 Activation.

PubMed

Lu, Chang; Liu, Xin; Zhang, Chen-Song; Gong, Haipeng; Wu, Jia-Wei; Wang, Zhi-Xin

2017-11-21

The mitogen-activated protein kinases (MAPKs) are key components of cellular signal transduction pathways, which are down-regulated by the MAPK phosphatases (MKPs). Catalytic activity of the MKPs is controlled both by their ability to recognize selective MAPKs and by allosteric activation upon binding to MAPK substrates. Here, we use a combination of experimental and computational techniques to elucidate the molecular mechanism for the ERK2-induced MKP3 activation. Mutational and kinetic study shows that the 334 FNFM 337 motif in the MKP3 catalytic domain is essential for MKP3-mediated ERK2 inactivation and is responsible for ERK2-mediated MKP3 activation. The long-term molecular dynamics (MD) simulations further reveal a complete dynamic process in which the catalytic domain of MKP3 gradually changes to a conformation that resembles an active MKP catalytic domain over the time scale of the simulation, providing a direct time-dependent observation of allosteric signal transmission in ERK2-induced MKP3 activation.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation.

PubMed

Fallahi-Sichani, Mohammad; El-Kebir, Mohammed; Marino, Simeone; Kirschner, Denise E; Linderman, Jennifer J

2011-03-15

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.

Red blood cell dynamics: from cell deformation to ATP release.

PubMed

Wan, Jiandi; Forsyth, Alison M; Stone, Howard A

2011-10-01

The mechanisms of red blood cell (RBC) deformation under both static and dynamic, i.e., flow, conditions have been studied extensively since the mid 1960s. Deformation-induced biochemical reactions and possible signaling in RBCs, however, were proposed only fifteen years ago. Therefore, the fundamental relationship between RBC deformation and cellular signaling dynamics i.e., mechanotransduction, remains incompletely understood. Quantitative understanding of the mechanotransductive pathways in RBCs requires integrative studies of physical models of RBC deformation and cellular biochemical reactions. In this article we review the physical models of RBC deformation, spanning from continuum membrane mechanics to cellular skeleton dynamics under both static and flow conditions, and elaborate the mechanistic links involved in deformation-induced ATP release. This journal is © The Royal Society of Chemistry 2011

Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

PubMed Central

Chen, Min; Hao, Yixue; Qiu, Meikang; Song, Jeungeun; Wu, Di; Humar, Iztok

2016-01-01

Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user’s quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities. PMID:27347975

Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks.

PubMed

Chen, Min; Hao, Yixue; Qiu, Meikang; Song, Jeungeun; Wu, Di; Humar, Iztok

2016-06-25

Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user's quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities.

Discrete dynamic modeling of cellular signaling networks.

PubMed

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Structural Plasticity and Conformational Transitions of HIV Envelope Glycoprotein gp120

PubMed Central

Korkut, Anil; Hendrickson, Wayne A.

2012-01-01

HIV envelope glycoproteins undergo large-scale conformational changes as they interact with cellular receptors to cause the fusion of viral and cellular membranes that permits viral entry to infect targeted cells. Conformational dynamics in HIV gp120 are also important in masking conserved receptor epitopes from being detected for effective neutralization by the human immune system. Crystal structures of HIV gp120 and its complexes with receptors and antibody fragments provide high-resolution pictures of selected conformational states accessible to gp120. Here we describe systematic computational analyses of HIV gp120 plasticity in such complexes with CD4 binding fragments, CD4 mimetic proteins, and various antibody fragments. We used three computational approaches: an isotropic elastic network analysis of conformational plasticity, a full atomic normal mode analysis, and simulation of conformational transitions with our coarse-grained virtual atom molecular mechanics (VAMM) potential function. We observe collective sub-domain motions about hinge points that coordinate those motions, correlated local fluctuations at the interfacial cavity formed when gp120 binds to CD4, and concerted changes in structural elements that form at the CD4 interface during large-scale conformational transitions to the CD4-bound state from the deformed states of gp120 in certain antibody complexes. PMID:23300605

Real-Time Kinetic Modeling of Voltage-Gated Ion Channels Using Dynamic Clamp

PubMed Central

Milescu, Lorin S.; Yamanishi, Tadashi; Ptak, Krzysztof; Mogri, Murtaza Z.; Smith, Jeffrey C.

2008-01-01

We propose what to our knowledge is a new technique for modeling the kinetics of voltage-gated ion channels in a functional context, in neurons or other excitable cells. The principle is to pharmacologically block the studied channel type, and to functionally replace it with dynamic clamp, on the basis of a computational model. Then, the parameters of the model are modified in real time (manually or automatically), with the objective of matching the dynamical behavior of the cell (e.g., action potential shape and spiking frequency), but also the transient and steady-state properties of the model (e.g., those derived from voltage-clamp recordings). Through this approach, one may find a model and parameter values that explain both the observed cellular dynamics and the biophysical properties of the channel. We extensively tested the method, focusing on Nav models. Complex Markov models (10–12 states or more) could be accurately integrated in real time at >50 kHz using the transition probability matrix, but not the explicit Euler method. The practicality of the technique was tested with experiments in raphe pacemaker neurons. Through automated real-time fitting, a Hodgkin-Huxley model could be found that reproduced well the action potential shape and the spiking frequency. Adding a virtual axonal compartment with a high density of Nav channels further improved the action potential shape. The computational procedure was implemented in the free QuB software, running under Microsoft Windows and featuring a friendly graphical user interface. PMID:18375511

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

Dynamics of proteins aggregation. II. Dynamic scaling in confined media

NASA Astrophysics Data System (ADS)

Zheng, Size; Shing, Katherine S.; Sahimi, Muhammad

2018-03-01

In this paper, the second in a series devoted to molecular modeling of protein aggregation, a mesoscale model of proteins together with extensive discontinuous molecular dynamics simulation is used to study the phenomenon in a confined medium. The medium, as a model of a crowded cellular environment, is represented by a spherical cavity, as well as cylindrical tubes with two aspect ratios. The aggregation process leads to the formation of β sheets and eventually fibrils, whose deposition on biological tissues is believed to be a major factor contributing to many neuro-degenerative diseases, such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis diseases. Several important properties of the aggregation process, including dynamic evolution of the total number of the aggregates, the mean aggregate size, and the number of peptides that contribute to the formation of the β sheets, have been computed. We show, similar to the unconfined media studied in Paper I [S. Zheng et al., J. Chem. Phys. 145, 134306 (2016)], that the computed properties follow dynamic scaling, characterized by power laws. The existence of such dynamic scaling in unconfined media was recently confirmed by experiments. The exponents that characterize the power-law dependence on time of the properties of the aggregation process in spherical cavities are shown to agree with those in unbounded fluids at the same protein density, while the exponents for aggregation in the cylindrical tubes exhibit sensitivity to the geometry of the system. The effects of the number of amino acids in the protein, as well as the size of the confined media, have also been studied. Similarities and differences between aggregation in confined and unconfined media are described, including the possibility of no fibril formation, if confinement is severe.

Single-Cell RNA Sequencing of Human T Cells.

PubMed

Villani, Alexandra-Chloé; Shekhar, Karthik

2017-01-01

Understanding how populations of human T cells leverage cellular heterogeneity, plasticity, and diversity to achieve a wide range of functional flexibility, particularly during dynamic processes such as development, differentiation, and antigenic response, is a core challenge that is well suited for single-cell analysis. Hypothesis-free evaluation of cellular states and subpopulations by transcriptional profiling of single T cells can identify relationships that may be obscured by targeted approaches such as FACS sorting on cell-surface antigens, or bulk expression analysis. While this approach is relevant to all cell types, it is of particular interest in the study of T cells for which classical phenotypic criteria are now viewed as insufficient for distinguishing different T cell subtypes and transitional states, and defining the changes associated with dysfunctional T cell states in autoimmunity and tumor-related exhaustion. This unit describes a protocol to generate single-cell transcriptomic libraries of human blood CD4 + and CD8 + T cells, and also introduces the basic bioinformatic steps to process the resulting sequence data for further computational analysis. We show how cellular subpopulations can be identified from transcriptional data, and derive characteristic gene expression signatures that distinguish these states. We believe single-cell RNA-seq is a powerful technique to study the cellular heterogeneity in complex tissues, a paradigm that will be of great value for the immune system.

Cellular dynamical mechanisms for encoding the time and place of events along spatiotemporal trajectories in episodic memory.

PubMed

Hasselmo, Michael E; Giocomo, Lisa M; Brandon, Mark P; Yoshida, Motoharu

2010-12-31

Understanding the mechanisms of episodic memory requires linking behavioral data and lesion effects to data on the dynamics of cellular membrane potentials and population interactions within brain regions. Linking behavior to specific membrane channels and neurochemicals has implications for therapeutic applications. Lesions of the hippocampus, entorhinal cortex and subcortical nuclei impair episodic memory function in humans and animals, and unit recording data from these regions in behaving animals indicate episodic memory processes. Intracellular recording in these regions demonstrates specific cellular properties including resonance, membrane potential oscillations and bistable persistent spiking that could underlie the encoding and retrieval of episodic trajectories. A model presented here shows how intrinsic dynamical properties of neurons could mediate the encoding of episodic memories as complex spatiotemporal trajectories. The dynamics of neurons allow encoding and retrieval of unique episodic trajectories in multiple continuous dimensions including temporal intervals, personal location, the spatial coordinates and sensory features of perceived objects and generated actions, and associations between these elements. The model also addresses how cellular dynamics could underlie unit firing data suggesting mechanisms for coding continuous dimensions of space, time, sensation and action. Copyright © 2010 Elsevier B.V. All rights reserved.

Cellular dynamical mechanisms for encoding the time and place of events along spatiotemporal trajectories in episodic memory

PubMed Central

Hasselmo, Michael E.; Giocomo, Lisa M.; Yoshida, Motoharu

2010-01-01

Understanding the mechanisms of episodic memory requires linking behavioural data and lesion effects to data on the dynamics of cellular membrane potentials and population interactions within these brain regions. Linking behavior to specific membrane channels and neurochemicals has implications for therapeutic applications. Lesions of the hippocampus, entorhinal cortex and subcortical nuclei impair episodic memory function in humans and animals, and unit recording data from these regions in behaving animals indicate episodic memory processes. Intracellular recording in these regions demonstrates specific cellular properties including resonance, membrane potential oscillations and bistable persistent spiking that could underlie the encoding and retrieval of episodic trajectories. A model presented here shows how intrinsic dynamical properties of neurons could mediate the encoding of episodic memories as complex spatiotemporal trajectories. The dynamics of neurons allow encoding and retrieval of unique episodic trajectories in multiple continuous dimensions including temporal intervals, personal location, the spatial coordinates and sensory features of perceived objects and generated actions, and associations between these elements. The model also addresses how cellular dynamics could underlie unit firing data suggesting mechanisms for coding continuous dimensions of space, time, sensation and action. PMID:20018213

Computer vision in cell biology.

PubMed

Danuser, Gaudenz

2011-11-23

Computer vision refers to the theory and implementation of artificial systems that extract information from images to understand their content. Although computers are widely used by cell biologists for visualization and measurement, interpretation of image content, i.e., the selection of events worth observing and the definition of what they mean in terms of cellular mechanisms, is mostly left to human intuition. This Essay attempts to outline roles computer vision may play and should play in image-based studies of cellular life. Copyright © 2011 Elsevier Inc. All rights reserved.

Exploring the concept of interaction computing through the discrete algebraic analysis of the Belousov-Zhabotinsky reaction.

PubMed

Dini, Paolo; Nehaniv, Chrystopher L; Egri-Nagy, Attila; Schilstra, Maria J

2013-05-01

Interaction computing (IC) aims to map the properties of integrable low-dimensional non-linear dynamical systems to the discrete domain of finite-state automata in an attempt to reproduce in software the self-organizing and dynamically stable properties of sub-cellular biochemical systems. As the work reported in this paper is still at the early stages of theory development it focuses on the analysis of a particularly simple chemical oscillator, the Belousov-Zhabotinsky (BZ) reaction. After retracing the rationale for IC developed over the past several years from the physical, biological, mathematical, and computer science points of view, the paper presents an elementary discussion of the Krohn-Rhodes decomposition of finite-state automata, including the holonomy decomposition of a simple automaton, and of its interpretation as an abstract positional number system. The method is then applied to the analysis of the algebraic properties of discrete finite-state automata derived from a simplified Petri net model of the BZ reaction. In the simplest possible and symmetrical case the corresponding automaton is, not surprisingly, found to contain exclusively cyclic groups. In a second, asymmetrical case, the decomposition is much more complex and includes five different simple non-abelian groups whose potential relevance arises from their ability to encode functionally complete algebras. The possible computational relevance of these findings is discussed and possible conclusions are drawn. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

PubMed Central

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-01-01

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

PubMed

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-03-14

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Dynamics of Cell Ensembles on Adhesive Micropatterns: Bridging the Gap between Single Cell Spreading and Collective Cell Migration

PubMed Central

Albert, Philipp J.; Schwarz, Ulrich S.

2016-01-01

The collective dynamics of multicellular systems arise from the interplay of a few fundamental elements: growth, division and apoptosis of single cells; their mechanical and adhesive interactions with neighboring cells and the extracellular matrix; and the tendency of polarized cells to move. Micropatterned substrates are increasingly used to dissect the relative roles of these fundamental processes and to control the resulting dynamics. Here we show that a unifying computational framework based on the cellular Potts model can describe the experimentally observed cell dynamics over all relevant length scales. For single cells, the model correctly predicts the statistical distribution of the orientation of the cell division axis as well as the final organisation of the two daughters on a large range of micropatterns, including those situations in which a stable configuration is not achieved and rotation ensues. Large ensembles migrating in heterogeneous environments form non-adhesive regions of inward-curved arcs like in epithelial bridge formation. Collective migration leads to swirl formation with variations in cell area as observed experimentally. In each case, we also use our model to predict cell dynamics on patterns that have not been studied before. PMID:27054883

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

PubMed

Zhang, Jinao; Zhong, Yongmin; Gu, Chengfan

2018-05-30

Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

Computational modelling of cellular level metabolism

NASA Astrophysics Data System (ADS)

Calvetti, D.; Heino, J.; Somersalo, E.

2008-07-01

The steady and stationary state inverse problems consist of estimating the reaction and transport fluxes, blood concentrations and possibly the rates of change of some of the concentrations based on data which are often scarce noisy and sampled over a population. The Bayesian framework provides a natural setting for the solution of this inverse problem, because a priori knowledge about the system itself and the unknown reaction fluxes and transport rates can compensate for the insufficiency of measured data, provided that the computational costs do not become prohibitive. This article identifies the computational challenges which have to be met when analyzing the steady and stationary states of multicompartment model for cellular metabolism and suggest stable and efficient ways to handle the computations. The outline of a computational tool based on the Bayesian paradigm for the simulation and analysis of complex cellular metabolic systems is also presented.

The Neurona at Home project: Simulating a large-scale cellular automata brain in a distributed computing environment

NASA Astrophysics Data System (ADS)

Acedo, L.; Villanueva-Oller, J.; Moraño, J. A.; Villanueva, R.-J.

2013-01-01

The Berkeley Open Infrastructure for Network Computing (BOINC) has become the standard open source solution for grid computing in the Internet. Volunteers use their computers to complete an small part of the task assigned by a dedicated server. We have developed a BOINC project called Neurona@Home whose objective is to simulate a cellular automata random network with, at least, one million neurons. We consider a cellular automata version of the integrate-and-fire model in which excitatory and inhibitory nodes can activate or deactivate neighbor nodes according to a set of probabilistic rules. Our aim is to determine the phase diagram of the model and its behaviour and to compare it with the electroencephalographic signals measured in real brains.

Implementing vertex dynamics models of cell populations in biology within a consistent computational framework.

PubMed

Fletcher, Alexander G; Osborne, James M; Maini, Philip K; Gavaghan, David J

2013-11-01

The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell-cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable. Copyright © 2013 Elsevier Ltd. All rights reserved.

Monitoring of seismic time-series with advanced parallel computational tools and complex networks

NASA Astrophysics Data System (ADS)

Kechaidou, M.; Sirakoulis, G. Ch.; Scordilis, E. M.

2012-04-01

Earthquakes have been in the focus of human and research interest for several centuries due to their catastrophic effect to the everyday life as they occur almost all over the world demonstrating a hard to be modelled unpredictable behaviour. On the other hand, their monitoring with more or less technological updated instruments has been almost continuous and thanks to this fact several mathematical models have been presented and proposed so far to describe possible connections and patterns found in the resulting seismological time-series. Especially, in Greece, one of the most seismically active territories on earth, detailed instrumental seismological data are available from the beginning of the past century providing the researchers with valuable and differential knowledge about the seismicity levels all over the country. Considering available powerful parallel computational tools, such as Cellular Automata, these data can be further successfully analysed and, most important, modelled to provide possible connections between different parameters of the under study seismic time-series. More specifically, Cellular Automata have been proven very effective to compose and model nonlinear complex systems resulting in the advancement of several corresponding models as possible analogues of earthquake fault dynamics. In this work preliminary results of modelling of the seismic time-series with the help of Cellular Automata so as to compose and develop the corresponding complex networks are presented. The proposed methodology will be able to reveal under condition hidden relations as found in the examined time-series and to distinguish the intrinsic time-series characteristics in an effort to transform the examined time-series to complex networks and graphically represent their evolvement in the time-space. Consequently, based on the presented results, the proposed model will eventually serve as a possible efficient flexible computational tool to provide a generic understanding of the possible triggering mechanisms as arrived from the adequately monitoring and modelling of the regional earthquake phenomena.

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

PubMed Central

Westermark, Stefanie; Steuer, Ralf

2016-01-01

Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes. PMID:28083530

Toward a systems-level view of dynamic phosphorylation networks

PubMed Central

Newman, Robert H.; Zhang, Jin; Zhu, Heng

2014-01-01

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks. PMID:25177341

Accelerating sino-atrium computer simulations with graphic processing units.

PubMed

Zhang, Hong; Xiao, Zheng; Lin, Shien-fong

2015-01-01

Sino-atrial node cells (SANCs) play a significant role in rhythmic firing. To investigate their role in arrhythmia and interactions with the atrium, computer simulations based on cellular dynamic mathematical models are generally used. However, the large-scale computation usually makes research difficult, given the limited computational power of Central Processing Units (CPUs). In this paper, an accelerating approach with Graphic Processing Units (GPUs) is proposed in a simulation consisting of the SAN tissue and the adjoining atrium. By using the operator splitting method, the computational task was made parallel. Three parallelization strategies were then put forward. The strategy with the shortest running time was further optimized by considering block size, data transfer and partition. The results showed that for a simulation with 500 SANCs and 30 atrial cells, the execution time taken by the non-optimized program decreased 62% with respect to a serial program running on CPU. The execution time decreased by 80% after the program was optimized. The larger the tissue was, the more significant the acceleration became. The results demonstrated the effectiveness of the proposed GPU-accelerating methods and their promising applications in more complicated biological simulations.

A comparative study on fluorescent cholesterol analogs as versatile cellular reporters[S

PubMed Central

Sezgin, Erdinc; Can, Fatma Betul; Schneider, Falk; Clausen, Mathias P.; Galiani, Silvia; Stanly, Tess A.; Waithe, Dominic; Colaco, Alexandria; Honigmann, Alf; Wüstner, Daniel; Platt, Frances; Eggeling, Christian

2016-01-01

Cholesterol (Chol) is a crucial component of cellular membranes, but knowledge of its intracellular dynamics is scarce. Thus, it is of utmost interest to develop tools for visualization of Chol organization and dynamics in cells and tissues. For this purpose, many studies make use of fluorescently labeled Chol analogs. Unfortunately, the introduction of the label may influence the characteristics of the analog, such as its localization, interaction, and trafficking in cells; hence, it is important to get knowledge of such bias. In this report, we compared different fluorescent lipid analogs for their performance in cellular assays: 1) plasma membrane incorporation, specifically the preference for more ordered membrane environments in phase-separated giant unilamellar vesicles and giant plasma membrane vesicles; 2) cellular trafficking, specifically subcellular localization in Niemann-Pick type C disease cells; and 3) applicability in fluorescence correlation spectroscopy (FCS)-based and super-resolution stimulated emission depletion-FCS-based measurements of membrane diffusion dynamics. The analogs exhibited strong differences, with some indicating positive performance in the membrane-based experiments and others in the intracellular trafficking assay. However, none showed positive performance in all assays. Our results constitute a concise guide for the careful use of fluorescent Chol analogs in visualizing cellular Chol dynamics. PMID:26701325

Predicting Bone Mechanical State During Recovery After Long-Duration Skeletal Unloading Using QCT and Finite Element Modeling

NASA Technical Reports Server (NTRS)

Chang, Katarina L.; Pennline, James A.

2013-01-01

During long-duration missions at the International Space Station, astronauts experience weightlessness leading to skeletal unloading. Unloading causes a lack of a mechanical stimulus that triggers bone cellular units to remove mass from the skeleton. A mathematical system of the cellular dynamics predicts theoretical changes to volume fractions and ash fraction in response to temporal variations in skeletal loading. No current model uses image technology to gather information about a skeletal site s initial properties to calculate bone remodeling changes and then to compare predicted bone strengths with the initial strength. The goal of this study is to use quantitative computed tomography (QCT) in conjunction with a computational model of the bone remodeling process to establish initial bone properties to predict changes in bone mechanics during bone loss and recovery with finite element (FE) modeling. Input parameters for the remodeling model include bone volume fraction and ash fraction, which are both computed from the QCT images. A non-destructive approach to measure ash fraction is also derived. Voxel-based finite element models (FEM) created from QCTs provide initial evaluation of bone strength. Bone volume fraction and ash fraction outputs from the computational model predict changes to the elastic modulus of bone via a two-parameter equation. The modulus captures the effect of bone remodeling and functions as the key to evaluate of changes in strength. Application of this time-dependent modulus to FEMs and composite beam theory enables an assessment of bone mechanics during recovery. Prediction of bone strength is not only important for astronauts, but is also pertinent to millions of patients with osteoporosis and low bone density.

Path planning on cellular nonlinear network using active wave computing technique

NASA Astrophysics Data System (ADS)

Yeniçeri, Ramazan; Yalçın, Müstak E.

2009-05-01

This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

NASA Astrophysics Data System (ADS)

White, Forest M.; Wolf-Yadlin, Alejandro

2016-06-01

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

Cellular computational platform and neurally inspired elements thereof

DOEpatents

Okandan, Murat

2016-11-22

A cellular computational platform is disclosed that includes a multiplicity of functionally identical, repeating computational hardware units that are interconnected electrically and optically. Each computational hardware unit includes a reprogrammable local memory and has interconnections to other such units that have reconfigurable weights. Each computational hardware unit is configured to transmit signals into the network for broadcast in a protocol-less manner to other such units in the network, and to respond to protocol-less broadcast messages that it receives from the network. Each computational hardware unit is further configured to reprogram the local memory in response to incoming electrical and/or optical signals.

Doctors going mobile.

PubMed

Romano, Ron; Baum, Neil

2014-01-01

Having a Web page and a blog site are the minimum requirements for an Internet presence in the new millennium. However, a Web page that loads on a personal computer or a laptop will be ineffective on a mobile or cellular phone. Today, with more existing and potential patients having access to cellular technology, it is necessary to reconfigure the appearance of your Web site that appears on a mobile phone. This article discusses mobile computing and suggestions for improving the appearance of your Web site on a mobile or cellular phone.

Cellular chirality arising from the self-organization of the actin cytoskeleton.

PubMed

Tee, Yee Han; Shemesh, Tom; Thiagarajan, Visalatchi; Hariadi, Rizal Fajar; Anderson, Karen L; Page, Christopher; Volkmann, Niels; Hanein, Dorit; Sivaramakrishnan, Sivaraj; Kozlov, Michael M; Bershadsky, Alexander D

2015-04-01

Cellular mechanisms underlying the development of left-right asymmetry in tissues and embryos remain obscure. Here, the development of a chiral pattern of actomyosin was revealed by studying actin cytoskeleton self-organization in cells with isotropic circular shape. A radially symmetrical system of actin bundles consisting of α-actinin-enriched radial fibres (RFs) and myosin-IIA-enriched transverse fibres (TFs) evolved spontaneously into the chiral system as a result of the unidirectional tilting of all RFs, which was accompanied by a tangential shift in the retrograde movement of TFs. We showed that myosin-IIA-dependent contractile stresses within TFs drive their movement along RFs, which grow centripetally in a formin-dependent fashion. The handedness of the chiral pattern was shown to be regulated by α-actinin-1. Computational modelling demonstrated that the dynamics of the RF-TF system can explain the pattern transition from radial to chiral. Thus, actin cytoskeleton self-organization provides built-in machinery that potentially allows cells to develop left-right asymmetry.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Modeling molecular mechanisms in the axon

NASA Astrophysics Data System (ADS)

de Rooij, R.; Miller, K. E.; Kuhl, E.

2017-03-01

Axons are living systems that display highly dynamic changes in stiffness, viscosity, and internal stress. However, the mechanistic origin of these phenomenological properties remains elusive. Here we establish a computational mechanics model that interprets cellular-level characteristics as emergent properties from molecular-level events. We create an axon model of discrete microtubules, which are connected to neighboring microtubules via discrete crosslinking mechanisms that obey a set of simple rules. We explore two types of mechanisms: passive and active crosslinking. Our passive and active simulations suggest that the stiffness and viscosity of the axon increase linearly with the crosslink density, and that both are highly sensitive to the crosslink detachment and reattachment times. Our model explains how active crosslinking with dynein motors generates internal stresses and actively drives axon elongation. We anticipate that our model will allow us to probe a wide variety of molecular phenomena—both in isolation and in interaction—to explore emergent cellular-level features under physiological and pathological conditions.

Evolving binary classifiers through parallel computation of multiple fitness cases.

PubMed

Cagnoni, Stefano; Bergenti, Federico; Mordonini, Monica; Adorni, Giovanni

2005-06-01

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

A biopolymer transistor: electrical amplification by microtubules.

PubMed

Priel, Avner; Ramos, Arnolt J; Tuszynski, Jack A; Cantiello, Horacio F

2006-06-15

Microtubules (MTs) are important cytoskeletal structures engaged in a number of specific cellular activities, including vesicular traffic, cell cyto-architecture and motility, cell division, and information processing within neuronal processes. MTs have also been implicated in higher neuronal functions, including memory and the emergence of "consciousness". How MTs handle and process electrical information, however, is heretofore unknown. Here we show new electrodynamic properties of MTs. Isolated, taxol-stabilized MTs behave as biomolecular transistors capable of amplifying electrical information. Electrical amplification by MTs can lead to the enhancement of dynamic information, and processivity in neurons can be conceptualized as an "ionic-based" transistor, which may affect, among other known functions, neuronal computational capabilities.

Neuronal excitability level transition induced by electrical stimulation

NASA Astrophysics Data System (ADS)

Florence, G.; Kurths, J.; Machado, B. S.; Fonoff, E. T.; Cerdeira, H. A.; Teixeira, M. J.; Sameshima, K.

2014-12-01

In experimental studies, electrical stimulation (ES) has been applied to induce neuronal activity or to disrupt pathological patterns. Nevertheless, the underlying mechanisms of these activity pattern transitions are not clear. To study these phenomena, we simulated a model of the hippocampal region CA1. The computational simulations using different amplitude levels and duration of ES revealed three states of neuronal excitability: burst-firing mode, depolarization block and spreading depression wave. We used the bifurcation theory to analyse the interference of ES in the cellular excitability and the neuronal dynamics. Understanding this process would help to improve the ES techniques to control some neurological disorders.

Computational method for multi-modal microscopy based on transport of intensity equation

NASA Astrophysics Data System (ADS)

Li, Jiaji; Chen, Qian; Sun, Jiasong; Zhang, Jialin; Zuo, Chao

2017-02-01

In this paper, we develop the requisite theory to describe a hybrid virtual-physical multi-modal imaging system which yields quantitative phase, Zernike phase contrast, differential interference contrast (DIC), and light field moment imaging simultaneously based on transport of intensity equation(TIE). We then give the experimental demonstration of these ideas by time-lapse imaging of live HeLa cell mitosis. Experimental results verify that a tunable lens based TIE system, combined with the appropriate post-processing algorithm, can achieve a variety of promising imaging modalities in parallel with the quantitative phase images for the dynamic study of cellular processes.

Evolution of Cellular Automata toward a LIFE-Like Rule Guided by 1/ƒ Noise

NASA Astrophysics Data System (ADS)

Ninagawa, Shigeru

There is evidence in favor of a relationship between the presence of 1/ƒ noise and computational universality in cellular automata. To confirm the relationship, we search for two-dimensional cellular automata with a 1/ƒ power spectrum by means of genetic algorithms. The power spectrum is calculated from the evolution of the state of the cell, starting from a random initial configuration. The fitness is estimated by the power spectrum with consideration of the spectral similarity to the 1/ƒ spectrum. The result shows that the rule with the highest fitness over the most runs exhibits a 1/ƒ type spectrum and its transition function and behavior are quite similar to those of the Game of Life, which is known to be a computationally universal cellular automaton. These results support the relationship between the presence of 1/ƒ noise and computational universality.

In silico reconstitution of Listeria propulsion exhibits nano-saltation.

PubMed

Alberts, Jonathan B; Odell, Garrett M

2004-12-01

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein-protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our "in silico reconstitution" produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes,in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions.

PubMed

Kirschner, Denise E; Linderman, Jennifer J

2009-04-01

In addition to traditional and novel experimental approaches to study host-pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host-pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two-photon intravital microscopy. Two others explore virtual or 'in silico' deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi-scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host-pathogen interactions.

Asynchronous Rate Chaos in Spiking Neuronal Circuits

PubMed Central

Harish, Omri; Hansel, David

2015-01-01

The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

New cellular automaton model for magnetohydrodynamics

NASA Technical Reports Server (NTRS)

Chen, Hudong; Matthaeus, William H.

1987-01-01

A new type of two-dimensional cellular automation method is introduced for computation of magnetohydrodynamic fluid systems. Particle population is described by a 36-component tensor referred to a hexagonal lattice. By appropriate choice of the coefficients that control the modified streaming algorithm and the definition of the macroscopic fields, it is possible to compute both Lorentz-force and magnetic-induction effects. The method is local in the microscopic space and therefore suited to massively parallel computations.

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

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

Lewis, Daniel D.; Department of Biomedical Engineering, University of California Davis, Davis, CA; Villarreal, Fernando D.

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with amore » special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.« less

Collective Traffic-like Movement of Ants on a Trail: Dynamical Phases and Phase Transitions

NASA Astrophysics Data System (ADS)

Kunwar, Ambarish; John, Alexander; Nishinari, Katsuhiro; Schadschneider, Andreas; Chowdhury, Debashish

2004-11-01

The traffic-like collective movement of ants on a trail can be described by a stochastic cellular automaton model. We have earlier investigated its unusual flow-density relation by using various mean field approximations and computer simulations. In this paper, we study the model following an alternative approach based on the analogy with the zero range process, which is one of the few known exactly solvable stochastic dynamical models. We show that our theory can quantitatively account for the unusual non-monotonic dependence of the average speed of the ants on their density for finite lattices with periodic boundary conditions. Moreover, we argue that the model exhibits a continuous phase transition at the critial density only in a limiting case. Furthermore, we investigate the phase diagram of the model by replacing the periodic boundary conditions by open boundary conditions.

Simulation tools for particle-based reaction-diffusion dynamics in continuous space

PubMed Central

2014-01-01

Particle-based reaction-diffusion algorithms facilitate the modeling of the diffusional motion of individual molecules and the reactions between them in cellular environments. A physically realistic model, depending on the system at hand and the questions asked, would require different levels of modeling detail such as particle diffusion, geometrical confinement, particle volume exclusion or particle-particle interaction potentials. Higher levels of detail usually correspond to increased number of parameters and higher computational cost. Certain systems however, require these investments to be modeled adequately. Here we present a review on the current field of particle-based reaction-diffusion software packages operating on continuous space. Four nested levels of modeling detail are identified that capture incrementing amount of detail. Their applicability to different biological questions is discussed, arching from straight diffusion simulations to sophisticated and expensive models that bridge towards coarse grained molecular dynamics. PMID:25737778

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

PubMed Central

Lewis, Daniel D.; Villarreal, Fernando D.; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems. PMID:25538941

A Strategic Interaction Model of Punishment Favoring Contagion of Honest Behavior

PubMed Central

Cremene, Marcel; Dumitrescu, D.; Cremene, Ligia

2014-01-01

The punishment effect on social behavior is analyzed within the strategic interaction framework of Cellular Automata and computational Evolutionary Game Theory. A new game, called Social Honesty (SH), is proposed. The SH game is analyzed in spatial configurations. Probabilistic punishment is used as a dishonesty deterrence mechanism. In order to capture the intrinsic uncertainty of social environments, payoffs are described as random variables. New dynamics, with a new relation between punishment probability and punishment severity, are revealed. Punishment probability proves to be more important than punishment severity in guiding convergence towards honesty as predominant behavior. This result is confirmed by empirical evidence and reported experiments. Critical values and transition intervals for punishment probability and severity are identified and analyzed. Clusters of honest or dishonest players emerge spontaneously from the very first rounds of interaction and are determinant for the future dynamics and outcomes. PMID:24489917

Synthetic biology outside the cell: linking computational tools to cell-free systems.

PubMed

Lewis, Daniel D; Villarreal, Fernando D; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

An undergraduate course, and new textbook, on ``Physical Models of Living Systems''

NASA Astrophysics Data System (ADS)

Nelson, Philip

2015-03-01

I'll describe an intermediate-level course on ``Physical Models of Living Systems.'' The only prerequisite is first-year university physics and calculus. The course is a response to rapidly growing interest among undergraduates in several science and engineering departments. Students acquire several research skills that are often not addressed in traditional courses, including: basic modeling skills, probabilistic modeling skills, data analysis methods, computer programming using a general-purpose platform like MATLAB or Python, dynamical systems, particularly feedback control. These basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including: virus dynamics; bacterial genetics and evolution of drug resistance; statistical inference; superresolution microscopy; synthetic biology; naturally evolved cellular circuits. Publication of a new textbook by WH Freeman and Co. is scheduled for December 2014. Supported in part by EF-0928048 and DMR-0832802.

Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

PubMed Central

Zhu, Hao; Sun, Yan; Rajagopal, Gunaretnam; Mondry, Adrian; Dhar, Pawan

2004-01-01

Background Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. Methods We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. Results We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. Conclusions Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described. PMID:15339335

[Computer simulation of thyroid regulatory mechanisms in health and malignancy].

PubMed

Abduvaliev, A A; Gil'dieva, M S; Khidirov, B N; Saĭdalieva, M; Saatov, T S

2010-07-01

The paper describes a computer model for regulation of the number of thyroid follicular cells in health and malignancy. The authors'computer program for mathematical simulation of the regulatory mechanisms of a thyroid follicular cellular community cannot be now referred to as good commercial products. For commercialization of this product, it is necessary to draw up a direct relation of the introduced corrected values from the actually existing normal values, such as the peripheral blood concentrations of thyroid hormones or the mean values of endocrine tissue mitotic activity. However, the described computer program has been also used in researches by our scientific group in the study of thyroid cancer. The available biological experimental data and theoretical provisions on thyroid structural and functional organization at the cellular level allow one to construct mathematical models for quantitative analysis of the regulation of the size of a cellular community of a thyroid follicle in health and abnormalities, by using the method for simulation of the regulatory mechanisms of living systems and the equations of cellular community regulatory communities.

Spiking network simulation code for petascale computers.

PubMed

Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

2014-01-01

Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today.

Spiking network simulation code for petascale computers

PubMed Central

Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.; Plesser, Hans E.; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

2014-01-01

Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682

Quantification of substrate and cellular strains in stretchable 3D cell cultures: an experimental and computational framework.

PubMed

González-Avalos, P; Mürnseer, M; Deeg, J; Bachmann, A; Spatz, J; Dooley, S; Eils, R; Gladilin, E

2017-05-01

The mechanical cell environment is a key regulator of biological processes . In living tissues, cells are embedded into the 3D extracellular matrix and permanently exposed to mechanical forces. Quantification of the cellular strain state in a 3D matrix is therefore the first step towards understanding how physical cues determine single cell and multicellular behaviour. The majority of cell assays are, however, based on 2D cell cultures that lack many essential features of the in vivo cellular environment. Furthermore, nondestructive measurement of substrate and cellular mechanics requires appropriate computational tools for microscopic image analysis and interpretation. Here, we present an experimental and computational framework for generation and quantification of the cellular strain state in 3D cell cultures using a combination of 3D substrate stretcher, multichannel microscopic imaging and computational image analysis. The 3D substrate stretcher enables deformation of living cells embedded in bead-labelled 3D collagen hydrogels. Local substrate and cell deformations are determined by tracking displacement of fluorescent beads with subsequent finite element interpolation of cell strains over a tetrahedral tessellation. In this feasibility study, we debate diverse aspects of deformable 3D culture construction, quantification and evaluation, and present an example of its application for quantitative analysis of a cellular model system based on primary mouse hepatocytes undergoing transforming growth factor (TGF-β) induced epithelial-to-mesenchymal transition. © 2017 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

A Simple Microscopy Assay to Teach the Processes of Phagocytosis and Exocytosis

ERIC Educational Resources Information Center

Gray, Ross; Gray, Andrew; Fite, Jessica L.; Jordan, Renee; Stark, Sarah; Naylor, Kari

2012-01-01

Phagocytosis and exocytosis are two cellular processes involving membrane dynamics. While it is easy to understand the purpose of these processes, it can be extremely difficult for students to comprehend the actual mechanisms. As membrane dynamics play a significant role in many cellular processes ranging from cell signaling to cell division to…

Dynamic positional fate map of the primary heart-forming region.

PubMed

Cui, Cheng; Cheuvront, Tracey J; Lansford, Rusty D; Moreno-Rodriguez, Ricardo A; Schultheiss, Thomas M; Rongish, Brenda J

2009-08-15

Here we show the temporal-spatial orchestration of early heart morphogenesis at cellular level resolution, in vivo, and reconcile conflicting positional fate mapping data regarding the primary heart-forming field(s). We determined the positional fates of precardiac cells using a precision electroporation approach in combination with wide-field time-lapse microscopy in the quail embryo, a warm-blooded vertebrate (HH Stages 4 through 10). Contrary to previous studies, the results demonstrate the existence of a "continuous" circle-shaped heart field that spans the midline, appearing at HH Stage 4, which then expands to form a wide arc of progenitors at HH Stages 5-7. Our time-resolved image data show that a subset of these cardiac progenitor cells do not overlap with the expression of common cardiogenic factors, Nkx-2.5 and Bmp-2, until HH Stage 10, when a tubular heart has formed, calling into question when cardiac fate is specified and by which key factors. Sub-groups and anatomical bands (cohorts) of heart precursor cells dramatically change their relative positions in a process largely driven by endodermal folding and other large-scale tissue deformations. Thus, our novel dynamic positional fate maps resolve the origin of cardiac progenitor cells in amniotes. The data also establish the concept that tissue motion contributes significantly to cellular position fate - i.e., much of the cellular displacement that occurs during assembly of a midline heart tube (HH Stage 9) is NOT due to "migration" (autonomous motility), a commonly held belief. Computational analysis of our time-resolved data lays the foundation for more precise analyses of how cardiac gene regulatory networks correlate with early heart tissue morphogenesis in birds and mammals.

Role of Compartmentalization on HiF-1α Degradation Dynamics during Changing Oxygen Conditions: A Computational Approach

PubMed Central

Bedessem, Baptiste; Stéphanou, Angélique

2014-01-01

HiF-1α is the central protein driving the cellular response to hypoxia. Its accumulation in cancer cells is linked to the appearance of chemoresistant and aggressive tumor phenotypes. As a consequence, understanding the regulation of HiF-1α dynamics is a major issue to design new anti-cancer therapies. In this paper, we propose a model of the hypoxia pathway, involving HiF-1α and its inhibitor pVHL. Based on data from the literature, we made the hypothesis that the regulation of HiF-1α involves two compartments (nucleus and cytoplasm) and a constitutive shuttle of the pVHL protein between them. We first show that this model captures correctly the main features of HiF-1α dynamics, including the bi-exponential degradation profile in normoxia, the kinetics of induction in hypoxia, and the switch-like accumulation. Second, we simulated the effects of a hypoxia/reoxygenation event, and show that it generates a strong instability of HiF-1α. The protein concentration rapidly increases 3 hours after the reoxygenation, and exhibits an oscillating pattern. This effect vanishes if we do not consider compartmentalization of HiF-1α. This result can explain various counter-intuitive observations about the specific molecular and cellular response to the reoxygenation process. Third, we simulated the HiF-1α dynamics in the tumor case. We considered different types of mutations associated with tumorigenesis, and we compared their consequences on HiF-1α dynamics. Then, we tested different therapeutics strategies. We show that a therapeutic decrease of HiF-1α nuclear level is not always correlated with an attenuation of reoxygenation-induced instabilities. Thus, it appears that the design of anti-HiF-1α therapies have to take into account these two aspects to maximize their efficiency. PMID:25338163

Computational modeling of radiobiological effects in bone metastases for different radionuclides.

PubMed

Liberal, Francisco D C Guerra; Tavares, Adriana Alexandre S; Tavares, João Manuel R S

2017-06-01

Computational simulation is a simple and practical way to study and to compare a variety of radioisotopes for different medical applications, including the palliative treatment of bone metastases. This study aimed to evaluate and compare cellular effects modelled for different radioisotopes currently in use or under research for treatment of bone metastases using computational methods. Computational models were used to estimate the radiation-induced cellular effects (Virtual Cell Radiobiology algorithm) post-irradiation with selected particles emitted by Strontium-89 ( 89 Sr), Samarium-153 ( 153 Sm), Lutetium-177 ( 177 Lu), and Radium-223 ( 223 Ra). Cellular kinetics post-irradiation using 89 Sr β - particles, 153 Sm β -  particles, 177 Lu β -  particles and 223 Ra α particles showed that the cell response was dose- and radionuclide-dependent. 177 Lu beta minus particles and, in particular, 223 Ra alpha particles, yielded the lowest survival fraction of all investigated particles. 223 Ra alpha particles induced the highest cell death of all investigated particles on metastatic prostate cells in comparison to irradiation with β -  radionuclides, two of the most frequently used radionuclides in the palliative treatment of bone metastases in clinical routine practice. Moreover, the data obtained suggest that the used computational methods might provide some perception about cellular effects following irradiation with different radionuclides.

Cellular reprogramming dynamics follow a simple 1D reaction coordinate

NASA Astrophysics Data System (ADS)

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

2018-01-01

Cellular reprogramming, the conversion of one cell type to another, induces global changes in gene expression involving thousands of genes, and understanding how cells globally alter their gene expression profile during reprogramming is an ongoing problem. Here we reanalyze time-course data on cellular reprogramming from differentiated cell types to induced pluripotent stem cells (iPSCs) and show that gene expression dynamics during reprogramming follow a simple 1D reaction coordinate. This reaction coordinate is independent of both the time it takes to reach the iPSC state as well as the details of the experimental protocol used. Using Monte-Carlo simulations, we show that such a reaction coordinate emerges from epigenetic landscape models where cellular reprogramming is viewed as a ‘barrier-crossing’ process between cell fates. Overall, our analysis and model suggest that gene expression dynamics during reprogramming follow a canonical trajectory consistent with the idea of an ‘optimal path’ in gene expression space for reprogramming.

Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy.

PubMed

Traenkle, Bjoern; Rothbauer, Ulrich

2017-01-01

Single-domain antibodies (sdAbs) have substantially expanded the possibilities of advanced cellular imaging such as live-cell or super-resolution microscopy to visualize cellular antigens and their dynamics. In addition to their unique properties including small size, high stability, and solubility in many environments, sdAbs can be efficiently functionalized according to the needs of the respective imaging approach. Genetically encoded intrabodies fused to fluorescent proteins (chromobodies) have become versatile tools to study dynamics of endogenous proteins in living cells. Additionally, sdAbs conjugated to organic dyes were shown to label cellular structures with high density and minimal fluorophore displacement making them highly attractive probes for super-resolution microscopy. Here, we review recent advances of the chromobody technology to visualize localization and dynamics of cellular targets and the application of chromobody-based cell models for compound screening. Acknowledging the emerging importance of super-resolution microscopy in cell biology, we further discuss advantages and challenges of sdAbs for this technology.

Numerical and experimental validation for the thermal transmittance of windows with cellular shades

DOE PAGES

Hart, Robert

2018-02-21

Some highly energy efficient window attachment products are available today, but more rapid market adoption would be facilitated by fair performance metrics. It is important to have validated simulation tools to provide a basis for this analysis. This paper outlines a review and validation of the ISO 15099 center-of-glass zero-solar-load heat transfer correlations for windows with cellular shades. Thermal transmittance was measured experimentally, simulated using computational fluid dynamics (CFD) analysis, and simulated utilizing correlations from ISO 15099 as implemented in Berkeley Lab WINDOW and THERM software. CFD analysis showed ISO 15099 underestimates heat flux of rectangular cavities by up tomore » 60% when aspect ratio (AR) = 1 and overestimates heat flux up to 20% when AR = 0.5. CFD analysis also showed that wave-type surfaces of cellular shades have less than 2% impact on heat flux through the cavities and less than 5% for natural convection of room-side surface. WINDOW was shown to accurately represent heat flux of the measured configurations to a mean relative error of 0.5% and standard deviation of 3.8%. Finally, several shade parameters showed significant influence on correlation accuracy, including distance between shade and glass, inconsistency in cell stretch, size of perimeter gaps, and the mounting hardware.« less

Numerical and experimental validation for the thermal transmittance of windows with cellular shades

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

Hart, Robert

Some highly energy efficient window attachment products are available today, but more rapid market adoption would be facilitated by fair performance metrics. It is important to have validated simulation tools to provide a basis for this analysis. This paper outlines a review and validation of the ISO 15099 center-of-glass zero-solar-load heat transfer correlations for windows with cellular shades. Thermal transmittance was measured experimentally, simulated using computational fluid dynamics (CFD) analysis, and simulated utilizing correlations from ISO 15099 as implemented in Berkeley Lab WINDOW and THERM software. CFD analysis showed ISO 15099 underestimates heat flux of rectangular cavities by up tomore » 60% when aspect ratio (AR) = 1 and overestimates heat flux up to 20% when AR = 0.5. CFD analysis also showed that wave-type surfaces of cellular shades have less than 2% impact on heat flux through the cavities and less than 5% for natural convection of room-side surface. WINDOW was shown to accurately represent heat flux of the measured configurations to a mean relative error of 0.5% and standard deviation of 3.8%. Finally, several shade parameters showed significant influence on correlation accuracy, including distance between shade and glass, inconsistency in cell stretch, size of perimeter gaps, and the mounting hardware.« less

Exploring the Spatial and Temporal Organization of a Cell’s Proteome

PubMed Central

Beck, Martin; Topf, Maya; Frazier, Zachary; Tjong, Harianto; Xu, Min; Zhang, Shihua; Alber, Frank

2013-01-01

To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteome’s spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteome’s organization into a spatially explicit, predictive model of cellular processes. PMID:21094684

Understanding cellular architecture in cancer cells

NASA Astrophysics Data System (ADS)

Bianco, Simone; Tang, Chao

2011-03-01

Understanding the development of cancer is an important goal for today's science. The morphology of cellular organelles, such as the nucleus, the nucleoli and the mitochondria, which is referred to as cellular architecture or cytoarchitecture, is an important indicator of the state of the cell. In particular, there are striking difference between the cellular architecture of a healthy cell versus a cancer cell. In this work we present a dynamical model for the evolution of organelles morphology in cancer cells. Using a dynamical systems approach, we describe the evolution of a cell on its way to cancer as a trajectory in a multidimensional morphology state. The results provided by this work may increase our insight on the mechanism of tumorigenesis and help build new therapeutic strategies.

Endoplasmic Reticulum and the Unfolded Protein Response: Dynamics and Metabolic Integration

PubMed Central

Bravo, Roberto; Parra, Valentina; Gatica, Damián; Rodriguez, Andrea E.; Torrealba, Natalia; Paredes, Felipe; Wang, Zhao V.; Zorzano, Antonio; Hill, Joseph A.; Jaimovich, Enrique; Quest, Andrew F.G.; Lavandero, Sergio

2013-01-01

The endoplasmic reticulum (ER) is a dynamic intracellular organelle with multiple functions essential for cellular homeostasis, development, and stress responsiveness. In response to cellular stress, a well-established signaling cascade, the unfolded protein response (UPR), is activated. This intricate mechanism is an important means of reestablishing cellular homeostasis and alleviating the inciting stress. Now, emerging evidence has demonstrated that the UPR influences cellular metabolism through diverse mechanisms, including calcium and lipid transfer, raising the prospect of involvement of these processes in the pathogenesis of disease, including neurodegeneration, cancer, diabetes mellitus and cardiovascular disease. Here, we review the distinct functions of the ER and UPR from a metabolic point of view, highlighting their association with prevalent pathologies. PMID:23317820

Epidermal Homeostasis and Radiation Responses in a Multiscale Tissue Modeling Framework

NASA Technical Reports Server (NTRS)

Hu, Shaowen; Cucinotta, Francis A.

2013-01-01

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

Regulation of calreticulin–major histocompatibility complex (MHC) class I interactions by ATP

PubMed Central

Wijeyesakere, Sanjeeva Joseph; Gagnon, Jessica K.; Arora, Karunesh; Brooks, Charles L.; Raghavan, Malini

2015-01-01

The MHC class I peptide loading complex (PLC) facilitates the assembly of MHC class I molecules with peptides, but factors that regulate the stability and dynamics of the assembly complex are largely uncharacterized. Based on initial findings that ATP, in addition to MHC class I-specific peptide, is able to induce MHC class I dissociation from the PLC, we investigated the interaction of ATP with the chaperone calreticulin, an endoplasmic reticulum (ER) luminal, calcium-binding component of the PLC that is known to bind ATP. We combined computational and experimental measurements to identify residues within the globular domain of calreticulin, in proximity to the high-affinity calcium-binding site, that are important for high-affinity ATP binding and for ATPase activity. High-affinity calcium binding by calreticulin is required for optimal nucleotide binding, but both ATP and ADP destabilize enthalpy-driven high-affinity calcium binding to calreticulin. ATP also selectively destabilizes the interaction of calreticulin with cellular substrates, including MHC class I molecules. Calreticulin mutants that affect ATP or high-affinity calcium binding display prolonged associations with monoglucosylated forms of cellular MHC class I, delaying MHC class I dissociation from the PLC and their transit through the secretory pathway. These studies reveal central roles for ATP and calcium binding as regulators of calreticulin–substrate interactions and as key determinants of PLC dynamics. PMID:26420867

Cellular dynamics in the muscle satellite cell niche

PubMed Central

Bentzinger, C Florian; Wang, Yu Xin; Dumont, Nicolas A; Rudnicki, Michael A

2013-01-01

Satellite cells, the quintessential skeletal muscle stem cells, reside in a specialized local environment whose anatomy changes dynamically during tissue regeneration. The plasticity of this niche is attributable to regulation by the stem cells themselves and to a multitude of functionally diverse cell types. In particular, immune cells, fibrogenic cells, vessel-associated cells and committed and differentiated cells of the myogenic lineage have emerged as important constituents of the satellite cell niche. Here, we discuss the cellular dynamics during muscle regeneration and how disease can lead to perturbation of these mechanisms. To define the role of cellular components in the muscle stem cell niche is imperative for the development of cell-based therapies, as well as to better understand the pathobiology of degenerative conditions of the skeletal musculature. PMID:24232182

Mitochondrial morphology transitions and functions: implications for retrograde signaling?

PubMed Central

Picard, Martin; Shirihai, Orian S.; Gentil, Benoit J.

2013-01-01

In response to cellular and environmental stresses, mitochondria undergo morphology transitions regulated by dynamic processes of membrane fusion and fission. These events of mitochondrial dynamics are central regulators of cellular activity, but the mechanisms linking mitochondrial shape to cell function remain unclear. One possibility evaluated in this review is that mitochondrial morphological transitions (from elongated to fragmented, and vice-versa) directly modify canonical aspects of the organelle's function, including susceptibility to mitochondrial permeability transition, respiratory properties of the electron transport chain, and reactive oxygen species production. Because outputs derived from mitochondrial metabolism are linked to defined cellular signaling pathways, fusion/fission morphology transitions could regulate mitochondrial function and retrograde signaling. This is hypothesized to provide a dynamic interface between the cell, its genome, and the fluctuating metabolic environment. PMID:23364527

The Cellular Automata for modelling of spreading of lava flow on the earth surface

NASA Astrophysics Data System (ADS)

Jarna, A.

2012-12-01

Volcanic risk assessment is a very important scientific, political and economic issue in densely populated areas close to active volcanoes. Development of effective tools for early prediction of a potential volcanic hazard and management of crises are paramount. However, to this date volcanic hazard maps represent the most appropriate way to illustrate the geographical area that can potentially be affected by a volcanic event. Volcanic hazard maps are usually produced by mapping out old volcanic deposits, however dynamic lava flow simulation gaining popularity and can give crucial information to corroborate other methodologies. The methodology which is used here for the generation of volcanic hazard maps is based on numerical simulation of eruptive processes by the principle of Cellular Automata (CA). The python script is integrated into ArcToolbox in ArcMap (ESRI) and the user can select several input and output parameters which influence surface morphology, size and shape of the flow, flow thickness, flow velocity and length of lava flows. Once the input parameters are selected, the software computes and generates hazard maps on the fly. The results can be exported to Google Maps (.klm format) to visualize the results of the computation. For validation of the simulation code are used data from a real lava flow. Comparison of the simulation results with real lava flows mapped out from satellite images will be presented.

Matrix remodeling between cells and cellular interactions with collagen bundle

NASA Astrophysics Data System (ADS)

Kim, Jihan; Sun, Bo

When cells are surrounded by complex environment, they continuously probe and interact with it by applying cellular traction forces. As cells apply traction forces, they can sense rigidity of their local environment and remodel the matrix microstructure simultaneously. Previous study shows that single human carcinoma cell (MDA-MB-231) remodeled its surrounding extracellular matrix (ECM) and the matrix remodeling was reversible. In this study we examined the matrix microstructure between cells and cellular interaction between them using quantitative confocal microscopy. The result shows that the matrix microstructure is the most significantly remodeled between cells consisting of aligned, and densified collagen fibers (collagen bundle)., the result shows that collagen bundle is irreversible and significantly change micromechanics of ECM around the bundle. We further examined cellular interaction with collagen bundle by analyzing dynamics of actin and talin formation along with the direction of bundle. Lastly, we analyzed dynamics of cellular protrusion and migrating direction of cells along the bundle.

Dynamic interactions between 14-3-3 proteins and phosphoproteins regulate diverse cellular processes

PubMed Central

2004-01-01

14-3-3 proteins exert an extraordinarily widespread influence on cellular processes in all eukaryotes. They operate by binding to specific phosphorylated sites on diverse target proteins, thereby forcing conformational changes or influencing interactions between their targets and other molecules. In these ways, 14-3-3s ‘finish the job’ when phosphorylation alone lacks the power to drive changes in the activities of intracellular proteins. By interacting dynamically with phosphorylated proteins, 14-3-3s often trigger events that promote cell survival – in situations from preventing metabolic imbalances caused by sudden darkness in leaves to mammalian cell-survival responses to growth factors. Recent work linking specific 14-3-3 isoforms to genetic disorders and cancers, and the cellular effects of 14-3-3 agonists and antagonists, indicate that the cellular complement of 14-3-3 proteins may integrate the specificity and strength of signalling through to different cellular responses. PMID:15167810

The role of water molecules in computational drug design.

PubMed

de Beer, Stephanie B A; Vermeulen, Nico P E; Oostenbrink, Chris

2010-01-01

Although water molecules are small and only consist of two different atom types, they play various roles in cellular systems. This review discusses their influence on the binding process between biomacromolecular targets and small molecule ligands and how this influence can be modeled in computational drug design approaches. Both the structure and the thermodynamics of active site waters will be discussed as these influence the binding process significantly. Structurally conserved waters cannot always be determined experimentally and if observed, it is not clear if they will be replaced upon ligand binding, even if sufficient space is available. Methods to predict the presence of water in protein-ligand complexes will be reviewed. Subsequently, we will discuss methods to include water in computational drug research. Either as an additional factor in automated docking experiments, or explicitly in detailed molecular dynamics simulations, the effect of water on the quality of the simulations is significant, but not easily predicted. The most detailed calculations involve estimates of the free energy contribution of water molecules to protein-ligand complexes. These calculations are computationally demanding, but give insight in the versatility and importance of water in ligand binding.

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

Zhang, Na; Zhang, Peng; Kang, Wei

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters aremore » systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately.« less

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

PubMed

He, Fei; Murabito, Ettore; Westerhoff, Hans V

2016-04-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

Dynamics of traffic flow with real-time traffic information

NASA Astrophysics Data System (ADS)

Yokoya, Yasushi

2004-01-01

We studied dynamics of traffic flow with real-time information provided. Provision of the real-time traffic information based on advancements in telecommunication technology is expected to facilitate the efficient utilization of available road capacity. This system has a potentiality of not only engineering for road usage but also the science of complexity series. In the system, the information plays a role of feedback connecting microscopic and macroscopic phenomena beyond the hierarchical structure of statistical physics. In this paper, we tried to clarify how the information works in a network of traffic flow from the perspective of statistical physics. The dynamical feature of the traffic flow is abstracted by a contrastive study between the nonequilibrium statistical physics and a computer simulation based on cellular automaton. We found that the information disrupts the local equilibrium of traffic flow by a characteristic dissipation process due to interaction between the information and individual vehicles. The dissipative structure was observed in the time evolution of traffic flow driven far from equilibrium as a consequence of the breakdown of the local-equilibrium hypothesis.

Dynamics of VEGF matrix-retention in vascular network patterning

NASA Astrophysics Data System (ADS)

Köhn-Luque, A.; de Back, W.; Yamaguchi, Y.; Yoshimura, K.; Herrero, M. A.; Miura, T.

2013-12-01

Vascular endothelial growth factor (VEGF) is a central regulator of blood vessel morphogenesis, although its role in patterning of endothelial cells into vascular networks is not fully understood. It has been suggested that binding of soluble VEGF to extracellular matrix components causes spatially restricted cues that guide endothelial cells into network patterns. Yet, current evidence for such a mechanism remains indirect. In this study, we quantitatively analyse the dynamics of VEGF retention in a controlled in vitro situation of human umbilical vascular endothelial cells (HUVECs) in Matrigel. We show that fluorescent VEGF accumulates in pericellular areas and colocalizes with VEGF binding molecules. Analysis of fluorescence recovery after photobleaching reveals that binding/unbinding to matrix molecules dominates VEGF dynamics in the pericellular region. Computational simulations using our experimental measurements of kinetic parameters show that matrix retention of chemotactic signals can lead to the formation of reticular cellular networks on a realistic timescale. Taken together, these results show that VEGF binds to matrix molecules in proximity of HUVECs in Matrigel, and suggest that bound VEGF drives vascular network patterning.

Parvovirus Induced Alterations in Nuclear Architecture and Dynamics

PubMed Central

Ihalainen, Teemu O.; Niskanen, Einari A.; Jylhävä, Juulia; Paloheimo, Outi; Dross, Nicolas; Smolander, Hanna; Langowski, Jörg; Timonen, Jussi; Vihinen-Ranta, Maija

2009-01-01

The nucleus of interphase eukaryotic cell is a highly compartmentalized structure containing the three-dimensional network of chromatin and numerous proteinaceous subcompartments. DNA viruses induce profound changes in the intranuclear structures of their host cells. We are applying a combination of confocal imaging including photobleaching microscopy and computational methods to analyze the modifications of nuclear architecture and dynamics in parvovirus infected cells. Upon canine parvovirus infection, expansion of the viral replication compartment is accompanied by chromatin marginalization to the vicinity of the nuclear membrane. Dextran microinjection and fluorescence recovery after photobleaching (FRAP) studies revealed the homogeneity of this compartment. Markedly, in spite of increase in viral DNA content of the nucleus, a significant increase in the protein mobility was observed in infected compared to non-infected cells. Moreover, analyzis of the dynamics of photoactivable capsid protein demonstrated rapid intranuclear dynamics of viral capsids. Finally, quantitative FRAP and cellular modelling were used to determine the duration of viral genome replication. Altogether, our findings indicate that parvoviruses modify the nuclear structure and dynamics extensively. Intranuclear crowding of viral components leads to enlargement of the interchromosomal domain and to chromatin marginalization via depletion attraction. In conclusion, parvoviruses provide a useful model system for understanding the mechanisms of virus-induced intranuclear modifications. PMID:19536327

An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images.

PubMed

Haass-Koffler, Carolina L; Naeemuddin, Mohammad; Bartlett, Selena E

2012-08-31

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology even in complex tissue sections. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

Multimodal computational microscopy based on transport of intensity equation

NASA Astrophysics Data System (ADS)

Li, Jiaji; Chen, Qian; Sun, Jiasong; Zhang, Jialin; Zuo, Chao

2016-12-01

Transport of intensity equation (TIE) is a powerful tool for phase retrieval and quantitative phase imaging, which requires intensity measurements only at axially closely spaced planes without a separate reference beam. It does not require coherent illumination and works well on conventional bright-field microscopes. The quantitative phase reconstructed by TIE gives valuable information that has been encoded in the complex wave field by passage through a sample of interest. Such information may provide tremendous flexibility to emulate various microscopy modalities computationally without requiring specialized hardware components. We develop a requisite theory to describe such a hybrid computational multimodal imaging system, which yields quantitative phase, Zernike phase contrast, differential interference contrast, and light field moment imaging, simultaneously. It makes the various observations for biomedical samples easy. Then we give the experimental demonstration of these ideas by time-lapse imaging of live HeLa cell mitosis. Experimental results verify that a tunable lens-based TIE system, combined with the appropriate postprocessing algorithm, can achieve a variety of promising imaging modalities in parallel with the quantitative phase images for the dynamic study of cellular processes.

High performance cellular level agent-based simulation with FLAME for the GPU.

PubMed

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

Markov State Models Provide Insights into Dynamic Modulation of Protein Function

PubMed Central

2015-01-01

Conspectus Protein function is inextricably linked to protein dynamics. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational paradigms are required for observing and understanding conformational dynamics of proteins and its functional implications. In principle, molecular dynamics simulations can provide the time evolution of atomistic models of proteins, but the long time scales associated with functional dynamics make it difficult to observe rare dynamical transitions. The issue of extracting essential functional components of protein dynamics from noisy simulation data presents another set of challenges in obtaining an unbiased understanding of protein motions. Therefore, a methodology that provides a statistical framework for efficient sampling and a human-readable view of the key aspects of functional dynamics from data analysis is required. The Markov state model (MSM), which has recently become popular worldwide for studying protein dynamics, is an example of such a framework. In this Account, we review the use of Markov state models for efficient sampling of the hierarchy of time scales associated with protein dynamics, automatic identification of key conformational states, and the degrees of freedom associated with slow dynamical processes. Applications of MSMs for studying long time scale phenomena such as activation mechanisms of cellular signaling proteins has yielded novel insights into protein function. In particular, from MSMs built using large-scale simulations of GPCRs and kinases, we have shown that complex conformational changes in proteins can be described in terms of structural changes in key structural motifs or “molecular switches” within the protein, the transitions between functionally active and inactive states of proteins proceed via multiple pathways, and ligand or substrate binding modulates the flux through these pathways. Finally, MSMs also provide a theoretical toolbox for studying the effect of nonequilibrium perturbations on conformational dynamics. Considering that protein dynamics in vivo occur under nonequilibrium conditions, MSMs coupled with nonequilibrium statistical mechanics provide a way to connect cellular components to their functional environments. Nonequilibrium perturbations of protein folding MSMs reveal the presence of dynamically frozen glass-like states in their conformational landscape. These frozen states are also observed to be rich in β-sheets, which indicates their possible role in the nucleation of β-sheet rich aggregates such as those observed in amyloid-fibril formation. Finally, we describe how MSMs have been used to understand the dynamical behavior of intrinsically disordered proteins such as amyloid-β, human islet amyloid polypeptide, and p53. While certainly not a panacea for studying functional dynamics, MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions. PMID:25625937

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Systematic parameter estimation in data-rich environments for cell signalling dynamics

PubMed Central

Nim, Tri Hieu; Luo, Le; Clément, Marie-Véronique; White, Jacob K.; Tucker-Kellogg, Lisa

2013-01-01

Motivation: Computational models of biological signalling networks, based on ordinary differential equations (ODEs), have generated many insights into cellular dynamics, but the model-building process typically requires estimating rate parameters based on experimentally observed concentrations. New proteomic methods can measure concentrations for all molecular species in a pathway; this creates a new opportunity to decompose the optimization of rate parameters. Results: In contrast with conventional parameter estimation methods that minimize the disagreement between simulated and observed concentrations, the SPEDRE method fits spline curves through observed concentration points, estimates derivatives and then matches the derivatives to the production and consumption of each species. This reformulation of the problem permits an extreme decomposition of the high-dimensional optimization into a product of low-dimensional factors, each factor enforcing the equality of one ODE at one time slice. Coarsely discretized solutions to the factors can be computed systematically. Then the discrete solutions are combined using loopy belief propagation, and refined using local optimization. SPEDRE has unique asymptotic behaviour with runtime polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. SPEDRE performance is comparatively evaluated on a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN (phosphatase and tensin homologue). Availability and implementation: Web service, software and supplementary information are available at www.LtkLab.org/SPEDRE Supplementary information: Supplementary data are available at Bioinformatics online. Contact: LisaTK@nus.edu.sg PMID:23426255

Formalizing Knowledge in Multi-Scale Agent-Based Simulations

PubMed Central

Somogyi, Endre; Sluka, James P.; Glazier, James A.

2017-01-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused. PMID:29338063

Time-spatial model on the dynamics of the proliferation of Aedes aegypti

NASA Astrophysics Data System (ADS)

Gouvêa, Maury Meirelles, Jr.

2017-03-01

Some complex physical systems, such as cellular regulation, ecosystems, and societies, can be represented by local interactions between agents. Then, complex behaviors may emerge. A cellular automaton is a discrete dynamic system with these features. Among the several complex systems, epidemic diseases are given special attention by researchers with respect to their dynamics. Understanding the behavior of an epidemic may well benefit a society. For instance, different proliferation scenarios may be produced and a prevention policy set. This paper presents a new simulation method of the time-spatial spread of the Dengue mosquito with a cellular automaton. Thus, it will be possible to create different dissemination scenarios and preventive policies for these in several regions. Simulations were performed with different initial conditions and parameters as a result of which the behavior of the proposed method was characterized.

Cellular automata approach for the dynamics of HIV infection under antiretroviral therapies: The role of the virus diffusion

NASA Astrophysics Data System (ADS)

González, Ramón E. R.; de Figueirêdo, Pedro Hugo; Coutinho, Sérgio

2013-10-01

We study a cellular automata model to test the timing of antiretroviral therapy strategies for the dynamics of infection with human immunodeficiency virus (HIV). We focus on the role of virus diffusion when its population is included in previous cellular automata model that describes the dynamics of the lymphocytes cells population during infection. This inclusion allows us to consider the spread of infection by the virus-cell interaction, beyond that which occurs by cell-cell contagion. The results show an acceleration of the infectious process in the absence of treatment, but show better efficiency in reducing the risk of the onset of AIDS when combined antiretroviral therapies are used even with drugs of low effectiveness. Comparison of results with clinical data supports the conclusions of this study.

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

PubMed

Somogyi, Endre; Sluka, James P; Glazier, James A

2016-10-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.

Simulating Molecular Mechanisms of the MDM2-Mediated Regulatory Interactions: A Conformational Selection Model of the MDM2 Lid Dynamics

PubMed Central

Verkhivker, Gennady M.

2012-01-01

Diversity and complexity of MDM2 mechanisms govern its principal function as the cellular antagonist of the p53 tumor suppressor. Structural and biophysical studies have demonstrated that MDM2 binding could be regulated by the dynamics of a pseudo-substrate lid motif. However, these experiments and subsequent computational studies have produced conflicting mechanistic models of MDM2 function and dynamics. We propose a unifying conformational selection model that can reconcile experimental findings and reveal a fundamental role of the lid as a dynamic regulator of MDM2-mediated binding. In this work, structure, dynamics and energetics of apo-MDM2 are studied as a function of posttranslational modifications and length of the lid. We found that the dynamic equilibrium between “closed” and “semi-closed” lid forms may be a fundamental characteristic of MDM2 regulatory interactions, which can be modulated by phosphorylation, phosphomimetic mutation as well as by the lid size. Our results revealed that these factors may regulate p53-MDM2 binding by fine-tuning the thermodynamic equilibrium between preexisting conformational states of apo-MDM2. In agreement with NMR studies, the effect of phosphorylation on MDM2 interactions was more pronounced with the truncated lid variant that favored the thermodynamically dominant closed form. The phosphomimetic mutation S17D may alter the lid dynamics by shifting the thermodynamic equilibrium towards the ensemble of “semi-closed” conformations. The dominant “semi-closed” lid form and weakened dependence on the phosphorylation seen in simulations with the complete lid can provide a rationale for binding of small p53-based mimetics and inhibitors without a direct competition with the lid dynamics. The results suggested that a conformational selection model of preexisting MDM2 states may provide a robust theoretical framework for understanding MDM2 dynamics. Probing biological functions and mechanisms of MDM2 regulation would require further integration of computational and experimental studies and may help to guide drug design of novel anti-cancer therapeutics. PMID:22815859

Computational approaches to metabolic engineering utilizing systems biology and synthetic biology.

PubMed

Fong, Stephen S

2014-08-01

Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.

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

PubMed

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

2016-01-01

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

Bio-inspired nano-sensor-enhanced CNN visual computer.

PubMed

Porod, Wolfgang; Werblin, Frank; Chua, Leon O; Roska, Tamas; Rodriguez-Vazquez, Angel; Roska, Botond; Fay, Patrick; Bernstein, Gary H; Huang, Yih-Fang; Csurgay, Arpad I

2004-05-01

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.

Relation of exact Gaussian basis methods to the dephasing representation: Theory and application to time-resolved electronic spectra

NASA Astrophysics Data System (ADS)

Sulc, Miroslav; Hernandez, Henar; Martinez, Todd J.; Vanicek, Jiri

2014-03-01

We recently showed that the Dephasing Representation (DR) provides an efficient tool for computing ultrafast electronic spectra and that cellularization yields further acceleration [M. Šulc and J. Vaníček, Mol. Phys. 110, 945 (2012)]. Here we focus on increasing its accuracy by first implementing an exact Gaussian basis method (GBM) combining the accuracy of quantum dynamics and efficiency of classical dynamics. The DR is then derived together with ten other methods for computing time-resolved spectra with intermediate accuracy and efficiency. These include the Gaussian DR (GDR), an exact generalization of the DR, in which trajectories are replaced by communicating frozen Gaussians evolving classically with an average Hamiltonian. The methods are tested numerically on time correlation functions and time-resolved stimulated emission spectra in the harmonic potential, pyrazine S0 /S1 model, and quartic oscillator. Both the GBM and the GDR are shown to increase the accuracy of the DR. Surprisingly, in chaotic systems the GDR can outperform the presumably more accurate GBM, in which the two bases evolve separately. This research was supported by the Swiss NSF Grant No. 200021_124936/1 and NCCR Molecular Ultrafast Science & Technology (MUST), and by the EPFL.

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

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

Endele, Max; Etzrodt, Martin; Schroeder, Timm, E-mail: timm.schroeder@bsse.ethz.ch

Hematopoiesis is the cumulative consequence of finely tuned signaling pathways activated through extrinsic factors, such as local niche signals and systemic hematopoietic cytokines. Whether extrinsic factors actively instruct the lineage choice of hematopoietic stem and progenitor cells or are only selectively allowing survival and proliferation of already intrinsically lineage-committed cells has been debated over decades. Recent results demonstrated that cytokines can instruct lineage choice. However, the precise function of individual cytokine-triggered signaling molecules in inducing cellular events like proliferation, lineage choice, and differentiation remains largely elusive. Signal transduction pathways activated by different cytokine receptors are highly overlapping, but support themore » production of distinct hematopoietic lineages. Cellular context, signaling dynamics, and the crosstalk of different signaling pathways determine the cellular response of a given extrinsic signal. New tools to manipulate and continuously quantify signaling events at the single cell level are therefore required to thoroughly interrogate how dynamic signaling networks yield a specific cellular response. - Highlights: • Recent studies provided definite proof for lineage-instructive action of cytokines. • Signaling pathways involved in hematopoietic lineage instruction remain elusive. • New tools are emerging to quantitatively study dynamic signaling networks over time.« less

Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering

PubMed Central

Prescott, Thomas P.; Lang, Moritz; Papachristodoulou, Antonis

2015-01-01

Large, naturally evolved biomolecular networks typically fulfil multiple functions. When modelling or redesigning such systems, functional subsystems are often analysed independently first, before subsequent integration into larger-scale computational models. In the design and analysis process, it is therefore important to quantitatively analyse and predict the dynamics of the interactions between integrated subsystems; in particular, how the incremental effect of integrating a subsystem into a network depends on the existing dynamics of that network. In this paper we present a framework for simulating the contribution of any given functional subsystem when integrated together with one or more other subsystems. This is achieved through a cascaded layering of a network into functional subsystems, where each layer is defined by an appropriate subset of the reactions. We exploit symmetries in our formulation to exhaustively quantify each subsystem’s incremental effects with minimal computational effort. When combining subsystems, their isolated behaviour may be amplified, attenuated, or be subject to more complicated effects. We propose the concept of mutual dynamics to quantify such nonlinear phenomena, thereby defining the incompatibility and cooperativity between all pairs of subsystems when integrated into any larger network. We exemplify our theoretical framework by analysing diverse behaviours in three dynamic models of signalling and metabolic pathways: the effect of crosstalk mechanisms on the dynamics of parallel signal transduction pathways; reciprocal side-effects between several integral feedback mechanisms and the subsystems they stabilise; and consequences of nonlinear interactions between elementary flux modes in glycolysis for metabolic engineering strategies. Our analysis shows that it is not sufficient to just specify subsystems and analyse their pairwise interactions; the environment in which the interaction takes place must also be explicitly defined. Our framework provides a natural representation of nonlinear interaction phenomena, and will therefore be an important tool for modelling large-scale evolved or synthetic biomolecular networks. PMID:25933116

Dynamic Finite Element Predictions for Mars Sample Return Cellular Impact Test #4

NASA Technical Reports Server (NTRS)

Fasanella, Edwin L.; Billings, Marcus D.

2001-01-01

The nonlinear, transient dynamic finite element code, MSC.Dytran, was used to simulate an impact test of an energy absorbing Earth Entry Vehicle (EEV) that will impact without a parachute. EEVOs are designed to return materials from asteroids, comets, or planets for laboratory analysis on Earth. The EEV concept uses an energy absorbing cellular structure designed to contain and limit the acceleration of space exploration samples during Earth impact. The spherical shaped cellular structure is composed of solid hexagonal and pentagonal foam-filled cells with hybrid graphite-epoxy/Kevlar cell walls. Space samples fit inside a smaller sphere at the center of the EEVOs cellular structure. Pre-test analytical predictions were compared with the test results from a bungee accelerator. The model used to represent the foam and the proper failure criteria for the cell walls were critical in predicting the impact loads of the cellular structure. It was determined that a FOAM1 model for the foam and a 20% failure strain criteria for the cell walls gave an accurate prediction of the acceleration pulse for cellular impact.

Cellular Spacing Selection During the Directional Solidification of Binary Alloys. A Numerical Approach

NASA Technical Reports Server (NTRS)

Catalina, Adrian V.; Sen, S.; Rose, M. Franklin (Technical Monitor)

2001-01-01

The evolution of cellular solid/liquid interfaces from an initially unstable planar front was studied by means of a two-dimensional computer simulation. The developed numerical model makes use of an interface tracking procedure and has the capability to describe the dynamics of the interface morphology based on local changes of the thermodynamic conditions. The fundamental physics of this formulation was validated against experimental microgravity results and the predictions of the analytical linear stability theory. The performed simulations revealed that in certain conditions, based on a competitive growth mechanism, an interface could become unstable to random perturbations of infinitesimal amplitude even at wavelengths smaller than the neutral wavelength, lambda(sub c), predicted by the linear stability theory. Furthermore, two main stages of spacing selection have been identified. In the first stage, at low perturbations amplitude, the selection mechanism is driven by the maximum growth rate of instabilities while in the second stage the selection is influenced by nonlinear phenomena caused by the interactions between the neighboring cells. Comparison of these predictions with other existing theories of pattern formation and experimental results will be discussed.

Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward Organism-Level Systems Biology in Mammals.

PubMed

Susaki, Etsuo A; Ueda, Hiroki R

2016-01-21

Organism-level systems biology aims to identify, analyze, control and design cellular circuits in organisms. Many experimental and computational approaches have been developed over the years to allow us to conduct these studies. Some of the most powerful methods are based on using optical imaging in combination with fluorescent labeling, and for those one of the long-standing stumbling blocks has been tissue opacity. Recently, the solutions to this problem have started to emerge based on whole-body and whole-organ clearing techniques that employ innovative tissue-clearing chemistry. Here, we review these advancements and discuss how combining new clearing techniques with high-performing fluorescent proteins or small molecule tags, rapid volume imaging and efficient image informatics is resulting in comprehensive and quantitative organ-wide, single-cell resolution experimental data. These technologies are starting to yield information on connectivity and dynamics in cellular circuits at unprecedented resolution, and bring us closer to system-level understanding of physiology and diseases of complex mammalian systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Particle-based membrane model for mesoscopic simulation of cellular dynamics

NASA Astrophysics Data System (ADS)

Sadeghi, Mohsen; Weikl, Thomas R.; Noé, Frank

2018-01-01

We present a simple and computationally efficient coarse-grained and solvent-free model for simulating lipid bilayer membranes. In order to be used in concert with particle-based reaction-diffusion simulations, the model is purely based on interacting and reacting particles, each representing a coarse patch of a lipid monolayer. Particle interactions include nearest-neighbor bond-stretching and angle-bending and are parameterized so as to reproduce the local membrane mechanics given by the Helfrich energy density over a range of relevant curvatures. In-plane fluidity is implemented with Monte Carlo bond-flipping moves. The physical accuracy of the model is verified by five tests: (i) Power spectrum analysis of equilibrium thermal undulations is used to verify that the particle-based representation correctly captures the dynamics predicted by the continuum model of fluid membranes. (ii) It is verified that the input bending stiffness, against which the potential parameters are optimized, is accurately recovered. (iii) Isothermal area compressibility modulus of the membrane is calculated and is shown to be tunable to reproduce available values for different lipid bilayers, independent of the bending rigidity. (iv) Simulation of two-dimensional shear flow under a gravity force is employed to measure the effective in-plane viscosity of the membrane model and show the possibility of modeling membranes with specified viscosities. (v) Interaction of the bilayer membrane with a spherical nanoparticle is modeled as a test case for large membrane deformations and budding involved in cellular processes such as endocytosis. The results are shown to coincide well with the predicted behavior of continuum models, and the membrane model successfully mimics the expected budding behavior. We expect our model to be of high practical usability for ultra coarse-grained molecular dynamics or particle-based reaction-diffusion simulations of biological systems.

Quantitative Analysis of Intra Urban Growth Modeling using socio economic agents by combining cellular automata model with agent based model

NASA Astrophysics Data System (ADS)

Singh, V. K.; Jha, A. K.; Gupta, K.; Srivastav, S. K.

2017-12-01

Recent studies indicate that there is a significant improvement in the urban land use dynamics through modeling at finer spatial resolutions. Geo-computational models such as cellular automata and agent based model have given evident proof regarding the quantification of the urban growth pattern with urban boundary. In recent studies, socio- economic factors such as demography, education rate, household density, parcel price of the current year, distance to road, school, hospital, commercial centers and police station are considered to the major factors influencing the Land Use Land Cover (LULC) pattern of the city. These factors have unidirectional approach to land use pattern which makes it difficult to analyze the spatial aspects of model results both quantitatively and qualitatively. In this study, cellular automata model is combined with generic model known as Agent Based Model to evaluate the impact of socio economic factors on land use pattern. For this purpose, Dehradun an Indian city is selected as a case study. Socio economic factors were collected from field survey, Census of India, Directorate of economic census, Uttarakhand, India. A 3X3 simulating window is used to consider the impact on LULC. Cellular automata model results are examined for the identification of hot spot areas within the urban area and agent based model will be using logistic based regression approach where it will identify the correlation between each factor on LULC and classify the available area into low density, medium density, high density residential or commercial area. In the modeling phase, transition rule, neighborhood effect, cell change factors are used to improve the representation of built-up classes. Significant improvement is observed in the built-up classes from 84 % to 89 %. However after incorporating agent based model with cellular automata model the accuracy improved from 89 % to 94 % in 3 classes of urban i.e. low density, medium density and commercial classes. Sensitivity study of the model indicated that southern and south-west part of the city have shown improvement and small patches of growth are also observed in the north western part of the city.The study highlights the growing importance of socio economic factors and geo-computational modeling approach on changing LULC of newly growing cities of modern India.

Allosteric dynamics of SAMHD1 studied by molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Patra, K. K.; Bhattacharya, A.; Bhattacharya, S.

2016-10-01

SAMHD1 is a human cellular enzyme that blocks HIV-1 infection in myeloid cells and non-cycling CD4+T cells. The enzyme is an allosterically regulated triphosphohydrolase that modulates the level of cellular dNTP. The virus restriction is attributed to the lowering of the pool of dNTP in the cell to a point where reverse-transcription is impaired. Mutations in SAMHD1 are also implicated in Aicardi-Goutieres syndrome. A mechanistic understanding of the allosteric activation of the enzyme is still elusive. We have performed molecular dynamics simulations to examine the allosteric site dynamics of the protein and to examine the connection between the stability of the tetrameric complex and the Allosite occupancy.

Collective dynamics in heterogeneous networks of neuronal cellular automata

NASA Astrophysics Data System (ADS)

Manchanda, Kaustubh; Bose, Amitabha; Ramaswamy, Ramakrishna

2017-12-01

We examine the collective dynamics of heterogeneous random networks of model neuronal cellular automata. Each automaton has b active states, a single silent state and r - b - 1 refractory states, and can show 'spiking' or 'bursting' behavior, depending on the values of b. We show that phase transitions that occur in the dynamical activity can be related to phase transitions in the structure of Erdõs-Rényi graphs as a function of edge probability. Different forms of heterogeneity allow distinct structural phase transitions to become relevant. We also show that the dynamics on the network can be described by a semi-annealed process and, as a result, can be related to the Boolean Lyapunov exponent.

The endoplasmic reticulum: structure, function and response to cellular signaling.

PubMed

Schwarz, Dianne S; Blower, Michael D

2016-01-01

The endoplasmic reticulum (ER) is a large, dynamic structure that serves many roles in the cell including calcium storage, protein synthesis and lipid metabolism. The diverse functions of the ER are performed by distinct domains; consisting of tubules, sheets and the nuclear envelope. Several proteins that contribute to the overall architecture and dynamics of the ER have been identified, but many questions remain as to how the ER changes shape in response to cellular cues, cell type, cell cycle state and during development of the organism. Here we discuss what is known about the dynamics of the ER, what questions remain, and how coordinated responses add to the layers of regulation in this dynamic organelle.

Predictive oncology: multidisciplinary, multi-scale in-silico modeling linking phenotype, morphology and growth

PubMed Central

Sanga, Sandeep; Frieboes, Hermann B.; Zheng, Xiaoming; Gatenby, Robert; Bearer, Elaine L.; Cristini, Vittorio

2007-01-01

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically review advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we propose and discuss a multi-scale, i.e., from the molecular to the gross tumor scale, mathematical and computational “first-principle” approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We demonstrate that this methodology, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as phenotype-diagnostic tool and thus to predict collective and individual tumor cell invasion of surrounding host. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior. PMID:17629503

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

Predictive Modeling and Computational Toxicology

EPA Science Inventory

Embryonic development is orchestrated via a complex series of cellular interactions controlling behaviors such as mitosis, migration, differentiation, adhesion, contractility, apoptosis, and extracellular matrix remodeling. Any chemical exposure that perturbs these cellular proce...

Replica Exchange Molecular Dynamics Study of Dimerization in Prion Protein: Multiple Modes of Interaction and Stabilization.

PubMed

Chamachi, Neharika G; Chakrabarty, Suman

2016-08-04

The pathological forms of prions are known to be a result of misfolding, oligomerization, and aggregation of the cellular prion. While the mechanism of misfolding and aggregation in prions has been widely studied using both experimental and computational tools, the structural and energetic characterization of the dimer form have not garnered as much attention. On one hand dimerization can be the first step toward a nucleation-like pathway to aggregation, whereas on the other hand it may also increase the conformational stability preventing self-aggregation. In this work, we have used extensive all-atom replica exchange molecular dynamics simulations of both monomer and dimer forms of a mouse prion protein to understand the structural, dynamic, and thermodynamic stability of dimeric prion as compared to the monomeric form. We show that prion proteins can dimerize spontaneously being stabilized by hydrophobic interactions as well as intermolecular hydrogen bonding and salt bridge formation. We have computed the conformational free energy landscapes for both monomer and dimer forms to compare the thermodynamic stability and misfolding pathways. We observe large conformational heterogeneity among the various modes of interactions between the monomers and the strong intermolecular interactions may lead to as high as 20% β-content. The hydrophobic regions in helix-2, surrounding coil regions, terminal regions along with the natively present β-sheet region appear to actively participate in prion-prion intermolecular interactions. Dimerization seems to considerably suppress the inherent dynamic instability observed in monomeric prions, particularly because the regions of structural frustration constitute the dimer interface. Further, we demonstrate an interesting reversible coupling between the Q160-G131 interaction (which leads to inhibition of β-sheet extension) and the G131-V161 H-bond formation.

Perspective: A Dynamics-Based Classification of Ventricular Arrhythmias

PubMed Central

Weiss, James N.; Garfinkel, Alan; Karagueuzian, Hrayr S.; Nguyen, Thao P.; Olcese, Riccardo; Chen, Peng-Sheng; Qu, Zhilin

2015-01-01

Despite key advances in the clinical management of life-threatening ventricular arrhythmias, culminating with the development of implantable cardioverter-defibrillators and catheter ablation techniques, pharmacologic/biologic therapeutics have lagged behind. The fundamental issue is that biological targets are molecular factors. Diseases, however, represent emergent properties at the scale of the organism that result from dynamic interactions between multiple constantly changing molecular factors. For a pharmacologic/biologic therapy to be effective, it must target the dynamic processes that underlie the disease. Here we propose a classification of ventricular arrhythmias that is based on our current understanding of the dynamics occurring at the subcellular, cellular, tissue and organism scales, which cause arrhythmias by simultaneously generating arrhythmia triggers and exacerbating tissue vulnerability. The goal is to create a framework that systematically links these key dynamic factors together with fixed factors (structural and electrophysiological heterogeneity) synergistically promoting electrical dispersion and increased arrhythmia risk to molecular factors that can serve as biological targets. We classify ventricular arrhythmias into three primary dynamic categories related generally to unstable Ca cycling, reduced repolarization, and excess repolarization, respectively. The clinical syndromes, arrhythmia mechanisms, dynamic factors and what is known about their molecular counterparts are discussed. Based on this framework, we propose a computational-experimental strategy for exploring the links between molecular factors, fixed factors and dynamic factors that underlie life-threatening ventricular arrhythmias. The ultimate objective is to facilitate drug development by creating an in silico platform to evaluate and predict comprehensively how molecular interventions affect not only a single targeted arrhythmia, but all primary arrhythmia dynamics categories as well as normal cardiac excitation-contraction coupling. PMID:25769672

On the spatial dynamics and oscillatory behavior of a predator-prey model based on cellular automata and local particle swarm optimization.

PubMed

Molina, Mario Martínez; Moreno-Armendáriz, Marco A; Carlos Seck Tuoh Mora, Juan

2013-11-07

A two-dimensional lattice model based on Cellular Automata theory and swarm intelligence is used to study the spatial and population dynamics of a theoretical ecosystem. It is found that the social interactions among predators provoke the formation of clusters, and that by increasing the mobility of predators the model enters into an oscillatory behavior. © 2013 Elsevier Ltd. All rights reserved.

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm.

PubMed

Yu, Isseki; Mori, Takaharu; Ando, Tadashi; Harada, Ryuhei; Jung, Jaewoon; Sugita, Yuji; Feig, Michael

2016-11-01

Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology.

Motifs, modules and games in bacteria.

PubMed

Wolf, Denise M; Arkin, Adam P

2003-04-01

Global explorations of regulatory network dynamics, organization and evolution have become tractable thanks to high-throughput sequencing and molecular measurement of bacterial physiology. From these, a nascent conceptual framework is developing, that views the principles of regulation in term of motifs, modules and games. Motifs are small, repeated, and conserved biological units ranging from molecular domains to small reaction networks. They are arranged into functional modules, genetically dissectible cellular functions such as the cell cycle, or different stress responses. The dynamical functioning of modules defines the organism's strategy to survive in a game, pitting cell against cell, and cell against environment. Placing pathway structure and dynamics into an evolutionary context begins to allow discrimination between those physical and molecular features that particularize a species to its surroundings, and those that provide core physiological function. This approach promises to generate a higher level understanding of cellular design, pathway evolution and cellular bioengineering.

Dynamic Reciprocity in the Wound Microenvironment

PubMed Central

Schultz, Gregory S.; Davidson, Jeffrey M.; Kirsner, Robert S.; Bornstein, Paul; Herman, Ira M.

2011-01-01

Here, we define dynamic reciprocity (DR) as an ongoing, bidirectional interaction amongst cells and their surrounding microenvironment. In the review, we posit that DR is especially meaningful during wound healing as the DR-driven biochemical, biophysical and cellular responses to injury play pivotal roles in regulating tissue regenerative responses. Such cell-extracellular matrix interactions not only guide and regulate cellular morphology, but cellular differentiation, migration, proliferation, and survival during tissue development, including e.g. embryogenesis, angiogenesis, as well as during pathologic processes including cancer diabetes, hypertension and chronic wound healing. Herein, we examine DR within the wound microenvironment while considering specific examples across acute and chronic wound healing. This review also considers how a number of hypotheses that attempt to explain chronic wound pathophysiology, which may be understood within the DR framework. The implications of applying the principles of dynamic reciprocity to optimize wound care practice and future development of innovative wound healing therapeutics are also briefly considered. PMID:21362080

Motifs, modules and games in bacteria

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

Wolf, Denise M.; Arkin, Adam P.

2003-04-01

Global explorations of regulatory network dynamics, organization and evolution have become tractable thanks to high-throughput sequencing and molecular measurement of bacterial physiology. From these, a nascent conceptual framework is developing, that views the principles of regulation in term of motifs, modules and games. Motifs are small, repeated, and conserved biological units ranging from molecular domains to small reaction networks. They are arranged into functional modules, genetically dissectible cellular functions such as the cell cycle, or different stress responses. The dynamical functioning of modules defines the organism's strategy to survive in a game, pitting cell against cell, and cell against environment.more » Placing pathway structure and dynamics into an evolutionary context begins to allow discrimination between those physical and molecular features that particularize a species to its surroundings, and those that provide core physiological function. This approach promises to generate a higher level understanding of cellular design, pathway evolution and cellular bioengineering.« less

Extinction rates in tumour public goods games.

PubMed

Gerlee, Philip; Altrock, Philipp M

2017-09-01

Cancer evolution and progression are shaped by cellular interactions and Darwinian selection. Evolutionary game theory incorporates both of these principles, and has been proposed as a framework to understand tumour cell population dynamics. A cornerstone of evolutionary dynamics is the replicator equation, which describes changes in the relative abundance of different cell types, and is able to predict evolutionary equilibria. Typically, the replicator equation focuses on differences in relative fitness. We here show that this framework might not be sufficient under all circumstances, as it neglects important aspects of population growth. Standard replicator dynamics might miss critical differences in the time it takes to reach an equilibrium, as this time also depends on cellular turnover in growing but bounded populations. As the system reaches a stable manifold, the time to reach equilibrium depends on cellular death and birth rates. These rates shape the time scales, in particular, in coevolutionary dynamics of growth factor producers and free-riders. Replicator dynamics might be an appropriate framework only when birth and death rates are of similar magnitude. Otherwise, population growth effects cannot be neglected when predicting the time to reach an equilibrium, and cell-type-specific rates have to be accounted for explicitly. © 2017 The Authors.

Elements of the cellular metabolic structure

PubMed Central

De la Fuente, Ildefonso M.

2015-01-01

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell. PMID:25988183

A new course and textbook on Physical Models of Living Systems, for science and engineering undergraduates

NASA Astrophysics Data System (ADS)

Nelson, Philip

2015-03-01

I'll describe an intermediate-level course on ``Physical Models of Living Systems.'' The only prerequisite is first-year university physics and calculus. The course is a response to rapidly growing interest among undergraduates in a broad range of science and engineering majors. Students acquire several research skills that are often not addressed in traditional courses: Basic modeling skills Probabilistic modeling skills Data analysis methods Computer programming using a general-purpose platform like MATLAB or Python Dynamical systems, particularly feedback control. These basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including: Virus dynamics Bacterial genetics and evolution of drug resistance Statistical inference Superresolution microscopy Synthetic biology Naturally evolved cellular circuits. Work supported by NSF Grants EF-0928048 and DMR-0832802.

Notch1-Dll4 signaling and mechanical force regulate leader cell formation during collective cell migration

PubMed Central

Riahi, Reza; Sun, Jian; Wang, Shue; Long, Min; Zhang, Donna D.; Wong, Pak Kin

2015-01-01

At the onset of collective cell migration, a subset of cells within an initially homogenous population acquires a distinct “leader” phenotype with characteristic morphology and motility. However, the factors driving leader cell formation as well as the mechanisms regulating leader cell density during the migration process remain to be determined. Here, we use single cell gene expression analysis and computational modeling to show that leader cell identity is dynamically regulated by Dll4 signaling through both Notch1 and cellular stress in a migrating epithelium. Time-lapse microscopy reveals that Dll4 is induced in leader cells after the creation of the cell-free region and leader cells are regulated via Notch1-Dll4 lateral inhibition. Furthermore, mechanical stress inhibits Dll4 expression and leader cell formation in the monolayer. Collectively, our findings suggest that a reduction of mechanical force near the boundary promotes Notch1-Dll4 signaling to dynamically regulate the density of leader cells during collective cell migration. PMID:25766473

Complexity in Nature and Society: Complexity Management in the Age of Globalization

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

The theory of nonlinear complex systems has become a proven problem-solving approach in the natural sciences from cosmic and quantum systems to cellular organisms and the brain. Even in modern engineering science self-organizing systems are developed to manage complex networks and processes. It is now recognized that many of our ecological, social, economic, and political problems are also of a global, complex, and nonlinear nature. What are the laws of sociodynamics? Is there a socio-engineering of nonlinear problem solving? What can we learn from nonlinear dynamics for complexity management in social, economic, financial and political systems? Is self-organization an acceptable strategy to handle the challenges of complexity in firms, institutions and other organizations? It is a main thesis of the talk that nature and society are basically governed by nonlinear and complex information dynamics. How computational is sociodynamics? What can we hope for social, economic and political problem solving in the age of globalization?.

Inverse problems and computational cell metabolic models: a statistical approach

NASA Astrophysics Data System (ADS)

Calvetti, D.; Somersalo, E.

2008-07-01

In this article, we give an overview of the Bayesian modelling of metabolic systems at the cellular and subcellular level. The models are based on detailed description of key biochemical reactions occurring in tissue, which may in turn be compartmentalized into cytosol and mitochondria, and of transports between the compartments. The classical deterministic approach which models metabolic systems as dynamical systems with Michaelis-Menten kinetics, is replaced by a stochastic extension where the model parameters are interpreted as random variables with an appropriate probability density. The inverse problem of cell metabolism in this setting consists of estimating the density of the model parameters. After discussing some possible approaches to solving the problem, we address the issue of how to assess the reliability of the predictions of a stochastic model by proposing an output analysis in terms of model uncertainties. Visualization modalities for organizing the large amount of information provided by the Bayesian dynamic sensitivity analysis are also illustrated.

Resolving dynamics of cell signaling via real-time imaging of the immunological synapse.

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

Stevens, Mark A.; Pfeiffer, Janet R.; Wilson, Bridget S.

2009-10-01

This highly interdisciplinary team has developed dual-color, total internal reflection microscopy (TIRF-M) methods that enable us to optically detect and track in real time protein migration and clustering at membrane interfaces. By coupling TIRF-M with advanced analysis techniques (image correlation spectroscopy, single particle tracking) we have captured subtle changes in membrane organization that characterize immune responses. We have used this approach to elucidate the initial stages of cell activation in the IgE signaling network of mast cells and the Toll-like receptor (TLR-4) response in macrophages stimulated by bacteria. To help interpret these measurements, we have undertaken a computational modeling effortmore » to connect the protein motion and lipid interactions. This work provides a deeper understanding of the initial stages of cellular response to external agents, including dynamics of interaction of key components in the signaling network at the 'immunological synapse,' the contact region of the cell and its adversary.« less

Using Long-Term Time-Lapse Imaging of Mammalian Cell Cycle Progression for Laboratory Instruction and Analysis

ERIC Educational Resources Information Center

Hinchcliffe, Edward H.

2005-01-01

Cinemicrography--the capture of moving cellular sequences through the microscope--has been influential in revealing the dynamic nature of cellular behavior. One of the more dramatic cellular events is mitosis, the division of sister chromatids into two daughter cells. Mitosis has been extensively studied in a variety of organisms, both…

Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

PubMed Central

Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali

2012-01-01

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250

Example-Based Super-Resolution Fluorescence Microscopy.

PubMed

Jia, Shu; Han, Boran; Kutz, J Nathan

2018-04-23

Capturing biological dynamics with high spatiotemporal resolution demands the advancement in imaging technologies. Super-resolution fluorescence microscopy offers spatial resolution surpassing the diffraction limit to resolve near-molecular-level details. While various strategies have been reported to improve the temporal resolution of super-resolution imaging, all super-resolution techniques are still fundamentally limited by the trade-off associated with the longer image acquisition time that is needed to achieve higher spatial information. Here, we demonstrated an example-based, computational method that aims to obtain super-resolution images using conventional imaging without increasing the imaging time. With a low-resolution image input, the method provides an estimate of its super-resolution image based on an example database that contains super- and low-resolution image pairs of biological structures of interest. The computational imaging of cellular microtubules agrees approximately with the experimental super-resolution STORM results. This new approach may offer potential improvements in temporal resolution for experimental super-resolution fluorescence microscopy and provide a new path for large-data aided biomedical imaging.

Computational and Pharmacological Target of Neurovascular Unit for Drug Design and Delivery

PubMed Central

2015-01-01

The blood-brain barrier (BBB) is a dynamic and highly selective permeable interface between central nervous system (CNS) and periphery that regulates the brain homeostasis. Increasing evidences of neurological disorders and restricted drug delivery process in brain make BBB as special target for further study. At present, neurovascular unit (NVU) is a great interest and highlighted topic of pharmaceutical companies for CNS drug design and delivery approaches. Some recent advancement of pharmacology and computational biology makes it convenient to develop drugs within limited time and affordable cost. In this review, we briefly introduce current understanding of the NVU, including molecular and cellular composition, physiology, and regulatory function. We also discuss the recent technology and interaction of pharmacogenomics and bioinformatics for drug design and step towards personalized medicine. Additionally, we develop gene network due to understand NVU associated transporter proteins interactions that might be effective for understanding aetiology of neurological disorders and new target base protective therapies development and delivery. PMID:26579539

A micro-epidemic model for primary dengue infection

NASA Astrophysics Data System (ADS)

Mishra, Arti; Gakkhar, Sunita

2017-06-01

In this paper, a micro-epidemic non-linear dynamical model has been proposed and analyzed for primary dengue infection. The model incorporates the effects of T cells immune response as well as humoral response during pathogenesis of dengue infection. The time delay has been accounted for production of antibodies from B cells. The basic reproduction number (R0) has been computed. Three equilibrium states are obtained. The existence and stability conditions for infection-free and ineffective cellular immune response state have been discussed. The conditions for existence of endemic state have been obtained. Further, the parametric region is obtained where system exhibits complex behavior. The threshold value of time delay has been computed which is critical for change in stability of endemic state. A threshold level for antibodies production rate has been obtained over which the infection will die out even though R0 > 1. The model is in line with the clinical observation that viral load decreases within 7-14 days from the onset of primary infection.

Computational exploration of neuron and neural network models in neurobiology.

PubMed

Prinz, Astrid A

2007-01-01

The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters-such as the number of ion channels of a given type in a neuron's membrane or the strength of a particular synapse-influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods.

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

PubMed

Oyetunde, Tolutola; Bao, Forrest Sheng; Chen, Jiung-Wen; Martin, Hector Garcia; Tang, Yinjie J

2018-05-03

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O 2 ). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development. Copyright © 2018. Published by Elsevier Inc.

Modeling of flow-induced shear stress applied on 3D cellular scaffolds: Implications for vascular tissue engineering.

PubMed

Lesman, Ayelet; Blinder, Yaron; Levenberg, Shulamit

2010-02-15

Novel tissue-culture bioreactors employ flow-induced shear stress as a means of mechanical stimulation of cells. We developed a computational fluid dynamics model of the complex three-dimensional (3D) microstructure of a porous scaffold incubated in a direct perfusion bioreactor. Our model was designed to predict high shear-stress values within the physiological range of those naturally sensed by vascular cells (1-10 dyne/cm(2)), and will thereby provide suitable conditions for vascular tissue-engineering experiments. The model also accounts for cellular growth, which was designed as an added cell layer grown on all scaffold walls. Five model variants were designed, with geometric differences corresponding to cell-layer thicknesses of 0, 50, 75, 100, and 125 microm. Four inlet velocities (0.5, 1, 1.5, and 2 cm/s) were applied to each model. Wall shear-stress distribution and overall pressure drop calculations were then used to characterize the relation between flow rate, shear stress, cell-layer thickness, and pressure drop. The simulations showed that cellular growth within 3D scaffolds exposes cells to elevated shear stress, with considerably increasing average values in correlation to cell growth and inflow velocity. Our results provide in-depth analysis of the microdynamic environment of cells cultured within 3D environments, and thus provide advanced control over tissue development in vitro. 2009 Wiley Periodicals, Inc.

Focus on the emerging new fields of network physiology and network medicine

NASA Astrophysics Data System (ADS)

Ivanov, Plamen Ch; Liu, Kang K. L.; Bartsch, Ronny P.

2016-10-01

Despite the vast progress and achievements in systems biology and integrative physiology in the last decades, there is still a significant gap in understanding the mechanisms through which (i) genomic, proteomic and metabolic factors and signaling pathways impact vertical processes across cells, tissues and organs leading to the expression of different disease phenotypes and influence the functional and clinical associations between diseases, and (ii) how diverse physiological systems and organs coordinate their functions over a broad range of space and time scales and horizontally integrate to generate distinct physiologic states at the organism level. Two emerging fields, network medicine and network physiology, aim to address these fundamental questions. Novel concepts and approaches derived from recent advances in network theory, coupled dynamical systems, statistical and computational physics show promise to provide new insights into the complexity of physiological structure and function in health and disease, bridging the genetic and sub-cellular level with inter-cellular interactions and communications among integrated organ systems and sub-systems. These advances form first building blocks in the methodological formalism and theoretical framework necessary to address fundamental problems and challenges in physiology and medicine. This ‘focus on’ issue contains 26 articles representing state-of-the-art contributions covering diverse systems from the sub-cellular to the organism level where physicists have key role in laying the foundations of these new fields.

Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics.

PubMed

Börlin, Christoph S; Lang, Verena; Hamacher-Brady, Anne; Brady, Nathan R

2014-09-10

Autophagy is a vesicle-mediated pathway for lysosomal degradation, essential under basal and stressed conditions. Various cellular components, including specific proteins, protein aggregates, organelles and intracellular pathogens, are targets for autophagic degradation. Thereby, autophagy controls numerous vital physiological and pathophysiological functions, including cell signaling, differentiation, turnover of cellular components and pathogen defense. Moreover, autophagy enables the cell to recycle cellular components to metabolic substrates, thereby permitting prolonged survival under low nutrient conditions. Due to the multi-faceted roles for autophagy in maintaining cellular and organismal homeostasis and responding to diverse stresses, malfunction of autophagy contributes to both chronic and acute pathologies. We applied a systems biology approach to improve the understanding of this complex cellular process of autophagy. All autophagy pathway vesicle activities, i.e. creation, movement, fusion and degradation, are highly dynamic, temporally and spatially, and under various forms of regulation. We therefore developed an agent-based model (ABM) to represent individual components of the autophagy pathway, subcellular vesicle dynamics and metabolic feedback with the cellular environment, thereby providing a framework to investigate spatio-temporal aspects of autophagy regulation and dynamic behavior. The rules defining our ABM were derived from literature and from high-resolution images of autophagy markers under basal and activated conditions. Key model parameters were fit with an iterative method using a genetic algorithm and a predefined fitness function. From this approach, we found that accurate prediction of spatio-temporal behavior required increasing model complexity by implementing functional integration of autophagy with the cellular nutrient state. The resulting model is able to reproduce short-term autophagic flux measurements (up to 3 hours) under basal and activated autophagy conditions, and to measure the degree of cell-to-cell variability. Moreover, we experimentally confirmed two model predictions, namely (i) peri-nuclear concentration of autophagosomes and (ii) inhibitory lysosomal feedback on mTOR signaling. Agent-based modeling represents a novel approach to investigate autophagy dynamics, function and dysfunction with high biological realism. Our model accurately recapitulates short-term behavior and cell-to-cell variability under basal and activated conditions of autophagy. Further, this approach also allows investigation of long-term behaviors emerging from biologically-relevant alterations to vesicle trafficking and metabolic state.

WESTPA: An interoperable, highly scalable software package for weighted ensemble simulation and analysis

PubMed Central

Zwier, Matthew C.; Adelman, Joshua L.; Kaus, Joseph W.; Pratt, Adam J.; Wong, Kim F.; Rego, Nicholas B.; Suárez, Ernesto; Lettieri, Steven; Wang, David W.; Grabe, Michael; Zuckerman, Daniel M.; Chong, Lillian T.

2015-01-01

The weighted ensemble (WE) path sampling approach orchestrates an ensemble of parallel calculations with intermittent communication to enhance the sampling of rare events, such as molecular associations or conformational changes in proteins or peptides. Trajectories are replicated and pruned in a way that focuses computational effort on under-explored regions of configuration space while maintaining rigorous kinetics. To enable the simulation of rare events at any scale (e.g. atomistic, cellular), we have developed an open-source, interoperable, and highly scalable software package for the execution and analysis of WE simulations: WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis). WESTPA scales to thousands of CPU cores and includes a suite of analysis tools that have been implemented in a massively parallel fashion. The software has been designed to interface conveniently with any dynamics engine and has already been used with a variety of molecular dynamics (e.g. GROMACS, NAMD, OpenMM, AMBER) and cell-modeling packages (e.g. BioNetGen, MCell). WESTPA has been in production use for over a year, and its utility has been demonstrated for a broad set of problems, ranging from atomically detailed host-guest associations to non-spatial chemical kinetics of cellular signaling networks. The following describes the design and features of WESTPA, including the facilities it provides for running WE simulations, storing and analyzing WE simulation data, as well as examples of input and output. PMID:26392815

Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure.

EPA Pesticide Factsheets

Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals.Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the ??tipping point?? at which the cells did not show recovery towards a normal phenotypic state.Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 03bcM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, 03b1-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories.Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 03bcM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories.Conclusions: These findings show t

Command-line cellular electrophysiology for conventional and real-time closed-loop experiments.

PubMed

Linaro, Daniele; Couto, João; Giugliano, Michele

2014-06-15

Current software tools for electrophysiological experiments are limited in flexibility and rarely offer adequate support for advanced techniques such as dynamic clamp and hybrid experiments, which are therefore limited to laboratories with a significant expertise in neuroinformatics. We have developed lcg, a software suite based on a command-line interface (CLI) that allows performing both standard and advanced electrophysiological experiments. Stimulation protocols for classical voltage and current clamp experiments are defined by a concise and flexible meta description that allows representing complex waveforms as a piece-wise parametric decomposition of elementary sub-waveforms, abstracting the stimulation hardware. To perform complex experiments lcg provides a set of elementary building blocks that can be interconnected to yield a large variety of experimental paradigms. We present various cellular electrophysiological experiments in which lcg has been employed, ranging from the automated application of current clamp protocols for characterizing basic electrophysiological properties of neurons, to dynamic clamp, response clamp, and hybrid experiments. We finally show how the scripting capabilities behind a CLI are suited for integrating experimental trials into complex workflows, where actual experiment, online data analysis and computational modeling seamlessly integrate. We compare lcg with two open source toolboxes, RTXI and RELACS. We believe that lcg will greatly contribute to the standardization and reproducibility of both simple and complex experiments. Additionally, on the long run the increased efficiency due to a CLI will prove a great benefit for the experimental community. Copyright © 2014 Elsevier B.V. All rights reserved.

Classification of Dynamical Diffusion States in Single Molecule Tracking Microscopy

PubMed Central

Bosch, Peter J.; Kanger, Johannes S.; Subramaniam, Vinod

2014-01-01

Single molecule tracking of membrane proteins by fluorescence microscopy is a promising method to investigate dynamic processes in live cells. Translating the trajectories of proteins to biological implications, such as protein interactions, requires the classification of protein motion within the trajectories. Spatial information of protein motion may reveal where the protein interacts with cellular structures, because binding of proteins to such structures often alters their diffusion speed. For dynamic diffusion systems, we provide an analytical framework to determine in which diffusion state a molecule is residing during the course of its trajectory. We compare different methods for the quantification of motion to utilize this framework for the classification of two diffusion states (two populations with different diffusion speed). We found that a gyration quantification method and a Bayesian statistics-based method are the most accurate in diffusion-state classification for realistic experimentally obtained datasets, of which the gyration method is much less computationally demanding. After classification of the diffusion, the lifetime of the states can be determined, and images of the diffusion states can be reconstructed at high resolution. Simulations validate these applications. We apply the classification and its applications to experimental data to demonstrate the potential of this approach to obtain further insights into the dynamics of cell membrane proteins. PMID:25099798

An extended model of vesicle fusion at the plasma membrane to estimate protein lateral diffusion from TIRF microscopy images.

PubMed

Basset, Antoine; Bouthemy, Patrick; Boulanger, Jérôme; Waharte, François; Salamero, Jean; Kervrann, Charles

2017-07-24

Characterizing membrane dynamics is a key issue to understand cell exchanges with the extra-cellular medium. Total internal reflection fluorescence microscopy (TIRFM) is well suited to focus on the late steps of exocytosis at the plasma membrane. However, it is still a challenging task to quantify (lateral) diffusion and estimate local dynamics of proteins. A new model was introduced to represent the behavior of cargo transmembrane proteins during the vesicle fusion to the plasma membrane at the end of the exocytosis process. Two biophysical parameters, the diffusion coefficient and the release rate parameter, are automatically estimated from TIRFM image sequences, to account for both the lateral diffusion of molecules at the membrane and the continuous release of the proteins from the vesicle to the plasma membrane. Quantitative evaluation on 300 realistic computer-generated image sequences demonstrated the efficiency and accuracy of the method. The application of our method on 16 real TIRFM image sequences additionally revealed differences in the dynamic behavior of Transferrin Receptor (TfR) and Langerin proteins. An automated method has been designed to simultaneously estimate the diffusion coefficient and the release rate for each individual vesicle fusion event at the plasma membrane in TIRFM image sequences. It can be exploited for further deciphering cell membrane dynamics.

Using process algebra to develop predator-prey models of within-host parasite dynamics.

PubMed

McCaig, Chris; Fenton, Andy; Graham, Andrea; Shankland, Carron; Norman, Rachel

2013-07-21

As a first approximation of immune-mediated within-host parasite dynamics we can consider the immune response as a predator, with the parasite as its prey. In the ecological literature of predator-prey interactions there are a number of different functional responses used to describe how a predator reproduces in response to consuming prey. Until recently most of the models of the immune system that have taken a predator-prey approach have used simple mass action dynamics to capture the interaction between the immune response and the parasite. More recently Fenton and Perkins (2010) employed three of the most commonly used prey-dependent functional response terms from the ecological literature. In this paper we make use of a technique from computing science, process algebra, to develop mathematical models. The novelty of the process algebra approach is to allow stochastic models of the population (parasite and immune cells) to be developed from rules of individual cell behaviour. By using this approach in which individual cellular behaviour is captured we have derived a ratio-dependent response similar to that seen in the previous models of immune-mediated parasite dynamics, confirming that, whilst this type of term is controversial in ecological predator-prey models, it is appropriate for models of the immune system. Copyright © 2013 Elsevier Ltd. All rights reserved.

WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

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

Stewart, R.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

Decoding the dynamics of cellular metabolism and the action of 3-bromopyruvate and 2-deoxyglucose using pulsed stable isotope-resolved metabolomics.

PubMed

Pietzke, Matthias; Zasada, Christin; Mudrich, Susann; Kempa, Stefan

2014-01-01

Cellular metabolism is highly dynamic and continuously adjusts to the physiological program of the cell. The regulation of metabolism appears at all biological levels: (post-) transcriptional, (post-) translational, and allosteric. This regulatory information is expressed in the metabolome, but in a complex manner. To decode such complex information, new methods are needed in order to facilitate dynamic metabolic characterization at high resolution. Here, we describe pulsed stable isotope-resolved metabolomics (pSIRM) as a tool for the dynamic metabolic characterization of cellular metabolism. We have adapted gas chromatography-coupled mass spectrometric methods for metabolomic profiling and stable isotope-resolved metabolomics. In addition, we have improved robustness and reproducibility and implemented a strategy for the absolute quantification of metabolites. By way of examples, we have applied this methodology to characterize central carbon metabolism of a panel of cancer cell lines and to determine the mode of metabolic inhibition of glycolytic inhibitors in times ranging from minutes to hours. Using pSIRM, we observed that 2-deoxyglucose is a metabolic inhibitor, but does not directly act on the glycolytic cascade.

Free energy calculation of permeant-membrane interactions using molecular dynamics simulations.

PubMed

Elvati, Paolo; Violi, Angela

2012-01-01

Nanotoxicology, the science concerned with the safe use of nanotechnology and nanostructure design for biological applications, is a field of research that has recently received great attention, as a result of the rapid growth in nanotechnology. Many nanostructures are of a scale and chemical composition similar to many biomolecular environments, and recent papers have reported evident toxicity of selected nanoparticles. Molecular simulations can help develop a mechanistic understanding of how structural properties affect bioactivity. In this chapter, we describe how to compute the free energy of interactions between cellular membranes and benzene, the main constituent of some toxic carbonaceous particles, with well-tempered metadynamics. This algorithm reconstructs the free energy surface and accelerates rare events in a coarse-grained representation of the system.

Parallel Implementation of Triangular Cellular Automata for Computing Two-Dimensional Elastodynamic Response on Arbitrary Domains

NASA Astrophysics Data System (ADS)

Leamy, Michael J.; Springer, Adam C.

In this research we report parallel implementation of a Cellular Automata-based simulation tool for computing elastodynamic response on complex, two-dimensional domains. Elastodynamic simulation using Cellular Automata (CA) has recently been presented as an alternative, inherently object-oriented technique for accurately and efficiently computing linear and nonlinear wave propagation in arbitrarily-shaped geometries. The local, autonomous nature of the method should lead to straight-forward and efficient parallelization. We address this notion on symmetric multiprocessor (SMP) hardware using a Java-based object-oriented CA code implementing triangular state machines (i.e., automata) and the MPI bindings written in Java (MPJ Express). We use MPJ Express to reconfigure our existing CA code to distribute a domain's automata to cores present on a dual quad-core shared-memory system (eight total processors). We note that this message passing parallelization strategy is directly applicable to computer clustered computing, which will be the focus of follow-on research. Results on the shared memory platform indicate nearly-ideal, linear speed-up. We conclude that the CA-based elastodynamic simulator is easily configured to run in parallel, and yields excellent speed-up on SMP hardware.

Traffic jam dynamics in stochastic cellular automata

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

Nagel, K.; Schreckenberg, M.

1995-09-01

Simple models for particles hopping on a grid (cellular automata) are used to simulate (single lane) traffic flow. Despite their simplicity, these models are astonishingly realistic in reproducing start-stop-waves and realistic fundamental diagrams. One can use these models to investigate traffic phenomena near maximum flow. A so-called phase transition at average maximum flow is visible in the life-times of jams. The resulting dynamic picture is consistent with recent fluid-dynamical results by Kuehne/Kerner/Konhaeuser, and with Treiterer`s hysteresis description. This places CA models between car-following models and fluid-dynamical models for traffic flow. CA models are tested in projects in Los Alamos (USA)more » and in NRW (Germany) for large scale microsimulations of network traffic.« less

Traffic dynamics of an on-ramp system with a cellular automaton model

NASA Astrophysics Data System (ADS)

Li, Xin-Gang; Gao, Zi-You; Jia, Bin; Jiang, Rui

2010-06-01

This paper uses the cellular automaton model to study the dynamics of traffic flow around an on-ramp with an acceleration lane. It adopts a parameter, which can reflect different lane-changing behaviour, to represent the diversity of driving behaviour. The refined cellular automaton model is used to describe the lower acceleration rate of a vehicle. The phase diagram and the capacity of the on-ramp system are investigated. The simulation results show that in the single cell model, the capacity of the on-ramp system will stay at the highest flow of a one lane system when the driver is moderate and careful; it will be reduced when the driver is aggressive. In the refined cellular automaton model, the capacity is always reduced even when the driver is careful. It proposes that the capacity drop of the on-ramp system is caused by aggressive lane-changing behaviour and lower acceleration rate.

Tissue organization by cadherin adhesion molecules: dynamic molecular and cellular mechanisms of morphogenetic regulation

PubMed Central

Niessen, Carien M.; Leckband, Deborah; Yap, Alpha S.

2013-01-01

This review addresses the cellular and molecular mechanisms of cadherin-based tissue morphogenesis. Tissue physiology is profoundly influenced by the distinctive organizations of cells in organs and tissues. In metazoa, adhesion receptors of the classical cadherin family play important roles in establishing and maintaining such tissue organization. Indeed, it is apparent that cadherins participate in a range of morphogenetic events that range from support of tissue integrity to dynamic cellular rearrangements. A comprehensive understanding of cadherin-based morphogenesis must then define the molecular and cellular mechanisms that support these distinct cadherin biologies. Here we focus on four key mechanistic elements: the molecular basis for adhesion through cadherin ectodomains; the regulation of cadherin expression at the cell surface; cooperation between cadherins and the actin cytoskeleton; and regulation by cell signaling. We discuss current progress and outline issues for further research in these fields. PMID:21527735

Nanoparticles engineered to bind cellular motors for efficient delivery.

PubMed

Dalmau-Mena, Inmaculada; Del Pino, Pablo; Pelaz, Beatriz; Cuesta-Geijo, Miguel Ángel; Galindo, Inmaculada; Moros, María; de la Fuente, Jesús M; Alonso, Covadonga

2018-03-30

Dynein is a cytoskeletal molecular motor protein that transports cellular cargoes along microtubules. Biomimetic synthetic peptides designed to bind dynein have been shown to acquire dynamic properties such as cell accumulation and active intra- and inter-cellular motion through cell-to-cell contacts and projections to distant cells. On the basis of these properties dynein-binding peptides could be used to functionalize nanoparticles for drug delivery applications. Here, we show that gold nanoparticles modified with dynein-binding delivery sequences become mobile, powered by molecular motor proteins. Modified nanoparticles showed dynamic properties, such as travelling the cytosol, crossing intracellular barriers and shuttling the nuclear membrane. Furthermore, nanoparticles were transported from one cell to another through cell-to-cell contacts and quickly spread to distant cells through cell projections. The capacity of these motor-bound nanoparticles to spread to many cells and increasing cellular retention, thus avoiding losses and allowing lower dosage, could make them candidate carriers for drug delivery.

Mobile Computing and Ubiquitous Networking: Concepts, Technologies and Challenges.

ERIC Educational Resources Information Center

Pierre, Samuel

2001-01-01

Analyzes concepts, technologies and challenges related to mobile computing and networking. Defines basic concepts of cellular systems. Describes the evolution of wireless technologies that constitute the foundations of mobile computing and ubiquitous networking. Presents characterization and issues of mobile computing. Analyzes economical and…

Protein dynamics and motions in relation to their functions: several case studies and the underlying mechanisms

PubMed Central

Yang, Li-Quan; Sang, Peng; Tao, Yan; Fu, Yun-Xin; Zhang, Ke-Qin; Xie, Yue-Hui; Liu, Shu-Qun

2013-01-01

Proteins are dynamic entities in cellular solution with functions governed essentially by their dynamic personalities. We review several dynamics studies on serine protease proteinase K and HIV-1 gp120 envelope glycoprotein to demonstrate the importance of investigating the dynamic behaviors and molecular motions for a complete understanding of their structure–function relationships. Using computer simulations and essential dynamic (ED) analysis approaches, the dynamics data obtained revealed that: (i) proteinase K has highly flexible substrate-binding site, thus supporting the induced-fit or conformational selection mechanism of substrate binding; (ii) Ca2+ removal from proteinase K increases the global conformational flexibility, decreases the local flexibility of substrate-binding region, and does not influence the thermal motion of catalytic triad, thus explaining the experimentally determined decreased thermal stability, reduced substrate affinity, and almost unchanged catalytic activity upon Ca2+ removal; (iii) substrate binding affects the large concerted motions of proteinase K, and the resulting dynamic pocket can be connected to substrate binding, orientation, and product release; (iv) amino acid mutations 375 S/W and 423 I/P of HIV-1 gp120 have distinct effects on molecular motions of gp120, facilitating 375 S/W mutant to assume the CD4-bound conformation, while 423 I/P mutant to prefer for CD4-unliganded state. The mechanisms underlying protein dynamics and protein–ligand binding, including the concept of the free energy landscape (FEL) of the protein–solvent system, how the ruggedness and variability of FEL determine protein's dynamics, and how the three ligand-binding models, the lock-and-key, induced-fit, and conformational selection are rationalized based on the FEL theory are discussed in depth. PMID:23527883

Protein dynamics and motions in relation to their functions: several case studies and the underlying mechanisms.

PubMed

Yang, Li-Quan; Sang, Peng; Tao, Yan; Fu, Yun-Xin; Zhang, Ke-Qin; Xie, Yue-Hui; Liu, Shu-Qun

2014-01-01

Proteins are dynamic entities in cellular solution with functions governed essentially by their dynamic personalities. We review several dynamics studies on serine protease proteinase K and HIV-1 gp120 envelope glycoprotein to demonstrate the importance of investigating the dynamic behaviors and molecular motions for a complete understanding of their structure-function relationships. Using computer simulations and essential dynamic (ED) analysis approaches, the dynamics data obtained revealed that: (i) proteinase K has highly flexible substrate-binding site, thus supporting the induced-fit or conformational selection mechanism of substrate binding; (ii) Ca(2+) removal from proteinase K increases the global conformational flexibility, decreases the local flexibility of substrate-binding region, and does not influence the thermal motion of catalytic triad, thus explaining the experimentally determined decreased thermal stability, reduced substrate affinity, and almost unchanged catalytic activity upon Ca(2+) removal; (iii) substrate binding affects the large concerted motions of proteinase K, and the resulting dynamic pocket can be connected to substrate binding, orientation, and product release; (iv) amino acid mutations 375 S/W and 423 I/P of HIV-1 gp120 have distinct effects on molecular motions of gp120, facilitating 375 S/W mutant to assume the CD4-bound conformation, while 423 I/P mutant to prefer for CD4-unliganded state. The mechanisms underlying protein dynamics and protein-ligand binding, including the concept of the free energy landscape (FEL) of the protein-solvent system, how the ruggedness and variability of FEL determine protein's dynamics, and how the three ligand-binding models, the lock-and-key, induced-fit, and conformational selection are rationalized based on the FEL theory are discussed in depth.

[Homeostasis and Disorder of Musculoskeletal System.Cellular dynamics in musculoskeletal system visualized by intravital imaging techniques.

PubMed

Kikuta, Junichi; Ishii, Masaru

Bone is continually remodeled by bone-resorbing osteoclasts and bone-forming osteoblasts. Although it has long been believed that bone homeostasis is tightly regulated by communication between osteoclasts and osteoblasts, the fundamental process and dynamics have remained elusive. We originally established an advanced imaging system to visualize living bone tissues using intravital two-photon microscopy. By means of this system, we revealed the in vivo behavior of bone-resorbing osteoclasts and bone-forming osteoblasts in bone tissues. This approach facilitates investigation of cellular dynamics in the pathogenesis of musculoskeletal disorders, and would thus be useful for evaluating the efficacy of novel therapeutic agents.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Using Movies to Analyse Gene Circuit Dynamics in Single Cells

PubMed Central

Locke, James CW; Elowitz, Michael B

2010-01-01

Preface Many bacterial systems rely on dynamic genetic circuits to control critical processes. A major goal of systems biology is to understand these behaviours in terms of individual genes and their interactions. However, traditional techniques based on population averages wash out critical dynamics that are either unsynchronized between cells or driven by fluctuations, or ‘noise,’ in cellular components. Recently, the combination of time-lapse microscopy, quantitative image analysis, and fluorescent protein reporters has enabled direct observation of multiple cellular components over time in individual cells. In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic circuit behaviour in diverse microbial systems. PMID:19369953

Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

DOE PAGES

Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

2015-04-09

For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are onemore » of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.« less

Parameterizing the Morse Potential for Coarse-Grained Modeling of Blood Plasma

PubMed Central

Zhang, Na; Zhang, Peng; Kang, Wei; Bluestein, Danny; Deng, Yuefan

2014-01-01

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters are systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately. PMID:24910470

Measuring competitive fitness in dynamic environments.

PubMed

Razinkov, Ivan A; Baumgartner, Bridget L; Bennett, Matthew R; Tsimring, Lev S; Hasty, Jeff

2013-10-24

Most yeast genes are dispensable for optimal growth in laboratory cultures. However, this apparent lack of fitness contribution is difficult to reconcile with the theory of natural selection. Here we use stochastic modeling to show that environmental fluctuations can select for a genetic mechanism that does not affect growth in static laboratory environments. We then present a novel experimental platform for measuring the fitness levels of specific genotypes in fluctuating environments. We test this platform by monitoring a mixed culture of two yeast strains that differ in their ability to respond to changes in carbon source yet exhibit the same fitness level in static conditions. When the sugar in the growth medium was switched between galactose and glucose, the wild-type strain gained a growth advantage over the mutant strain. Interestingly, both our computational and experimental results show that the strength of the adaptive advantage conveyed by the wild-type genotype depends on the total number of carbon source switches, not on the frequency of these fluctuations. Our results illustrate the selective power of environmental fluctuations on seemingly slight phenotypic differences in cellular response dynamics and underscore the importance of dynamic processes in the evolution of species.

Fasting and refeeding induces changes in the mouse hepatic lipid droplet proteome.

PubMed

Kramer, David A; Quiroga, Ariel D; Lian, Jihong; Fahlman, Richard P; Lehner, Richard

2018-06-15

During fasting, the liver increases lipid storage as a mean to reserve and provide energy for vital cellular functions. After re-feeding, hepatocytes rapidly decrease the amount of triacylglycerol that is stored in lipid droplets (LDs), visible as the size of hepatic LDs significantly decreases after re-feeding. Little is known about the changes in the liver LD proteome that occur during the fasting/re-feeding transition. This study aimed to investigate the hepatic LD proteome in fasted and re-fed conditions in the mouse. Using label-free LC-MS/MS analysis the relative abundance of 817 proteins was determined in highly purified LDs. Comparative analysis for differential protein abundance with respect to feeding states revealed 130 with higher abundance in LDs from fasted mice and 31 in LDs from re-fed mice. Among proteins observed to have higher abundance on LDs in the fasted state we found perilipin-5, and several mitochondrial and peroxisomal marker proteins, supporting the role of LDs in the provision of substrates for fatty acid oxidation. Proteins of higher abundance upon re-feeding included several peroxisomal and mitochondrial marker proteins and expand our understanding of the dynamic nature of the hepatic LD proteome according to the energetic requirements of the cell. Proteomic investigations have been revealing the complexities and dynamics of cellular LDs from a variety of cell types. As these sub-cellular structures are truly dynamic in nature, our investigations reveal that simply the feeding state of an animal leads to significant changes to the protein composition of LDs and suggest a variety of dynamic interactions with other cellular organelles, such as the mitochondria and peroxisomes. As such, the experimental design for investigations of this cellular structure must consider this dynamic baseline. Lastly our analysis on global protein abundance has revealed the unforeseen high abundance of murine major urinary proteins associated with hepatic lipid droplets, which warrants further investigations. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Crash energy management on the base of Movable cellular automata method

NASA Astrophysics Data System (ADS)

Psakhie, Serguei; Dmitriev, Andrei; Shilko, Evgueni; Tatarintsev, Evgueni; Korostelev, Serguei

2001-06-01

One of the main problems of materials science is increasing of structure's viability under dynamic loading. In general, a solution is the management of transformation of the energy of loading to the energy of destroying of the least important parts and details of the structure. It has to be noted that similar problem also exists in materials science, since a majority of modern materials are heterogeneous and have a complex internal structure. To optimize this structure for working under dynamic loading it is necessary to take into account the redistribution of elastic energy including phase transformation, generation and accumulation of micro-damages, etc. As far as real experiments on destroying the complex objects are sufficiently expensive and getting of detailed information is often associates with essential difficulties, the methods of computer modeling are used in solving the similar problems. As a rule, these are the methods of continuum mechanics. Although essential achievements have been obtained on the basis of these methods the continuum approach has several limitations, connected first of all with the possibility of description of generation of damages, formation and development of cracks and mass mixing effects. These problems may be solved on the basis of the Movable Cellular Automata (MCA) method, which has been successfully used for modeling fracture of the different material and structures In the paper behavior and peculiarities of failure of complex structures and materials under dynamic loading are studied on the basis of computer modeling. The results shown that sometimes even small changes of the internal structure leads to the significant increasing of the viability of the complex structures and materials. It is due to the elastic energy flux change over during the dynamical loading. This effect may be explained by the fact that elastic energy fluxes define the current stress concentration. Namely, because the area of inclusions are subjected by the largest displacement and due to less Young modulus of inclusions the loading pulses are transferred towards the other parts of the sample. This leads to "blurring" of the stress concentrators and conservation of wholeness of the structure. In its turn, this leads to essential raising up of threshold value of "injected" energy, i.e. the energy absorbed by the structure before loss of its carrying capacity. Practically, elastic energy "circulates" in the structure until a stress concentrator appears, which power will be sufficient for forming a macro-cracks. The results demonstrate a possibility of managing the fracture process under dynamic loading and raising viability of structures and heterogeneous materials by changing their internal structure, geometry, so by entering the specific inclusions.

WE-DE-202-00: Connecting Radiation Physics with Computational Biology

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

NONE

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

The Development of a Novel High Throughput Computational Tool for Studying Individual and Collective Cellular Migration

PubMed Central

Chapnick, Douglas A.; Jacobsen, Jeremy; Liu, Xuedong

2013-01-01

Understanding how cells migrate individually and collectively during development and cancer metastasis can be significantly aided by a computation tool to accurately measure not only cellular migration speed, but also migration direction and changes in migration direction in a temporal and spatial manner. We have developed such a tool for cell migration researchers, named Pathfinder, which is capable of simultaneously measuring the migration speed, migration direction, and changes in migration directions of thousands of cells both instantaneously and over long periods of time from fluorescence microscopy data. Additionally, we demonstrate how the Pathfinder software can be used to quantify collective cell migration. The novel capability of the Pathfinder software to measure the changes in migration direction of large populations of cells in a spatiotemporal manner will aid cellular migration research by providing a robust method for determining the mechanisms of cellular guidance during individual and collective cell migration. PMID:24386097

Self-organized perturbations enhance class IV behavior and 1/f power spectrum in elementary cellular automata.

PubMed

Nakajima, Kohei; Haruna, Taichi

2011-09-01

In this paper, we propose a new class of cellular automata based on the modification of its state space. It is introduced to model a computation which is exposed to an environment. We formalized the computation as extension and projection processes of its state space and resulting misidentifications of the state. This is motivated to embed the role of an environment into the system itself, which naturally induces self-organized internal perturbations rather than the usual external perturbations. Implementing this structure into the elementary cellular automata, we characterized its effect by means of input entropy and power spectral analysis. As a result, the cellular automata with this structure showed robust class IV behavior and a 1/f power spectrum in a wide range of rule space comparative to the notion of the edge of chaos. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Inferring cortical function in the mouse visual system through large-scale systems neuroscience.

PubMed

Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

2016-07-05

The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.

Biophysical Discovery through the Lens of a Computational Microscope

NASA Astrophysics Data System (ADS)

Amaro, Rommie

With exascale computing power on the horizon, improvements in the underlying algorithms and available structural experimental data are enabling new paradigms for chemical discovery. My work has provided key insights for the systematic incorporation of structural information resulting from state-of-the-art biophysical simulations into protocols for inhibitor and drug discovery. We have shown that many disease targets have druggable pockets that are otherwise ``hidden'' in high resolution x-ray structures, and that this is a common theme across a wide range of targets in different disease areas. We continue to push the limits of computational biophysical modeling by expanding the time and length scales accessible to molecular simulation. My sights are set on, ultimately, the development of detailed physical models of cells, as the fundamental unit of life, and two recent achievements highlight our efforts in this arena. First is the development of a molecular and Brownian dynamics multi-scale modeling framework, which allows us to investigate drug binding kinetics in addition to thermodynamics. In parallel, we have made significant progress developing new tools to extend molecular structure to cellular environments. Collectively, these achievements are enabling the investigation of the chemical and biophysical nature of cells at unprecedented scales.

SIRT6 knockout cells resist apoptosis initiation but not progression: a computational method to evaluate the progression of apoptosis.

PubMed

Domanskyi, Sergii; Nicholatos, Justin W; Schilling, Joshua E; Privman, Vladimir; Libert, Sergiy

2017-11-01

Apoptosis is essential for numerous processes, such as development, resistance to infections, and suppression of tumorigenesis. Here, we investigate the influence of the nutrient sensing and longevity-assuring enzyme SIRT6 on the dynamics of apoptosis triggered by serum starvation. Specifically, we characterize the progression of apoptosis in wild type and SIRT6 deficient mouse embryonic fibroblasts using time-lapse flow cytometry and computational modelling based on rate-equations and cell distribution analysis. We find that SIRT6 deficient cells resist apoptosis by delaying its initiation. Interestingly, once apoptosis is initiated, the rate of its progression is higher in SIRT6 null cells compared to identically cultured wild type cells. However, SIRT6 null cells succumb to apoptosis more slowly, not only in response to nutrient deprivation but also in response to other stresses. Our data suggest that SIRT6 plays a role in several distinct steps of apoptosis. Overall, we demonstrate the utility of our computational model to describe stages of apoptosis progression and the integrity of the cellular membrane. Such measurements will be useful in a broad range of biological applications.

Examining the architecture of cellular computing through a comparative study with a computer

PubMed Central

Wang, Degeng; Gribskov, Michael

2005-01-01

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software–hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's ‘hardware’ equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the ‘bandwidth’ of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed. PMID:16849179

Examining the architecture of cellular computing through a comparative study with a computer.

PubMed

Wang, Degeng; Gribskov, Michael

2005-06-22

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software-hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's "hardware" equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the "bandwidth" of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed.

Dynamics of propagation of premature impulses in structurally remodeled infarcted myocardium: a computational analysis

PubMed Central

Cabo, Candido

2014-01-01

Initiation of cardiac arrhythmias typically follows one or more premature impulses either occurring spontaneously or applied externally. In this study, we characterize the dynamics of propagation of single (S2) and double premature impulses (S3), and the mechanisms of block of premature impulses at structural heterogeneities caused by remodeling of gap junctional conductance (Gj) in infarcted myocardium. Using a sub-cellular computer model of infarcted tissue, we found that |INa,max|, prematurity (coupling interval with the previous impulse), and conduction velocity (CV) of premature impulses change dynamically as they propagate away from the site of initiation. There are fundamental differences between the dynamics of propagation of S2 and S3 premature impulses: for S2 impulses |INa,max| recovers fast, prematurity decreases and CV increases as propagation proceeds; for S3 impulses low values of |INa,max| persist, prematurity could increase, and CV could decrease as impulses propagate away from the site of initiation. As a consequence it is more likely that S3 impulses block at sites of structural heterogeneities causing source/sink mismatch than S2 impulses block. Whether premature impulses block at Gj heterogeneities or not is also determined by the values of Gj (and the space constant λ) in the regions proximal and distal to the heterogeneity: when λ in the direction of propagation increases >40%, premature impulses could block. The maximum slope of CV restitution curves for S2 impulses is larger than for S3 impulses. In conclusion: (1) The dynamics of propagation of premature impulses make more likely that S3 impulses block at sites of structural heterogeneities than S2 impulses block; (2) Structural heterogeneities causing an increase in λ (or CV) of >40% could result in block of premature impulses; (3) A decrease in the maximum slope of CV restitution curves of propagating premature impulses is indicative of an increased potential for block at structural heterogeneities. PMID:25566085

Dynamics of propagation of premature impulses in structurally remodeled infarcted myocardium: a computational analysis.

PubMed

Cabo, Candido

2014-01-01

Initiation of cardiac arrhythmias typically follows one or more premature impulses either occurring spontaneously or applied externally. In this study, we characterize the dynamics of propagation of single (S2) and double premature impulses (S3), and the mechanisms of block of premature impulses at structural heterogeneities caused by remodeling of gap junctional conductance (Gj) in infarcted myocardium. Using a sub-cellular computer model of infarcted tissue, we found that |INa,max|, prematurity (coupling interval with the previous impulse), and conduction velocity (CV) of premature impulses change dynamically as they propagate away from the site of initiation. There are fundamental differences between the dynamics of propagation of S2 and S3 premature impulses: for S2 impulses |INa,max| recovers fast, prematurity decreases and CV increases as propagation proceeds; for S3 impulses low values of |INa,max| persist, prematurity could increase, and CV could decrease as impulses propagate away from the site of initiation. As a consequence it is more likely that S3 impulses block at sites of structural heterogeneities causing source/sink mismatch than S2 impulses block. Whether premature impulses block at Gj heterogeneities or not is also determined by the values of Gj (and the space constant λ) in the regions proximal and distal to the heterogeneity: when λ in the direction of propagation increases >40%, premature impulses could block. The maximum slope of CV restitution curves for S2 impulses is larger than for S3 impulses. (1) The dynamics of propagation of premature impulses make more likely that S3 impulses block at sites of structural heterogeneities than S2 impulses block; (2) Structural heterogeneities causing an increase in λ (or CV) of >40% could result in block of premature impulses; (3) A decrease in the maximum slope of CV restitution curves of propagating premature impulses is indicative of an increased potential for block at structural heterogeneities.

Differential effects of LifeAct-GFP and actin-GFP on cell mechanics assessed using micropipette aspiration.

PubMed

Sliogeryte, Kristina; Thorpe, Stephen D; Wang, Zhao; Thompson, Clare L; Gavara, Nuria; Knight, Martin M

2016-01-25

The actin cytoskeleton forms a dynamic structure involved in many fundamental cellular processes including the control of cell morphology, migration and biomechanics. Recently LifeAct-GFP (green fluorescent protein) has been proposed for visualising actin structure and dynamics in live cells as an alternative to actin-GFP which has been shown to affect cell mechanics. Here we compare the two approaches in terms of their effect on cellular mechanical behaviour. Human mesenchymal stem cells (hMSCs) were analysed using micropipette aspiration and the effective cellular equilibrium and instantaneous moduli calculated using the standard linear solid model. We show that LifeAct-GFP provides clearer visualisation of F-actin organisation and dynamics. Furthermore, LifeAct-GFP does not alter effective cellular mechanical properties whereas actin-GFP expression causes an increase in the cell modulus. Interestingly, LifeAct-GFP expression did produce a small (~10%) increase in the percentage of cells exhibiting aspiration-induced membrane bleb formation, whilst actin-GFP expression reduced blebbing. Further studies examined the influence of LifeAct-GFP in other cell types, namely chondrogenically differentiated hMSCs and murine chondrocytes. LifeAct-GFP also had no effect on the moduli of these non-blebbing cells for which mechanical properties are largely dependent on the actin cortex. In conclusion we show that LifeAct-GFP enables clearer visualisation of actin organisation and dynamics without disruption of the biomechanical properties of either the whole cell or the actin cortex. Thus the study provides new evidence supporting the use of LifeAct-GFP rather than actin-GFP for live cell microscopy and the study of cellular mechanobiology. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

Bisio, Alessandro; D’Ariano, Giacomo Mauro; Tosini, Alessandro, E-mail: alessandro.tosini@unipv.it

We present a quantum cellular automaton model in one space-dimension which has the Dirac equation as emergent. This model, a discrete-time and causal unitary evolution of a lattice of quantum systems, is derived from the assumptions of homogeneity, parity and time-reversal invariance. The comparison between the automaton and the Dirac evolutions is rigorously set as a discrimination problem between unitary channels. We derive an exact lower bound for the probability of error in the discrimination as an explicit function of the mass, the number and the momentum of the particles, and the duration of the evolution. Computing this bound withmore » experimentally achievable values, we see that in that regime the QCA model cannot be discriminated from the usual Dirac evolution. Finally, we show that the evolution of one-particle states with narrow-band in momentum can be efficiently simulated by a dispersive differential equation for any regime. This analysis allows for a comparison with the dynamics of wave-packets as it is described by the usual Dirac equation. This paper is a first step in exploring the idea that quantum field theory could be grounded on a more fundamental quantum cellular automaton model and that physical dynamics could emerge from quantum information processing. In this framework, the discretization is a central ingredient and not only a tool for performing non-perturbative calculation as in lattice gauge theory. The automaton model, endowed with a precise notion of local observables and a full probabilistic interpretation, could lead to a coherent unification of a hypothetical discrete Planck scale with the usual Fermi scale of high-energy physics. - Highlights: • The free Dirac field in one space dimension as a quantum cellular automaton. • Large scale limit of the automaton and the emergence of the Dirac equation. • Dispersive differential equation for the evolution of smooth states on the automaton. • Optimal discrimination between the automaton evolution and the Dirac equation.« less

Dissecting the Calcium-Induced Differentiation of Human Primary Keratinocytes Stem Cells by Integrative and Structural Network Analyses

PubMed Central

Toufighi, Kiana; Yang, Jae-Seong; Luis, Nuno Miguel; Aznar Benitah, Salvador; Lehner, Ben; Serrano, Luis; Kiel, Christina

2015-01-01

The molecular details underlying the time-dependent assembly of protein complexes in cellular networks, such as those that occur during differentiation, are largely unexplored. Focusing on the calcium-induced differentiation of primary human keratinocytes as a model system for a major cellular reorganization process, we look at the expression of genes whose products are involved in manually-annotated protein complexes. Clustering analyses revealed only moderate co-expression of functionally related proteins during differentiation. However, when we looked at protein complexes, we found that the majority (55%) are composed of non-dynamic and dynamic gene products (‘di-chromatic’), 19% are non-dynamic, and 26% only dynamic. Considering three-dimensional protein structures to predict steric interactions, we found that proteins encoded by dynamic genes frequently interact with a common non-dynamic protein in a mutually exclusive fashion. This suggests that during differentiation, complex assemblies may also change through variation in the abundance of proteins that compete for binding to common proteins as found in some cases for paralogous proteins. Considering the example of the TNF-α/NFκB signaling complex, we suggest that the same core complex can guide signals into diverse context-specific outputs by addition of time specific expressed subunits, while keeping other cellular functions constant. Thus, our analysis provides evidence that complex assembly with stable core components and competition could contribute to cell differentiation. PMID:25946651

Computing the binding affinity of a ligand buried deep inside a protein with the hybrid steered molecular dynamics

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

Villarreal, Oscar D.; Yu, Lili; Department of Laboratory Medicine, Yancheng Vocational Institute of Health Sciences, Yancheng, Jiangsu 224006

Computing the ligand-protein binding affinity (or the Gibbs free energy) with chemical accuracy has long been a challenge for which many methods/approaches have been developed and refined with various successful applications. False positives and, even more harmful, false negatives have been and still are a common occurrence in practical applications. Inevitable in all approaches are the errors in the force field parameters we obtain from quantum mechanical computation and/or empirical fittings for the intra- and inter-molecular interactions. These errors propagate to the final results of the computed binding affinities even if we were able to perfectly implement the statistical mechanicsmore » of all the processes relevant to a given problem. And they are actually amplified to various degrees even in the mature, sophisticated computational approaches. In particular, the free energy perturbation (alchemical) approaches amplify the errors in the force field parameters because they rely on extracting the small differences between similarly large numbers. In this paper, we develop a hybrid steered molecular dynamics (hSMD) approach to the difficult binding problems of a ligand buried deep inside a protein. Sampling the transition along a physical (not alchemical) dissociation path of opening up the binding cavity- -pulling out the ligand- -closing back the cavity, we can avoid the problem of error amplifications by not relying on small differences between similar numbers. We tested this new form of hSMD on retinol inside cellular retinol-binding protein 1 and three cases of a ligand (a benzylacetate, a 2-nitrothiophene, and a benzene) inside a T4 lysozyme L99A/M102Q(H) double mutant. In all cases, we obtained binding free energies in close agreement with the experimentally measured values. This indicates that the force field parameters we employed are accurate and that hSMD (a brute force, unsophisticated approach) is free from the problem of error amplification suffered by many sophisticated approaches in the literature.« less

Live CLEM imaging to analyze nuclear structures at high resolution.

PubMed

Haraguchi, Tokuko; Osakada, Hiroko; Koujin, Takako

2015-01-01

Fluorescence microscopy (FM) and electron microscopy (EM) are powerful tools for observing molecular components in cells. FM can provide temporal information about cellular proteins and structures in living cells. EM provides nanometer resolution images of cellular structures in fixed cells. We have combined FM and EM to develop a new method of correlative light and electron microscopy (CLEM), called "Live CLEM." In this method, the dynamic behavior of specific molecules of interest is first observed in living cells using fluorescence microscopy (FM) and then cellular structures in the same cell are observed using electron microscopy (EM). Following image acquisition, FM and EM images are compared to enable the fluorescent images to be correlated with the high-resolution images of cellular structures obtained using EM. As this method enables analysis of dynamic events involving specific molecules of interest in the context of specific cellular structures at high resolution, it is useful for the study of nuclear structures including nuclear bodies. Here we describe Live CLEM that can be applied to the study of nuclear structures in mammalian cells.

Microfluidic platform for single cell analysis under dynamic spatial and temporal stimulation.

PubMed

Song, Jiyoung; Ryu, Hyunryul; Chung, Minhwan; Kim, Youngtaek; Blum, Yannick; Lee, Sung Sik; Pertz, Olivier; Jeon, Noo Li

2018-05-01

Recent research on cellular responses is shifting from static observations recorded under static stimuli to real-time monitoring in a dynamic environment. Since cells sense and interact with their surrounding microenvironment, an experimental platform where dynamically changing cellular microenvironments should be recreated in vitro. There has been a lack of microfluidic devices to support spatial and temporal stimulations in a simple and robust manner. Here, we describe a microfluidic device that generates dynamic chemical gradients and pulses in both space and time using a single device. This microfluidic device provides at least 12h of continuous stimulations that can be used to observe responses from mammalian cells. Combination of the microfluidic de-vice with live-cell imaging facilitates real-time observation of dynamic cellular response at single cell level. Using stable HEK cells with biosensors, ERK (Extracellular signal-Regulated Kinase) activities were observed un-der the pulsatile and ramping stimulations of EGF (Epidermal Growth Factor). We quantified ERK activation even at extremely low EGF concentration (0.0625µg/ml), which can not be observed using conventional techniques such as western blot. Cytoskeleton re-arrangement of the 3T3 fibroblast (stable transfection with Lifeact-GFP) was compared under abrupt and gradually changing gradient of PDGF. Copyright © 2017 Elsevier B.V. All rights reserved.

Approximate probabilistic cellular automata for the dynamics of single-species populations under discrete logisticlike growth with and without weak Allee effects.

PubMed

Mendonça, J Ricardo G; Gevorgyan, Yeva

2017-05-01

We investigate one-dimensional elementary probabilistic cellular automata (PCA) whose dynamics in first-order mean-field approximation yields discrete logisticlike growth models for a single-species unstructured population with nonoverlapping generations. Beginning with a general six-parameter model, we find constraints on the transition probabilities of the PCA that guarantee that the ensuing approximations make sense in terms of population dynamics and classify the valid combinations thereof. Several possible models display a negative cubic term that can be interpreted as a weak Allee factor. We also investigate the conditions under which a one-parameter PCA derived from the more general six-parameter model can generate valid population growth dynamics. Numerical simulations illustrate the behavior of some of the PCA found.

Virtual Embryo: Cell-Agent Based Modeling of Developmental Processes and Toxicities (CSS BOSC)

EPA Science Inventory

Spatial regulation of cellular dynamics is fundamental to morphological development. As such, chemical disruption of spatial dynamics is a determinant of developmental toxicity. Incorporating spatial dynamics into AOPs for developmental toxicity is desired but constrained by the ...

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

PubMed Central

Yu, Isseki; Mori, Takaharu; Ando, Tadashi; Harada, Ryuhei; Jung, Jaewoon; Sugita, Yuji; Feig, Michael

2016-01-01

Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology. DOI: http://dx.doi.org/10.7554/eLife.19274.001 PMID:27801646

A dynamic cellular vertex model of growing epithelial tissues

NASA Astrophysics Data System (ADS)

Lin, Shao-Zhen; Li, Bo; Feng, Xi-Qiao

2017-04-01

Intercellular interactions play a significant role in a wide range of biological functions and processes at both the cellular and tissue scales, for example, embryogenesis, organogenesis, and cancer invasion. In this paper, a dynamic cellular vertex model is presented to study the morphomechanics of a growing epithelial monolayer. The regulating role of stresses in soft tissue growth is revealed. It is found that the cells originating from the same parent cell in the monolayer can orchestrate into clustering patterns as the tissue grows. Collective cell migration exhibits a feature of spatial correlation across multiple cells. Dynamic intercellular interactions can engender a variety of distinct tissue behaviors in a social context. Uniform cell proliferation may render high and heterogeneous residual compressive stresses, while stress-regulated proliferation can effectively release the stresses, reducing the stress heterogeneity in the tissue. The results highlight the critical role of mechanical factors in the growth and morphogenesis of epithelial tissues and help understand the development and invasion of epithelial tumors.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

PubMed Central

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-01-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications. PMID:26525841

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope.

PubMed

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-03

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

NASA Astrophysics Data System (ADS)

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Quantitative measurement of intracellular protein dynamics using photobleaching or photoactivation of fluorescent proteins.

PubMed

Matsuda, Tomoki; Nagai, Takeharu

2014-12-01

Unlike in vitro protein dynamics, intracellular protein dynamics are intricately regulated by protein-protein interactions or interactions between proteins and other cellular components, including nucleic acids, the plasma membrane and the cytoskeleton. Alteration of these dynamics plays a crucial role in physiological phenomena such as gene expression and cell division. Live-cell imaging via microscopy with the inherent properties of fluorescent proteins, i.e. photobleaching and photoconversion, or fluorescence correlation spectroscopy, provides insight into the movement of proteins and their interactions with cellular components. This article reviews techniques based on photo-induced changes in the physicochemical properties of fluorescent proteins to measure protein dynamics inside living cells, and it also discusses the strengths and weaknesses of these techniques. © The Author 2014. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Optimization of industrial microorganisms: recent advances in synthetic dynamic regulators.

PubMed

Min, Byung Eun; Hwang, Hyun Gyu; Lim, Hyun Gyu; Jung, Gyoo Yeol

2017-01-01

Production of biochemicals by industrial fermentation using microorganisms requires maintaining cellular production capacity, because maximal productivity is economically important. High-productivity microbial strains can be developed using static engineering, but these may not maintain maximal productivity throughout the culture period as culture conditions and cell states change dynamically. Additionally, economic reasons limit heterologous protein expression using inducible promoters to prevent metabolic burden for commodity chemical and biofuel production. Recently, synthetic and systems biology has been used to design genetic circuits, precisely controlling gene expression or influencing genetic behavior toward a desired phenotype. Development of dynamic regulators can maintain cellular phenotype in a maximum production state in response to factors including cell concentration, oxygen, temperature, pH, and metabolites. Herein, we introduce dynamic regulators of industrial microorganism optimization and discuss metabolic flux fine control by dynamic regulators in response to metabolites or extracellular stimuli, robust production systems, and auto-induction systems using quorum sensing.

Exploring transmembrane transport through alpha-hemolysin with grid-steered molecular dynamics.

PubMed

Wells, David B; Abramkina, Volha; Aksimentiev, Aleksei

2007-09-28

The transport of biomolecules across cell boundaries is central to cellular function. While structures of many membrane channels are known, the permeation mechanism is known only for a select few. Molecular dynamics (MD) is a computational method that can provide an accurate description of permeation events at the atomic level, which is required for understanding the transport mechanism. However, due to the relatively short time scales accessible to this method, it is of limited utility. Here, we present a method for all-atom simulation of electric field-driven transport of large solutes through membrane channels, which in tens of nanoseconds can provide a realistic account of a permeation event that would require a millisecond simulation using conventional MD. In this method, the average distribution of the electrostatic potential in a membrane channel under a transmembrane bias of interest is determined first from an all-atom MD simulation. This electrostatic potential, defined on a grid, is subsequently applied to a charged solute to steer its permeation through the membrane channel. We apply this method to investigate permeation of DNA strands, DNA hairpins, and alpha-helical peptides through alpha-hemolysin. To test the accuracy of the method, we computed the relative permeation rates of DNA strands having different sequences and global orientations. The results of the G-SMD simulations were found to be in good agreement in experiment.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research.

PubMed

Zhu, Xin-Guang; Lynch, Jonathan P; LeBauer, David S; Millar, Andrew J; Stitt, Mark; Long, Stephen P

2016-05-01

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels. © 2015 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

Computational modeling of ion transport through nanopores.

PubMed

Modi, Niraj; Winterhalter, Mathias; Kleinekathöfer, Ulrich

2012-10-21

Nanoscale pores are ubiquitous in biological systems while artificial nanopores are being fabricated for an increasing number of applications. Biological pores are responsible for the transport of various ions and substrates between the different compartments of biological systems separated by membranes while artificial pores are aimed at emulating such transport properties. As an experimental method, electrophysiology has proven to be an important nano-analytical tool for the study of substrate transport through nanopores utilizing ion current measurements as a probe for the detection. Independent of the pore type, i.e., biological or synthetic, and objective of the study, i.e., to model cellular processes of ion transport or electrophysiological experiments, it has become increasingly important to understand the dynamics of ions in nanoscale confinements. To this end, numerical simulations have established themselves as an indispensable tool to decipher ion transport processes through biological as well as artificial nanopores. This article provides an overview of different theoretical and computational methods to study ion transport in general and to calculate ion conductance in particular. Potential new improvements in the existing methods and their applications are highlighted wherever applicable. Moreover, representative examples are given describing the ion transport through biological and synthetic nanopores as well as the high selectivity of ion channels. Special emphasis is placed on the usage of molecular dynamics simulations which already have demonstrated their potential to unravel ion transport properties at an atomic level.

Life's Still Lifes

NASA Astrophysics Data System (ADS)

McIntosh, Harold V.

The de Bruijn diagram describing those decompositions of the neighborhoods of a one dimensional cellular automaton which conform to predetermined requirements of periodicity and translational symmetry shows how to construct extended configurations satisfying the same requirements. Similar diagrams, formed by stages, describe higher dimensional automata, although they become more laborious to compute with increasing neighborhood size. The procedure is illustrated by computing some still lifes for Conway's game of Life, a widely known two dimensional cellular automaton. This paper is written in September 10, 1988.

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

Growth and setting of gas bubbles in a viscoelastic matrix imaged by X-ray microtomography: the evolution of cellular structures in fermenting wheat flour dough.

PubMed

Turbin-Orger, A; Babin, P; Boller, E; Chaunier, L; Chiron, H; Della Valle, G; Dendievel, R; Réguerre, A L; Salvo, L

2015-05-07

X-ray tomography is a relevant technique for the dynamic follow-up of gas bubbles in an opaque viscoelastic matrix, especially using image analysis. It has been applied here to pieces of fermenting wheat flour dough of various compositions, at two different voxel sizes (15 and 5 μm). The resulting evolution of the main cellular features shows that the creation of cellular structures follows two regimes that are defined by a characteristic time of connectivity, tc [30 and 80 min]: first (t ≤ tc), bubbles grow freely and then (t ≥ tc) they become connected since the percolation of the gas phase is limited by liquid films. During the first regime, bubbles can be tracked and the local strain rate can be measured. Its values (10(-4)-5 × 10(-4) s(-1)) are in agreement with those computed from dough viscosity and internal gas pressure, both of which depend on the composition. For higher porosity, P = 0.64 in our case, and thus occurring in the second regime, different cellular structures are obtained and XRT images show deformed gas cells that display complex shapes. The comparison of these images with confocal laser scanning microscopy images suggests the presence of liquid films that separate these cells. The dough can therefore be seen as a three-phase medium: viscoelastic matrix/gas cell/liquid phase. The contributions of the different levels of matter organization can be integrated by defining a capillary number (C = 0.1-1) that makes it possible to predict the macroscopic dough behavior.

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.

Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study

NASA Astrophysics Data System (ADS)

Li, Jun; Fu, Siyao; He, Haibo; Jia, Hongfei; Li, Yanzhong; Guo, Yi

2015-11-01

Large-scale regional evacuation is an important part of national security emergency response plan. Large commercial shopping area, as the typical service system, its emergency evacuation is one of the hot research topics. A systematic methodology based on Cellular Automata with the Dynamic Floor Field and event driven model has been proposed, and the methodology has been examined within context of a case study involving the evacuation within a commercial shopping mall. Pedestrians walking is based on Cellular Automata and event driven model. In this paper, the event driven model is adopted to simulate the pedestrian movement patterns, the simulation process is divided into normal situation and emergency evacuation. The model is composed of four layers: environment layer, customer layer, clerk layer and trajectory layer. For the simulation of movement route of pedestrians, the model takes into account purchase intention of customers and density of pedestrians. Based on evacuation model of Cellular Automata with Dynamic Floor Field and event driven model, we can reflect behavior characteristics of customers and clerks at the situations of normal and emergency evacuation. The distribution of individual evacuation time as a function of initial positions and the dynamics of the evacuation process is studied. Our results indicate that the evacuation model using the combination of Cellular Automata with Dynamic Floor Field and event driven scheduling can be used to simulate the evacuation of pedestrian flows in indoor areas with complicated surroundings and to investigate the layout of shopping mall.

In Vitro Experimental Model for the Long-Term Analysis of Cellular Dynamics During Bronchial Tree Development from Lung Epithelial Cells

PubMed Central

Maruta, Naomichi; Marumoto, Moegi

2017-01-01

Lung branching morphogenesis has been studied for decades, but the underlying developmental mechanisms are still not fully understood. Cellular movements dynamically change during the branching process, but it is difficult to observe long-term cellular dynamics by in vivo or tissue culture experiments. Therefore, developing an in vitro experimental model of bronchial tree would provide an essential tool for developmental biology, pathology, and systems biology. In this study, we succeeded in reconstructing a bronchial tree in vitro by using primary human bronchial epithelial cells. A high concentration gradient of bronchial epithelial cells was required for branching initiation, whereas homogeneously distributed endothelial cells induced the formation of successive branches. Subsequently, the branches grew in size to the order of millimeter. The developed model contains only two types of cells and it facilitates the analysis of lung branching morphogenesis. By taking advantage of our experimental model, we carried out long-term time-lapse observations, which revealed self-assembly, collective migration with leader cells, rotational motion, and spiral motion of epithelial cells in each developmental event. Mathematical simulation was also carried out to analyze the self-assembly process and it revealed simple rules that govern cellular dynamics. Our experimental model has provided many new insights into lung development and it has the potential to accelerate the study of developmental mechanisms, pattern formation, left–right asymmetry, and disease pathogenesis of the human lung. PMID:28471293

Tempo-spatially resolved cellular dynamics of human immunodeficiency virus transacting activator of transcription (Tat) peptide-modified nanocargos in living cells

NASA Astrophysics Data System (ADS)

Wei, Lin; Yang, Qiaoyu; Xiao, Lehui

2014-08-01

Understanding the cellular uptake mechanism and intracellular fate of nanocarriers in living cells is of great importance for the rational design of efficient drug delivery cargos as well as the development of robust biomedical diagnostic probes. In present study, with a dual wavelength view darkfield microscope (DWVD), the tempo-spatially resolved dynamics of Tat peptide-functionalized gold nanoparticles (TGNPs, with size similar to viruses) in living HeLa cells were extensively explored. It was found that energy-dependent endocytosis (both clathrin- and caveolae-mediated processes were involved) was the prevailing pathway for the cellular uptake of TGNPs. The time-correlated dynamic spatial distribution information revealed that TGNPs could not actively target the cell nuclei, which is contrary to previous observations based on fixed cell results. More importantly, the inheritance of TGNPs to the daughter cells through mitosis was found to be the major route to metabolize TGNPs by HeLa cells. These understandings on the cellular uptake mechanism and intracellular fate of nanocargos in living cells would provide deep insight on how to improve and controllably manipulate their translocation efficiency for targeted drug delivery.Understanding the cellular uptake mechanism and intracellular fate of nanocarriers in living cells is of great importance for the rational design of efficient drug delivery cargos as well as the development of robust biomedical diagnostic probes. In present study, with a dual wavelength view darkfield microscope (DWVD), the tempo-spatially resolved dynamics of Tat peptide-functionalized gold nanoparticles (TGNPs, with size similar to viruses) in living HeLa cells were extensively explored. It was found that energy-dependent endocytosis (both clathrin- and caveolae-mediated processes were involved) was the prevailing pathway for the cellular uptake of TGNPs. The time-correlated dynamic spatial distribution information revealed that TGNPs could not actively target the cell nuclei, which is contrary to previous observations based on fixed cell results. More importantly, the inheritance of TGNPs to the daughter cells through mitosis was found to be the major route to metabolize TGNPs by HeLa cells. These understandings on the cellular uptake mechanism and intracellular fate of nanocargos in living cells would provide deep insight on how to improve and controllably manipulate their translocation efficiency for targeted drug delivery. Electronic supplementary information (ESI) available: Experimental section and additional supporting results as noted in the text. See DOI: 10.1039/c4nr02732a

77 FR 57571 - Center For Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-18

...: Genes, Genomes, and Genetics Integrated Review Group; Genomics, Computational Biology and Technology... Reproductive Sciences Integrated Review Group; Cellular, Molecular and Integrative Reproduction Study Section...: Immunology Integrated Review Group; Cellular and Molecular Immunology--B Study Section. [[Page 57572

Mosquito population dynamics from cellular automata-based simulation

NASA Astrophysics Data System (ADS)

Syafarina, Inna; Sadikin, Rifki; Nuraini, Nuning

2016-02-01

In this paper we present an innovative model for simulating mosquito-vector population dynamics. The simulation consist of two stages: demography and dispersal dynamics. For demography simulation, we follow the existing model for modeling a mosquito life cycles. Moreover, we use cellular automata-based model for simulating dispersal of the vector. In simulation, each individual vector is able to move to other grid based on a random walk. Our model is also capable to represent immunity factor for each grid. We simulate the model to evaluate its correctness. Based on the simulations, we can conclude that our model is correct. However, our model need to be improved to find a realistic parameters to match real data.

Rho GTPases at the crossroad of signaling networks in mammals: impact of Rho-GTPases on microtubule organization and dynamics.

PubMed

Wojnacki, José; Quassollo, Gonzalo; Marzolo, María-Paz; Cáceres, Alfredo

2014-01-01

Microtubule (MT) organization and dynamics downstream of external cues is crucial for maintaining cellular architecture and the generation of cell asymmetries. In interphase cells RhoA, Rac, and Cdc42, conspicuous members of the family of small Rho GTPases, have major roles in modulating MT stability, and hence polarized cell behaviors. However, MTs are not mere targets of Rho GTPases, but also serve as signaling platforms coupling MT dynamics to Rho GTPase activation in a variety of cellular conditions. In this article, we review some of the key studies describing the reciprocal relationship between small Rho-GTPases and MTs during migration and polarization.

Stochastic modeling for dynamics of HIV-1 infection using cellular automata: A review.

PubMed

Precharattana, Monamorn

2016-02-01

Recently, the description of immune response by discrete models has emerged to play an important role to study the problems in the area of human immunodeficiency virus type 1 (HIV-1) infection, leading to AIDS. As infection of target immune cells by HIV-1 mainly takes place in the lymphoid tissue, cellular automata (CA) models thus represent a significant step in understanding when the infected population is dispersed. Motivated by these, the studies of the dynamics of HIV-1 infection using CA in memory have been presented to recognize how CA have been developed for HIV-1 dynamics, which issues have been studied already and which issues still are objectives in future studies.

A Fast Microfluidic Temperature Control Device for Studying Microtubule Dynamics in Fission Yeast

PubMed Central

Velve-Casquillas, Guilhem; Costa, Judite; Carlier-Grynkorn, Frédérique; Mayeux, Adeline; Tran, Phong T.

2010-01-01

Recent development in soft lithography and microfluidics enables biologists to create tools to control the cellular microenvironment. One such control is the ability to quickly change the temperature of the cells. Genetic model organism such as fission yeast has been useful for studies of the cell cytoskeleton. In particular, the dynamic microtubule cytoskeleton responds to changes in temperature. In addition, there are temperature-sensitive mutations of cytoskeletal proteins. We describe here the fabrication and use of a microfluidic device to quickly and reversibly change cellular temperature between 2°C and 50°C. We demonstrate the use of this device while imaging at high-resolution microtubule dynamics in fission yeast. PMID:20719272

Far-field photostable optical nanoscopy (PHOTON) for real-time super-resolution single-molecular imaging of signaling pathways of single live cells

NASA Astrophysics Data System (ADS)

Huang, Tao; Browning, Lauren M.; Xu, Xiao-Hong Nancy

2012-04-01

Cellular signaling pathways play crucial roles in cellular functions and design of effective therapies. Unfortunately, study of cellular signaling pathways remains formidably challenging because sophisticated cascades are involved, and a few molecules are sufficient to trigger signaling responses of a single cell. Here we report the development of far-field photostable-optical-nanoscopy (PHOTON) with photostable single-molecule-nanoparticle-optical-biosensors (SMNOBS) for mapping dynamic cascades of apoptotic signaling pathways of single live cells in real-time at single-molecule (SM) and nanometer (nm) resolutions. We have quantitatively imaged single ligand molecules (tumor necrosis factor α, TNFα) and their binding kinetics with their receptors (TNFR1) on single live cells; tracked formation and internalization of their clusters and their initiation of intracellular signaling pathways in real-time; and studied apoptotic signaling dynamics and mechanisms of single live cells with sufficient temporal and spatial resolutions. This study provides new insights into complex real-time dynamic cascades and molecular mechanisms of apoptotic signaling pathways of single live cells. PHOTON provides superior imaging and sensing capabilities and SMNOBS offer unrivaled biocompatibility and photostability, which enable probing of signaling pathways of single live cells in real-time at SM and nm resolutions.Cellular signaling pathways play crucial roles in cellular functions and design of effective therapies. Unfortunately, study of cellular signaling pathways remains formidably challenging because sophisticated cascades are involved, and a few molecules are sufficient to trigger signaling responses of a single cell. Here we report the development of far-field photostable-optical-nanoscopy (PHOTON) with photostable single-molecule-nanoparticle-optical-biosensors (SMNOBS) for mapping dynamic cascades of apoptotic signaling pathways of single live cells in real-time at single-molecule (SM) and nanometer (nm) resolutions. We have quantitatively imaged single ligand molecules (tumor necrosis factor α, TNFα) and their binding kinetics with their receptors (TNFR1) on single live cells; tracked formation and internalization of their clusters and their initiation of intracellular signaling pathways in real-time; and studied apoptotic signaling dynamics and mechanisms of single live cells with sufficient temporal and spatial resolutions. This study provides new insights into complex real-time dynamic cascades and molecular mechanisms of apoptotic signaling pathways of single live cells. PHOTON provides superior imaging and sensing capabilities and SMNOBS offer unrivaled biocompatibility and photostability, which enable probing of signaling pathways of single live cells in real-time at SM and nm resolutions. Electronic supplementary information (ESI) available. See DOI: 10.1039/c2nr11739h

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

PubMed

Stylianidou, Stella; Brennan, Connor; Nissen, Silas B; Kuwada, Nathan J; Wiggins, Paul A

2016-11-01

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. © 2016 John Wiley & Sons Ltd.

An attempt of modelling debris flows characterised by strong inertial effects through Cellular Automata

NASA Astrophysics Data System (ADS)

Iovine, G.; D'Ambrosio, D.

2003-04-01

Cellular Automata models do represent a valid method for the simulation of complex phenomena, when these latter can be described in "a-centric" terms - i.e. through local interactions within a discrete time-space. In particular, flow-type landslides (such as debris flows) can be viewed as a-centric dynamical system. SCIDDICA S4b, the last release of a family of two-dimensional hexagonal Cellular Automata models, has recently been developed for simulating debris flows characterised by strong inertial effects. It has been derived by progressively enriching an initial simplified CA model, originally derived for simulating very simple cases of slow-moving flow-type landslides. In S4b, by applying an empirical strategy, the inertial characters of the flowing mass have been translated into CA terms. In the transition function of the model, the distribution of landslide debris among the cells is computed by considering the momentum of the debris which move among the cells of the neighbourhood, and privileging the flow direction. By properly setting the value of one of the global parameters of the model (the "inertial factor"), the mechanism of distribution of the landslide debris among the cells can be influenced in order to emphasise the inertial effects, according to the energy of the flowing mass. Moreover, the high complexity of both the model and of the phenomena to be simulated (e.g. debris flows characterised by severe erosion along their path, and by strong inertial effects) suggested to employ an automated technique of evaluation, for the determination of the best set of global parameters. Accordingly, the calibration of the model has been performed through Genetic Algorithms, by considering several real cases of study: these latter have been selected among the population of landslides triggered in Campania (Southern Italy) in May 1998 and December 1999. Obtained results are satisfying: errors computed by comparing the simulations with the map of the real landslides are smaller than those previously obtained either through previous releases of the same model or without Genetic Algorithms. Nevertheless, results are still only preliminary, as the experiments have been realised in sequential computing environment. A more efficient calibration of the model would certainly be possible by adopting a parallel environment of computing, as a great number of tests could be performed in reasonable times; moreover, the parameters' optimisation could be realised in wider ranges, and in greater detail.

Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks

PubMed Central

Hallen, Mark; Li, Bochong; Tanouchi, Yu; Tan, Cheemeng; West, Mike; You, Lingchong

2011-01-01

Cellular processes are “noisy”. In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry. PMID:22022252

Particle Models with Self Sustained Current

NASA Astrophysics Data System (ADS)

Colangeli, M.; De Masi, A.; Presutti, E.

2017-06-01

We present some computer simulations run on a stochastic cellular automaton (CA). The CA simulates a gas of particles which are in a channel,the interval [1, L] in Z, but also in "reservoirs" R_1 and R_2. The evolution in the channel simulates a lattice gas with Kawasaki dynamics with attractive Kac interactions; the temperature is chosen smaller than the mean field critical one. There are also exchanges of particles between the channel and the reservoirs and among reservoirs. When the rate of exchanges among reservoirs is in a suitable interval the CA reaches an apparently stationary state with a non zero current; for different choices of the initial condition the current changes sign. We have a quite satisfactory theory of the phenomenon but we miss a full mathematical proof.

Recent Advances in Transferable Coarse-Grained Modeling of Proteins

PubMed Central

Kar, Parimal; Feig, Michael

2017-01-01

Computer simulations are indispensable tools for studying the structure and dynamics of biological macromolecules. Biochemical processes occur on different scales of length and time. Atomistic simulations cannot cover the relevant spatiotemporal scales at which the cellular processes occur. To address this challenge, coarse-grained (CG) modeling of the biological systems are employed. Over the last few years, many CG models for proteins continue to be developed. However, many of them are not transferable with respect to different systems and different environments. In this review, we discuss those CG protein models that are transferable and that retain chemical specificity. We restrict ourselves to CG models of soluble proteins only. We also briefly review recent progress made in the multi-scale hybrid all-atom/coarse-grained simulations of proteins. PMID:25443957

The Cybersecurity Challenge in Acquisition

DTIC Science & Technology

2016-04-30

problems. Scarier yet, another group took control of a car’s computers through a cellular telephone and Bluetooth connections and could access...did more extensive work, hacking their way into a 2009 midsize car through its cellular, Bluetooth , and other wireless connections. Stefan Savage, a

RchyOptimyx: Cellular Hierarchy Optimization for Flow Cytometry

PubMed Central

Aghaeepour, Nima; Jalali, Adrin; O’Neill, Kieran; Chattopadhyay, Pratip K.; Roederer, Mario; Hoos, Holger H.; Brinkman, Ryan R.

2013-01-01

Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population. PMID:23044634

The use of many-body physics and thermodynamics to describe the dynamics of rhythmic generators in sensory cortices engaged in memory and learning.

PubMed

Vitiello, Giuseppe

2015-04-01

The problem of the transition from the molecular and cellular level to the macroscopic level of observed assemblies of myriads of neurons is the subject addressed in this report. The great amount of detailed information available at molecular and cellular level seems not sufficient to account for the high effectiveness and reliability observed in the brain macroscopic functioning. It is suggested that the dissipative many-body model and thermodynamics might offer the dynamical frame underlying the rich phenomenology observed at microscopic and macroscopic level and help in the understanding on how to fill the gap between the bio-molecular and cellular level and the one of brain macroscopic functioning. Copyright © 2014 Elsevier Ltd. All rights reserved.

Perspective: On the importance of hydrodynamic interactions in the subcellular dynamics of macromolecules

PubMed Central

Skolnick, Jeffrey

2016-01-01

An outstanding challenge in computational biophysics is the simulation of a living cell at molecular detail. Over the past several years, using Stokesian dynamics, progress has been made in simulating coarse grained molecular models of the cytoplasm. Since macromolecules comprise 20%-40% of the volume of a cell, one would expect that steric interactions dominate macromolecular diffusion. However, the reduction in cellular diffusion rates relative to infinite dilution is due, roughly equally, to steric and hydrodynamic interactions, HI, with nonspecific attractive interactions likely playing rather a minor role. HI not only serve to slow down long time diffusion rates but also cause a considerable reduction in the magnitude of the short time diffusion coefficient relative to that at infinite dilution. More importantly, the long range contribution of the Rotne-Prager-Yamakawa diffusion tensor results in temporal and spatial correlations that persist up to microseconds and for intermolecular distances on the order of protein radii. While HI slow down the bimolecular association rate in the early stages of lipid bilayer formation, they accelerate the rate of large scale assembly of lipid aggregates. This is suggestive of an important role for HI in the self-assembly kinetics of large macromolecular complexes such as tubulin. Since HI are important, questions as to whether continuum models of HI are adequate as well as improved simulation methodologies that will make simulations of more complex cellular processes practical need to be addressed. Nevertheless, the stage is set for the molecular simulations of ever more complex subcellular processes. PMID:27634243

An improved cellular automata model for train operation simulation with dynamic acceleration

NASA Astrophysics Data System (ADS)

Li, Wen-Jun; Nie, Lei

2018-03-01

Urban rail transit plays an important role in the urban public traffic because of its advantages of fast speed, large transport capacity, high safety, reliability and low pollution. This study proposes an improved cellular automaton (CA) model by considering the dynamic characteristic of the train acceleration to analyze the energy consumption and train running time. Constructing an effective model for calculating energy consumption to aid train operation improvement is the basis for studying and analyzing energy-saving measures for urban rail transit system operation.

Topometry optimization of sheet metal structures for crashworthiness design using hybrid cellular automata

NASA Astrophysics Data System (ADS)

Mozumder, Chandan K.

The objective in crashworthiness design is to generate plastically deformable energy absorbing structures which can satisfy the prescribed force-displacement (FD) response. The FD behavior determines the reaction force, displacement and the internal energy that the structure should withstand. However, attempts to include this requirement in structural optimization problems remain scarce. The existing commercial optimization tools utilize models under static loading conditions because of the complexities associated with dynamic/impact loading. Due to the complexity of a crash event and the consequent time required to numerically analyze the dynamic response of the structure, classical methods (i.e., gradient-based and direct) are not well developed to solve this undertaking. This work presents an approach under the framework of the hybrid cellular automaton (HCA) method to solve the above challenge. The HCA method has been successfully applied to nonlinear transient topology optimization for crashworthiness design. In this work, the HCA algorithm has been utilized to develop an efficient methodology for synthesizing shell-based sheet metal structures with optimal material thickness distribution under a dynamic loading event using topometry optimization. This method utilizes the cellular automata (CA) computing paradigm and nonlinear transient finite element analysis (FEA) via ls-dyna. In this method, a set field variables is driven to their target states by changing a convenient set of design variables (e.g., thickness). These rules operate locally in cells within a lattice that only know local conditions. The field variables associated with the cells are driven to a setpoint to obtain the desired structure. This methodology is used to design for structures with controlled energy absorption with specified buckling zones. The peak reaction force and the maximum displacement are also constrained to meet the desired safety level according to passenger safety regulations. Design for prescribed FD response by minimizing the error between the actual response and desired FD curve is implemented. With the use of HCA rules, manufacturability constraints (e.g., rolling) and structures which can be manufactured by special techniques, such as, tailor-welded blanks (TWB), have also been implemented. This methodology is applied to shock-absorbing structural components for passengers in a crashing vehicle. These results are compared to previous designs showing the benefits of the method introduced in this work.

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

McMahon, S.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

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

Schuemann, J.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

Bistability in the chemical master equation for dual phosphorylation cycles.

PubMed

Bazzani, Armando; Castellani, Gastone C; Giampieri, Enrico; Remondini, Daniel; Cooper, Leon N

2012-06-21

Dual phospho/dephosphorylation cycles, as well as covalent enzymatic-catalyzed modifications of substrates are widely diffused within cellular systems and are crucial for the control of complex responses such as learning, memory, and cellular fate determination. Despite the large body of deterministic studies and the increasing work aimed at elucidating the effect of noise in such systems, some aspects remain unclear. Here we study the stationary distribution provided by the two-dimensional chemical master equation for a well-known model of a two step phospho/dephosphorylation cycle using the quasi-steady state approximation of enzymatic kinetics. Our aim is to analyze the role of fluctuations and the molecules distribution properties in the transition to a bistable regime. When detailed balance conditions are satisfied it is possible to compute equilibrium distributions in a closed and explicit form. When detailed balance is not satisfied, the stationary non-equilibrium state is strongly influenced by the chemical fluxes. In the last case, we show how the external field derived from the generation and recombination transition rates, can be decomposed by the Helmholtz theorem, into a conservative and a rotational (irreversible) part. Moreover, this decomposition allows to compute the stationary distribution via a perturbative approach. For a finite number of molecules there exists diffusion dynamics in a macroscopic region of the state space where a relevant transition rate between the two critical points is observed. Further, the stationary distribution function can be approximated by the solution of a Fokker-Planck equation. We illustrate the theoretical results using several numerical simulations.

Triangles bridge the scales: Quantifying cellular contributions to tissue deformation

NASA Astrophysics Data System (ADS)

Merkel, Matthias; Etournay, Raphaël; Popović, Marko; Salbreux, Guillaume; Eaton, Suzanne; Jülicher, Frank

2017-03-01

In this article, we propose a general framework to study the dynamics and topology of cellular networks that capture the geometry of cell packings in two-dimensional tissues. Such epithelia undergo large-scale deformation during morphogenesis of a multicellular organism. Large-scale deformations emerge from many individual cellular events such as cell shape changes, cell rearrangements, cell divisions, and cell extrusions. Using a triangle-based representation of cellular network geometry, we obtain an exact decomposition of large-scale material deformation. Interestingly, our approach reveals contributions of correlations between cellular rotations and elongation as well as cellular growth and elongation to tissue deformation. Using this triangle method, we discuss tissue remodeling in the developing pupal wing of the fly Drosophila melanogaster.

The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability.

PubMed

Mahrooghy, Majid; Yarahmadian, Shantia; Menon, Vineetha; Rezania, Vahid; Tuszynski, Jack A

2015-10-01

Microtubules (MTs) are intra-cellular cylindrical protein filaments. They exhibit a unique phenomenon of stochastic growth and shrinkage, called dynamic instability. In this paper, we introduce a theoretical framework for applying Compressive Sensing (CS) to the sampled data of the microtubule length in the process of dynamic instability. To reduce data density and reconstruct the original signal with relatively low sampling rates, we have applied CS to experimental MT lament length time series modeled as a Dichotomous Markov Noise (DMN). The results show that using CS along with the wavelet transform significantly reduces the recovery errors comparing in the absence of wavelet transform, especially in the low and the medium sampling rates. In a sampling rate ranging from 0.2 to 0.5, the Root-Mean-Squared Error (RMSE) decreases by approximately 3 times and between 0.5 and 1, RMSE is small. We also apply a peak detection technique to the wavelet coefficients to detect and closely approximate the growth and shrinkage of MTs for computing the essential dynamic instability parameters, i.e., transition frequencies and specially growth and shrinkage rates. The results show that using compressed sensing along with the peak detection technique and wavelet transform in sampling rates reduces the recovery errors for the parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Toxicological Tipping Points: Learning Boolean Networks from High-Content Imaging Data. (BOSC meeting)

EPA Science Inventory

The objective of this work is to elucidate biological networks underlying cellular tipping points using time-course data. We discretized the high-content imaging (HCI) data and inferred Boolean networks (BNs) that could accurately predict dynamic cellular trajectories. We found t...

Engineering cellular fibers for musculoskeletal soft tissues using directed self-assembly.

PubMed

Schiele, Nathan R; Koppes, Ryan A; Chrisey, Douglas B; Corr, David T

2013-05-01

Engineering strategies guided by developmental biology may enhance and accelerate in vitro tissue formation for tissue engineering and regenerative medicine applications. In this study, we looked toward embryonic tendon development as a model system to guide our soft tissue engineering approach. To direct cellular self-assembly, we utilized laser micromachined, differentially adherent growth channels lined with fibronectin. The micromachined growth channels directed human dermal fibroblast cells to form single cellular fibers, without the need for a provisional three-dimensional extracellular matrix or scaffold to establish a fiber structure. Therefore, the resulting tissue structure and mechanical characteristics were determined solely by the cells. Due to the self-assembly nature of this approach, the growing fibers exhibit some key aspects of embryonic tendon development, such as high cellularity, the rapid formation (within 24 h) of a highly organized and aligned cellular structure, and the expression of cadherin-11 (indicating direct cell-to-cell adhesions). To provide a dynamic mechanical environment, we have also developed and characterized a method to apply precise cyclic tensile strain to the cellular fibers as they develop. After an initial period of cellular fiber formation (24 h postseeding), cyclic strain was applied for 48 h, in 8-h intervals, with tensile strain increasing from 0.7% to 1.0%, and at a frequency of 0.5 Hz. Dynamic loading dramatically increased cellular fiber mechanical properties with a nearly twofold increase in both the linear region stiffness and maximum load at failure, thereby demonstrating a mechanism for enhancing cellular fiber formation and mechanical properties. Tissue engineering strategies, designed to capture key aspects of embryonic development, may provide unique insight into accelerated maturation of engineered replacement tissue, and offer significant advances for regenerative medicine applications in tendon, ligament, and other fibrous soft tissues.

Exploration of cellular reaction systems.

PubMed

Kirkilionis, Markus

2010-01-01

We discuss and review different ways to map cellular components and their temporal interaction with other such components to different non-spatially explicit mathematical models. The essential choices made in the literature are between discrete and continuous state spaces, between rule and event-based state updates and between deterministic and stochastic series of such updates. The temporal modelling of cellular regulatory networks (dynamic network theory) is compared with static network approaches in two first introductory sections on general network modelling. We concentrate next on deterministic rate-based dynamic regulatory networks and their derivation. In the derivation, we include methods from multiscale analysis and also look at structured large particles, here called macromolecular machines. It is clear that mass-action systems and their derivatives, i.e. networks based on enzyme kinetics, play the most dominant role in the literature. The tools to analyse cellular reaction networks are without doubt most complete for mass-action systems. We devote a long section at the end of the review to make a comprehensive review of related tools and mathematical methods. The emphasis is to show how cellular reaction networks can be analysed with the help of different associated graphs and the dissection into modules, i.e. sub-networks.

Continuum Approaches to Understanding Ion and Peptide Interactions with the Membrane

PubMed Central

Latorraca, Naomi R.; Callenberg, Keith M.; Boyle, Jon P.; Grabe, Michael

2014-01-01

Experimental and computational studies have shown that cellular membranes deform to stabilize the inclusion of transmembrane (TM) proteins harboring charge. Recent analysis suggests that membrane bending helps to expose charged and polar residues to the aqueous environment and polar head groups. We previously used elasticity theory to identify membrane distortions that minimize the insertion of charged TM peptides into the membrane. Here, we extend our work by showing that it also provides a novel, computationally efficient method for exploring the energetics of ion and small peptide penetration into membranes. First, we show that the continuum method accurately reproduces energy profiles and membrane shapes generated from molecular simulations of bare ion permeation at a fraction of the computational cost. Next, we demonstrate that the dependence of the ion insertion energy on the membrane thickness arises primarily from the elastic properties of the membrane. Moreover, the continuum model readily provides a free energy decomposition into components not easily determined from molecular dynamics. Finally, we show that the energetics of membrane deformation strongly depend on membrane patch size both for ions and peptides. This dependence is particularly strong for peptides based on simulations of a known amphipathic, membrane binding peptide from the human pathogen Toxoplasma gondii. In total, we address shortcomings and advantages that arise from using a variety of computational methods in distinct biological contexts. PMID:24652510

Substrate stress relaxation regulates cell spreading

NASA Astrophysics Data System (ADS)

Chaudhuri, Ovijit; Gu, Luo; Darnell, Max; Klumpers, Darinka; Bencherif, Sidi A.; Weaver, James C.; Huebsch, Nathaniel; Mooney, David J.

2015-02-01

Studies of cellular mechanotransduction have converged upon the idea that cells sense extracellular matrix (ECM) elasticity by gauging resistance to the traction forces they exert on the ECM. However, these studies typically utilize purely elastic materials as substrates, whereas physiological ECMs are viscoelastic, and exhibit stress relaxation, so that cellular traction forces exerted by cells remodel the ECM. Here we investigate the influence of ECM stress relaxation on cell behaviour through computational modelling and cellular experiments. Surprisingly, both our computational model and experiments find that spreading for cells cultured on soft substrates that exhibit stress relaxation is greater than cells spreading on elastic substrates of the same modulus, but similar to that of cells spreading on stiffer elastic substrates. These findings challenge the current view of how cells sense and respond to the ECM.

Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics

PubMed Central

Lythgoe, Katrina A.; Blanquart, François

2016-01-01

The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical models, which are widely used to interpret and predict viral and immune dynamics in infection, typically treat the infected host as a well-mixed homogeneous environment. Here, we present mathematical, analytical, and computational results that demonstrate that consideration of the spatial structure of the viral population within the host radically alters predictions of previous models. We study the dynamics of virus replication and cytotoxic T lymphocytes (CTLs) within a metapopulation of spatially segregated patches, representing T cell areas connected by circulating blood and lymph. The dynamics of the system depend critically on the interaction between CTLs and infected cells at the within-patch level. We show that for a wide range of parameters, the system admits an unexpected outcome called the shifting-mosaic steady state. In this state, the whole body’s viral population is stable over time, but the equilibrium results from an underlying, highly dynamic process of local infection and clearance within T-cell centers. Notably, and in contrast to previous models, this new model can explain the large differences in set-point viral load (SPVL) observed between patients and their distribution, as well as the relatively low proportion of cells infected at any one time, and alters the predicted determinants of viral load variation. PMID:27706164

Computation and Dynamics: Classical and Quantum

NASA Astrophysics Data System (ADS)

Kisil, Vladimir V.

2010-05-01

We discuss classical and quantum computations in terms of corresponding Hamiltonian dynamics. This allows us to introduce quantum computations which involve parallel processing of both: the data and programme instructions. Using mixed quantum-classical dynamics we look for a full cost of computations on quantum computers with classical terminals.

The Design of Fault Tolerant Quantum Dot Cellular Automata Based Logic

NASA Technical Reports Server (NTRS)

Armstrong, C. Duane; Humphreys, William M.; Fijany, Amir

2002-01-01

As transistor geometries are reduced, quantum effects begin to dominate device performance. At some point, transistors cease to have the properties that make them useful computational components. New computing elements must be developed in order to keep pace with Moore s Law. Quantum dot cellular automata (QCA) represent an alternative paradigm to transistor-based logic. QCA architectures that are robust to manufacturing tolerances and defects must be developed. We are developing software that allows the exploration of fault tolerant QCA gate architectures by automating the specification, simulation, analysis and documentation processes.

Distributed sensor networks: a cellular nonlinear network perspective.

PubMed

Haenggi, Martin

2003-12-01

Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.

How to Make a Synthetic Multicellular Computer

PubMed Central

Macia, Javier; Sole, Ricard

2014-01-01

Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined. PMID:24586222

Progress and challenges in bioinformatics approaches for enhancer identification

PubMed Central

Kleftogiannis, Dimitrios; Kalnis, Panos

2016-01-01

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

Fluorescent Reporters and Biosensors for Probing the Dynamic Behavior of Protein Kinases

PubMed Central

González-Vera, Juan A.; Morris, May C.

2015-01-01

Probing the dynamic activities of protein kinases in real-time in living cells constitutes a major challenge that requires specific and sensitive tools tailored to meet the particular demands associated with cellular imaging. The development of genetically-encoded and synthetic fluorescent biosensors has provided means of monitoring protein kinase activities in a non-invasive fashion in their native cellular environment with high spatial and temporal resolution. Here, we review existing technologies to probe different dynamic features of protein kinases and discuss limitations where new developments are required to implement more performant tools, in particular with respect to infrared and near-infrared fluorescent probes and strategies which enable improved signal-to-noise ratio and controlled activation of probes. PMID:28248276

A Causal Relation between Bioluminescence and Oxygen to Quantify the Cell Niche

PubMed Central

Lambrechts, Dennis; Roeffaers, Maarten; Goossens, Karel; Hofkens, Johan; Van de Putte, Tom; Schrooten, Jan; Van Oosterwyck, Hans

2014-01-01

Bioluminescence imaging assays have become a widely integrated technique to quantify effectiveness of cell-based therapies by monitoring fate and survival of transplanted cells. To date these assays are still largely qualitative and often erroneous due to the complexity and dynamics of local micro-environments (niches) in which the cells reside. Here, we report, using a combined experimental and computational approach, on oxygen that besides being a critical niche component responsible for cellular energy metabolism and cell-fate commitment, also serves a primary role in regulating bioluminescent light kinetics. We demonstrate the potential of an oxygen dependent Michaelis-Menten relation in quantifying intrinsic bioluminescence intensities by resolving cell-associated oxygen gradients from bioluminescent light that is emitted from three-dimensional (3D) cell-seeded hydrogels. Furthermore, the experimental and computational data indicate a strong causal relation of oxygen concentration with emitted bioluminescence intensities. Altogether our approach demonstrates the importance of oxygen to evolve towards quantitative bioluminescence and holds great potential for future microscale measurement of oxygen tension in an easily accessible manner. PMID:24840204

The Binding Interface between Human APOBEC3F and HIV-1 Vif Elucidated by Genetic and Computational Approaches.

PubMed

Richards, Christopher; Albin, John S; Demir, Özlem; Shaban, Nadine M; Luengas, Elizabeth M; Land, Allison M; Anderson, Brett D; Holten, John R; Anderson, John S; Harki, Daniel A; Amaro, Rommie E; Harris, Reuben S

2015-12-01

APOBEC3 family DNA cytosine deaminases provide overlapping defenses against pathogen infections. However, most viruses have elaborate evasion mechanisms such as the HIV-1 Vif protein, which subverts cellular CBF-β and a polyubiquitin ligase complex to neutralize these enzymes. Despite advances in APOBEC3 and Vif biology, a full understanding of this direct host-pathogen conflict has been elusive. We combine virus adaptation and computational studies to interrogate the APOBEC3F-Vif interface and build a robust structural model. A recurring compensatory amino acid substitution from adaptation experiments provided an initial docking constraint, and microsecond molecular dynamic simulations optimized interface contacts. Virus infectivity experiments validated a long-lasting electrostatic interaction between APOBEC3F E289 and HIV-1 Vif R15. Taken together with mutagenesis results, we propose a wobble model to explain how HIV-1 Vif has evolved to bind different APOBEC3 enzymes and, more generally, how pathogens may evolve to escape innate host defenses. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

Smith, Jeremy C.; Cheng, Xiaolin; Nickels, Jonathan D.

Understanding of cell membrane organization has evolved significantly from the classic fluid mosaic model. It is now recognized that biological membranes are highly organized structures, with differences in lipid compositions between inner and outer leaflets and in lateral structures within the bilayer plane, known as lipid rafts. These organizing principles are important for protein localization and function as well as cellular signaling. However, the mechanisms and biophysical basis of lipid raft formation, structure, dynamics and function are not clearly understood. One key question, which we focus on in this review, is how lateral organization and leaflet compositional asymmetry are coupled.more » Detailed information elucidating this question has been sparse because of the small size and transient nature of rafts and the experimental challenges in constructing asymmetric bilayers. Resolving this mystery will require advances in both experimentation and modeling. We discuss here the preparation of model systems along with experimental and computational approaches that have been applied in efforts to address this key question in membrane biology. Furthermore, we seek to place recent and future advances in experimental and computational techniques in context, providing insight into in-plane and transverse organization of biological membranes.« less

A substrate selectivity and inhibitor design lesson from the PDE10-cAMP crystal structure: a computational study.

PubMed

Lau, Justin Kai-Chi; Li, Xiao-Bo; Cheng, Yuen-Kit

2010-04-22

Phosphodiesterases (PDEs) catalyze the hydrolysis of second messengers cAMP and cGMP in regulating many important cellular signals and have been recognized as important drug targets. Experimentally, a range of specificity/selectivity toward cAMP and cGMP is well-known for the individual PDE families. The study reported here reveals that PDEs might also exhibit selectivity toward conformations of the endogenous substrates cAMP and cGMP. Molecular dynamics simulations and free energy study have been applied to study the binding of the cAMP torsional conformers about the glycosyl bond in PDE10A2. The computational results elucidated that PDE10A2 is energetically more favorable in complex with the syn cAMP conformer (as reported in the crystal structure) and the binding of anti cAMP to PDE10A2 would lead to either a nonreactive configuration or significant perturbation on the catalytic pocket of the enzyme. This experimentally inaccessible information provides important molecular insights for the development of effective PDE10 ligands.