Publications by authors named "James A Glazier"

65 Publications

A mechanical model of early somite segmentation.

iScience 2021 Apr 16;24(4):102317. Epub 2021 Mar 16.

Department of Physics, North Carolina State University, Raleigh, NC 27607, USA.

Somitogenesis is often described using the clock-and-wavefront (CW) model, which does not explain how molecular signaling rearranges the pre-somitic mesoderm (PSM) cells into somites. Our scanning electron microscopy analysis of chicken embryos reveals a caudally-progressing epithelialization front in the dorsal PSM that precedes somite formation. Signs of apical constriction and tissue segmentation appear in this layer 3-4 somite lengths caudal to the last-formed somite. We propose a mechanical instability model in which a steady increase of apical contractility leads to periodic failure of adhesion junctions within the dorsal PSM and positions the future inter-somite boundaries. This model produces spatially periodic segments whose size depends on the speed of the activation front of contraction (), and the buildup rate of contractility (Λ). The Λ/ ratio determines whether this mechanism produces spatially and temporally regular or irregular segments, and whether segment size increases with the front speed.
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http://dx.doi.org/10.1016/j.isci.2021.102317DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050378PMC
April 2021

Using digital twins in viral infection.

Science 2021 03;371(6534):1105-1106

Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.

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http://dx.doi.org/10.1126/science.abf3370DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170388PMC
March 2021

A computational model of liver tissue damage and repair.

PLoS One 2020 21;15(12):e0243451. Epub 2020 Dec 21.

Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America.

Drug induced liver injury (DILI) and cell death can result from oxidative stress in hepatocytes. An initial pattern of centrilobular damage in the APAP model of DILI is amplified by communication from stressed cells and immune system activation. While hepatocyte proliferation counters cell loss, high doses are still lethal to the tissue. To understand the progression of disease from the initial damage to tissue recovery or death, we computationally model the competing biological processes of hepatocyte proliferation, necrosis and injury propagation. We parametrize timescales of proliferation (α), conversion of healthy to stressed cells (β) and further sensitization of stressed cells towards necrotic pathways (γ) and model them on a Cellular Automaton (CA) based grid of lattice sites. 1D simulations show that a small α/β (fast proliferation), combined with a large γ/β (slow death) have the lowest probabilities of tissue survival. At large α/β, tissue fate can be described by a critical γ/β* ratio alone; this value is dependent on the initial amount of damage and proportional to the tissue size N. Additionally, the 1D model predicts a minimum healthy population size below which damage is irreversible. Finally, we compare 1D and 2D phase spaces and discuss outcomes of bistability where either survival or death is possible, and of coexistence where simulated tissue never completely recovers or dies but persists as a mixture of healthy, stressed and necrotic cells. In conclusion, our model sheds light on the evolution of tissue damage or recovery and predicts potential for divergent fates given different rates of proliferation, necrosis, and injury propagation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243451PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752149PMC
January 2021

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.

PLoS Comput Biol 2020 12 21;16(12):e1008451. Epub 2020 Dec 21.

Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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http://dx.doi.org/10.1371/journal.pcbi.1008451DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785254PMC
December 2020

Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D.

Phys Med Biol 2021 02 11;66(4):045026. Epub 2021 Feb 11.

Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America.

Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a 'bridging' module, RADCELL, that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D, and the cell dose and cell DNA damage information were fed back through RADCELL to CC3D for updating the cell properties. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a framework for quantifying the biological consequences of radiation therapy. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.
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http://dx.doi.org/10.1088/1361-6560/abd4f9DOI Listing
February 2021

Unification of aggregate growth models by emergence from cellular and intracellular mechanisms.

R Soc Open Sci 2020 Aug 12;7(8):192148. Epub 2020 Aug 12.

Department of Mechanical and Energy Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.

Multicellular aggregate growth is regulated by nutrient availability and removal of metabolites, but the specifics of growth dynamics are dependent on cell type and environment. Classical models of growth are based on differential equations. While in some cases these classical models match experimental observations, they can only predict growth of a limited number of cell types and so can only be selectively applied. Currently, no classical model provides a general mathematical representation of growth for any cell type and environment. This discrepancy limits their range of applications, which a general modelling framework can enhance. In this work, a hybrid cellular Potts model is used to explain the discrepancy between classical models as emergent behaviours from the same mathematical system. Intracellular processes are described using probability distributions of local chemical conditions for proliferation and death and simulated. By fitting simulation results to a generalization of the classical models, their emergence is demonstrated. Parameter variations elucidate how aggregate growth may behave like one classical growth model or another. Three classical growth model fits were tested, and emergence of the Gompertz equation was demonstrated. Effects of shape changes are demonstrated, which are significant for final aggregate size and growth rate, and occur stochastically.
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http://dx.doi.org/10.1098/rsos.192148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481681PMC
August 2020

Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection.

bioRxiv 2020 Jun 16. Epub 2020 Jun 16.

To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 infection of a human lung epithelial cell line. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. At the individual-gene level, EGR3 was significantly upregulated in infected cells. Similar activation in T-cells and fibroblasts in infected lung could explain the T-cell anergy and eventual fibrosis seen in SARS-CoV-1 infection. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples.
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http://dx.doi.org/10.1101/2020.06.16.155267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310616PMC
June 2020

A modular framework for multiscale multicellular spatial modeling of viral infection, immune response and drug therapy timing and efficacy in epithelial tissues: A multiscale model of viral infection in epithelial tissues.

bioRxiv 2020 May 7. Epub 2020 May 7.

Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.

Development of predictive quantitative models of all aspects of COVID-19 is essential for rapidly understanding the causes of differing disease outcomes and vulnerabilities, suggesting drug and therapeutic targets, and designing optimized personalized interventions. Easy to implement, predictive multiscale modeling frameworks to integrate the wide variety of clinical and research datasets into actionable insights, which could inform therapeutic regime strategies are lacking. We present a multiscale, multicellular, spatiotemporal model of the infection of epithelial tissue by a generic virus, a simplified cellular immune response and viral and immune-induced tissue damage. Our initial model is built of modular components to allow it to be easily extended and adapted in a collaborative fashion to describe specific viral infections, tissue types and immune responses. The model allows us to define three parameter regimes: where viral infection coincides with a massive cytopathic effect, where the immune System rapidly controls the virus and where the immune System controls the virus but extensive tissue damage occurs. We use the model in a proof-of-concept application to evaluate a number of drug therapy concepts. Inhibition of viral internalization and faster immune-cell recruitment lead to containment of infection. Fast viral internalization and slower immune response lead to uncontrolled spread of infection. Simulation of a drug, whose mode of action is to reduce production of viral RNAs, shows that a relatively limited reduction of viral replication at the beginning of infection greatly decreases the total area of tissue damage and maximal viral load, while even a treatment that greatly reduces the rate of genomic replication rapidly loses efficacy as the infection progresses. A number of simulation conditions lead to stochastically variable outcomes, with some replicas clearing or controlling the virus, while others see virus-induced damage sweep the simulated lung patch. The model is open-source and modular, allowing rapid development and extension of its components by groups working in parallel.
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http://dx.doi.org/10.1101/2020.04.27.064139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7263495PMC
May 2020

Rapid community-driven development of a SARS-CoV-2 tissue simulator.

bioRxiv 2020 Apr 5. Epub 2020 Apr 5.

The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interactions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving rapid refinements with a two-to-four week release cycle. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health.
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http://dx.doi.org/10.1101/2020.04.02.019075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239052PMC
April 2020

CompuCell3D Simulations Reproduce Mesenchymal Cell Migration on Flat Substrates.

Biophys J 2020 06 30;118(11):2801-2815. Epub 2020 Apr 30.

Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Program de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil. Electronic address:

Mesenchymal cell crawling is a critical process in normal development, in tissue function, and in many diseases. Quantitatively predictive numerical simulations of cell crawling thus have multiple scientific, medical, and technological applications. However, we still lack a low-computational-cost approach to simulate mesenchymal three-dimensional (3D) cell crawling. Here, we develop a computationally tractable 3D model (implemented as a simulation in the CompuCell3D simulation environment) of mesenchymal cells crawling on a two-dimensional substrate. The Fürth equation, the usual characterization of mean-squared displacement (MSD) curves for migrating cells, describes a motion in which, for increasing time intervals, cell movement transitions from a ballistic to a diffusive regime. Recent experiments have shown that for very short time intervals, cells exhibit an additional fast diffusive regime. Our simulations' MSD curves reproduce the three experimentally observed temporal regimes, with fast diffusion for short time intervals, slow diffusion for long time intervals, and intermediate time -interval-ballistic motion. The resulting parameterization of the trajectories for both experiments and simulations allows the definition of time- and length scales that translate between computational and laboratory units. Rescaling by these scales allows direct quantitative comparisons among MSD curves and between velocity autocorrelation functions from experiments and simulations. Although our simulations replicate experimentally observed spontaneous symmetry breaking, short-timescale diffusive motion, and spontaneous cell-motion reorientation, their computational cost is low, allowing their use in multiscale virtual-tissue simulations. Comparisons between experimental and simulated cell motion support the hypothesis that short-time actomyosin dynamics affects longer-time cell motility. The success of the base cell-migration simulation model suggests its future application in more complex situations, including chemotaxis, migration through complex 3D matrices, and collective cell motion.
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http://dx.doi.org/10.1016/j.bpj.2020.04.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264849PMC
June 2020

Mitochondrial depolarization and repolarization in the early stages of acetaminophen hepatotoxicity in mice.

Toxicology 2020 06 19;439:152464. Epub 2020 Apr 19.

School of Public Health, Indiana University, Bloomington, IN, USA.

Mitochondrial injury and depolarization are primary events in acetaminophen hepatotoxicity. Previous studies have shown that restoration of mitochondrial function in surviving hepatocytes, which is critical to recovery, is at least partially accomplished via biogenesis of new mitochondria. However, other studies indicate that mitochondria also have the potential to spontaneously repolarize. Although repolarization was previously observed only at a sub-hepatotoxic dose of acetaminophen, we postulated that mitochondrial repolarization in hepatocytes outside the centrilobular regions of necrosis might contribute to recovery of mitochondrial function following acetaminophen-induced injury. Our studies utilized longitudinal intravital microscopy of millimeter-scale regions of the mouse liver to characterize the spatio-temporal relationship between mitochondrial polarization and necrosis early in acetaminophen-induced liver injury. Treatment of male C57BL/6J mice with a single intraperitoneal 250 mg/kg dose of acetaminophen resulted in hepatotoxicity that was apparent histologically within 2 h of treatment, leading to 20 and 60-fold increases in serum aspartate aminotransferase and alanine aminotransferase, respectively, within 6 h. Intravital microscopy of the livers of mice injected with rhodamine123, TexasRed-dextran, propidium iodide and Hoechst 33342 detected centrilobular foci of necrosis within extended regions of mitochondrial depolarization within 2 h of acetaminophen treatment. Although regions of necrosis were more apparent 6 h after acetaminophen treatment, the vast majority of hepatocytes with depolarized mitochondria did not progress to necrosis, but rather recovered mitochondrial polarization within 6 h. Recovery of mitochondrial function following acetaminophen hepatotoxicity thus involves not only biogenesis of new mitochondria, but also repolarization of existing mitochondria. These studies also revealed a spatial distribution of necrosis and mitochondrial depolarization whose single-cell granularity is inconsistent with the hypothesis that communication between neighboring cells plays an important role in the propagation of necrosis during the early stages of APAP hepatotoxicity. Small islands of healthy, intact cells were frequently found surrounded by necrotic cells, and small islands of necrotic cells were frequently found surrounded by healthy, intact cells. Time-series studies demonstrated that these "islands", consisting in some cases of single cells, are persistent; over a period of hours, injury does not spread from individual necrotic cells to their neighbors.
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http://dx.doi.org/10.1016/j.tox.2020.152464DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270714PMC
June 2020

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature.

J Vis Exp 2019 11 18(153). Epub 2019 Nov 18.

Department of Medicine, Indiana University;

Changes in blood flow velocity and distribution are vital in maintaining tissue and organ perfusion in response to varying cellular needs. Further, appearance of defects in microcirculation can be a primary indicator in the development of multiple pathologies. Advances in optical imaging have made intravital microscopy (IVM) a practical approach, permitting imaging at the cellular and subcellular level in live animals at high-speed over time. Yet, despite the importance of maintaining adequate tissue perfusion, spatial and temporal variability in capillary flow is seldom documented. In the standard approach, a small number of capillary segments are chosen for imaging over a limited time. To comprehensively quantify capillary flow in an unbiased way we developed Spatial Temporal Analysis of Fieldwise Flow (STAFF), a macro for FIJI open-source image analysis software. Using high-speed image sequences of full fields of blood flow within capillaries, STAFF produces images that represent motion over time called kymographs for every time interval for every vascular segment. From the kymographs STAFF calculates velocities from the distance that red blood cells move over time, and outputs the velocity data as a sequence of color-coded spatial maps for visualization and tabular output for quantitative analyses. In normal mouse livers, STAFF analyses quantified profound differences in flow velocity between pericentral and periportal regions within lobules. Even more unexpected are the differences in flow velocity seen between sinusoids that are side by side and fluctuations seen within individual vascular segments over seconds. STAFF is a powerful new tool capable of providing novel insights by enabling measurement of the complex spatiotemporal dynamics of capillary flow.
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http://dx.doi.org/10.3791/60493DOI Listing
November 2019

The 2019 mathematical oncology roadmap.

Phys Biol 2019 06 19;16(4):041005. Epub 2019 Jun 19.

Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA 91010, United States of America. Author to whom any correspondence should be addressed.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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http://dx.doi.org/10.1088/1478-3975/ab1a09DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6655440PMC
June 2019

A simple automated method for continuous fieldwise measurement of microvascular hemodynamics.

Microvasc Res 2019 05 28;123:7-13. Epub 2018 Nov 28.

Department of Medicine, Indiana University, Indianapolis, IN, USA. Electronic address:

Microvascular perfusion dynamics are vital to physiological function and are frequently dysregulated in injury and disease. Typically studies measure microvascular flow in a few selected vascular segments over limited time, failing to capture spatial and temporal variability. To quantify microvascular flow in a more complete and unbiased way we developed STAFF (Spatial Temporal Analysis of Fieldwise Flow), a macro for FIJI open-source image analysis software. Using high-speed microvascular flow movies, STAFF generates kymographs for every time interval for every vascular segment, calculates flow velocities from red blood cell shadow angles, and outputs the data as color-coded velocity map movies and spreadsheets. In untreated mice, analyses demonstrated profound variation even between adjacent sinusoids over seconds. In acetaminophen-treated mice we detected flow reduction localized to pericentral regions. STAFF is a powerful new tool capable of providing novel insights by enabling measurement of the complex spatiotemporal dynamics of microvascular flow.
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http://dx.doi.org/10.1016/j.mvr.2018.11.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379124PMC
May 2019

Modeling of xenobiotic transport and metabolism in virtual hepatic lobule models.

PLoS One 2018 13;13(9):e0198060. Epub 2018 Sep 13.

Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America.

Computational models of normal liver function and xenobiotic induced liver damage are increasingly being used to interpret in vitro and in vivo data and as an approach to the de novo prediction of the liver's response to xenobiotics. The microdosimetry (dose at the level of individual cells) of xenobiotics vary spatially within the liver because of both compound-independent and compound-dependent factors. In this paper, we build model liver lobules to investigate the interplay between vascular structure, blood flow and cellular transport that lead to regional variations in microdosimetry. We then compared simulation results obtained using this complex spatial model with a simpler linear pipe model of a sinusoid and a very simple single box model. We found that variations in diffusive transport, transporter-mediated transport and metabolism, coupled with complex liver sinusoid architecture and blood flow distribution, led to three essential patterns of xenobiotic exposure within the virtual liver lobule: (1) lobular-wise uniform, (2) radially varying and (3) both radially and azimuthally varying. We propose to use these essential patterns of exposure as a reference for selection of model representations when a computational study involves modeling detailed hepatic responses to xenobiotics.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198060PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136710PMC
February 2019

Molecular jenga: the percolation phase transition (collapse) in virus capsids.

Phys Biol 2018 06 6;15(5):056005. Epub 2018 Jun 6.

Department of Molecular and Cellular Biochemistry, Indiana University, Bloomington, IN 47405, United States of America. Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, United States of America.

Virus capsids are polymeric protein shells that protect the viral cargo. About half of known virus families have icosahedral capsids that self-assemble from tens to thousands of subunits. Capsid disassembly is critical to the lifecycles of many viruses yet is poorly understood. Here, we apply a graph and percolation theory to examine the effect of removing capsid subunits on capsid stability and fragmentation. Based on the structure of the icosahedral capsid of hepatitis B virus (HBV), we constructed a graph of rhombic subunits arranged with icosahedral symmetry. Though our approach neglects dependence on energetics, time, and molecular detail, it quantitatively predicts a percolation phase transition consistent with recent in vitro studies of HBV capsid dissociation. While the stability of the capsid graph followed a gradual quadratic decay, the rhombic tiling abruptly fragmented when we removed more than 25% of the 120 subunits, near the percolation threshold observed experimentally. This threshold may also affect results of capsid assembly, which also experimentally produces a preponderance of 90 mer intermediates, as the intermediate steps in these reactions are reversible and can thus resemble dissociation. Application of percolation theory to understanding capsid association and dissociation may prove a general approach to relating virus biology to the underlying biophysics of the virus particle.
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http://dx.doi.org/10.1088/1478-3975/aac194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004236PMC
June 2018

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

Symp Theory Model Simul 2017 Apr;2017

Department of Intelligent Systems Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN 47405.

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.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749416PMC
April 2017

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

Proc Winter Simul Conf 2016 Dec 19;2016:1230-1240. Epub 2017 Jan 19.

Dept. of Intelligent Systems Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA.

Living tissues are dynamic, heterogeneous compositions of , including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical and reorganize via transformation, birth, death and migration . 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 paradigm, the Continuous Concurrent Object Process Methodology () which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.
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http://dx.doi.org/10.1109/WSC.2016.7822179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742361PMC
December 2016

A Notch positive feedback in the intestinal stem cell niche is essential for stem cell self-renewal.

Mol Syst Biol 2017 04 28;13(4):927. Epub 2017 Apr 28.

School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

The intestinal epithelium is the fastest regenerative tissue in the body, fueled by fast-cycling stem cells. The number and identity of these dividing and migrating stem cells are maintained by a mosaic pattern at the base of the crypt. How the underlying regulatory scheme manages this dynamic stem cell niche is not entirely clear. We stimulated intestinal organoids with Notch ligands and inhibitors and discovered that intestinal stem cells employ a positive feedback mechanism via direct Notch binding to the second intron of the Notch1 gene. Inactivation of the positive feedback by CRISPR/Cas9 mutation of the binding sequence alters the mosaic stem cell niche pattern and hinders regeneration in organoids. Dynamical system analysis and agent-based multiscale stochastic modeling suggest that the positive feedback enhances the robustness of Notch-mediated niche patterning. This study highlights the importance of feedback mechanisms in spatiotemporal control of the stem cell niche.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408779PMC
http://dx.doi.org/10.15252/msb.20167324DOI Listing
April 2017

Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD.

Hum Genomics 2016 11 21;10(1):37. Epub 2016 Nov 21.

Division of Nephrology, Department of Medicine, Richard Roudebush VAMC and Indiana University School of Medicine, Indianapolis, IN, 46202, USA.

Background: Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression.

Results: We used the de Almeida laboratory's sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways.

Conclusions: Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD's mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease.
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http://dx.doi.org/10.1186/s40246-016-0095-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117508PMC
November 2016

A Computational Model of Peripheral Photocoagulation for the Prevention of Progressive Diabetic Capillary Occlusion.

J Diabetes Res 2016 25;2016:2508381. Epub 2016 Oct 25.

The Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA; Physics Department, Indiana University, Bloomington, IN 47405, USA; School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.

We developed a computational model of the propagation of retinal ischemia in diabetic retinopathy and analyzed the consequences of various patterns and sizes of burns in peripheral retinal photocoagulation. The model addresses retinal ischemia as a phenomenon of adverse local feedback in which once a capillary is occluded there is an elevated probability of occlusion of adjacent capillaries resulting in enlarging areas of retinal ischemia as is commonly seen clinically. Retinal burns of different sizes and patterns, treated as local oxygen sources, are predicted to have different effects on the propagation of retinal ischemia. The patterns of retinal burns are optimized with regard to minimization of the sum of the photocoagulated retina and computer predicted ischemic retina. Our simulations show that certain patterns of retinal burns are effective at preventing the spatial spread of ischemia by creating oxygenated boundaries across which the ischemia does not propagate. This model makes no statement about current PRP treatment of avascular peripheral retina and notes that the usual spot sizes used in PRP will not prevent ischemic propagation in still vascularized retinal areas. The model seems to show that a properly patterned laser treatment of still vascularized peripheral retina may be able to prevent or at least constrain the propagation of diabetic retinal ischemia in those retinal areas with intact capillaries.
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http://dx.doi.org/10.1155/2016/2508381DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5099465PMC
June 2017

Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease.

Mol Biol Cell 2016 11 18;27(22):3673-3685. Epub 2016 May 18.

Division of Nephrology, Richard L. Roudebush VA Medical Center, and Indiana University School of Medicine, Indianapolis, IN 46202

In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell-cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell-cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention.
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http://dx.doi.org/10.1091/mbc.E16-01-0059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5221598PMC
November 2016

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

Model Driven Eng Lang Syst 2016 Oct;16:115-122

Department of Intelligent Systems Engineering Biocomplexity Institute Indiana University Bloomington, IN 47405.

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.
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http://dx.doi.org/10.1145/2976767.2976790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761068PMC
October 2016

A Liver-Centric Multiscale Modeling Framework for Xenobiotics.

PLoS One 2016 16;11(9):e0162428. Epub 2016 Sep 16.

Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162428PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026379PMC
August 2017

Filopodial-Tension Model of Convergent-Extension of Tissues.

PLoS Comput Biol 2016 06 20;12(6):e1004952. Epub 2016 Jun 20.

Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, Indiana, United States of America.

In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo.
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http://dx.doi.org/10.1371/journal.pcbi.1004952DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4913901PMC
June 2016

Progression of Diabetic Capillary Occlusion: A Model.

PLoS Comput Biol 2016 06 14;12(6):e1004932. Epub 2016 Jun 14.

School of Optometry, Indiana University, Bloomington, Indiana, United States of America.

An explanatory computational model is developed of the contiguous areas of retinal capillary loss which play a large role in diabetic maculapathy and diabetic retinal neovascularization. Strictly random leukocyte mediated capillary occlusion cannot explain the occurrence of large contiguous areas of retinal ischemia. Therefore occlusion of an individual capillary must increase the probability of occlusion of surrounding capillaries. A retinal perifoveal vascular sector as well as a peripheral retinal capillary network and a deleted hexagonal capillary network are modelled using Compucell3D. The perifoveal modelling produces a pattern of spreading capillary loss with associated macular edema. In the peripheral network, spreading ischemia results from the progressive loss of the ladder capillaries which connect peripheral arterioles and venules. System blood flow was elevated in the macular model before a later reduction in flow in cases with progression of capillary occlusions. Simulations differing only in initial vascular network structures but with identical dynamics for oxygen, growth factors and vascular occlusions, replicate key clinical observations of ischemia and macular edema in the posterior pole and ischemia in the retinal periphery. The simulation results also seem consistent with quantitative data on macular blood flow and qualitative data on venous oxygenation. One computational model applied to distinct capillary networks in different retinal regions yielded results comparable to clinical observations in those regions.
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http://dx.doi.org/10.1371/journal.pcbi.1004932DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4907516PMC
June 2016

libRoadRunner: a high performance SBML simulation and analysis library.

Bioinformatics 2015 Oct 17;31(20):3315-21. Epub 2015 Jun 17.

Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.

Motivation: This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations.

Results: libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB ( WWWMATHWORKSCOM: ) and SciPy ( HTTP//WWWSCIPYORG/: ), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix.

Availability And Implementation: libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository.

Contacts: [email protected] or [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv363DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607739PMC
October 2015

Emergent Stratification in Solid Tumors Selects for Reduced Cohesion of Tumor Cells: A Multi-Cell, Virtual-Tissue Model of Tumor Evolution Using CompuCell3D.

PLoS One 2015 17;10(6):e0127972. Epub 2015 Jun 17.

Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, Indiana, USA.

Tumor cells and structure both evolve due to heritable variation of cell behaviors and selection over periods of weeks to years (somatic evolution). Micro-environmental factors exert selection pressures on tumor-cell behaviors, which influence both the rate and direction of evolution of specific behaviors, especially the development of tumor-cell aggression and resistance to chemotherapies. In this paper, we present, step-by-step, the development of a multi-cell, virtual-tissue model of tumor somatic evolution, simulated using the open-source CompuCell3D modeling environment. Our model includes essential cell behaviors, microenvironmental components and their interactions. Our model provides a platform for exploring selection pressures leading to the evolution of tumor-cell aggression, showing that emergent stratification into regions with different cell survival rates drives the evolution of less cohesive cells with lower levels of cadherins and higher levels of integrins. Such reduced cohesivity is a key hallmark in the progression of many types of solid tumors.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127972PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470639PMC
March 2016

3D simulations of wet foam coarsening evidence a self similar growth regime.

Colloids Surf A Physicochem Eng Asp 2015 May 14;473:109-114. Epub 2015 Feb 14.

Instituto de Física, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, C.P. 15051 - 91501-970 Porto Alegre, RS, Brazil; Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, 47405, United States of America; Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Av. Bento Gonçalves 9500, C.P. 15051 - 91501-970 Porto Alegre, RS, Brazil.

In wet liquid foams, slow diffusion of gas through bubble walls changes bubble pressure, volume and wall curvature. Large bubbles grow at the expenses of smaller ones. The smaller the bubble, the faster it shrinks. As the number of bubbles in a given volume decreases in time, the average bubble size increases: the foam coarsens. During coarsening, bubbles also move relative to each other, changing bubble topology and shape, while liquid moves within the regions separating the bubbles. Analyzing the combined effects of these mechanisms requires examining a volume with enough bubbles to provide appropriate statistics throughout coarsening. Using a Cellular Potts model, we simulate these mechanisms during the evolution of three-dimensional foams with wetnesses of = 0.00, 0.05 and 0.20. We represent the liquid phase as an ensemble of many small fluid particles, which allows us to monitor liquid flow in the region between bubbles. The simulations begin with 2 × 10 bubbles for = 0.00 and 1.25 × 10 bubbles for = 0.05 and 0.20, allowing us to track the distribution functions for bubble size, topology and growth rate over two and a half decades of volume change. All simulations eventually reach a growth regime, with the distribution functions time independent and the number of bubbles decreasing with time as a power law whose exponent depends on the wetness.
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http://dx.doi.org/10.1016/j.colsurfa.2015.02.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019577PMC
May 2015

The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents.

Bioinformatics 2014 Aug 22;30(16):2367-74. Epub 2014 Apr 22.

Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA.

Motivation: Currently, there are no ontologies capable of describing both the spatial organization of groups of cells and the behaviors of those cells. The lack of a formalized method for describing the spatiality and intrinsic biological behaviors of cells makes it difficult to adequately describe cells, tissues and organs as spatial objects in living tissues, in vitro assays and in computational models of tissues.

Results: We have developed an OWL-2 ontology to describe the intrinsic physical and biological characteristics of cells and tissues. The Cell Behavior Ontology (CBO) provides a basis for describing the spatial and observable behaviors of cells and extracellular components suitable for describing in vivo, in vitro and in silico multicell systems. Using the CBO, a modeler can create a meta-model of a simulation of a biological model and link that meta-model to experiment or simulation results. Annotation of a multicell model and its computational representation, using the CBO, makes the statement of the underlying biology explicit. The formal representation of such biological abstraction facilitates the validation, falsification, discovery, sharing and reuse of both models and experimental data.

Availability And Implementation: The CBO, developed using Protégé 4, is available at http://cbo.biocomplexity.indiana.edu/cbo/ and at BioPortal (http://bioportal.bioontology.org/ontologies/CBO).
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http://dx.doi.org/10.1093/bioinformatics/btu210DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133580PMC
August 2014