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Maqsood Z, Clark JC, Martin EM, Cheung YFH, Morán LA, Watson SET, Pike JA, Di Y, Poulter NS, Slater A, Lange BMH, Nieswandt B, Eble JA, Tomlinson MG, Owen DM, Stegner D, Bridge LJ, Wierling C, Watson SP. Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins. PLoS Comput Biol 2022; 18:e1010708. [PMID: 36441766 PMCID: PMC9731471 DOI: 10.1371/journal.pcbi.1010708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/08/2022] [Accepted: 11/04/2022] [Indexed: 11/29/2022] Open
Abstract
The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects.
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Affiliation(s)
- Zahra Maqsood
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Alacris Theranostics, GmbH, Berlin, Germany
- Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg and Institute of Experimental Biomedicine I, University Hospital of Würzburg, Würzburg, Germany
| | - Joanne C. Clark
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
| | - Eleyna M. Martin
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Yam Fung Hilaire Cheung
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Leibniz-Institut für Analytische Wissenschaften–ISAS—e. V., Dortmund, Germany
- School of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Luis A. Morán
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Sean E. T. Watson
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jeremy A. Pike
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
| | - Ying Di
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Natalie S. Poulter
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
| | - Alexandre Slater
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | | | - Bernhard Nieswandt
- Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg and Institute of Experimental Biomedicine I, University Hospital of Würzburg, Würzburg, Germany
| | - Johannes A. Eble
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Mike G. Tomlinson
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
- Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Dylan M. Owen
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
- Institute of Immunology and Immunotherapy, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - David Stegner
- Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg and Institute of Experimental Biomedicine I, University Hospital of Würzburg, Würzburg, Germany
| | - Lloyd J. Bridge
- Faculty of Environment & Technology, Department of Computer Science and Creative Technologies, University of the West England, Bristol, United Kingdom
| | | | - Steve P. Watson
- Institute of Cardiovascular Sciences, IBR Building, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, United Kingdom
- School of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
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Liew CW, Polanco L, Manalang K, Kurt RA. An experimental and computational approach to unraveling interconnected TLR signaling cascades. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Nitz M, Smith D, Wysocki B, Knoell D, Wysocki T. Modeling of an immune response: Queuing network analysis of the impact of zinc and cadmium on macrophage activation. Biotechnol Bioeng 2020; 118:412-422. [PMID: 32970332 DOI: 10.1002/bit.27579] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/19/2020] [Accepted: 09/09/2020] [Indexed: 12/13/2022]
Abstract
Chronic obstructive pulmonary disease is characterized by progressive, irreversible airflow obstruction resulting from an abnormal inflammatory response to noxious gases and particles. Alveolar macrophages rely on the transcription factors, nuclear factor κB and mitogen-activated protein kinase, among others, to facilitate the production of inflammatory mediators designed to help rid the lung of foreign pathogens and noxious stimuli. Building a kinetic model using queuing networks, provides a quantitative approach incorporating an initial number of individual molecules along with rates of the reactions in any given pathway. Accordingly, this model has been shown useful to model cell behavior including signal transduction, transcription, and metabolic pathways. The aim of this study was to determine whether a queuing theory model that involves lipopolysaccharide-mediated macrophage activation in tandem with changes in intracellular Cd and zinc (Zn) content or a lack thereof, would be useful to predict their impact on immune activation. We then validate our model with biologic cytokine output from human macrophages relative to the timing of innate immune activation. We believe that our results further prove the validity of the queuing theory approach to model intracellular molecular signaling and postulate that it can be useful to predict additional cell signaling pathways and the corresponding biological outcomes.
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Affiliation(s)
- Marissa Nitz
- Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Deandra Smith
- Department of Pharmacy Practice, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Beata Wysocki
- Biology, University of Nebraska Omaha, Omaha, Nebraska, USA
| | - Daren Knoell
- Pharmacy Practice and Science, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Tadeusz Wysocki
- Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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Soheilypour M, Mofrad MRK. Agent-Based Modeling in Molecular Systems Biology. Bioessays 2018; 40:e1800020. [PMID: 29882969 DOI: 10.1002/bies.201800020] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Indexed: 12/13/2022]
Abstract
Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.
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Affiliation(s)
- Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
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Mathematical Models for Immunology: Current State of the Art and Future Research Directions. Bull Math Biol 2016; 78:2091-2134. [PMID: 27714570 PMCID: PMC5069344 DOI: 10.1007/s11538-016-0214-9] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 09/26/2016] [Indexed: 01/01/2023]
Abstract
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.
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Hussain F, Langmead CJ, Mi Q, Dutta-Moscato J, Vodovotz Y, Jha SK. Automated parameter estimation for biological models using Bayesian statistical model checking. BMC Bioinformatics 2015; 16 Suppl 17:S8. [PMID: 26679759 PMCID: PMC4674867 DOI: 10.1186/1471-2105-16-s17-s8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system. Results Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. Conclusions We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.
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Day JD, Metes DM, Vodovotz Y. Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation. Front Immunol 2015; 6:484. [PMID: 26441988 PMCID: PMC4585194 DOI: 10.3389/fimmu.2015.00484] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/07/2015] [Indexed: 12/22/2022] Open
Abstract
A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.
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Affiliation(s)
- Judy D. Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
- National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA
| | - Diana M. Metes
- Department of Surgery and Immunology, Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA
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Vodovotz Y. Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience. Interface Focus 2014; 4:20140004. [PMID: 25285195 DOI: 10.1098/rsfs.2014.0004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery , University of Pittsburgh , W944 Starzl Biomedical Sciences Tower, 200 Lothrop Street, Pittsburgh, PA 15213 , USA
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Chapleau RR, Robinson PJ, Schlager JJ, Gearhart JM. Potential new therapeutic modality revealed through agent-based modeling of the neuromuscular junction and acetylcholinesterase inhibition. Theor Biol Med Model 2014; 11:42. [PMID: 25273339 PMCID: PMC4209019 DOI: 10.1186/1742-4682-11-42] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 09/24/2014] [Indexed: 11/23/2022] Open
Abstract
Background One of the leading causes of death and illness within the agriculture industry is through unintentionally ingesting or inhaling organophosphate pesticides. OP intoxication directly inhibits acetylcholinesterase, resulting in an excitatory signaling cascade leading to fasciculation, loss of control of bodily fluids, and seizures. Methods Our model was developed using a discrete, rules-based modeling approach in NetLogo. This model includes acetylcholinesterase, the nicotinic acetylcholine receptor responsible for signal transduction, a single release of acetylcholine, organophosphate inhibitors, and a theoretical novel medical countermeasure. We have parameterized the system considering the molecular reaction rate constants in an agent-based approach, as opposed to apparent macroscopic rates used in differential equation models. Results Our model demonstrates how the cholinergic crisis can be mitigated by therapeutic intervention with an acetylcholinesterase activator. Our model predicts signal rise rates and half-lives consistent with in vitro and in vivo data in the absence and presence of inhibitors. It also predicts the efficacy of theoretical countermeasures acting through three mechanisms: increasing catalytic turnover of acetylcholine, increasing acetylcholine binding affinity to the enzyme, and decreasing binding rates of inhibitors. Conclusion We present a model of the neuromuscular junction confirming observed acetylcholine signaling data and suggesting that developing a countermeasure capable of reducing inhibitor binding, and not activator concentration, is the most important parameter for reducing organophosphate (OP) intoxication. Electronic supplementary material The online version of this article (doi:10.1186/1742-4682-11-42) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Richard R Chapleau
- Henry M Jackson Foundation for the Advancement of Military Medicine, 2729 R Street, Wright Patterson AFB, OH 45433, USA.
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Peer X, An G. Agent-based model of fecal microbial transplant effect on bile acid metabolism on suppressing Clostridium difficile infection: an example of agent-based modeling of intestinal bacterial infection. J Pharmacokinet Pharmacodyn 2014; 41:493-507. [PMID: 25168489 DOI: 10.1007/s10928-014-9381-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2014] [Accepted: 08/19/2014] [Indexed: 01/05/2023]
Abstract
Agent-based modeling is a computational modeling method that represents system-level behavior as arising from multiple interactions between the multiple components that make up a system. Biological systems are thus readily described using agent-based models (ABMs), as multi-cellular organisms can be viewed as populations of interacting cells, and microbial systems manifest as colonies of individual microbes. Intersections between these two domains underlie an increasing number of pathophysiological processes, and the intestinal tract represents one of the most significant locations for these inter-domain interactions, so much so that it can be considered an internal ecology of varying robustness and function. Intestinal infections represent significant disturbances of this internal ecology, and one of the most clinically relevant intestinal infections is Clostridium difficile infection (CDI). CDI is precipitated by the use of broad-spectrum antibiotics, involves the depletion of commensal microbiota, and alterations in bile acid composition in the intestinal lumen. We present an example ABM of CDI (the C. difficile Infection ABM, or CDIABM) to examine fundamental dynamics of the pathogenesis of CDI and its response to treatment with anti-CDI antibiotics and a newer treatment therapy, fecal microbial transplant. The CDIABM focuses on one specific mechanism of potential CDI suppression: commensal modulation of bile acid composition. Even given its abstraction, the CDIABM reproduces essential dynamics of CDI and its response to therapy, and identifies a paradoxical zone of behavior that provides insight into the role of intestinal nutritional status and the efficacy of anti-CDI therapies. It is hoped that this use case example of the CDIABM can demonstrate the usefulness of both agent-based modeling and the application of abstract functional representation as the biomedical community seeks to address the challenges of increasingly complex diseases with the goal of personalized medicine.
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Affiliation(s)
- Xavier Peer
- Department of Surgery, University of Chicago, 5841 South Maryland Ave, MC 5094 S-032, Chicago, IL, 60637, USA
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Agent-based modeling of the immune system: NetLogo, a promising framework. BIOMED RESEARCH INTERNATIONAL 2014; 2014:907171. [PMID: 24864263 PMCID: PMC4016927 DOI: 10.1155/2014/907171] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 04/02/2014] [Indexed: 12/12/2022]
Abstract
Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.
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Aerts JM, Haddad WM, An G, Vodovotz Y. From data patterns to mechanistic models in acute critical illness. J Crit Care 2014; 29:604-10. [PMID: 24768566 DOI: 10.1016/j.jcrc.2014.03.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 03/14/2014] [Accepted: 03/14/2014] [Indexed: 12/13/2022]
Abstract
The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.
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Affiliation(s)
- Jean-Marie Aerts
- Division Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, Leuven, Belgium B-3001
| | - Wassim M Haddad
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150
| | - Gary An
- Department of Surgery, University of Chicago Medicine, Chicago, IL 60637
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219.
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Gopalakrishnan V, Kim M, An G. Using an Agent-Based Model to Examine the Role of Dynamic Bacterial Virulence Potential in the Pathogenesis of Surgical Site Infection. Adv Wound Care (New Rochelle) 2013; 2:510-526. [PMID: 24761337 PMCID: PMC3842882 DOI: 10.1089/wound.2012.0400] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 01/11/2013] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Despite clinical advances, surgical site infections (SSIs) remain a problem. The development of SSIs involves a complex interplay between the cellular and molecular mechanisms of wound healing and contaminating bacteria, and here, we utilize an agent-based model (ABM) to investigate the role of bacterial virulence potential in the pathogenesis of SSI. APPROACH The Muscle Wound ABM (MWABM) incorporates muscle cells, neutrophils, macrophages, myoblasts, abstracted blood vessels, and avirulent/virulent bacteria to simulate the pathogenesis of SSIs. Simulated bacteria with virulence potential can mutate to possess resistance to reactive oxygen species and increased invasiveness. Simulated experiments (t=7 days) involved parameter sweeps of initial wound size to identify transition zones between healed and nonhealed wounds/SSIs, and to evaluate the effect of avirulent/virulent bacteria. RESULTS The MWABM reproduced the dynamics of normal successful healing, including a transition zone in initial wound size beyond which healing was significantly impaired. Parameter sweeps with avirulent bacteria demonstrated that smaller wound sizes were associated with healing failure. This effect was even more pronounced with the addition of virulence potential to the contaminating bacteria. INNOVATION The MWABM integrates the myriad factors involved in the healing of a normal wound and the pathogenesis of SSIs. This type of model can serve as a useful framework into which more detailed mechanistic knowledge can be embedded. CONCLUSION Future work will involve more comprehensive representation of host factors, and especially the ability of those host factors to activate virulence potential in the microbes involved.
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Affiliation(s)
| | - Moses Kim
- Department of Surgery, University of Chicago, Chicago, Illinois
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, Illinois
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Stern JR, Olivas AD, Valuckaite V, Zaborina O, Alverdy JC, An G. Agent-based model of epithelial host-pathogen interactions in anastomotic leak. J Surg Res 2013; 184:730-8. [PMID: 23290531 PMCID: PMC4184143 DOI: 10.1016/j.jss.2012.12.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2012] [Revised: 11/24/2012] [Accepted: 12/06/2012] [Indexed: 10/27/2022]
Abstract
BACKGROUND There is a growing recognition of the significance of host-pathogen interactions (HPIs) in gut biology leading to a reassessment of the role of bacteria in intestinal anastomotic leak. Understanding the complexities of the early postsurgical gut HPI requires integrating knowledge of both epithelial and bacterial behaviors to generate hypotheses of potential mechanisms of interaction. Agent-based modeling is a computational method well suited to achieve this goal, and we use an agent-based model (ABM) to examine alterations in the HPI affecting reestablishment of the epithelial barrier that may subsequently lead to anastomotic leak. METHODS Computational agents representing Pseudomonas aeruginosa were added to a previously validated ABM of epithelial restitution. Simulated experiments were performed examining the effect of radiation on bacterial binding to epithelial cells, plausibility of putative binding targets, and potential mechanisms of epithelial cell killing by virulent bacteria. RESULTS Simulation experiments incorporating radiation effects on epithelial monolayers produced binding patterns akin to those seen in vitro and suggested that promotility integrin-laminin associations represent potential sites for bacterial binding and disruption of restitution. Simulations of potential mechanisms of epithelial cell killing suggested that an injected cytotoxin was the means by which virulent bacteria produced the tissue destruction needed to generate an anastomotic leak, a mechanism subsequently confirmed with genotyping of the virulent P aeruginosa strain. CONCLUSIONS This study emphasizes the utility of ABM as an adjunct to traditional research methods and provides insights into the potentially critical role of HPI in the pathogenesis of anastomotic leak.
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Affiliation(s)
| | | | | | | | | | - Gary An
- The University of Chicago, Department of Surgery
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Abstract
OBJECTIVES To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis. DATA SOURCES PubMed search and review of relevant medical literature. SUMMARY Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed. CONCLUSIONS Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.
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A mechanistic pharmacodynamic model of IRAK-4 drug inhibition in the Toll-like receptor pathway. J Pharmacokinet Pharmacodyn 2013; 40:609-22. [DOI: 10.1007/s10928-013-9334-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 08/22/2013] [Indexed: 12/17/2022]
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Vasilescu C, Olteanu M, Flondor P, Calin GA. Fractal-like kinetics of intracellular enzymatic reactions: a chemical framework of endotoxin tolerance and a possible non-specific contribution of macromolecular crowding to cross-tolerance. Theor Biol Med Model 2013; 10:55. [PMID: 24034421 PMCID: PMC3849556 DOI: 10.1186/1742-4682-10-55] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Accepted: 09/10/2013] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The response to endotoxin (LPS), and subsequent signal transduction lead to the production of cytokines such as tumor necrosis factor-α (TNF-α) by innate immune cells. Cells or organisms pretreated with endotoxin enter into a transient state of hyporesponsiveness, referred to as endotoxin tolerance (ET) which represents a particular case of negative preconditioning. Despite recent progress in understanding the molecular basis of ET, there is no consensus yet on the primary mechanism responsible for ET and for the more complex cases of cross tolerance. In this study, we examined the consequences of the macromolecular crowding (MMC) and of fractal-like kinetics (FLK) of intracellular enzymatic reactions on the LPS signaling machinery. We hypothesized that this particular type of enzyme kinetics may explain the development of ET phenomenon. METHOD Our aim in the present study was to characterize the chemical kinetics framework in ET and determine whether fractal-like kinetics explains, at least in part, ET. We developed an ordinary differential equations (ODE) mathematical model that took into account the links between the MMC and the LPS signaling machinery leading to ET. We proposed that the intracellular fractal environment (MMC) contributes to ET and developed two mathematical models of enzyme kinetics: one based on Kopelman's fractal-like kinetics framework and the other based on Savageau's power law model. RESULTS Kopelman's model provides a good image of the potential influence of a fractal intracellular environment (MMC) on ET. The Savageau power law model also partially explains ET. The computer simulations supported the hypothesis that MMC and FLK may play a role in ET. CONCLUSION The model highlights the links between the organization of the intracellular environment, MMC and the LPS signaling machinery leading to ET. Our FLK-based model does not minimize the role of the numerous negative regulatory factors. It simply draws attention to the fact that macromolecular crowding can contribute significantly to the induction of ET by imposing geometric constrains and a particular chemical kinetic for the intracellular reactions.
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Affiliation(s)
- Catalin Vasilescu
- Fundeni Clinical Institute, Carol Davila University of Medicine and Pharmacy, 258 Fundeni Street, Bucharest, Romania.
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18
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Chapa J, Bourgo RJ, Greene GL, Kulkarni S, An G. Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics. PLoS One 2013; 8:e64091. [PMID: 23704974 PMCID: PMC3660364 DOI: 10.1371/journal.pone.0064091] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 04/08/2013] [Indexed: 02/06/2023] Open
Abstract
The study of the pathogenesis of breast cancer is challenged by the long time-course of the disease process and the multi-factorial nature of generating oncogenic insults. The characterization of the longitudinal pathogenesis of malignant transformation from baseline normal breast duct epithelial dynamics may provide vital insight into the cascading systems failure that leads to breast cancer. To this end, extensive information on the baseline behavior of normal mammary epithelium and breast cancer oncogenesis was integrated into a computational model termed the Ductal Epithelium Agent-Based Model (DEABM). The DEABM is composed of computational agents that behave according to rules established from published cellular and molecular mechanisms concerning breast duct epithelial dynamics and oncogenesis. The DEABM implements DNA damage and repair, cell division, genetic inheritance and simulates the local tissue environment with hormone excretion and receptor signaling. Unrepaired DNA damage impacts the integrity of the genome within individual cells, including a set of eight representative oncogenes and tumor suppressors previously implicated in breast cancer, with subsequent consequences on successive generations of cells. The DEABM reproduced cellular population dynamics seen during the menstrual cycle and pregnancy, and demonstrated the oncogenic effect of known genetic factors associated with breast cancer, namely TP53 and Myc, in simulations spanning ∼40 years of simulated time. Simulations comparing normal to BRCA1-mutant breast tissue demonstrated rates of invasive cancer development similar to published epidemiologic data with respect to both cumulative incidence over time and estrogen-receptor status. Investigation of the modeling of ERα-positive (ER+) tumorigenesis led to a novel hypothesis implicating the transcription factor and tumor suppressor RUNX3. These data suggest that the DEABM can serve as a potentially valuable framework to augment the traditional investigatory workflow for future hypothesis generation and testing of the mechanisms of breast cancer oncogenesis.
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Affiliation(s)
- Joaquin Chapa
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Ryan J. Bourgo
- Ben May Department of Cancer Research, University of Chicago, Chicago, Illinois, United States of America
| | - Geoffrey L. Greene
- Ben May Department of Cancer Research, University of Chicago, Chicago, Illinois, United States of America
| | - Swati Kulkarni
- Department of Surgery, University of Chicago, Chicago, Illinois, United States of America
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, Illinois, United States of America
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19
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Hoque R, Vodovotz Y, Mehal W. Therapeutic strategies in inflammasome mediated diseases of the liver. J Hepatol 2013; 58:1047-52. [PMID: 23266490 PMCID: PMC4113326 DOI: 10.1016/j.jhep.2012.12.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2012] [Revised: 12/12/2012] [Accepted: 12/13/2012] [Indexed: 12/20/2022]
Abstract
Tissue stress and cell death result in inflammation even in the absence of pathogens. Such sterile inflammation is dependent on a cytosolic complex of proteins inside immune cells termed the inflammasome. This complex converts two groups of extracellular signals into an inflammatory response via activation of caspase-1 and secretion of IL-1β and IL-18. Group 1 signals are typically TOLL like receptor agonists and result in transcriptional upregulation of inflammasome components and pro-cytokines. Group 2 signals are diverse, ranging from uric acid to ATP, and lead to assembly and activation of the inflammasome complex. Inflammasome components are required for a wide range of acute and chronic pathologies, including experimental alcoholic and non-alcoholic steatohepatitis, and drug-induced liver injury. Collectively, group 1 and 2 signals, inflammasome components, and cytokine receptors provide a rich source of therapeutic targets. Many of the advances in the field have come from standard reductionist experiments. Progress in the understanding of complex human systems will, however, be dependent on novel strategies such as systems analysis, which analyze large data sets to provide new insights.
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Affiliation(s)
- Rafaz Hoque
- Section of Digestive Diseases, Yale University, and West Haven Veterans Medical Center, New Haven, CT, United States
| | - Yoram Vodovotz
- Department of Surgery and Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Wajahat Mehal
- Section of Digestive Diseases, Yale University, and West Haven Veterans Medical Center, New Haven, CT, United States
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20
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An agent-based model of cellular dynamics and circadian variability in human endotoxemia. PLoS One 2013; 8:e55550. [PMID: 23383223 PMCID: PMC3559552 DOI: 10.1371/journal.pone.0055550] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2012] [Accepted: 12/30/2012] [Indexed: 01/01/2023] Open
Abstract
As cellular variability and circadian rhythmicity play critical roles in immune and inflammatory responses, we present in this study an agent-based model of human endotoxemia to examine the interplay between circadian controls, cellular variability and stochastic dynamics of inflammatory cytokines. The model is qualitatively validated by its ability to reproduce circadian dynamics of inflammatory mediators and critical inflammatory responses after endotoxin administration in vivo. Novel computational concepts are proposed to characterize the cellular variability and synchronization of inflammatory cytokines in a population of heterogeneous leukocytes. Our results suggest that there is a decrease in cell-to-cell variability of inflammatory cytokines while their synchronization is increased after endotoxin challenge. Model parameters that are responsible for IκB production stimulated by NFκB activation and for the production of anti-inflammatory cytokines have large impacts on system behaviors. Additionally, examining time-dependent systemic responses revealed that the system is least vulnerable to endotoxin in the early morning and most vulnerable around midnight. Although much remains to be explored, proposed computational concepts and the model we have pioneered will provide important insights for future investigations and extensions, especially for single-cell studies to discover how cellular variability contributes to clinical implications.
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21
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Günel A. Modelling the interactions between TLR4 and IFNβ pathways. J Theor Biol 2012; 307:137-48. [PMID: 22575970 DOI: 10.1016/j.jtbi.2012.04.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2011] [Revised: 04/17/2012] [Accepted: 04/18/2012] [Indexed: 02/06/2023]
Abstract
Bacterial lipopolysaccharide (LPS) association with their connate receptor TLR4 triggers Type I interferon signaling cascade through its MyD88 independent downstream. Compared to plethora of reported empirical data on both TLR4 and Type I interferon pathways, there is no known model to decipher crosstalk mechanisms between these two crucial innate immune pathogen activated pathways regulating vital transcriptional factors such as nuclear factor-κB (NFκB), IFNβ, the interferon-stimulated gene factor-3 (ISGF3) and an important cancer drug target protein kinase-R (PKR). Innate immune system is based on a sensitive balance of intricate interactions. In elucidating these interactions, in silico integration of pathways has great potential. Attempts confined to single pathway may not be effective in truly addressing source of real systems behavior. This is the first report combining toll-like receptor-4 (TLR4) and interferon beta (IFNβ) pathways in a single in silico model, analyzing their interactions, pinpointing the source of delay in PKR late phase activity and limiting the transcription of IFN and PKR by using a method including an statistical physics technique in reaction equations. The model quite successfully recapitulates published interferon regulatory factor-3 (IRF3) and IFNβ data from mouse macrophages and PKR data from mouse embryonic fibroblast cell lines. The simulations end up with an estimate of IRF3, IFNβ, ISGF3 dose dependent profiles mimicking nonlinear dose response characteristic of the system. Involvement of concomitant PKR downstream can unravel elusive mechanisms in specific profiles like NFκB regulation.
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Affiliation(s)
- Aylin Günel
- Istanbul Technical University Informatics Institute, Maslak, 34469, Istanbul, Turkiye.
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22
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Bode JG, Ehlting C, Häussinger D. The macrophage response towards LPS and its control through the p38(MAPK)-STAT3 axis. Cell Signal 2012; 24:1185-94. [PMID: 22330073 DOI: 10.1016/j.cellsig.2012.01.018] [Citation(s) in RCA: 298] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2012] [Accepted: 01/27/2012] [Indexed: 12/19/2022]
Abstract
In macrophages detection of gram-negative bacteria particularly involves binding of the outer-wall component lipopolysaccharide (LPS) to its cognate receptor complex, comprising Toll like receptor 4 (TLR4), CD14 and MD2. LPS-induced formation of the LPS receptor complex elicits a signaling network, including intra-cellular signal-transduction directly activated by the TLR4 receptor complex as well as successional induction of indirect autocrine and paracrine signaling events. All these different pathways are integrated into the macrophage response towards an inflammatory stimulus by a highly complex cross-talk of the pathways engaged. This also includes a tight control by several intra- and inter-cellular feedback loops warranting an inflammatory response sufficient to battle invading pathogens and to avoid non-essential tissue damage caused by an overwhelming inflammatory response. Several evidences indicate that the reciprocal cross-talk between the p38(MAPK)-pathway and signal transducer and activator of transcription (STAT)3-mediated signal-transduction forms a critical axis successively activated by LPS. The balanced activation of this axis is essential for both induction and propagation of the inflammatory macrophage response as well as for the control of the resolution phase, which is largely driven by IL-10 and sustained STAT3 activation. In this context regulation of suppressor of cytokine signaling (SOCS)3 expression and the recently described divergent regulatory roles of the two p38(MAPK)-activated protein kinases MK2 and MK3 for the regulation of LPS-induced NF-κB- and IRF3-mediated signal-transduction and gene expression, which includes the regulation of IFNβ, IL-10 and DUSP1, appears to play an important role.
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Affiliation(s)
- Johannes G Bode
- Department of Gastroenterology, Hepatology and Infectious Disease, University Hospital, Heinrich Heine University of Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany.
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23
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Kim M, Christley S, Alverdy JC, Liu D, An G. Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model. Surg Infect (Larchmt) 2012; 13:18-32. [PMID: 22217195 DOI: 10.1089/sur.2011.057] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Necrotizing enterocolitis (NEC) is a complex disease involving prematurity, enteral feeding, and bacterial effects. We propose that the underlying initial condition in its pathogenesis is reduced ability of the neonatal gut epithelial cells (NGECs) to clear oxidative stress (OS), and that when such a NGEC population is exposed to enteral feeding, the increased metabolic OS tips the population toward apoptosis, inflammation, bacterial activation, and eventual necrosis. The multi-factorial complexity of NEC requires characterization with computational modeling, and herein, we used an agent-based model (ABM) to instantiate and examine our unifying hypothesis of the pathogenesis of NEC. METHODS An ABM of the neonatal gut was created with NGEC computational agents incorporating rules for pathways for OS, p53, tight junctions, Toll-like receptor (TLR)-4, nitric oxide, and nuclear factor-kappa beta (NF-κB). The modeled bacteria activated TLR-4 on contact with NGECs. Simulations included parameter sweeps of OS response, response to feeding, addition of bacteria, and alterations in gut mucus production. RESULTS The ABM reproduced baseline cellular respiration and clearance of OS. Reduction in OS clearance consistent with clinical NEC led to senescence, apoptosis, or inflammation, with disruption of tight junctions, but rarely to NGEC necrosis. An additional "hit" of bacteria activating TLR-4 potentiated a shift to NGEC necrosis across the entire population. The mucus layer was modeled to limit bacterial-NGEC interactions and reduce this effect, but concomitant apoptosis in the goblet cell population reduced the efficacy of the mucus layer and limited its protective effect in simulated experiments. This finding suggests a means by which increased apoptosis at the cellular population level can lead to a transition to the necrosis outcome. CONCLUSIONS Our ABM incorporates known components of NEC and demonstrates that impaired OS management can lead to apoptosis and inflammation of NGECs, rendering the system susceptible to an additional insult involving regionalized mucus barrier failure and TLR-4 activation, which potentiates the necrosis outcome. This type of integrative dynamic knowledge representation can be a useful adjunct to help guide and contextualize research.
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Affiliation(s)
- Moses Kim
- Department of Surgery, University of Chicago, Chicago, Illinois 60637, USA
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24
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Abstract
Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.
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Affiliation(s)
- Gary An
- Department of Surgery, University of Chicago, Chicago, IL 60637
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
| | - Rami A. Namas
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213
| | - Yoram Vodovotz
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213
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25
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Machado D, Costa RS, Rocha M, Ferreira EC, Tidor B, Rocha I. Modeling formalisms in Systems Biology. AMB Express 2011; 1:45. [PMID: 22141422 PMCID: PMC3285092 DOI: 10.1186/2191-0855-1-45] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2011] [Accepted: 12/05/2011] [Indexed: 12/18/2022] Open
Abstract
Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.
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Affiliation(s)
- Daniel Machado
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Rafael S Costa
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Eugénio C Ferreira
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Bruce Tidor
- Department of Biological Engineering/Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Isabel Rocha
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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26
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Seal JB, Alverdy JC, Zaborina O, An G. Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis. Theor Biol Med Model 2011; 8:33. [PMID: 21929759 PMCID: PMC3184268 DOI: 10.1186/1742-4682-8-33] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2011] [Accepted: 09/19/2011] [Indexed: 01/07/2023] Open
Abstract
Background There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis. Methodology/Principal Findings An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses. Conclusions/Significance Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.
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Affiliation(s)
- John B Seal
- Department of Surgery, University of Chicago, 5841 South Maryland Ave, MC 5031, Chicago, IL 60637, USA
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27
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Yang Q, Calvano SE, Lowry SF, Androulakis IP. A dual negative regulation model of Toll-like receptor 4 signaling for endotoxin preconditioning in human endotoxemia. Math Biosci 2011; 232:151-63. [PMID: 21624378 DOI: 10.1016/j.mbs.2011.05.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2010] [Revised: 05/10/2011] [Accepted: 05/16/2011] [Indexed: 12/17/2022]
Abstract
We discuss a model illustrating how the outcome of repeated endotoxin administration experiments can emerge as a natural consequence of the tightly regulated signaling pathways and also highlight the importance of a dual negative feedback regulation including PI3K/Akt and IRAK-M (IRAK3). We identify the relative time scales of the onset and the magnitude of the stimulus as key determinants of outcome in repeated administration experiments. The results of our simulations involve potentiated response, tolerance, and protective tolerance. Moreover, the knockout of negative regulators shows that IRAK-M is a necessary and sufficient factor for generation of endotoxin tolerance (ET). The effects of the knockout of IRAK-M gene or administration of PI3K inhibitor do yield predictions that have been verified experimentally. Finally, the pretreatment with PI3K inhibitor reveals the interaction between these two negative regulations.
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Affiliation(s)
- Qian Yang
- Chemical Engineering, Rutgers University, Piscataway, NJ 08854, USA.
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28
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An G, Christley S. Agent‐based modeling and biomedical ontologies: a roadmap. ACTA ACUST UNITED AC 2011. [DOI: 10.1002/wics.167] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Gary An
- Department of Surgery, University of Chicago, Chicago, IL, USA
| | - Scott Christley
- Department of Surgery, University of Chicago, Chicago, IL, USA
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29
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An G, Mi Q, Dutta-Moscato J, Vodovotz Y. Agent-based models in translational systems biology. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2011; 1:159-171. [PMID: 20835989 DOI: 10.1002/wsbm.45] [Citation(s) in RCA: 159] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing.
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Affiliation(s)
- Gary An
- Department of Surgery, Northwestern University, Chicago, IL 60611.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
| | - Qi Mi
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA 15260.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
| | - Joyeeta Dutta-Moscato
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
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30
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An G, Bartels J, Vodovotz Y. In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation. Drug Dev Res 2010; 72:187-200. [PMID: 21552346 DOI: 10.1002/ddr.20415] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.
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Affiliation(s)
- Gary An
- Department of Surgery, University of Chicago, Chicago, IL 60637
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31
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Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, Okonkwo DO, Verdolini-Abbott K, Constantine G, An G, Vodovotz Y. Translational systems biology of inflammation: potential applications to personalized medicine. Per Med 2010; 7:549-559. [PMID: 21339856 PMCID: PMC3041597 DOI: 10.2217/pme.10.45] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A central goal of industrialized nations is to provide personalized, preemptive and predictive medicine, while maintaining healthcare costs at a minimum. To do so, we must confront and gain an understanding of inflammation, a complex, nonlinear process central to many diseases that affect both industrialized and developing nations. Herein, we describe the work aimed at creating a rational, engineering-oriented and evidence-based synthesis of inflammation geared towards rapid clinical application. This comprehensive approach, which we call 'Translational Systems Biology', to date has been utilized for in silico studies of sepsis, trauma/hemorrhage/traumatic brain injury, acute liver failure and wound healing. This framework has now allowed us to suggest how to modulate acute inflammation in a rational and individually optimized fashion using engineering principles applied to a biohybrid device. We suggest that we are on the cusp of fulfilling the promise of in silico modeling for personalized medicine for inflammatory disease.
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Affiliation(s)
- Qi Mi
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Sports Medicine & Nutrition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nicole Yee-Key Li
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Communication Science & Disorders, University of Pittsburgh, Pittsburgh, PA, USA
| | - Cordelia Ziraldo
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ali Ghuma
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maxim Mikheev
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Squires
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
| | - David O Okonkwo
- Department of Neurological Surgery, University of Pittsburgh, PA, USA
| | - Katherine Verdolini-Abbott
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Communication Science & Disorders, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gregory Constantine
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Departments of Mathematics & Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gary An
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Chicago, Chicago, IL, USA
| | - Yoram Vodovotz
- Center for Inflammation & Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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Vodovotz Y, Constantine G, Faeder J, Mi Q, Rubin J, Bartels J, Sarkar J, Squires RH, Okonkwo DO, Gerlach J, Zamora R, Luckhart S, Ermentrout B, An G. Translational systems approaches to the biology of inflammation and healing. Immunopharmacol Immunotoxicol 2010; 32:181-95. [PMID: 20170421 PMCID: PMC3134151 DOI: 10.3109/08923970903369867] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Inflammation is a complex, non-linear process central to many of the diseases that affect both developed and emerging nations. A systems-based understanding of inflammation, coupled to translational applications, is therefore necessary for efficient development of drugs and devices, for streamlining analyses at the level of populations, and for the implementation of personalized medicine. We have carried out an iterative and ongoing program of literature analysis, generation of prospective data, data analysis, and computational modeling in various experimental and clinical inflammatory disease settings. These simulations have been used to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals for in silico studies associated with the clinical settings of sepsis, trauma, acute liver failure, and wound healing to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation; and to gain insight into host-pathogen interactions in malaria, necrotizing enterocolitis, and sepsis. These simulations have converged with other systems biology approaches (e.g., functional genomics) to aid in the design of new drugs or devices geared towards modulating inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ impacts via tissue damage/dysfunction. This framework has now allowed us to suggest how to modulate acute inflammation in a rational, individually optimized fashion. This plethora of computational and intertwined experimental/engineering approaches is the cornerstone of Translational Systems Biology approaches for inflammatory diseases.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
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Abstract
Inflammation is a complex, multiscale biological response to threats - both internal and external - to the body, which is also required for proper healing of injured tissue. In turn, damaged or dysfunctional tissue stimulates further inflammation. Despite continued advances in characterizing the cellular and molecular processes involved in the interactions between inflammation and tissue damage, there exists a significant gap between the knowledge of mechanistic pathophysiology and the development of effective therapies for various inflammatory conditions. We have suggested the concept of translational systems biology, defined as a focused application of computational modeling and engineering principles to pathophysiology primarily in order to revise clinical practice. This chapter reviews the existing, translational applications of computational simulations and related approaches as applied to inflammation.
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Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO, An G. Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 2008; 217:1-10. [PMID: 18835282 DOI: 10.1016/j.mbs.2008.07.013] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2008] [Accepted: 07/11/2008] [Indexed: 12/15/2022]
Abstract
Inflammation is a normal, robust physiological process. It can also be viewed as a complex system that senses and attempts to resolve homeostatic perturbations initiated from within the body (for example, in autoimmune disease) or from the outside (for example, in infections). Virtually all acute and chronic diseases are either driven or modulated by inflammation. The complex interplay between beneficial and harmful arms of the inflammatory response may underlie the lack of fully effective therapies for many diseases. Mathematical modeling is emerging as a frontline tool for understanding the complexity of the inflammatory response. A series of articles in this issue highlights various modeling approaches to inflammation in the larger context of health and disease, from intracellular signaling to whole-animal physiology. Here we discuss the state of this emerging field. We note several common features of inflammation models, as well as challenges and prospects for future studies.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
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