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Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
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Zhu X, Deng J, Zuo Z, Lam TN. An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein. AAPS JOURNAL 2016; 18:1475-1488. [PMID: 27480317 DOI: 10.1208/s12248-016-9955-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 07/06/2016] [Indexed: 11/30/2022]
Abstract
Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.
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Affiliation(s)
- Xiao Zhu
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Jianyuan Deng
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Zhong Zuo
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Tai Ning Lam
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
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Kassab GS, An G, Sander EA, Miga MI, Guccione JM, Ji S, Vodovotz Y. Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective. Ann Biomed Eng 2016; 44:2611-25. [PMID: 27015816 DOI: 10.1007/s10439-016-1596-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 03/18/2016] [Indexed: 12/18/2022]
Abstract
In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.
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Affiliation(s)
- Ghassan S Kassab
- California Medical Innovations Institute, San Diego, CA, 92121, USA
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, 60637, USA
| | - Edward A Sander
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Julius M Guccione
- Department of Surgery, University of California, San Francisco, CA, 94143, USA
| | - Songbai Ji
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.,Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, W944 Starzl Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA, 15213, USA. .,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.
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4
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Brown D, Namas RA, Almahmoud K, Zaaqoq A, Sarkar J, Barclay DA, Yin J, Ghuma A, Abboud A, Constantine G, Nieman G, Zamora R, Chang SC, Billiar TR, Vodovotz Y. Trauma in silico: Individual-specific mathematical models and virtual clinical populations. Sci Transl Med 2016; 7:285ra61. [PMID: 25925680 DOI: 10.1126/scitranslmed.aaa3636] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.
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Affiliation(s)
| | - Rami A Namas
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Khalid Almahmoud
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Akram Zaaqoq
- Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | | | - Derek A Barclay
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jinling Yin
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ali Ghuma
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Andrew Abboud
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Gregory Constantine
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Gary Nieman
- Department of Surgery, Upstate Medical University, Syracuse, NY 13210, USA
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA 15219, USA
| | | | - Timothy R Billiar
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA 15219, USA.
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5
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Cosgrove J, Butler J, Alden K, Read M, Kumar V, Cucurull-Sanchez L, Timmis J, Coles M. Agent-Based Modeling in Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:615-29. [PMID: 26783498 PMCID: PMC4716580 DOI: 10.1002/psp4.12018] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 06/29/2015] [Accepted: 07/31/2015] [Indexed: 02/06/2023]
Abstract
Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.
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Affiliation(s)
- J Cosgrove
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - J Butler
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - K Alden
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK
| | - M Read
- Charles Perkins Centre University of Sydney Sydney Australia
| | - V Kumar
- University of California School of Medicine LA Jolla California USA
| | | | - J Timmis
- York Computational Immunology LabUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK; SimOmicsYorkUK
| | - M Coles
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; SimOmicsYorkUK
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Ziraldo C, Mi Q, An G, Vodovotz Y. Computational Modeling of Inflammation and Wound Healing. Adv Wound Care (New Rochelle) 2013; 2:527-537. [PMID: 24527362 DOI: 10.1089/wound.2012.0416] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 01/20/2013] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients. APPROACH To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing. INNOVATION We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models. RESULTS A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment. CONCLUSIONS The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.
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Affiliation(s)
- Cordelia Ziraldo
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Qi Mi
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, Illinois
| | - Yoram Vodovotz
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
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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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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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Hunt CA, Kennedy RC, Kim SHJ, Ropella GEP. Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 5:461-80. [PMID: 23737142 PMCID: PMC3739932 DOI: 10.1002/wsbm.1222] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.
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Affiliation(s)
- C Anthony Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2012; 59:893-903. [PMID: 21712727 DOI: 10.2310/jim.0b013e318224d8cc] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX 77030, USA.
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Cohen MJ. Use of models in identification and prediction of physiology in critically ill surgical patients. Br J Surg 2012; 99:487-93. [PMID: 22287099 DOI: 10.1002/bjs.7798] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2011] [Indexed: 11/08/2022]
Abstract
BACKGROUND With higher-throughput data acquisition and processing, increasing computational power, and advancing computer and mathematical techniques, modelling of clinical and biological data is advancing rapidly. Although exciting, the goal of recreating or surpassing in silico the clinical insight of the experienced clinician remains difficult. Advances toward this goal and a brief overview of various modelling and statistical techniques constitute the purpose of this review. METHODS A review of the literature and experience with models and physiological state representation and prediction after injury was undertaken. RESULTS A brief overview of models and the thinking behind their use for surgeons new to the field is presented, including an introduction to visualization and modelling work in surgical care, discussion of state identification and prediction, discussion of causal inference statistical approaches, and a brief introduction to new vital signs and waveform analysis. CONCLUSION Modelling in surgical critical care can provide a useful adjunct to traditional reductionist biological and clinical analysis. Ultimately the goal is to model computationally the clinical acumen of the experienced clinician.
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Affiliation(s)
- M J Cohen
- Department of Surgery, University of California San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Ward 3A, San Francisco, California 94110, USA.
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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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