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Forceville X, Van Antwerpen P, Annane D, Vincent JL. Selenocompounds and Sepsis-Redox Bypass Hypothesis: Part B-Selenocompounds in the Management of Early Sepsis. Antioxid Redox Signal 2022; 37:998-1029. [PMID: 35287478 DOI: 10.1089/ars.2020.8062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Significance: Endothelial barrier damage, which is in part caused by excess production of reactive oxygen, halogen and nitrogen species (ROHNS), especially peroxynitrite (ONOO-), is a major event in early sepsis and, with leukocyte hyperactivation, part of the generalized dysregulated immune response to infection, which may even become a complex maladaptive state. Selenoenzymes have major antioxidant functions. Their synthesis is related to the need to limit deleterious oxidant redox cycling by small selenocompounds, which may be of therapeutic cytotoxic interest. Plasma selenoprotein-P is crucial for selenium transport from the liver to the tissues and for antioxidant endothelial protection, especially against ONOO-. Above micromolar concentrations, sodium selenite (Na2SeO3) becomes cytotoxic, with a lower cytotoxicity threshold in activated cells, which has led to cancer research. Recent Advances: Plasma selenium (<2% of total body selenium) is mainly contained in selenoprotein-P, and concentrations decrease rapidly in the early phase of sepsis, because of increased selenoprotein-P binding and downregulation of hepatic synthesis and excretion. At low concentrations, Na2SeO3 acts as a selenium donor, favoring selenoprotein-P synthesis in physiology, but probably not in the acute phase of sepsis. Critical Issues: The cytotoxic effects of Na2SeO3 against hyperactivated leukocytes, especially the most immature forms that liberate ROHNS, may be beneficial, but they may also be harmful for activated endothelial cells. Endothelial protection against ROHNS by selenoprotein-P may reduce Na2SeO3 toxicity, which is increased in sepsis. Future Direction: The combination of selenoprotein-P for endothelial protection and the cytotoxic effects of Na2SeO3 against hyperactivated leukocytes may be a promising intervention for early sepsis. Antioxid. Redox Signal. 37, 998-1029.
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Affiliation(s)
- Xavier Forceville
- Medico-surgical Intensive Care Unit, Great Hospital of East Francilien - Meaux site, Meaux, France.,Clinical Investigation Centre (CIC Inserm1414) CHU de Rennes - Université de Rennes 1, Rennes, France
| | - Pierre Van Antwerpen
- Pharmacognosy, Bioanalysis and Drug Discovery and Analytical Platform of the Faculty of Pharmacy, Univesité libre de Bruxelles (ULB), Bruxelles, Belgium
| | - Djillali Annane
- Service de Réanimation Médicale, Hôpital Raymond Poincaré (APHP), Garches, France.,U1173 Lab. of Inflammation & Infection, (Fédération Hospitalo-Universitaire) FHU SEPSIS, Université Paris Saclay-campus (Université de Versailles Saint-Quentin-en-Yvelines) UVSQ, Versailles, France
| | - Jean Louis Vincent
- Department of Intensive Care, Erasme University Hospital, Université libre de Bruxelles, Brussels, Belgium
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2
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Korkmaz HI, Niessen FB, Pijpe A, Sheraton VM, Vermolen FJ, Krijnen PA, Niessen HW, Sloot PM, Middelkoop E, Gibbs S, van Zuijlen PP. Scar formation from the perspective of complexity science: a new look at the biological system as a whole. J Wound Care 2022; 31:178-184. [PMID: 35148632 DOI: 10.12968/jowc.2022.31.2.178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A burn wound is a complex systemic disease at multiple levels. Current knowledge of scar formation after burn injury has come from traditional biological and clinical studies. These are normally focused on just a small part of the entire process, which has limited our ability to sufficiently understand the underlying mechanisms and to predict systems behaviour. Scar formation after burn injury is a result of a complex biological system-wound healing. It is a part of a larger whole. In this self-organising system, many components form networks of interactions with each other. These networks of interactions are typically non-linear and change their states dynamically, responding to the environment and showing emergent long-term behaviour. How molecular and cellular data relate to clinical phenomena, especially regarding effective therapies of burn wounds to achieve minimal scarring, is difficult to unravel and comprehend. Complexity science can help bridge this gap by integrating small parts into a larger whole, such that relevant biological mechanisms and data are combined in a computational model to better understand the complexity of the entire biological system. A better understanding of the complex biological system of post-burn scar formation could bring research and treatment regimens to the next level. The aim of this review/position paper is to create more awareness of complexity in scar formation after burn injury by describing the basic principles of complexity science and its potential for burn care professionals.
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Affiliation(s)
- H Ibrahim Korkmaz
- Department of Plastic Reconstructive and Hand Surgery, Amsterdam Movement Sciences (AMS) Institute, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Department of Molecular Cell Biology and Immunology, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Burn Center and Department of Plastic and Reconstructive Surgery, Red Cross Hospital, Beverwijk, The Netherlands.,Association of Dutch Burn Centres (ADBC), Beverwijk, The Netherlands
| | - Frank B Niessen
- Department of Plastic Reconstructive and Hand Surgery, Amsterdam Movement Sciences (AMS) Institute, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Anouk Pijpe
- Burn Center and Department of Plastic and Reconstructive Surgery, Red Cross Hospital, Beverwijk, The Netherlands
| | - Vivek M Sheraton
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - Fred J Vermolen
- Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.,Computational Mathematics, Hasselt University, Diepenbeek, Belgium
| | - Paul Aj Krijnen
- Department of Pathology and Cardiac Surgery, Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Hans Wm Niessen
- Department of Pathology and Cardiac Surgery, Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Peter Ma Sloot
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands.,Complexity Institute, Nanyang Technological University, Singapore.,ITMO University, Saint Petersburg, Russian Federation
| | - Esther Middelkoop
- Department of Plastic Reconstructive and Hand Surgery, Amsterdam Movement Sciences (AMS) Institute, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Burn Center and Department of Plastic and Reconstructive Surgery, Red Cross Hospital, Beverwijk, The Netherlands.,Association of Dutch Burn Centres (ADBC), Beverwijk, The Netherlands
| | - Susan Gibbs
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Department of Oral Cell Biology, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paul Pm van Zuijlen
- Department of Plastic Reconstructive and Hand Surgery, Amsterdam Movement Sciences (AMS) Institute, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Burn Center and Department of Plastic and Reconstructive Surgery, Red Cross Hospital, Beverwijk, The Netherlands.,Paediatric Surgical Centre, Emma Children's Hospital, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
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3
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Forceville X, Van Antwerpen P, Preiser JC. Selenocompounds and Sepsis: Redox Bypass Hypothesis for Early Diagnosis and Treatment: Part A-Early Acute Phase of Sepsis: An Extraordinary Redox Situation (Leukocyte/Endothelium Interaction Leading to Endothelial Damage). Antioxid Redox Signal 2021; 35:113-138. [PMID: 33567962 DOI: 10.1089/ars.2020.8063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Significance: Sepsis is a health disaster. In sepsis, an initial, beneficial local immune response against infection evolves rapidly into a generalized, dysregulated response or a state of chaos, leading to multiple organ failure. Use of life-sustaining supportive therapies creates an unnatural condition, enabling the complex cascades of the sepsis response to develop in patients who would otherwise die. Multiple attempts to control sepsis at an early stage have been unsuccessful. Recent Advances: Major events in early sepsis include activation and binding of leukocytes and endothelial cells in the microcirculation, damage of the endothelial surface layer (ESL), and a decrease in the plasma concentration of the antioxidant enzyme, selenoprotein-P. These events induce an increase in intracellular redox potential and lymphocyte apoptosis, whereas apoptosis is delayed in monocytes and neutrophils. They also induce endothelial mitochondrial and cell damage. Critical Issues: Neutrophil production increases dramatically, and aggressive immature forms are released. Leukocyte cross talk with other leukocytes and with damaged endothelial cells amplifies the inflammatory response. The release of large quantities of reactive oxygen, halogen, and nitrogen species as a result of the leukocyte respiratory burst, endothelial mitochondrial damage, and ischemia/reperfusion processes, along with the marked decrease in selenoprotein-P concentrations, leads to peroxynitrite damage of the ESL, reducing flow and damaging the endothelial barrier. Future Directions: Endothelial barrier damage by activated leukocytes is a time-sensitive event in sepsis, occurring within hours and representing the first step toward organ failure and death. Reducing or stopping this event is necessary before irreversible damage occurs.
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Affiliation(s)
- Xavier Forceville
- Medico-Surgical Intensive Care Unit, Great Hospital of East Francilien-Meaux Site, Hôpital Saint Faron, Meaux, France.,Clinical Investigation Center (CIC Inserm 1414), CHU de Rennes, Université de Rennes 1, Rennes, France
| | - Pierre Van Antwerpen
- Pharmacognosy, Bioanalysis and Drug Discovery and Analytical Platform of the Faculty of Pharmacy, Université libre de Bruxelles (ULB), Bruxelles, Belgium
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5
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Vodovotz Y, An G. Agent-based models of inflammation in translational systems biology: A decade later. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1460. [PMID: 31260168 PMCID: PMC8140858 DOI: 10.1002/wsbm.1460] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/14/2019] [Accepted: 06/15/2019] [Indexed: 12/11/2022]
Abstract
Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, Immunology, Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gary An
- Department of Surgery, University of Vermont, Burlington, Vermont
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6
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Lamparello AJ, Namas RA, Constantine G, McKinley TO, Elster E, Vodovotz Y, Billiar TR. A conceptual time window-based model for the early stratification of trauma patients. J Intern Med 2019; 286:2-15. [PMID: 30623510 DOI: 10.1111/joim.12874] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.
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Affiliation(s)
- A J Lamparello
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R A Namas
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - G Constantine
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - T O McKinley
- Department of Orthopaedic Surgery, Indiana University School of Medicine, IU Health Methodist Hospital, Indianapolis, IN, USA
| | - E Elster
- Department of Surgery, University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Y Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - T R Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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7
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Yamakawa S, Hayashida K. Advances in surgical applications of growth factors for wound healing. BURNS & TRAUMA 2019; 7:10. [PMID: 30993143 PMCID: PMC6450003 DOI: 10.1186/s41038-019-0148-1] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 03/13/2019] [Indexed: 12/15/2022]
Abstract
Growth factors have recently gained clinical importance for wound management. Application of recombinant growth factors has been shown to mimic cell migration, proliferation, and differentiation in vivo, allowing for external modulation of the healing process. Perioperative drug delivery systems can enhance the biological activity of these growth factors, which have a very short in vivo half-life after topical administration. Although the basic mechanisms of these growth factors are well understood, most have yet to demonstrate a significant impact in animal studies or small-sized clinical trials. In this review, we emphasized currently approved growth factor therapies, including a sustained release system for growth factors, emerging therapies, and future research possibilities combined with surgical procedures. Approaches seeking to understand wound healing at a systemic level are currently ongoing. However, further research and consideration in surgery will be needed to provide definitive confirmation of the efficacy of growth factor therapies for intractable wounds.
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Affiliation(s)
- Sho Yamakawa
- Division of Plastic and Reconstructive Surgery, Shimane University Faculty of Medicine, 89-1 Enya-cho, Izumo, Shimane 693-8501 Japan
| | - Kenji Hayashida
- Division of Plastic and Reconstructive Surgery, Shimane University Faculty of Medicine, 89-1 Enya-cho, Izumo, Shimane 693-8501 Japan
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8
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Namas R, Ghuma A, Hermus L, Zamora R, Okonkwo D, Billiar T, Vodovotz Y. The Acute Inflammatory Response in Trauma /Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects. Libyan J Med 2016. [DOI: 10.3402/ljm.v4i3.4824] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
| | | | - L. Hermus
- Martini Hospital, Department of Surgery, Groningen, Netherlands
| | | | | | | | - Y. Vodovotz
- Department of Surgery
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine University of Pittsburgh, Pittsburgh, PA
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9
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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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10
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Namas RA, Mi Q, Namas R, Almahmoud K, Zaaqoq AM, Abdul-Malak O, Azhar N, Day J, Abboud A, Zamora R, Billiar TR, Vodovotz Y. Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation. Antioxid Redox Signal 2015; 23:1370-87. [PMID: 26560096 PMCID: PMC4685502 DOI: 10.1089/ars.2015.6398] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
SIGNIFICANCE Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. RECENT ADVANCES Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. CRITICAL ISSUES Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments. FUTURE DIRECTIONS These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.
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Affiliation(s)
- Rami A. Namas
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Qi Mi
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Rajaie Namas
- Department of Internal Medicine, Division of Rheumatology, University of Michigan, Ann Arbor, Michigan
| | - Khalid Almahmoud
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Akram M. Zaaqoq
- Department of Critical Care Medicine, University of Pittsburgh, Pennsylvania
| | - Othman Abdul-Malak
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Nabil Azhar
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Judy Day
- Department of Mathematics, University of Tennessee, Knoxville, Tennessee
| | - Andrew Abboud
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Timothy R. Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- 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
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11
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Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection. PLoS One 2015; 10:e0136139. [PMID: 26327290 PMCID: PMC4556515 DOI: 10.1371/journal.pone.0136139] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Accepted: 07/31/2015] [Indexed: 01/08/2023] Open
Abstract
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.
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12
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Abstract
Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.
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Affiliation(s)
- Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854 ; Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854 ; Department of Surgery, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901
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13
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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: 14] [Impact Index Per Article: 1.4] [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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14
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Innate immunity in disease: insights from mathematical modeling and analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 844:227-43. [PMID: 25480644 DOI: 10.1007/978-1-4939-2095-2_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. This robust responsiveness to biological stress likely endows the host with increased fitness, but over-robust or inadequate inflammation predisposes the host to various diseases. Importantly, well-compartmentalized inflammation is generally beneficial, but spillover of inflammation into the blood is a hallmark-and likely also a driver-of self-maintaining inflammation. The blood is also a key entry point and immunological interface for vectors of parasitic diseases, diseases that themselves incite systemic inflammation. The complex role of inflammation in health and disease has made this biological system difficult to understand comprehensively and modulate rationally for therapeutic purposes. Consequently, systems approaches have been applied in order to characterize dynamical properties and identify key control points in inflammation. This process begins with the collection of high-dimensional, experimental, and clinical data, followed by data reduction and data-driven modeling that finally informs mechanistic computational models for analysis, prediction, and rational modulation. These studies have suggested that the overall architecture of the inflammatory response includes a multiscale positive feedback from inflammation → tissue damage → inflammation, which is often inadequately controlled by negative feedback from anti-inflammatory mediators. Given the importance of the blood interface for the inflammatory response, and the accessibility of this compartment both as an immunological sampling reservoir for vectors as well as for diagnosis and therapy, we suggest that any rational efforts at modulating inflammation via the blood compartment must involve computational modeling.
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Abstract
Sepsis is a complicated syndrome that manifests within the complex adaptive system that is the human body. Complexity theory and the recently emerged applied complexity science illustrate these phenomena and the body's response in light of understanding these complex systems. Provision of careful observation by the nurse may recognize signs of moving into a nonadaptive or declining status, facilitating detection before complete sepsis or chaos. Systems or tools that aid the bedside nurse in prompt identification of sepsis and facilitate standardized evidence-based interventions are proving to be a valuable means in the care of the septic patient.
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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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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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Vodovotz Y, An G, Androulakis IP. A Systems Engineering Perspective on Homeostasis and Disease. Front Bioeng Biotechnol 2013; 1:6. [PMID: 25022216 PMCID: PMC4090890 DOI: 10.3389/fbioe.2013.00006] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2013] [Accepted: 08/16/2013] [Indexed: 01/06/2023] Open
Abstract
Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh , Pittsburgh, PA , USA ; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh , Pittsburgh, PA , USA
| | - Gary An
- Department of Surgery, The University of Chicago , Chicago, IL , USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University , Piscataway, NJ , USA ; Department of Chemical and Biochemical Engineering, Rutgers University , Piscataway, NJ , USA ; Department of Surgery, Rutgers Robert Wood Johnson Medical School , New Brunswick, NJ , USA
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Hybrid equation/agent-based model of ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects with spinal cord injury. PLoS Comput Biol 2013; 9:e1003070. [PMID: 23696726 PMCID: PMC3656105 DOI: 10.1371/journal.pcbi.1003070] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Accepted: 04/09/2013] [Indexed: 12/18/2022] Open
Abstract
Pressure ulcers are costly and life-threatening complications for people with spinal cord injury (SCI). People with SCI also exhibit differential blood flow properties in non-ulcerated skin. We hypothesized that a computer simulation of the pressure ulcer formation process, informed by data regarding skin blood flow and reactive hyperemia in response to pressure, could provide insights into the pathogenesis and effective treatment of post-SCI pressure ulcers. Agent-Based Models (ABM) are useful in settings such as pressure ulcers, in which spatial realism is important. Ordinary Differential Equation-based (ODE) models are useful when modeling physiological phenomena such as reactive hyperemia. Accordingly, we constructed a hybrid model that combines ODEs related to blood flow along with an ABM of skin injury, inflammation, and ulcer formation. The relationship between pressure and the course of ulcer formation, as well as several other important characteristic patterns of pressure ulcer formation, was demonstrated in this model. The ODE portion of this model was calibrated to data related to blood flow following experimental pressure responses in non-injured human subjects or to data from people with SCI. This model predicted a higher propensity to form ulcers in response to pressure in people with SCI vs. non-injured control subjects, and thus may serve as novel diagnostic platform for post-SCI ulcer formation.
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Dick TE, Molkov YI, Nieman G, Hsieh YH, Jacono FJ, Doyle J, Scheff JD, Calvano SE, Androulakis IP, An G, Vodovotz Y. Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling. Front Physiol 2012; 3:222. [PMID: 22783197 PMCID: PMC3387781 DOI: 10.3389/fphys.2012.00222] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Accepted: 06/03/2012] [Indexed: 01/10/2023] Open
Abstract
Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.
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Affiliation(s)
- Thomas E Dick
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University Cleveland, OH, USA
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A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine. Crit Care Med 2012; 40:1052-63. [PMID: 22425816 DOI: 10.1097/ccm.0b013e31823e986a] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
OBJECTIVE To gain insights into individual variations in acute inflammation and physiology. DESIGN Large-animal study combined with mathematical modeling. SETTING Academic large-animal and computational laboratories. SUBJECTS Outbred juvenile swine. INTERVENTIONS Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling. MEASUREMENTS AND MAIN RESULTS Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO(2)/FIO(2) ratio μ200 and static compliance μ10 L/cm H(2)O). Plasma interleukin-1β, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO(2)/NO(3) were significantly (p μ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1β from resident macrophages. The ordinary differential equation model also included blood pressure, PaO(2), and FIO(2), and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine. CONCLUSIONS The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.
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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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An G, Nieman G, Vodovotz Y. Computational and systems biology in trauma and sepsis: current state and future perspectives. INTERNATIONAL JOURNAL OF BURNS AND TRAUMA 2012; 2:1-10. [PMID: 22928162 PMCID: PMC3415970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 12/30/2011] [Accepted: 01/15/2012] [Indexed: 06/01/2023]
Abstract
Trauma, often accompanied by hemorrhage, is a leading cause of death worldwide, often leading to inflammation-related late complications that include sepsis and multiple organ failure. These secondary complications are a manifestation of the complexity of biological responses elicited by trauma/hemorrhage, responses that span most, if not all, cell types, tissues, and organ systems. This daunting complexity at the patient level is manifest by the near total dearth of available therapeutics, and we suggest that this dire condition is due in large part to the lack of a rational, systems-oriented framework for drug development, clinical trial design, in-hospital diagnostics, and post-hospital care. We have further suggested that mechanistic computational modeling can form the basis of such a rational framework, given the maturity of systems biology/computational biology. Herein, we briefly summarize the state of the art of these approaches, and highlight the biological insights and novel hypotheses derived from these approaches. We propose a rational framework for transitioning through the currently fragmented process from identification of biological networks that are potential therapeutic targets, through clinical trial design, to personalized diagnosis and care. Insights derived from systems and computational biology in trauma and sepsis include the centrality of Damage-Associated Molecular Pattern molecules as drivers of both beneficial and detrimental inflammation, along with a novel view of multiple organ dysfunction as a cascade of containment failures with distinct implications for therapy. Finally, we suggest how these insights might be best implemented to drive transformational change in the fields of trauma and sepsis.
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Affiliation(s)
- Gary An
- Department of Surgery, University of ChicagoChicago, IL 60637
| | - Gary Nieman
- Department of Surgery, Upstate Medical UniversitySyracuse, NY 13210
| | - Yoram Vodovotz
- Department of Surgery, University of PittsburghPittsburgh, PA 15213
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of PittsburghPittsburgh, PA 15219
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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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25
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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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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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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2011; 59. [PMID: 21712727 PMCID: PMC3196807 DOI: 10.231/jim.0b013e318224d8cc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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, Houston TX USA,Contact: Mary F. McGuire, School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin, #600, Houston, TX 77030 USA, , 1-832-364-6734
| | - M. Sriram Iyengar
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX USA
| | - David W. Mercer
- Department of Surgery, University of Nebraska Medical Center, Omaha NE USA
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Brown BN, Price IM, Toapanta FR, DeAlmeida DR, Wiley CA, Ross TM, Oury TD, Vodovotz Y. An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Math Biosci 2011; 231:186-96. [PMID: 21385589 PMCID: PMC3088650 DOI: 10.1016/j.mbs.2011.03.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2010] [Revised: 02/26/2011] [Accepted: 03/01/2011] [Indexed: 12/11/2022]
Abstract
Inflammation and airway remodeling occur in a variety of airway diseases. Modeling aspects of the inflammatory and fibrotic processes following repeated exposure to particulate matter may provide insights into a spectrum of airway diseases, as well as prevention/treatment strategies. An agent-based model (ABM) was created to examine the response of an abstracted population of inflammatory cells (nominally macrophages, but possibly including other inflammatory cells such as lymphocytes) and cells involved in remodeling (nominally fibroblasts) to particulate exposure. The model focused on a limited number of relevant interactions, specifically those among macrophages, fibroblasts, a pro-inflammatory cytokine (TNF-α), an anti-inflammatory cytokine (TGF-β1), collagen deposition, and tissue damage. The model yielded three distinct states that were equated with (1) self-resolving inflammation and a return to baseline, (2) a pro-inflammatory process of localized tissue damage and fibrosis, and (3) elevated pro- and anti-inflammatory cytokines, persistent tissue damage, and fibrosis outcomes. Experimental results consistent with these predicted states were observed in histology sections of lung tissue from mice exposed to particulate matter. Systematic in silico studies suggested that the development of each state depended primarily upon the degree and duration of exposure. Thus, a relatively simple ABM resulted in several, biologically feasible, emergent states, suggesting that the model captures certain salient features of inflammation following exposure of the lung to particulate matter. This ABM may hold future utility in the setting of airway disease resulting from inflammation and fibrosis following particulate exposure.
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Affiliation(s)
- Bryan N. Brown
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Ian M. Price
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Franklin R. Toapanta
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Dilhari R. DeAlmeida
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Clayton A. Wiley
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ted M. Ross
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tim D. Oury
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Yoram Vodovotz
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213
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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: 155] [Impact Index Per Article: 11.9] [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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Qian WJ, Petritis BO, Kaushal A, Finnerty CC, Jeschke MG, Monroe ME, Moore RJ, Schepmoes AA, Xiao W, Moldawer LL, Davis RW, Tompkins RG, Herndon DN, Camp DG, Smith RD. Plasma proteome response to severe burn injury revealed by 18O-labeled "universal" reference-based quantitative proteomics. J Proteome Res 2011; 9:4779-89. [PMID: 20698492 DOI: 10.1021/pr1005026] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
A burn injury represents one of the most severe forms of human trauma and is responsible for significant mortality worldwide. Here, we present the first quantitative proteomics investigation of the blood plasma proteome response to severe burn injury by comparing the plasma protein concentrations of 10 healthy control subjects with those of 15 severe burn patients at two time-points following the injury. The overall analytical strategy for this work integrated immunoaffinity depletion of the 12 most abundant plasma proteins with cysteinyl-peptide enrichment-based fractionation prior to LC-MS analyses of individual patient samples. Incorporation of an 18O-labeled "universal" reference among the sample sets enabled precise relative quantification across samples. In total, 313 plasma proteins confidently identified with two or more unique peptides were quantified. Following statistical analysis, 110 proteins exhibited significant abundance changes in response to the burn injury. The observed changes in protein concentrations suggest significant inflammatory and hypermetabolic response to the injury, which is supported by the fact that many of the identified proteins are associated with acute phase response signaling, the complement system, and coagulation system pathways. The regulation of approximately 35 proteins observed in this study is in agreement with previous results reported for inflammatory or burn response, but approximately 50 potentially novel proteins previously not known to be associated with burn response or inflammation are also found. Elucidating proteins involved in the response to severe burn injury may reveal novel targets for therapeutic interventions as well as potential predictive biomarkers for patient outcomes such as multiple organ failure.
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Affiliation(s)
- Wei-Jun Qian
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
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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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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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The molecular Koch's postulates and surgical infection: a view forward. Surgery 2010; 147:757-65. [PMID: 20494210 DOI: 10.1016/j.surg.2010.03.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2010] [Accepted: 03/12/2010] [Indexed: 11/23/2022]
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Solovyev A, Mikheev M, Zhou L, Dutta-Moscato J, Ziraldo C, An G, Vodovotz Y, Mi Q. SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling. ACTA ACUST UNITED AC 2010; 2:18-30. [PMID: 24163721 DOI: 10.4018/jats.2010070102] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.
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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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Arciero JC, Ermentrout GB, Upperman JS, Vodovotz Y, Rubin JE. Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis. PLoS One 2010; 5:e10066. [PMID: 20419099 PMCID: PMC2856678 DOI: 10.1371/journal.pone.0010066] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2010] [Accepted: 03/14/2010] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Necrotizing enterocolitis (NEC) is a severe disease of the gastrointestinal tract of pre-term babies and is thought to be related to the physiological immaturity of the intestine and altered levels of normal flora in the gut. Understanding the factors that contribute to the pathology of NEC may lead to the development of treatment strategies aimed at re-establishing the integrity of the epithelial wall and preventing the propagation of inflammation in NEC. Several studies have shown a reduced incidence and severity of NEC in neonates treated with probiotics (beneficial bacteria species). METHODOLOGY/PRINCIPAL FINDINGS The objective of this study is to use a mathematical model to predict the conditions under which probiotics may be successful in promoting the health of infants suffering from NEC. An ordinary differential equation model is developed that tracks the populations of pathogenic and probiotic bacteria in the intestinal lumen and in the blood/tissue region. The permeability of the intestinal epithelial layer is treated as a variable, and the role of the inflammatory response is included. The model predicts that in the presence of probiotics health is restored in many cases that would have been otherwise pathogenic. The timing of probiotic administration is also shown to determine whether or not health is restored. Finally, the model predicts that probiotics may be harmful to the NEC patient under very specific conditions, perhaps explaining the detrimental effects of probiotics observed in some clinical studies. CONCLUSIONS/SIGNIFICANCE The reduced, experimentally motivated mathematical model that we have developed suggests how a certain general set of characteristics of probiotics can lead to beneficial or detrimental outcomes for infants suffering from NEC, depending on the influences of probiotics on defined features of the inflammatory response.
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Affiliation(s)
- Julia C Arciero
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
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Abstract
Personalized medicine is a major goal for the future of healthcare, and we suggest that computational simulations are necessary in order to achieve it. Inflammatory diseases, both acute and chronic, represent an area in which personalized medicine is especially needed, given the high level of individual variability that characterizes these diseases. We have created such simulations, and have used them to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals; for in silico experiments and clinical trials in sepsis, trauma, and wound healing; and to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ damage via tissue damage/dysfunction. We suggest that computational simulations are the cornerstone of Translational Systems Biology approaches for inflammatory diseases.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
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Foteinou PT, Calvano SE, Lowry SF, Androulakis IP. Multiscale model for the assessment of autonomic dysfunction in human endotoxemia. Physiol Genomics 2010; 42:5-19. [PMID: 20233835 DOI: 10.1152/physiolgenomics.00184.2009] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Severe injury and infection are associated with autonomic dysfunction. The realization that a dysregulation in autonomic function may predispose a host to excessive inflammatory processes has renewed interest in understanding the role of central nervous system (CNS) in modulating systemic inflammatory processes. Assessment of heart rate variability (HRV) has been used to evaluate systemic abnormalities and as a predictor of the severity of illness. Dissecting the relevance of neuroimmunomodulation in controlling inflammatory processes requires an understanding of the multiscale interplay between CNS and the immune response. A vital enabler in that respect is the development of a systems-based approach that integrates data across multiple scales, and models the emerging host response as the outcome of interactions of critical modules. Thus, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is proposed that integrates processes across the host from the cellular to the systemic host response level. At the cellular level interacting components are associated with elementary signaling pathways that propagate extracellular signals to the transcriptional response level. Further, essential modules associated with the neuroendocrine immune crosstalk are considered. Finally, at the systemic level, phenotypic expressions such as HRV are incorporated to assess systemic decomplexification indicative of the severity of the host response. Thus, the proposed work intends to associate acquired endocrine dysfunction with diminished HRV as a critical enabler for clarifying how cellular inflammatory processes and neural-based pathways mediate the links between patterns of autonomic control (HRV) and clinical outcomes.
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Affiliation(s)
- Panagiota T Foteinou
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
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Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP. Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS One 2010; 5:e9249. [PMID: 20174629 PMCID: PMC2823776 DOI: 10.1371/journal.pone.0009249] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2009] [Accepted: 11/27/2009] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions. METHODOLOGY/PRINCIPAL FINDINGS An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades. CONCLUSIONS/SIGNIFICANCE The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system.
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Affiliation(s)
- Xu Dong
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America
| | - Panagiota T. Foteinou
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America
| | - Steven E. Calvano
- Department of Surgery, University of Medicine and Dentristry of New Jersey Robert Wood Johnson Medical School, New Brunswick, New Jersey, United States of America
| | - Stephen F. Lowry
- Department of Surgery, University of Medicine and Dentristry of New Jersey Robert Wood Johnson Medical School, New Brunswick, New Jersey, United States of America
| | - Ioannis P. Androulakis
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America
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An GC. Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research. Wound Repair Regen 2010; 18:8-12. [DOI: 10.1111/j.1524-475x.2009.00568.x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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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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Foteinou P, Yang E, Androulakis IP. NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION. Comput Chem Eng 2009; 33:2028-2041. [PMID: 20161495 PMCID: PMC2796781 DOI: 10.1016/j.compchemeng.2009.06.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance.
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Affiliation(s)
- P.T. Foteinou
- Biomedical Engineering Department, Rutgers University, 599 Taylor Road Piscataway, NJ 08854
| | - E. Yang
- Biomedical Engineering Department, Rutgers University, 599 Taylor Road Piscataway, NJ 08854
| | - I. P. Androulakis
- Biomedical Engineering Department, Rutgers University, 599 Taylor Road Piscataway, NJ 08854
- Chemical & Biochemical Engineering Department, Rutgers University, 98 Brett Road, Piscataway, NJ 08854
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Namas R, Ghuma A, Hermus L, Zamora R, Okonkwo DO, Billiar TR, Vodovotz Y. The acute inflammatory response in trauma / hemorrhage and traumatic brain injury: current state and emerging prospects. Libyan J Med 2009; 4:97-103. [PMID: 21483522 PMCID: PMC3066737 DOI: 10.4176/090325] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Traumatic injury/hemorrhagic shock (T/HS) elicits an acute inflammatory response that may result in death. Inflammation describes a coordinated series of molecular, cellular, tissue, organ, and systemic responses that drive the pathology of various diseases including T/HS and traumatic brain injury (TBI). Inflammation is a finely tuned, dynamic, highly-regulated process that is not inherently detrimental, but rather required for immune surveillance, optimal post-injury tissue repair, and regeneration. The inflammatory response is driven by cytokines and chemokines and is partially propagated by damaged tissue-derived products (Damage-associated Molecular Patterns; DAMP's). DAMPs perpetuate inflammation through the release of pro-inflammatory cytokines, but may also inhibit anti-inflammatory cytokines. Various animal models of T/HS in mice, rats, pigs, dogs, and non-human primates have been utilized in an attempt to move from bench to bedside. Novel approaches, including those from the field of systems biology, may yield therapeutic breakthroughs in T/HS and TBI in the near future.
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A model of TLR4 signaling and tolerance using a qualitative, particle–event-based method: Introduction of spatially configured stochastic reaction chambers (SCSRC). Math Biosci 2009; 217:43-52. [DOI: 10.1016/j.mbs.2008.10.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2008] [Revised: 09/02/2008] [Accepted: 10/02/2008] [Indexed: 12/12/2022]
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Rivière B, Epshteyn Y, Swigon D, Vodovotz Y. A simple mathematical model of signaling resulting from the binding of lipopolysaccharide with Toll-like receptor 4 demonstrates inherent preconditioning behavior. Math Biosci 2008; 217:19-26. [PMID: 18950647 DOI: 10.1016/j.mbs.2008.10.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2008] [Revised: 09/24/2008] [Accepted: 10/03/2008] [Indexed: 11/16/2022]
Abstract
The complex biology of Gram-negative bacterial lipopolysaccharide (LPS) is central to the acute inflammatory response in sepsis and related diseases. Repeated treatment with LPS can lead to desensitization or enhancement of subsequent responses both in vitro and in vivo (a phenomenon known as preconditioning). Previous computational studies have demonstrated a role for anti-inflammatory influences in this process (J. Day, J. Rubin, Y. Vodovotz, C.C. Chow, A. Reynolds, G. Clermont, A reduced mathematical model of the acute inflammatory response: II. Capturing scenarios of repeated endotoxin administration. J. Theor. Biol. 242 (2006) 237). Since LPS signals via Toll-like receptor 4 (TLR4), we created a simple mathematical model in order to address the role of this receptor in both the normal and preconditioned response to LPS. We created a non-linear system of ordinary differential equations, consisting of free LPS, free TLR4, bound complex LPS-TLR4, and an intracellular signaling cascade (lumped into a single variable). We simulate the effects of preconditioning by small and large repeated doses of LPS on the system, varying the timing of the doses as well as the rate of expression of TLR4. Our simulations suggest that a simplified model of LPS/TLR4 signaling can account for complex preconditioning phenomena without invoking a specific signaling inhibition mechanism, but rather based on the dynamics of the signaling response itself, as well as the timing and magnitude of the LPS stimuli.
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Affiliation(s)
- Beatrice Rivière
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Math Biosci 2008; 217:53-63. [PMID: 18835283 DOI: 10.1016/j.mbs.2008.08.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2008] [Revised: 08/16/2008] [Accepted: 08/21/2008] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. METHODS BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. RESULTS The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. CONCLUSIONS We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.
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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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Li NYK, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y. A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One 2008; 3:e2789. [PMID: 18665229 PMCID: PMC2481293 DOI: 10.1371/journal.pone.0002789] [Citation(s) in RCA: 89] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2008] [Accepted: 05/12/2008] [Indexed: 12/26/2022] Open
Abstract
The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains.
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Affiliation(s)
- Nicole Y. K. Li
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Katherine Verdolini
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- University of Pittsburgh Voice Center, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for Inflammation and Regenerative Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Gilles Clermont
- Center for Inflammation and Regenerative Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Qi Mi
- Center for Inflammation and Regenerative Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Elaine N. Rubinstein
- Office of Measurement and Evaluation of Teaching, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Patricia A. Hebda
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Otolaryngology Wound Healing Laboratory, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yoram Vodovotz
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for Inflammation and Regenerative Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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