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Critical Reviews™ in Biomedical Engineering

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ISSN Druckformat: 0278-940X

ISSN Online: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

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Addressing the Translational Dilemma: Dynamic Knowledge Representation of Inflammation Using Agent-Based Modeling

Volumen 40, Ausgabe 4, 2012, pp. 323-340
DOI: 10.1615/CritRevBiomedEng.v40.i4.70
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ABSTRAKT

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

REFERENZIERT VON
  1. Clancy Colleen E., An Gary, Cannon William R., Liu Yaling, May Elebeoba E., Ortoleva Peter, Popel Aleksander S., Sluka James P., Su Jing, Vicini Paolo, Zhou Xiaobo, Eckmann David M., Multiscale Modeling in the Clinic: Drug Design and Development, Annals of Biomedical Engineering, 44, 9, 2016. Crossref

  2. Christley Scott, Cockrell Chase, An Gary, Computational Studies of the Intestinal Host-Microbiota Interactome, Computation, 3, 1, 2015. Crossref

  3. Cockrell Chase, An Gary, Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model, Journal of Theoretical Biology, 430, 2017. Crossref

  4. Moyo Daniel, Beattie Lynette, Andrews Paul S., Moore John W. J., Timmis Jon, Sawtell Amy, Hoehme Stefan, Sampson Adam T., Kaye Paul M., Macrophage Transactivation for Chemokine Production Identified as a Negative Regulator of Granulomatous Inflammation Using Agent-Based Modeling, Frontiers in Immunology, 9, 2018. Crossref

  5. Gullo A., Celestre C. M., Paratore A. L., Silvestri L., van Saene H. K., Sepsis and Organ(s) Dysfunction, in Anaesthesia, Pharmacology, Intensive Care and Emergency A.P.I.C.E., 2014. Crossref

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