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Translational systems biology using an agent‐based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research
Author(s) -
An Gary C.
Publication year - 2010
Publication title -
wound repair and regeneration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.847
H-Index - 109
eISSN - 1524-475X
pISSN - 1067-1927
DOI - 10.1111/j.1524-475x.2009.00568.x
Subject(s) - computer science , representation (politics) , process (computing) , data science , knowledge representation and reasoning , management science , artificial intelligence , cognitive science , psychology , engineering , politics , political science , law , operating system
The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate “systems” processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing “thought experiments” to fill those gaps. Agent‐based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent‐based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community‐wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of “knowledge ecologies,” where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.