Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling.

The complexity of the acute inflammatory response (AIR) is, by now, generally recognized. The primary manifestation of this property has been the difficulty in translating the information derived from reductionist, basic science research into effective clinical treatment regimens for sepsis. However, the recognition of the "complexity" of the AIR is not without its pitfalls. Despite its limitations, reductionism remains the primary means of obtaining scientific information. Furthermore, a functional shortcoming of use of the term complex has been to make it equivalent to "essentially unsolvable." Therefore, a mechanism is needed to integrate the apparatus of reductionist analysis into a complex synthetic methodology that overcomes the current limitations of both. Toward this end, I propose a structure for a class of collaborative, community-wide in silico models that use the framework of agent-based modeling. Agent-based modeling is a type of mathematical modeling that focuses on the behaviors of the components of complex systems and is well suited for translating the results of basic science experiments. I will also introduce a preliminary version of a syntactical "grammar" that can potentially be used to facilitate the transfer of basic science data into computer code. It is hoped that when a mature version of this framework is implemented, the resulting models will provide a functional, synthetic data base on the AIR that could be used for directing research, testing hypotheses, teaching and training, and drug discovery/testing.

[1]  Steven F. Railsback,et al.  ANALYSIS OF HABITAT‐SELECTION RULES USING ANINDIVIDUAL‐BASED MODEL , 2002 .

[2]  Thorsten Tjardes,et al.  Sepsis Research in the Next Millennium: Concentrate on the Software Rather than the Hardware , 2002, Shock.

[3]  G. Clermont,et al.  Mathematical models of the acute inflammatory response , 2004, Current opinion in critical care.

[4]  G. An,et al.  AGENT BASED MODEL OF CELL CULTURE EPITHELIAL BARRIER FUNCTION: USING COMPUTER SIMULATION IN CONJUNTION WITH A BASIC SCIENCE MODEL: 36 , 2004 .

[5]  Timothy G Buchman,et al.  In vivo, in vitro, in silico... , 2004, Critical care medicine.

[6]  Stephen A. Racunas,et al.  HyBrow: a prototype system for computer-aided hypothesis evaluation , 2004, ISMB/ECCB.

[7]  G. An.  In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling* , 2004, Critical care medicine.

[8]  A. Seely,et al.  Multiple organ dysfunction syndrome: Exploring the paradigm of complex nonlinear systems , 2000, Critical care medicine.

[9]  Peter A. Ward,et al.  Role of C5a–C5aR Interaction in Sepsis , 2004, Shock.

[10]  Michael Travers,et al.  BioLingua: a programmable knowledge environment for biologists , 2005, Bioinform..

[11]  Gary An,et al.  IN-SILICO UNIFICATION OF DIFFERENT BASIC SCIENCE MODELS OF GUT EPITHELIAL BARRIER FUNCTION USING AGENT BASED MODELING: 348 , 2004 .

[12]  Paul E. Johnson,et al.  Simulation Modeling in Political Science , 1999 .

[13]  T G Buchman,et al.  Physiologic stability and physiologic state. , 1996, The Journal of trauma.

[14]  G An,et al.  Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. , 2001, Shock.

[15]  Alessandro Perrone,et al.  Agent-based methods in economics and finance : simulations in Swarm , 2002 .

[16]  G. Clermont,et al.  The dynamics of acute inflammation. , 2004, Journal of theoretical biology.

[17]  J. Marshall,et al.  Through a glass darkly: the brave new world of in silico modeling. , 2004, Critical care medicine.

[18]  E A Neugebauer,et al.  Complexity and non-linearity in shock research: reductionism or synthesis? , 2001, Shock.

[19]  G. Clermont,et al.  In silico design of clinical trials: A method coming of age , 2004, Critical care medicine.

[20]  Gary An,et al.  Mathematical modeling in medicine: a means, not an end. , 2005, Critical care medicine.

[21]  Glen E. P. Ropella,et al.  In silico representation of the liver-connecting function to anatomy, physiology and heterogeneous microenvironments , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.