User:Gary An

Revision as of 23:21, 19 March 2012

Current Position:

Associate Professor of Surgery

Section of General Surgery

Department of Surgery

University of Chicago Pritzker School of Medicine

Contact Information

5841 South Maryland Ave

MC5031 S-032

Chicago, IL 60637

Phone: 773-702-9742

Email: docgca@aol.com or docgca@gmail.com

Current Research

My research focuses on the utilization of agent-based modeling for multi-scale modeling of acute inflammation. Agent levels include cells and molecules, with the goal of simulating organ- and organism-level behaviors from these generative mechanisms. While the focus of my models have primarily been on the pathophysiology of sepsis and multiple organ failure, in a more general sense I am interested in using agent-based modeling as a means of dynamic knowledge representation to augment the biomedical research process. With respect to acute inflammation, the ubiquitousness of the underlying cellular and molecular mechanisms suggest the application of this methodology to such area as oncogenesis, transplant immunology/rejection, autoimmune disease, wound healing/scarring, atherosclerosis and aging. I am currently using NetLogo as my primary agent-based modeling platform, but am involved with the development of SPARK (Simple Platform for Agent-based Representation of Knowledge http://www.pitt.edu/~cirm/spark/ ) currently being done at the University of Pittsburgh.

I am also interested in automated text analysis/information extraction from biomedical texts, formal knowledge representation in biomedicine and the development of methods for evolving biomedical ontologies. These goals form a "front-end" to high-level modeling tools (such as the Agent Modeling Platform http://www.eclipse.org/amp/ , an Eclipse project ) and are intended to facilitate the ability for biomedical knowledge to be represented, communicated and evaluated via executable models. I am also involved in projects directed at developing methods to execute ABMs on massively parallel computing platforms. I believe this is a vital development necessary for true, multi-scale, biologically relevant simulation.

Finally, I am currently working on developing scalable methods to facilitate and semi-automate the ability of non-computationally-oriented biomedical researchers to express their knowledge and hypotheses using computational and mathematical models. Our approach uses an artificially intelligent computational agent to perform the mapping between biological conceptual models/hypotheses and computational/mathematical model specifications.

Translational Systems Biology/Agent Based Modeling Publications

An, G. Closing the scientific loop: Bridging correlation and causality in the petaflop age. Sci. Transl. Med. 2010. 2(41):41ps34. Doi: 10.1126/scitranslmed.3000390

An, G., Nieman, G. and Vodovotz, Y. Computational and Systems Biology in Trauma and Sepsis: Current State and Future Perspectives. International Journal of Burns and Trauma. 2012. 2(1):1-10.

An, G. and Christley, S. Addressing the Translational Dilemma: Dynamic Knowledge Representation of Inflammation using Agent-based Modeling. Critical Reviews in Biomedical Engineering. 2012. In press.

An, G., Namas, R. and Vodovotz, Y. Sepsis: from pattern to mechanism and back. Critical Reviews in Biomedical Engineering. 2012. In press.

Christley, S. and An, G. A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant. Computational and Mathematical Organization Theory. 2011. ePub November 4, 2011. 17(4). doi: 10.1007/s10588-011-9101-y

Kim, M., Christley, S., Alverdy, J.C., Liu, D. and An, G. Immature Oxidative Stress Management as a Unifying Hypothesis in the Pathogenesis of Necrotizing Enterocolitis: Insights from an Agent-based Model. Surgical Infections. 2012. Jan 4 epub ahead of print. PMID: 22217195

Seal, J.B., Alverdy, J.C., Zaborina, O. and 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. Theoretical Biology and Medical Modelling, ePub September 19, 2011. 8(1):33. doi:10.1186/1742-4682-8-33. PMID: 21929759

An, G and Christley, S. Agent-based Modeling and Biomedical Ontologies: A Roadmap. Wiley Interdisciplinary Reviews: Computational Statistics, ePublication: April 15, 2011. 3:343-356. Doi:10.1002/wics.167

Christley, S. and An, G. “A Proposed Method for Dynamic Knowledge Representation via Agent-directed Composition from Biomedical and Simulation Ontologies: An Example using Gut Mucus Layer Dynamics.” Proceedings of the 2011 Spring Simulation Multiconference(Agent-directed Simulation Symposium), in Press. * Won “Best Paper in Track”

An, G., Bartels, J and Vodovotz, Y. In silico augmentation of the drug development pipeline: Examples from the study of acute inflammation. Drug Development Research 2011 March 1:72(2):187-200. PMID: 21552346

Mi, Q., Li, N.Y.K., Ziraldo, C., Ghuma,A., Mikheev, M., Squires, R., Okonkwo, D.O., Verdolini,K., Constantine, G., An, G., Vodovotz, Y., Translational Systems Biology of Inflammation: Applications to Personalized Medicine, Personalized Medicine, September 2010, 7(5): 549-559. PMID: 21339856

Solovyev, A., Mikheev, M., Zhou, L., Dutta-Mosscato, J., Ziraldo, C., An, G., Vodovotz, Y. and Mi, Q. SPARK: A framework for multi-scale agent-based biomedical modeling. International Journal of Agent Technologies and Systems, 2011, 2(3):18-31. Doi:10.4018/jats.2010070102

Seal, J.B., Morowitz, M., Zaborina, O, An, G. and Alverdy, J.C. The Molecular Koch’s Postulates and Surgical Infection: a View Forward. Surgery. 2010, 147(6):757-765.

D’Onofrio, D. and An, G. A comparative approach for the investigation of biological information processing: An examination of the structure and function of computer hard drives and DNA. Theoretical Biology and Medical Modelling. 2010, 7:3. (January 21, 2010) http://www.tbiomed.com/content/7/1/3 Doi: 10.1186/1742-4682-7-3

An, G. Translational Systems Biology using an Agent-based Approach for Dynamic Knowledge Representation: An Evolutionary Paradigm for Biomedical Research. Wound Repair and Regeneration. Published Online: January 7, 2010. 18(1):8-12. Doi: 10.1111/j.1524-475X.2009.00568.x

Vodovotz, Y, Constantine, G., Faeder, J., Mi, Q., Rubin, J., Bartels, J., Sarkar, J., Squires, R., Okonkwo, D., Gerlach, J., Zamora, R., Luckhart, S., Ermentrout, B. and An, G. Translational Systems Approaches to the Biology of Inflammation and Healing. Immunopharmacology and Immunotoxicology. 2010 Jun;32(2):181-95.

Li, N.Y.K., Verdolini-Abbott, K., Rosen, C., An, G., Hebda, P.A. and Vodovotz, Y. Translational systems biology and voice pathophysiology. The Laryngoscope. Published Online: December 18, 2009. Doi: 10.1002/lary.20755

An, G, Mi, Q., Dutta-Moscato, J. and Vodovotz, Y. Agent-based models in translational systems biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. Published Online: June 25, 2009. Doi: 10:1002/wsbm.45

Tegner, J.N, Compte, A, Auffray, C., An, G., Cedersund, G., Clermont, G., Gutkin, B., Oltavai, Z.N., Stephan, K.N., Thomas, R. and Villoslada, P. Computational disease modeling – fact or fiction? BMC Systems Biology 2009; 3:56 (June 4, 2009). http://www.biomedcentral.com/1752-0509/3/56 Doi:10.1186/1752-0509-3-56

Chau, T.A., McCully, M.L., Brintnell, W., An, G., Kasper, K.J., Haeryfar, S.M.M., McCormick, J.K., Cairns, E., Heinrichs, D.E. and Madrenas, J. TLR2 Ligands on the Staphylococcal Cell Wall Down-regulate Superantigen-Induced T Cell Activation and Prevent Toxic Shock Syndrome. Nature Medicine 2009; 15:641-648. Doi: 10.1038/nm.1965

An, G and Wilensky, U. From Artificial Life to In Silico Medicine: NetLogo as a Means of Translational Knowledge Representation in Biomedical Research. In Adamatsky, A and Komosinski, M. (eds): Artificial Life in Software (Second Edition). Springer-Verlag, London, in press.

An, G: Dynamic Knowledge Representation using Agent Based Modeling: Ontology Instantiation and Verification of Conceptual Models. In Maly, I. (ed): Systems Biology: Methods in Molecular Biology Series. Humana Press, New York City, 2009.

An G. A model of TLR4 signaling and tolerance using a qualitative, particle-event-based method: Introduction of Spatially Configured Stochastic Reaction Chambers (SCSRC). Mathematical Biosciences 2009; 217:43-52.

An G. and Faeder JR. Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Mathematical Biosciences 2009; 217:53-63.

Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO and An G. Mechanistic Simulations of Inflammation: Current State and Future Prospects. Mathematical Biosciences 2009; 217:1-10.

An G. Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theoretical Biology and Medical Modelling 2008; 5:11 (May 27, 2008).

Vodovotz Y, Csete M, Bartels J, Chang S and An G. Translational Systems Biology of Inflammation. PLoS Comput Biol 4(4): e1000014. doi:10.1371/journal.pcbi.1000014

An G, Faeder J and Vodovotz Y. A Review of Translational Systems Biology as applied to Inflammation: Introduction of an Engineering Approach to the Pathophysiology of the Burn Patient. J Burn Care Res 2008; 29(2):277-285.

Folcik VA, An G and Orosz CG. The Basic Immune Simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theoretical Biology and Medical Modelling 2007; 4:39, Publish ahead of print September 27, 2007.

An, G: Agent Based Modeling and Endothelial Biomedicine. In Aird, W. (ed): Endothelial Biomedicine: A Comprehensive Treatise. Cambridge University Press, 2007. Pages 1754-1759.

An G, Hunt CA, Clermont G, Neugebauer EA and Vodovotz Y. Challenges and Rewards on the Road to Translational Systems Biology in Acute Illness: Four Case Reports from Interdisciplinary Teams. Journal of Critical Care 2007; 22(2):169-175.

Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, Hotchkiss J, Ta’asan S, Neugebauer EA and An G. Evidence-based Modeling of Critical Illness: An Initial Consensus from the Society of Complexity in Acute Illness. Journal of Critical Care 2007; 22(1):77-84.

Wakeland, W., Macovsky, L. and An, G.. A Hybrid Simulation for Studying Acute Inflammatory Response. Proceedings of the 2007 Spring Simulation Multiconference (Agent-directed Simulation Symposium). 1:39-46.

An G. Concepts for developing a collaborative in-silico model of the acute inflammatory response using agent based modeling. Journal of Critical Care 2006; 21(1): 105-110.

An G. Phenomenological issues related to measurement, mechanisms and manipulation of complex biological systems. Critical Care Medicine 2006; 34(1): 245-246. (Editorial)

An G. Introduction of an in-sIlico syntactical grammar for translating basic science research into agent based models of the acute inflammatory response. Proceedings from the 11th Congress European Shock Society 2005, H Redl editor. 161-165.

An G. Mathematical modeling in medicine: A means not an end. Critical Care Medicine 2005; 33(1): 253-254. (Editorial)

Vodovotz Y, Clermont G, Chow C, An G. Mathematical models of the acute inflammatory response. Current Opinions in Critical Care 2004; 10:383-390.

An G. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Critical Care Medicine 2004; 32(10): 2050-2060..

An G. Complexity theory and surgery: Introducing and integrating a new analytical paradigm. New Surgery 2001; 1(3): 175-179.

An G. Agent-based computer simulation and SIRS: Building a bridge between basic science and clinical trials. Shock 2001; 16(4): 266-273.

An G, Lee I. Agent-Based Computer simulation (ABCS) and the inflammatory response: Building a tool to study Systemic Inflammatory Response Syndrome (SIRS). Simulation and Gaming 2001; 32(3): 344-361.

An G, Lee I. Complexity, Emergence and Pathophysiology: Using Agent Based Computer Simulation to characterize the Non-Adaptive Inflammatory Response. InterJournal Complex Systems: http://www.interjournal.org. Manuscript # [344]. May, 2000.

Links

Link to Society of Complexity in Acute Illness (SCAI). This is an organization of physicians and biomedical researchers committed to promoting the use of mathematical modeling and computational methods to analyze the pathophysiology of acute illnesses with a focus on inflammation. http://www.scai-med.org

Link to Modeling Glossary developed and maintained by the Biosystems Research Group at the University of California, San Francisco under the leadership of C. Anthony Hunt. This Glossary can be used as a reference point by those interested in biomedical modeling as a means of fostering communication between modelers and domain specialists. http://biosystems.ucsf.edu/Researc/dictionary.html