Gary An, MD



Dr. An has worked on the application of complex systems analysis to sepsis and inflammation since 1999, primarily using agent based modeling to create mechanistic models of various aspects of the acute inflammatory response, work that has evolved to the use of agent-based models as a means of dynamic knowledge representation to integrate multiple scales of biological phenomenon. The impetus for his work is the recognition that the Translational Dilemma has arisen from a bottleneck in the scientific cycle at the point of experiment and hypothesis evaluation.  His research involves the development of: mechanism-based computer simulations in conjunction with biomedical research labs, high-performance/parallel computing architectures for agent-based models, artificial intelligence systems for modular model construction, and community-wide meta-science environments, all with the goal of facilitating transformative scientific research. Towards this end he has developed agent-based models of sepsis, multiple organ failure, wound healing, surgical site infections, necrotizing enterocolitis, tumor metastasis, breast cancer, C. difficile colitis, and the link between oncogenesis and inflammation.

Research highlights include:

1. The first agent-based models of systemic inflammation/sepsis, as well as one of the first examples of using in silico clinical trials to test potential multi-modal sepsis therapies. 

2. The development of a modular modeling framework based on the integration of multiple, organ and tissue specific agent-based models

3. The development of a unifying hypothesis of necrotizing enterocolitis using an agent based model, based on the integration of the known necessary factors involved in the pathogenesis of the disease and inferring. This hypothesis states the it is immaturity of oxidative stress management that underlies the propensity to develop necrotizing enterocolitis. This model was subsequently used to predict configurations of host-pathogen interactions that could be used to characterize the disease. 

4. The use of a agent-based model of epithelial cell behavior to link the TGF-β and EGFR signaling pathways in epithelial restitution. This model was subsequently used to impute potential mechanisms by which epithelial restitution could be impaired by host-pathogen interactions in anastomotic healing.

5. The use of an agent based model of breast ductal epithelial dynamics to examine the oncogenesis of breast cancer, with the specific identification of the role of RUNX3 in the development of ER+ tumors. This model links cellular and molecular mechanisms with epidemiological data from the SEER database. 

6. The development of an agent-based model linking inflammation with the development and subsequent behavior of cancers, including the identification and description of a generative hierarchy that integrates and orders the Hallmarks of cancer. 

7. High resolution modeling of the terminal airway unit at the alveolar level, to investigate what the actual mechanics present during inflation of the lung. This model will be used to examine the pathogenesis of ventilator induced lung injury. 

8. The development of the Spatially Explicit General Purpose Model of Enteric Tissue (SEGMEnT), a virtual gut platform that integrates cellular and molecular mechanisms to produce histological detail, and, in its supercomputing implementation, generate anatomic scale clinical detail. 

9. The development of an Artificial Intelligence system, the Computational Modeling Assistant (CMA), to automate various steps in the generation of dynamic computational models and aid in the performance of simulation experiments. The CMA is part of a larger vision aimed at developing robust and scalable cyberinfrastructures that can be used to accelerate the Scientific Process. 

10. Introduction of Translational Systems Biology, an approach that involves the use of dynamic computational modeling and systems biology approaches with an explicit goal of representing clinical conditions and improving human health. More information here, here, here and here

WATCH: Dr. An's work, featured in Student Science, shows an agent-based model of an immune cell receiving chemical signals from other cells.