Agent‐based modeling and biomedical ontologies: a roadmap

Abstract The translational dilemma represents a foundational challenge for the biomedical research community. Addressing the dilemma will require an enhancement in the throughput capacity of the expression and evaluation of mechanistic hypotheses. Doing so will require the ability to place biomedical knowledge into a format where hypotheses can be readily instantiated such that the dynamics inherent to biological systems can be represented, and also technological enhancement of the generation of such dynamic models. We suggest that the former goal can be approached by using the meta‐structure of agent‐based models (ABMs) to integrate different knowledge hierarchies currently represented with bio‐ontologies and increase the expressiveness of formal knowledge representation to account for mechanistic biological rules. The development of an agent‐based modeling format (ABMF) will provide a bridge between ontological knowledge representation and methods for modeling and simulation (M&S). We further suggest that the latter goal of process enhancement can be targeted by the development of intelligent computational agents that can concatenate bio‐ontologies with M&S ontologies to semiautomate the generation of bio‐simulations. We believe that the application of these two complementary approaches would address the foundational nature of the current throughput bottleneck in the scientific cycle. WIREs Comp Stat 2011 3 343–356 DOI: 10.1002/wics.167 This article is categorized under: Statistical Models > Agent-Based Models Applications of Computational Statistics > Computational and Molecular Biology Software for Computational Statistics > Artificial Intelligence and Expert Systems Statistical Models > Simulation Models