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A goal of systems biology and human genetics is to understand how DNA sequence variations impact human health through a hierarchy of biochemical, metabolic, and physiological systems. We present here a proof-of-principle study that demonstrates how artificial life in the form of agent-based simulation can be used to generate hypothetical systems biology models that are consistent with pre-defined genetic models of disease susceptibility. Here, an evolutionary computing strategy called grammatical evolution is utilized to discover artificial life models. The goal of these studies is to perform thought experiments about the nature of complex biological systems that are consistent with genetic models of disease susceptibility. It is anticipated that the utility of this approach will be the generation of biological hypotheses that can then be tested using experimental systems.