An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems

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Abstract

DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems. Understanding the hierarchical relationships in the genotype-phenotype mapping is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This strategy uses an evolutionary computation approach called grammatical evolution for symbolic manipulation and optimization of Petri net models. We previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of complex genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. However, the modeling strategy was generally not successful when extended to modeling nonlinear interactions between three DNA sequence variations. In the present study, we evaluate a modified grammar for building Petri net models of biochemical systems that are consistent with high-order genetic models of disease susceptibility. The results indicate that our hierarchical model-building approach is capable of identifying perfect Petri net models when an appropriate grammar is used.