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Updated: Aug 19, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human
Jason H Moore1, Erik M Boczko, Marshall L Summar
1Computational Genetics Laboratory, Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu
Abstract:
Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have 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 modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.
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