Integrated Bayesian Deterministic and Probabilistic Modeling of Phenotype-Genotype Networks in the Metabolic System
Rachael Hageman Blair Principal Investigator
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This project develops a comprehensive Bayesian methodology for mathematical modeling, which integrates across diverse data types, accounts for model uncertainty, and incorporates existing prior knowledge about the biological system. The Bayesian models integrate two well-established modeling paradigms: (1) deterministic mathematical models of cellular metabolism and (2) causal graphical models of phenotypes and genotypes. These modeling paradigms are widely used, but suffer major limitations: deterministic models do not reflect the individual variation in fluxes that result from allelic variation of enzymes, and graphical modeling techniques currently ignore all prior information about the biochemical pathway. This project overcomes these limitations by incorporating diverse forms of data and variables, which span the genome, transcriptome, metabolome, and clinical phenotypes. Applications focus on genome-scale metabolic reconstructions of breast and skin cancer cell metabolism to search out novel molecular targeted therapies, side-effects, and unravel the complex mechanisms underlying the disease. The methodology will be implemented in a publicly available software package, which is designed for users with a minimal background in mathematics and statistics. <br/><br/>Understanding how genetics influences metabolic and regulatory function is important for the prevention and treatment of disease. Genetics explains in part why some people are more responsive to drug treatments, while others have no response or may experience deadly side effects. Advances in high-throughput phenotyping technologies have made large-scale measurements of molecular traits possible. Mathematical models are widely used to elucidate networks of molecular traits from high-throughput data, and have become important tools for systems biology. Despite this progress, integrating diverse types of data remains a major challenge that has limited our ability to take full advantage of the wealth of post-genomics data for knowledge and discovery. This project addresses this challenge and represents a bold new direction in systems biology, which can be generalized to model different biological systems. A broader impact of this project is the software development, which aims to bridge the gap between computational and experimental biology by putting accessible tools in the hands of the biologist.