Improving Genome Scale Metabolic Models using Gene Regulatory Networks

Abstract

This thesis aims to enhance Genome Scale Metabolic Models (GEMs) by integrating Gene Regulatory Networks (GRNs) to improve modeling capabilities for the yeast Saccharomyces cerevisiae. The project involved constructing a pipeline to incorporate gene expression data into GEMs, resulting in a constrained metabolic model with a more diverse and characteristic flux distribution. By utilizing transcription data and transcription factor interactions, the integration aims to provide a more comprehensive understanding of cellular processes. The research demonstrates that incorporating GRNs can enhance the predictive accuracy of GEMs, despite the challenges associated with data complexity and integration methodologies. Potential future work includes upgrading the GRN to a dynamic Bayesian network and exploring the effects of network size on model outcomes.