A probabilistic model for genetic regulation of metabolic networks

Abstract

Recent advancements in gene expression pro ling and measurement of metabolic reaction rates have led to increased interest in predicting metabolic reaction rates. In this thesis we present a principled approach for using gene expression pro les to improve predictions of metabolic reaction rates. A probabilistic graphical model is presented, which addresses inherent weaknesses in the current state of the art method for data-driven reconstruction of regulatory-metabolic networks. Our model combines methods from systems biology and machine learning, and is shown to outperform the current state of the art on synthetic data. Results on real data from S. cerevisiae and M. tuberculosis are also presented.