Application of graphical models to inference and analysis of biological networks

Abstract

The inference of gene regulatory networks (GRNs) from gene-expression measurements, and the desire to understand genomic functions and the behavior of these complex networks form a core element of systems biology-based phenotyping. This inference, also known as reverse engineering, is one of the most challenging tasks in systems biology and bioinformatics, mainly due to the complex nature of biological systems, which involve many factors and uncertainties. However, with rapid biotechnological advancements, large-scale high-throughput biological data have become available. These data have enabled researchers to deduce and understand how interactions among the vast array of components in biological systems relate and a ect each other. Nevertheless, an up-to-date full understanding of such interactions has not been possible. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today's biology. However, little research focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression pro les. Furthermore, knowledge on how biological noise or error propagate up the development ladder of biological systems is currently lacking. This dissertation presents computational frameworks to explore the global behavior of biological systems and the consistency between experimentally veri ed GRNs against corresponding gene-expression dataset. Also considered is the general question of the e ect of perturbation on the dynamical network behavior, as well as developing an analytical technique to capture and characterize error evolution in biological networks. The developed computational tools are applied to assess network steady states, network state progression, and impact of gene deletion in Escherichia coli and Budding yeast models. The computational frameworks explained here provide useful graphical models and analytical tools to study biological networks. Moreover, the error propagation technique provides a step towards accurate prediction of noise propagation in biology, a key to understanding faithful signal propagation in gene networks as well as designing noise-tolerant arti cial gene circuits.