| dc.description.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. | |
