Tools from statistical physics for systems biology and for genomics

My graduate studies involved three broad classes of problems, each of which are presented in different chapters of this thesis. The first two parts of my work were related to studying dynamics of biochemical networks. I studied a mean-field/stochastic model of epigenetic chromatin silencing in yeast. The model gives rise to different dynamical behaviors possible within the same molecular model and provides qualitative predictions that are being investigated experimentally. In another part of my work, I studied a model of segment polarity network in Drosophila and analyzed the parameter space of the system. I particularly studied the relation between the geometry of parameter space and the robustness of the network. I will show that, in addition to the volume, the geometry of this region has important consequences for the robustness and the fragility of a network. A major part of my PhD work involved applications of high-throughput sequencing technologies for extracting information at the genomic level. I present SOPRA, a new algorithm for exploiting the mate pair information for assembly of short reads. I have successfully applied SOPRA to real data and were able to assemble scaffolds of significant length with very few errors introduced in the process.Ph.D.Includes bibliographical referencesIncludes vitaby Adel Dayaria