TitleTools from statistical physics for systems biology and for genomics
NameDayarian, Adel (author), Sengupta, Anirvan (chair), Baker, Andrew J (internal member), Bhanot, Gyan (internal member), Andrei, Eva (internal member), Stolovitzky, Gustavo (outside member), Rutgers University, Graduate School - New Brunswick,
SubjectPhysics and Astronomy,
DescriptionMy 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.
NoteIncludes bibliographical references
Noteby Adel Dayarian
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.