SEMINAR NOTICE
Department of Electrical and Systems Engineering
MODELING AND IDENTIFICATION OF DIFFERENTIALLY REGULATED GENES
USING TRANSCRIPTOMICS AND PROTEOMICS DATA
DISSERTATION DEFENSE
by
Thanura Ranmal Evitigala
PhD Candidate
Electronic Systems and Signals Research Laboratory
Abstract:
Photosynthetic organisms are complex dynamical systems, showing a remarkable ability to adapt to different
environmental conditions for their survival. Mechanisms underlying the coordination between cellular processes in these organisms
are still poorly understood. In this thesis we utilize various computational and modeling techniques to analyze transcriptomics and
proteomics data sets from several photosynthetic organisms. We try to use changes in expression levels of the genes to study the
response of these organisms to various environmental conditions such as availability of nutrients, concentrations of chemicals in
growth media, and temperature. Three specific problems studied are transcriptomic modifications in photosynthetic organisms to
reduction-oxidation (redox) stress conditions, circadian and diurnal rhythms of cyanobacteria and the effect of incident light patterns
on these rhythms, and the coordination between biological processes in cyanobacteria under various growth conditions.
Time course transcriptomics data from Cyanothece sp. ATCC 51142 has shown strong diurnal rhythms. By combining multiple
experimental conditions and using gene classification algorithms based on Fourier scores and angular distances, it is shown that
majority of the diurnal genes are in fact light responding. About 10% of genes in the genome are categorized as being circadian
controlled. A transcription control model based on feed-forward loops is employed to identify the interactions between diurnal genes.
A phase oscillator network is proposed to model the behavior of different biological processes. Both these models are shown to carry
biologically meaningful features.
To study the coordination between different biological processes to various environment and genetic modifications, interaction model
is derived using Bayesian network approach combining all publicly available microarray data sets for Synechocystis sp. PCC 6803.
Several novel relationships between biological processes are discovered from the model. Model is used to simulate several
experimental conditions, and the response of the model has been shown to agree with the experimentally observed behaviors.
DATE:
TIME:
PLACE:
Friday, September 18, 2009
10:00 a.m.
Bryan Hall, Room 305
Dissertation advisors:
Dr. Bijoy K. Ghosh
Dr. Himadri B. Pakrasi
Dr. Hiro Mukai
This seminar is in partial fulfillment
of the Doctor of Philosophy degree