Oral Candidacy Exam - Fazle Faisal
|Start:||5/12/2014 at 10:30AM|
|End:||5/12/2014 at 1:30PM|
|Location:||384 Fitzpatrick Hall|
Oral Candidacy Exam
May 12, 2014, 10:30 am, 384 Fitzpatrick Hall
Advisor: Dr. Tijana Milenkovic
Dr. Patricia Clark Dr. Gregory Madey Dr. Aaron Striegel
"Novel Strategies for Dynamic Analysis and Alignment of Biological Networks and Their Interdisciplinary Applications"
Network science spans many domains including computational biology. Biomolecules in the cell, such as genes or their protein products, do not function alone but instead interact with each other to carry our cellular processes. And this is exactly what biological networks model. Efficient graph theoretic and computational analyses of biological analyses have a potential to deepen our understanding of complex biological processes and diseases, which can lead to identification of disease genes, design of drugs targeting the disease genes, and consequently improvement in health care. However, several challenges exist that make it hard to efficiently extract biological knowledge from topology of biological networks: 1) biological network data are large, 2) biological network data are heterogeneous due to availability of various different data types, 3) biological network data are noisy, 4) current biological network data represent static snapshots of actually dynamic cellular functioning, and 5) many of network (or graph) theoretic problems are computationally hard.
In this context, computational data integration can take advantage of the complementary information of different data types and can lead to more accurate biological knowledge extraction from the integrated heterogeneous network data. Further, computational data integration can also help with the inference and analysis of dynamic biological networks (which cannot be inferred experimentally due to limitations of biotechnologies for data collection) and can thus allow for studying dynamic cellular processes, such as aging. Finally, efficient computational strategies for network comparison and alignment can revolutionize our biological understanding by facilitating a new way to transfer knowledge between species.
Therefore, the Ph.D. dissertation proposal focuses on the development of novel computational strategies for integrative, dynamic, and comparative biological network analysis. In the proposal, the already developed and proposed network-based strategies have been and will be used to address important biological problems, such as studying human aging, which is hard to study experimentally due to long life span as well as ethical constraints. Further, the proposal discusses two additional interdisciplinary collaborative applications of the network-based strategies related to: 1) protein synthesis and 2) protein degradation. To date, my Ph.D. research contributions have resulted in a journal publication, a conference publication, an additional journal paper that is under minor revision, and an additional conference paper that is under review.