Recently, Computational Biology has emerged as one of the most
exciting areas of computer science research, not only because of its
immediate impact on many biomedical applications, (e.g., personalized
medicine, drug and vaccine discovery, tools for diagnostics and
therapeutic interventions, etc.), but also because it raises many new
and interesting combinatorial and algorithmic questions, in the
process. In this thesis, we focus on robust and efficient algorithms
to analyze biological networks, primarily targeting protein networks,
possibly the most fascinating networks in computational biology in
terms of their structure, evolution and complexity, as well as because
of their role in various genetic and metabolic diseases.
Classically, protein networks have been studied statically, i.e., without taking into account time-dependent metamorphic changes in network topology and functionality. In this work, we introduce new analysis techniques that view protein networks as being dynamic in nature, evolving over time, and diverse in regulatory patterns at various stages of the system development. Our analysis is capable of dealing with multiple time-scales: ranging from the slowest time-scale corresponding to evolutionary time between species, speeding up to inter-species pathway evolution time, and finally, moving to the other extreme at the cellular developmental time-scale.
We also provide a new method to overcome limitations imposed by corrupting effects of experimental noise (e.g., high false positive and false negative rates) in Yeast Two-Hybrid (Y2H) networks, which often provide primary data for protein complexes. Our new combinatorial algorithm measures connectivity between proteins in Y2H network not by edges but by edge-disjoint paths, which reflects pathway evolution better within single specie network. This algorithm has been shown to be robust against increasing false positives and false negatives, as estimated using variation of information and separation measures.
In addition, we have devised a new way to incorporate evolutionary information in order to significantly improve classification of proteins, especially those isolated in their own networks or surrounded by poorly characterized neighbors. In our method, the networks of two (or more) species are joined by edges of high sequence similarity so that protein-homologs of different species can exchange information and acquire new and improved functional associations.
Finally, we have integrated many of these techniques into one tool to create a novel analysis of malaria parasite P. falciparum's life-cycle at the scale of reaction-time, single cell level, and encompassing its entire inter-erythrocytic developmental cycle (IDC). Our approach allows connecting time-course gene expression profiles of consecutive IDC stages in order to assign functions to un-annotated Malaria proteins and predict potential targets for vaccine and drug development.