Entire molecular biology revolves primarily around proteins and genes (DNA and RNA). They collaborate with each other facilitating various biomolecular systems. Thus, to comprehend any biological phenomenon from very basic cell division to most complex cancer, it is fundamental to decode the functional dynamics of proteins and genes. Recently, computational approaches are being widely used to supplement traditional experimental approaches. However, each automated approach has its own advantages and limitations. In this thesis, major shortcomings of existing computational approaches are identified and alternative fast yet precise methods are proposed. First, a strong need for reliable automated protein function prediction is identified. Almost half of protein functional interpretations are enigmatic. Lack of universal functional vocabulary further elevates the problem. NRProF, a novel neural response based method is proposed for protein functional annotation. Neural response algorithm simulates human brain in classifying images; the same is applied here for classifying proteins. Considering Gene Ontology (GO) hierarchical structure as background, NRProF classifies a protein of interest to a specific GO category and thus assigns the corresponding function. Having established reliable protein functional annotations, protein and gene collaborations are studied next. Interactions amongst transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental for gene regulation and are highly specific, even in evolution background. To explain this binding specificity a Co-Evo (co-evolutionary) relationship is hypothesized. Pearson correlation and Mutual Information (MI) metrics are used to validate the hypothesis. Residue level MI is used to infer specific binding residues of TFs and corresponding TFBSs, assisting a thorough understanding of gene regulatory mechanism and aid targeted gene therapies. After comprehending TF and TFBS associations, interplay between genes is abstracted as Gene Regulatory Networks. Several methods using expression correlations are proposed to infer gene networks. However, most of them ignore the embedded dynamic delay induced by complex molecular interactions and other riotous cellular mechanisms, involved in gene regulation. The delay is rather obvious in high frequency time series expression data. DDGni, a novel network inference strategy is proposed by adopting gapped smith-waterman algorithm. Gaps attune expression delays and local alignment unveils short regulatory windows, which traditional methods overlook. In addition to gene level expression data, recent studies demonstrated the merits of exon-level RNA-Seq data in profiling splice variants and constructing gene networks. However, the large number of exons versus small sample size limits their practical application. SpliceNet, a novel method based on Large Dimensional Trace is proposed to infer isoform specific co-expression networks from exon-level RNA-Seq data. It provides a more comprehensive picture to our understanding of complex diseases by inferring network rewiring between normal and diseased samples at isoform resolution. It can be applied to any exon level RNA-Seq data and exon array data. In summary, this thesis first identifies major shortcomings of existing computational approaches to functional association of proteins and genes, and develops several tools viz. NRProF, Co-Evo, DDGni and SpliceNet. Collectively, they offer a comprehensive picture of the biomolecular system under study. / published_or_final_version / Biochemistry / Doctoral / Doctor of Philosophy

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The past few decades have witnessed a revolution in recombinant DNA and nucleic acid sequencing technologies. Recently however, technologies capable of massively high-throughout, genome-wide data collection, combined with computational and statistical tools for data mining, integration and modeling have enabled the construction of predictive networks that capture cellular regulatory states, paving the way for ‘Systems biology’. Consequently, protein interactions can be captured in the context of a cellular interaction network and emergent ‘system’ properties arrived at, that may not have been possible by conventional biology. The ability to generate data from multiple, non-redundant experimental sources is one of the important facets to systems biology. Towards this end, we have established a novel platform called ‘spotted cell microarrays’ for conducting image-based genetic screens. We have subsequently used spotted cell microarrays for studying multidimensional phenotypes in yeast under different regulatory states. In particular, we studied the response to mating pheromone using a cell microarray comprised of the yeast non-essential deletion library and analyzed morphology changes to identify novel genes that were involved in mating. An important aspect of the mating response pathway is large-scale spatiotemporal changes to the proteome, an aspect of proteomics, still largely obscure. In our next study, we used an imaging screen and a computational approach to predict and validate the complement of proteins that polarize and change localization towards the mating projection tip. By adopting such hybrid approaches, we have been able to, not only study proteins involved in specific pathways, but also their behavior in a systemic context, leading to a broader comprehension of cell function. Lastly, we have performed a novel metabolic starvation-based screen using the GFP-tagged collection to study proteome dynamics in response to nutrient limitation and are currently in the process of rationalizing our observations through follow-up experiments. We believe this study to have implications in evolutionarily conserved cellular mechanisms such as protein turnover, quiescence and aging. Our technique has therefore been applied towards addressing several interesting aspects of yeast cellular physiology and behavior and is now being extended to mammalian cells. / text