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Assembling, analyzing, refining, and cataloging molecular interaction network

  • Author(s): Mak, Huajiang Craig
  • et al.

Life within an organism is sustained by biomolecular interactions. Mapping networks of interactions and deciphering their functions - at both individual and global scales - is a long-standing challenge. Network analyses promise to illuminate how complex behaviors emerge from collections of individual interactions. Many disease states and physiological responses, for instance, emerge from the complex interconnections between biological pathways. This dissertation addresses four challenges inherent in deriving biological meaning from networks. We studied transcriptional regulatory networks, although the insights gained are more widely applicable. First, we investigated methods for assembling regulatory networks by integrating physical and functional data. Second, we analyzed individual regulatory pathways and global properties of networks. Third, we investigated methods for efficient network validation and refinement. Fourth, we describe a database of network models: its value, its implementation, and its potential uses as a research tool. We present the first large-scale network model of the transcriptional response to a DNA damaging agent in a eukaryotic cell. We developed novel methods for statistically assigning confidence scores to high- throughput data and for integrating physical and functional data to generate a network that is induced by an environmental perturbation. In a second study, we developed computational methods to discover and characterize a group of 22 yeast transcription factors that specifically bind to targets in the subtelomeric regions near the ends of chromosomes. In a third study, we used a measure of information gain to prioritize potential experiments for validating a network. We then performed three top-ranking experiments and used the results to refine the network. In a fourth study, we describe CellCircuits, an online, searchable database of network models containing over 1000 models from eleven published studies. This database was made available to the wider research community. As a result of our work, we identified general guidelines and themes for working with molecular interaction networks. We conclude with remarks on grounding computational research in the realities of biology

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