Gene Regulatory Network

Identification of differentially expressed sub-networks from gene expression data sets has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Sub-networks can reveal the complex patterns of the whole bio-molecular network by extracting the interactions that depend on temporal or condition specific context. The identification of condition specific sub-networks is of great importance for investigating how a living cell adapts to changing environments. Our main goal is to obtain the most density sub-graph, which is described by its maximal scoring sub-network, from a whole gene regulatory network.

Finding maximal scoring sub-network is generally formulated as a combinatorial optimization problem. Bio-molecular networks are often large in scale. It is impossible to solve such a large combinatorial optimization problem exactly in reasonable time. We construct gene regulatory networks using some statistic based technique from gene expression datasets, and then reformulate the problems as optimization models that have strong theoretical validations.

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