Research in computational systems biology focuses on establishing the complex relationship and interactions between genes and how they work together to render a particular phenotype. This involves the development and application of systematic approaches to study the biological regulation in the context of a network in which genes are regulating each other. Our research aim to develop novel approaches to identify genes and their regulation that determine a phenotype, which involves the reverse engineering of regulatory mechanisms through identification of condition specific genes and interactions, as well as the systematical modeling and simulation to reconstruct context dependent regulatory networks.Chapter 1 introduces the fundamental approaches in systems biology. Data mining techniques have been developed to identify genes and interactions from gene expression data, while systems modeling integrate current knowledge to develop a functional context to address the complexity that arises in biological systems. We provide examples to demonstrate the practical aspects and biological relevance of the methodologies. Chapter 2 introduces and discusses the multi-layer approach that is able to reconstruct condition-specific genes and their regulation through an integrative analysis of large scale information of gene expression, protein interaction and transcriptional regulation. In Chapter 3 we explore a dynamic feature of gene network: the switch-like behavior, wherein we show that gene switches have specific pattern of gene expression which can be uncover by mining microarray data. This study demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network, thereby predicting gene switches that could be important for a phenotype and can participate in cell fate decision. In Chapter 4 the cancer phenotype is studied using systems modeling of the human metabolic network. We develop a novel approach to simulate context dependent metabolic states that upon perturbation of the gene(s) that modulate metabolic functions, can determine whether the gene is involved in conferring a phenotype. The approach is then applied to predict therapeutic microRNAs for human hepatocellular cancer. Chapter 5 provides a brief summary of the implications of the research towards a systematic understanding of gene network as well as a future perspective of the field.
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