||In the emergence of post-genomic research, one of the most important themes is to uncover the complex biological mechanisms involved in genetic regulation. The regulatory interactions controlled by cis-regulatory DNA modules provide clues about the development of biological processes. These regulatory links can be represented as network-like architectures, i.e. gene regulatory networks (GRNs), which indicate the causal gene expression relationships between instructional inputs and functional outputs of genes. Modeling GRNs, therefore, is essential for conceptualizing how genes express themselves as well as influence others. Thanks to modern measurement techniques for gene expression, researchers can investigate phenotypic behavior of a living being by reconstructing GRNs from expression data. Typically a reverse engineering approach is employed; it is an effective strategy to reproduce possible fitting models of GRNs. Under this strategy, however, two daunting tasks must be undertaken. One is to optimize the accuracy of inferred network behaviors; the other is to designate valid biological topologies for target networks. Though existing studies have addressed the two tasks for years, few are able to satisfy both requirements simultaneously.|
To cope with the difficulties, this thesis proposes an integrative modeling framework which consists of three aspects. First, a novel reverse engineering algorithm is developed to tackle the issue of efficiently optimizing network behaviors for GRNs. Second, a proposed sensitivity analysis approach coupling with the optimization algorithm is designed to identify critical regulatory interactions under the situation where biological knowledge is unavailable. Finally, an integrated modeling approach combining knowledge-based and data-driven input sources is constructed to conduct biological topologies with corresponding network behaviors. For each aspect, a series of experiments are performed. The results reveal that the proposed framework can successfully infer solutions that are satisfactory for both requirements of network behaviors and biological structures, and thus the outcomes are exploitable for future in vivo experimental design.