| During the past decades, with the advent of high-throughput proteomics, microarray and bioinformatics technologies, the biological community has been exposed to a new discipline: Systems Biology. The aim of systems biology is to understand the biological system atsystem level. Experiment data analysis and biochemical network modeling and simulation are two key parts of the methodology of System Biology. The dissertation major focus the analysis of multi-source and time series data and modeling and simulation of biochemical network basen on the agent technology, and the most important features include:1. Protein-protein interactions (PPI) play a key role in many fundamental biological processes, and comprehensively identifying them represents a crucial step towards systematically defining their cellular roles. A method of Predicting co-complexed protein pairs based on communication model using diverse biological data is given here. Theory analysis and experiment results showed that the given method is very useful.2. Living things are characterized by a very high degree of structure at every level. We study the order change of rat plasma proteomic data casused by Type 2 Diabetes using the Shannon Entropy. Our experiment results showed that GK rats (type 2 diabetes) preserved a larger entropic values than Wistar rats (normal) throughout time.3. Biomarkers can be used to track the process of disease such as cancer, diagnose the disease, and detect the drug target. We proposed a Localized-Statistic-Based Approach for identifying biomarker of omics data, which improving the the efficiency of biomarker selection.4. In order to present the phenome existed in the biology system, we given a new agent-based computational modeling and simulation paradigm for the biochemical reaction networks. The method can be combined with the popular methods and can be widely used to study the molecular biology system.
|
PDF Full Text Request