Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data
The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unpr
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Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data Tianhai Tian
Abstract The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data. Keywords Bayesian inference • Approximate Bayesian computation • Genetic regulation • Reverse engineering
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Introduction
Precision medicine involves using detailed, patient-specified molecular information to diagnose and categorize disease, then guide treatment to improve clinic outcome [42]. To achieve these goals, precision medicine aims to develop computational models that integrate data and knowledge from both clinic and basic research to gain a mechanistic understanding of disease [14]. Compared with bioinformatics approaches, computational models are able to predict mode of action and responses
T. Tian (*) School of Mathematical Science, Monash University, Clayton, VIC 3800, Australia e-mail: [email protected] © Springer Science+Business Media Singapore 2016 B. Shen et al. (eds.), Translational Biomedical Informatics, Advances in Experimental Medicine and Biology 939, DOI 10.1007/978-981-10-1503-8_12
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to treatments not only at the molecular level but across all levels of biological organizations as well, including molecular level (gene network, cell-signaling pathway, and metabolic network), cell population level, tissue level, and even whole organism levels. However, a significant challenge facing precision medicine is the incorporation of models at different levels into a single framework by the integration of heterogeneous datasets [58]. With the rapid advancement of high-throughput technologies such as microarray, RNA sequencing, and mass spectrometry (MS)-based proteomics, enormous amounts of information are available for gene expression and kinase activity in the genome-wide scale [9, 44, 55]. These datasets give opportunities to develop mathematical models to explore the regulatory mechanisms and study system dynamics of molecular networks. Although the datasets contain enormous amounts of information, it is still a challenge to develop effective methods to extract use
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