Titlebook: Computational Systems Biology; Reneé Ireton,Kristina Montgomery,Jason McDermott Book 2009 Humana Press 2009 Analysis.algorithms.bioinforma

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Prediction of Protein–Protein Interactions: A Study of the Co-evolution Modelmodel are considered, including algorithms that attempt to identify the subset of the database proteins (the homologs of the query proteins) that are more likely to interact. We test the models over a large set of protein interactions extracted from several sources, including BIND, DIP, and HPRD.

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Methods to Reconstruct and Compare Transcriptional Regulatory Networks. In this chapter, we will focus in detail on the first approach and describe methods to reconstruct and analyze the transcriptional regulatory networks of uncharacterized organisms by using a known regulatory network as a template.

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1064-3745 ological network analysis and data representation and manageComputational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (‘‘systems’’) involved in a living organism. Based on this

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Computational Reconstruction of Protein–Protein Interaction Networks: Algorithms and Issues of evidence using machine learning methods. Here we describe the commonly used algorithms for predicting protein–protein interaction by genome data integration, and discuss several important yet often overlooked issues in computational reconstruction of protein–protein interaction networks.

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Structure-Based , Prediction of Transcription Factor–Binding Sitest molecular water solvent and counter-ions. For computational efficiency, we use a standard additive approximation for the contribution of each DNA base pair to the total binding free energy. The additive approximation is not strictly necessary, and more detailed computations could be used to invest

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