Methods in Bioengineering: Systems Analysis of Biological Networks

by Arul Jayaraman, Juergen Hahn

Overview

This cutting-edge volume provides a detailed look at the two main aspects of systems biology: the design of sophisticated experimental methods and the development of complex models to analyze the data. Focusing on methods that are being used to solve current problems in biomedical science and engineering, this comprehensive, richly illustrated resource shows you how to: Design of state-of-the art methods for analyzing biological systems,implement experimental approaches for investigating cellular behavior in health and disease, use algorithms and modeling techniques for quantitatively describing biomedical problems, and integrate experimental and computational approaches for a more complete view of biological systems.

Product Details

ISBN-13:	9781608075959
Publisher:	Artech House, Incorporated
Publication date:	08/01/2009
Sold by:	Barnes & Noble
Format:	eBook
File size:	13 MB
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About the Author

Arul Jayaraman is an assistant professor in the Department of Chemical Engineering at Texas A&M University. He holds an M.S. and Ph.D. in biochemical engineering from Tufts University and the University of California, respectively. Juergen Hahn is an assistant professor in the Department of Chemical Engineering at Texas A&M University, where he earned his M.S. and Ph.D.

Table of Contents

Quantitative Immunofluorescence for Measuring Spatial Compartmentation of Covalently-Modified Signaling Proteins. Development of Green Fluorescent Protein-Based Reporter Cell Lines for Dynamic Profiling of Transcription Factor and Kinase Activation. Comparison of Algorithms for Analyzing Fluorescence Microscopy Images and Computation of Transcription Factor Profiles. Data-driven, Mechanistic Modeling of Biochemical Reaction Networks. Construction of Phenotype-Specific Gene Network by Synergy Analysis. Genome Scale Analysis of Metabolic Networks. Modeling the Dynamics of Cellular Networks. Steady State Sensitivity Analysis of Biochemical Networks: A Brief Review and new Methods. Determining Metabolite Production Capabilities of Engineered Saccharomyces cerevisiae using Dynamic Flux Balance Analysis. Experimental Design for Parameter Identifiability in Biological Signal Transduction Modeling. Parameter Identification with Adaptive Sparse Grid-based Optimization for Models of Cellular Processes. Reverse Engineering of Biological Networks. Transcriptome Analysis of Regulatory Networks. A Workflow From Time Series Gene Expression Profiling to Transcriptional Regulatory Networks.