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System Modeling in Cellular BiologyFrom Concepts to Nuts and Bolts$
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Zoltan Szallasi, Jorg Stelling, and Vipul Periwal

Print publication date: 2006

Print ISBN-13: 9780262195485

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262195485.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 05 August 2021

Methods to Identify Cellular Architecture and Dynamics from Experimental Data

Methods to Identify Cellular Architecture and Dynamics from Experimental Data

Chapter:
(p.221) 11 Methods to Identify Cellular Architecture and Dynamics from Experimental Data
Source:
System Modeling in Cellular Biology
Author(s):

Rudiyanto Gunawan

Kapil G. Gadkar

Francis J. Doyle

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262195485.003.0011

This chapter highlights the methodologies and challenges in the reverse engineering of cellular networks, in particular the identification of network dynamics using engineering approaches. The chapter is organized as follows. Section 11.2 gives a motivating example, which highlights some of the difficulties in a cellular network inference problem. Section 11.3 discusses the different issues and methodologies in reverse engineering with respect to both topology and dynamics. Tutorials are presented in the form of case studies involving a metabolic network in E. coli and a signal transduction pathway in a caspase-activated apoptosis. Finally, open research problems in this area are identified based on the analysis of the case studies.

Keywords:   cellular networks, reverse engineering, Escherichia coli, metabolic network, signal transduction pathway, caspase-activated apoptosis

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