Systems Biology: Mathematical Modeling and Model Analysis

Andreas Kremling

© 2013 - Chapman and Hall/CRC
Published November 12, 2013
Textbook - 379 Pages - 176 B/W Illustrations
ISBN 9781466567894 - CAT# K16367
Series: Chapman & Hall/CRC Mathematical and Computational Biology

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  • Presents methods for the analysis of motifs, modules, and large-scale networks
  • Explains how deterministic models and graphs can be used in the analysis and verification of networks
  • Shows how to model the complete synthesis of macromolecules and compare measured data
  • Uses the lactose uptake system to demonstrate various modeling and analysis tools
  • Contains many analytical and numerical examples
  • Includes exercises and a bibliography at the end of each chapter
  • Offers MATLAB® code on the book’s CRC Press web page

Solutions manual and figure slides available upon qualifying course adoption


Drawing on the latest research in the field, Systems Biology: Mathematical Modeling and Model Analysis presents many methods for modeling and analyzing biological systems, in particular cellular systems. It shows how to use predictive mathematical models to acquire and analyze knowledge about cellular systems. It also explores how the models are systematically applied in biotechnology.

The first part of the book introduces biological basics, such as metabolism, signaling, gene expression, and control as well as mathematical modeling fundamentals, including deterministic models and thermodynamics. The text also discusses linear regression methods, explains the differences between linear and nonlinear regression, and illustrates how to determine input variables to improve estimation accuracy during experimental design.

The second part covers intracellular processes, including enzymatic reactions, polymerization processes, and signal transduction. The author highlights the process–function–behavior sequence in cells and shows how modeling and analysis of signal transduction units play a mediating role between process and function.

The third part presents theoretical methods that address the dynamics of subsystems and the behavior near a steady state. It covers techniques for determining different time scales, sensitivity analysis, structural kinetic modeling, and theoretical control engineering aspects, including a method for robust control. It also explores frequent patterns (motifs) in biochemical networks, such as the feed-forward loop in the transcriptional network of E. coli.

Moving on to models that describe a large number of individual reactions, the last part looks at how these cellular models are used in biotechnology. The book also explains how graphs can illustrate the link between two components in large networks with several interactions.


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