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Computer Science - Bioinformatics | Computational Cancer Biology - An Interaction Network Approach

Computational Cancer Biology

An Interaction Network Approach

Vidyasagar, Mathukumalli

2012, XII, 80 p. 11 illus. in color.

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  • Gives the engineer and the mathematician a self-contained entry into the study of biological systems
  • Applications drawn from real cancer data – not simulations

This brief introduces readers to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics, building on only a basic background in these two topics.


Aside from providing a self-contained introduction to several aspects of basic biology and to cancer, as well as to the techniques from statistics most commonly used in cancer biology, the brief describes several methods for inferring gene interaction networks from expression data, including one that is reported for the first time in the brief.  The application of these methods is illustrated on actual data from cancer cell lines.  Some promising directions for new research are also discussed.


After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.

Content Level » Research

Keywords » Biologically-derived Control Systems - Cancer Biology - Computational Biology - Graph Theory - Information Theory - Markov Processes

Related subjects » Bioinformatics - Cancer Research - Control Engineering - Life Sciences, Medicine & Health

Table of contents 

Introduction.- Inferring Genetic Regulatory Networks.- Context-specific Genomic Networks.- Analyzing Statistical Significance.- Separating Drivers from Passengers.- Some Research Directions.

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