Creating, Modeling, and Visualizing Metabolic Networks

Date
2005-01-01
Authors
Dickerson, Julie
Berleant, Daniel
Du, Pan
Ding, Jing
Foster, Carol
Li, Ling
Wurtele, Eve
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Altmetrics
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Li, Ling
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Research Projects
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Abstract

Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and create due to the diverse types of information that need to be represented. This chapter describes a suite of interlinked tools for developing, displaying, and modeling metabolic networks. The metabolic network interactions database, MetNetDB, contains information on regulatory and metabolic interactions derived from a combination of web databases and input from biologists in their area of expertise. PathBinderA mines the biological “literaturome” by searching for new interactions or supporting evidence for existing interactions in metabolic networks. Sentences from abstracts are ranked in terms of the likelihood that an interaction is described and combined with evidence provided by other sentences. FCModeler, a publicly available software package, enables the biologist to visualize and model metabolic and regulatory network maps. FCModeler aids in the development and evaluation of hypotheses, and provides a modeling framework for assessing the large amounts of data captured by high-throughput gene expression experiments.

Description

This is a manuscript of a chapter published as Dickerson, Julie A., Daniel Berleant, Pan Du, Jing Ding, Carol M. Foster, Ling Li, and Eve Syrkin Wurtele. "Creating, modeling, and visualizing metabolic networks." In Medical Informatics, pp. 491-518. Springer, Boston, MA, 2005. doi: 10.1007/0-387-25739-X_17. Posted with permission.

Keywords
fuzzy logic, microarray analysis, gene expression networks, fuzzy cognitive maps, text mining, naïve bayes
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