Inferring pathways and networks with a Bayesian framework.

Zheng Li, Christina Chan

Research output: Research - peer-reviewArticle

  • 33 Citations

Abstract

Numerous mathematical methods have been adapted and developed to quantitatively reverse engineer biological networks, for example, signal transduction pathways, from experimental micro-array data. Compared with stochastic methods, such as Boolean networks, and deterministic methods, such as thermodynamic or differential equation-based models, Bayesian network analysis has the ability to assess, with scoring metrics, causal relations based on conditional probabilities and thus permit hypothesis testing. The goal of this paper is to illustrate the integration of several Bayesian based techniques into a unified Bayesian framework that can infer hepatocellular networks from metabolic data. Reverse engineering of pathways and networks provides a framework for predictive modeling and hypotheses testing to gain deeper insight into living organisms, disease mechanisms, and targeted therapeutics. Evaluating this methodology initially against the known biochemical network provides confidence in the networks that are uncovered from the experimental data using this framework. From the metabolic data we inferred the known sub-networks, such as the tricarboxylic acid (TCA) and urea cycles. In addition, we combined the relationships learned from the data and our current knowledge of the biological system to postulate several alternative metabolic sub-network models that can predict a particular cellular function, such as intracellular triglyceride accumulation.

LanguageEnglish (US)
Pages746-748
Number of pages3
JournalThe FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Volume18
Issue number6
StatePublished - 2004
Externally publishedYes

Profile

Testing
Metabolic Networks and Pathways
Tricarboxylic Acids
Signal transduction
Reverse engineering
Bayesian networks
Biological systems
Electric network analysis
Urea
Triglycerides
Differential equations
Thermodynamics
Engineers
Bayes Theorem
Citric Acid Cycle
Signal Transduction
Therapeutics

Cite this

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