Exploring pathways from gene co-expression to network dynamics.

Huai Li, Y. Sun, Ming Zhan

Research output: Contribution to journalReview articlepeer-review

19 Scopus citations


One of the major challenges in post-genomic research is to understand how physiological and pathological phenotypes arise from the networks or connectivity of expressed genes. In addressing this issue, we have developed two computational algorithms, CoExMiner and PathwayPro, to explore static features of gene co-expression and dynamic behaviors of gene networks. CoExMiner is based on B-spline approximation followed by the coefficient of determination (CoD) estimation for modeling gene co-expression patterns. The algorithm allows the exploration of transcriptional responses that involve coordinated expression of genes encoding proteins which work in concert in the cell. PathwayPro is based on a finite-state Markov chain model for mimicking dynamic behaviors of a transcriptional network. The algorithm allows quantitative assessment of a wide range of network responses, including susceptibility to disease, potential usefulness of a given drug, and consequences of such external stimuli as pharmacological interventions or caloric restriction. We demonstrated the applications of CoExMiner and PathwayPro by examining gene expression profiles of ligands and receptors in cancerous and non-cancerous cells and network dynamics of the leukemia-associated BCR-ABL pathway. The examinations disclosed both linear and nonlinear relationships of ligand-receptor interactions associated with cancer development, identified disease and drug targets of leukemia, and provided new insights into biology of the diseases. The analysis using these newly developed algorithms show the great usefulness of computational systems biology approaches for biological and medical research.

Original languageEnglish (US)
Pages (from-to)249-267
Number of pages19
JournalMethods in molecular biology (Clifton, N.J.)
StatePublished - 2009

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics


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