Theoretical and Computational Studies of the Glucose Signaling Pathways in Yeast Using Global Gene Expression Data

Xiaoxia Lin, Christodoulos A. Floudas, Ying Wang, James R. Broach

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

We have combined DNA microarray experiments with novel computational methods as a means of defining the topology of a biological signal transduction pathway. By DNA microarray techniques, we previously acquired data on expression over time of all genes in the yeast Saccharomyces following addition of glucose to wild-type cells and to cells mutated in one or more components of the Ras signaling network. In addition, we examined the time course of expression following activation of components of the Ras signaling network in the absence of glucose addition. In this current study, we have applied a novel theoretical and computational framework to these data to identify the network topology of the glucose signaling pathway in yeast and the role of Ras components in that network. The computational approach involves clustering genes by expression pattern, postulating a signaling network topology superstructure that includes all possible component interconnections and then evaluating the feasibility of the superstructure interconnections by optimization methods using Mixed Integer Linear Programming techniques. This approach is the first rigorous mathematical framework for addressing the biological network topology issue, and the novel formulation features the introduction of discrete variables for the connectivity and logical expressions that connect the experimental observations to the network structure. This analysis yields a topology for the glucose signaling pathway that is consistent with, and an extension of, known biological interactions in glucose signaling.

Original languageEnglish (US)
Pages (from-to)864-886
Number of pages23
JournalBiotechnology and bioengineering
Volume84
Issue number7
DOIs
StatePublished - Dec 30 2003

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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