Modeling Biological Networks

• IV.6.iv Subproject 4 Modeling the Regulation and Function of Metabolism
  • IV.6.iv.a Datasources
  • IV.6.iv.b Methods
  • IV.6.iv.c Methodology
    • IV.6.iv.c.1 Cluster analysis
    • IV.6.iv.c.2 Uncovering dynamical constraints using Singular Value Decomposition
  • IV.6.iv.d Research Plan
    • IV.6.iv.d.1 Model construction
    • IV.6.iv.d.2 Model validation
      • IV.6.iv.d.2.i Phenotypic validation
      • IV.6.iv.d.2.ii Molecular validation
      • IV.6.iv.d.2.iii Experimental validation
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The activity of a given enzyme affects each biochemical reaction in the metabolic network. Individual enzymes never act independently. Instead, enzymatic activities are co-regulated, both through their organization into operons, and through sophisticated feedback mechanisms. We will apply a variety of methods to discover co-regulated enzymes by examining their gene expression profiles and other data.

The simplest method for uncovering correlations between enzymes based on gene expression is through simple clustering, using their correlation matrix (Eisen et al., 1998). This technique, uses microarray data to determine the correlation coefficient φij between each pair of genes i and j, after which energy minimization algorithms or self-organized maps cluster the genes into co-regulated groups. The most widely used clustering method creates a tree, in which co-regulated genes typically lie on the same branch. We also will use more sophisticated methods to achieve better clustering (Bittner et al., 1999).

Our first aim is to determine the correlations between the metabolic enzymes at a transcriptional level. From the correlation matrix we expect to learn which pairs of enzymes have co-regulated transcription, and their degree of co-regulation. The results of the microarray based enzyme clustering should correlate strongly with the functional modules that Subproject 1 uncovers from the metabolic network topology. This analysis will provide a quantitative picture of regulatory co-dependency, but little biological insight into the origins of the correlations. Further correlating our results with known biological function, such as operon and regulon organization could provide such insight.