Modeling Biological Networks

IV.1 Coordinators
IV.2 Participants
IV.3 Introduction
IV.4 Background and Significance
IV.5 Research Plan
IV.6 Specific Subprojects

• IV.6.i Subproject 1 Topological Analysis of Selected Metabolic and Regulatory Networks for Large-scale Structure
  • IV.6.i.a Datasources
  • IV.6.i.b Methodology
  • IV.6.i.c Large-Scale Statistical Characterization of Pathways
  • IV.6.i.d Clustering
    • IV.6.i.d.1 Clustering algorithms
      • IV.6.i.d.1.i Spatial clustering algorithms
      • IV.6.i.d.1.ii Clustering by thermal annealing
      • IV.6.i.d.1.iii Network clustering
• [ Other Subprojects ]

IV.7 Connection to Specific Projects 2 (cytoskeleton) and 3 (organogenesis)
IV.8 Timeline

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IV.6.i Subproject 1 - Topological Analysis of Selected Metabolic and Regulatory Networks for Large-scale Structure:

To understand the cell's network organization requires computational approaches which simultaneously assess the generic features of living organisms and the unique features of distinct species. Two networks, often investigated independently, together determine a cell's metabolism. The metabolic network describes the genomic capacity for metabolism; a cell-wide genetic regulatory network controls metabolic enzyme activity. Figure IV.4 describes the topological analysis of metabolic, regulatory and biological networks to uncover and model their pathway structures and modularity.

Fig. IV.4. Outline of analysis methodology.


IV.6.i.a Datasources:
Several integrated pathway-genome databases, based on genomic and biochemical evidence, include all biochemical reactions found within the E. coli and S. cerevisiae metabolomes. Our analysis will use the data generated in subprojects 4-6, the data deposited in the WIT/ERGO database (Overbeek et al., 2000) plus biochemical reactions which only biochemical evidence reveals (Edwards and Palsson, 2000; Edwards et al., 2001). The new version (v 5.4) of the EcoCyc database (Karp et al., 2000) complements the E. coli database.

IV.6.i.b Methodology:

To understand the properties of complex metabolic networks requires mathematical tools to characterize their topology. In general, a topological network of N vertices corresponds uniquely to its connectivity matrix M of size N x N, where Mij = 1 if a directed link from node i to j exists and Mij = 0 otherwise (Figure IV.5). The stoichiometric matrix, S, characterizes the metabolic network more completely. Each element Sij is the stoichiometric coefficient ij of a metabolite Xi participating in reaction j, if we write each reaction (Reder, 1988). A complete description of the network requires the exact stoichiometric matrix Sij, which automatically provides Mij. However, the data contained in the stoichiometric matrix can be overwhelming, and are unnecessary to characterize the generic properties of the network. Consequently, we plan to develop and assign to the network elements unique measures which quantify the relative importance of each metabolite (node) and reaction (link). While understanding the large-scale structure of networks is important, experimentally testable predictions need to rely on organism-specific functions. An important goal of our research is to distinguish the generic properties of the metabolic network from specific features unique to each organism, through the parallel investigation of several prokaryotic organisms with known metabolic networks. We describe here our first quantitative measures of network topology. As we probe network properties and as experimental results accumulate, we will develop additional measures with direct or indirect biological and/or topological significance. Our main research objectives include: