| Santiago Trevino III, Kevin E. Bassler Determining the functional structure of biological networks is a central goal of systems biology. One approach is to identify functional communities of genes in a network inferred from correlated responses to environmental and genetic perturbations in gene expression data. However, commonly used methods for this purpose can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. In this talk, methods will be presented that aim to address these problems. The methods robustly detect a hierarchy of co-regulated and functionally enriched gene communities. We demonstrate their usefulness and validity by applying them to Escherichia coli gene expression data and comparing our predicted functional modules to gene ontology (GO) terms. Analysis of our most significantly enriched identified communities reveals several candidates for new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities. Additionally, we will present recent results that extend our methods to bipartite networks. |
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