Using pathway networks to model context dependent cellular function

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Abstract

Molecular networks are commonly used to explore cellular organisation and disease mechanisms. Function is studied using molecular interaction networks, such as protein-protein networks. Although much biological insight has been gained using these models of molecular function, they are hindered by their reliance on available experimental data and an inability to capture the complexity of biological processes. Functional modules can be identified based on molecular network topology, making it essential that the edges accurately depict molecular interactions. However, these networks struggle to depict the temporal nature of interactions, giving the impression that all interactions are constant. This misrepresentation can result in functionally heterogeneous clusters. The notoriously inaccurate nature of experimental protein interaction data, along with variable conformity among network clusters and functional modules further impedes functional module extraction. Representation of genes by single nodes artificially merges the functions of pleiotropic genes, distorting the arrangement of function within molecular networks. This thesis therefore explores a more suitable model for representing function. Pathways are composed of sets of proteins that are known to interact within a particular cellular context, corresponding to a discernible biological function. Their representation of context dependent cellular activity makes them ideal for use as nodes within a new pathway level model. Using combinatorial algorithms a reduced redundancy pathway set was produced to represent global cellular systems. Enrichment analysis provides reliable functional annotations for each pathway node, attributing independent functions to pleiotropic genes. Edges are based on functional semantic similarity, generating a network representation of functional organisation. Both yeast and human biological systems are presented as functionally connected pathway networks. Pathway annotation and experimentation with semantic similarity measures provides insight into the cross-talk between biological processes. Pathway functional modules elucidate the intracellular implementation of processes. Disease modules highlight the effects of functional perturbations and disease mechanisms. The pathway model provides a complementary, high-level functional model that begins to bridge the gap between molecular data and phenotype. The utilisation of pathway data provides a large, well-validated data source, avoiding the inaccuracies inherent with molecular data. Pathway models better represent components of biological complexity such as pleiotropy and linear implementation of functions.

Keyword(s)

bioinformatics; function; network; pathway

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