Abstract
Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.
Highlights
Biochemical experimentation has defined pathways or functional groupings of biomolecular interactions
We find that (1) the minimal pathways are biologically supported by independent biomolecular interaction networks, (2) the minimal pathways have stronger biological support than traditional human-defined metabolic pathways, and (3) the minimal pathways guided experimental discovery of novel regulatory roles for E. coli transcription factors
We found 54 metabolic pathways in E. coli that were not described in KEGG, Gene Ontology, or EcoCyc
Summary
Biochemical experimentation has defined pathways or functional groupings of biomolecular interactions. Systems biology has led to the elucidation and analysis of multiple cellular networks, representing metabolism (Mo et al, 2009; Orth et al, 2011), transcriptional regulation (Gama-Castro et al, 2011), protein-protein interactions (Han et al, 2004), and genetic interactions (Costanzo et al, 2010) These networks provide the opportunity to build unbiased pathway structures using statistical or mechanistic algorithms. The stoichiometric matrix (S) is a mathematical description of a genome-scale metabolic network, which can be queried by many available modeling methods (Lewis et al, 2012) These models and the calculated reaction fluxes are typically studied under a steady-state assumption (Fig 1A). We present a mixed-integer linear optimization algorithm (MinSpan) that can for the first time define the shortest, functional pathways for metabolism at the genome scale using metabolic networks thereby describing the totality of steady-state phenotypes. We find that (1) the minimal pathways are biologically supported by independent biomolecular interaction networks, (2) the minimal pathways have stronger biological support than traditional human-defined metabolic pathways, and (3) the minimal pathways guided experimental discovery of novel regulatory roles for E. coli transcription factors
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