Minimal metabolic pathway structure is consistent with associated biomolecular interactions

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 E scherichia 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. image The MinSpan algorithm is presented that defines the shortest functional metabolic pathways at the genome scale, based on whole network function and parsimonious use of cellular components. The pathways are biologically supported by biomolecular interaction networks. Pathways are traditionally defined by biochemical experimentation and are the universal paradigm for describing cellular processes. The MinSpan algorithm defines pathways at the genome scale using metabolic network reconstructions based on a principle of minimal use of biochemical transformations. MinSpan derived pathways are as or more representative of the underlying protein–protein, positive genetic, and transcriptional regulatory interactions compared to traditional pathways. The MinSpan pathways are used in conjunction with constraint‐based modeling to predict transcriptional regulation in E . coli .