Abstract
In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.
Highlights
Stoichiometric models have been extensively used since 1980s [1,2,3] and prediction capabilities of these networks have been proven to be very useful
Construction of subnetworks and lumped reactions for the target metabolites decision in order to control the flux through the reactions of RncGEM
We developed lumpGEM, a systems biology tool that captures the minimal sized subnetworks that are capable of producing target compounds from a set of defined core metabolites
Summary
Stoichiometric models have been extensively used since 1980s [1,2,3] and prediction capabilities of these networks have been proven to be very useful. One of the pioneering studies on E. coli through a small stoichiometric model is performed by Varma et al [4,5] in where the authors described the model as composed of core carbon metabolism pathways namely, glycolysis, pentose phosphate pathway, TCA cycle and formation of some by-product formations accompanied by a part of the Electron Transport Chain (ETC) This stoichiometric definition is further extended by the integration of a biomass composition formulation that is provided in the classic text published by Neidhardt [6]. The amounts of 12 precursor metabolites from the core carbon metabolism (erythrose-4-phosphate, ribose-5-phosphate, pyruvate, alpha-ketoglutarate, phosphoenolpyruvate etc.) along with the requirement of cofactors (ATP, NADH, NADPH) and inorganic compounds (S, NH4) to synthesize these biomass building blocks (BBB) were estimated Such representation has been used in different studies to understand the core carbon metabolism and its relation with biomass accumulation [8,9]. Similar metabolic models with a reduced representation of the biosynthesis of the building blocks have been used in many other studies for E. coli and other organisms [10,11,12,13,14,15]
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