redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Abstract

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

“…The allowable ranges of concentrations of common metabolites of D2 and GEM were consistent. Similar result was reported for the case of E. coli where the discrepancy in concentration ranges was reported for only few metabolites [60].…”

“…Reconstruction of core reduced models. Using as a basis the curated iJN1411, we employed the redGEM [60] and lumpGEM [61] algorithms to construct a family of three core Figure 1. The core networks generated by the redGEM algorithm from iJN1411 genomescale model.…”

“…Systematic reduction of iJN1411. We used the redGEM [60] and lumpGEM [61] algorithms to deliver reduced models of three different sizes (referred in the results section as D1, D2 and D3). The first step in the redGEM algorithm is to select the metabolic subsystems of interest around which the reduced models are built.…”

“…The D1 model consisted of: (i) the D1 core network formed by the reactions and metabolites from the six subsystems and the reactions that belonged to one-reactionstep pairwise connections between these six subsystems [60] (Fig 1); and (ii) lumped reactions that connected the D1 core network with the BBBs. The D2 model contained:…”

Improving the EFMs quality by augmenting their representativeness in LP methods

“…LP is being widely used to reduce the complexity of combinational problems in systems biology introducing optimization objectives that lead the search and constraint the space of solutions to a subset within an specific focus [22–24]. …”