COVRECON: Combining Genome-scale Metabolic Network Reconstruction and Data-driven Inverse Modeling to Reveal Changes in Metabolic Interaction Networks

Abstract: One central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organism phenotypes could be understood as a result of the metabolic interaction network dynamics. Previously, we have derived a mathematical method addressing this problem using metabolomics data for the inverse calculation of a biochemical Jacobian network. However, these algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems, which makes them inadequate for dealing with large-scale metabolic networks. In this work, we present a novel regression-loss based inverse Jacobian algorithm and related workflow COVRECON. It consists of two parts: a, Sim-Network and b, Inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian's structure for a specific metabolomics dataset. Instead of directly solving a regression problem, the new inverse differential Jacobian part is based on a more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. This approach is illustrated by in silico stochastic analysis with different-sized metabolic networks from the BioModels database. The advantages of COVRECON are that 1) it automatically reconstructs a data-driven superpathway metabolic interaction model; 2) more general network structures can be considered; 3) the new inverse algorithms improve stability, decrease computation time, and extend to large-scale models