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