The method uses data from metabolomics, which measures the levels of various metabolites in a system. The method can automatically construct a simplified model of the metabolic network and calculate the changes in the biochemical regulations using a novel algorithm. COVRECON can help to understand the complex dynamics and mechanisms of biological systems and diseases. Our paper demonstrates the method on several models and real data from breast cancer cells.

The strategy has broad implications for science, as it provides a new way to integrate different types of OMICS data and infer causal relationships between molecular components. COVRECON can also be applied to other biological systems, such as microbial communities, plants, or animals. The method can help to identify key regulators and targets for intervention in various biological processes and diseases. Our paper also contributes to the field of systems biology, which aims to understand the emergent properties and behaviors of complex biological systems. COVRECON is a powerful tool for systems biology research and applications.

Read the full open access paper here:
COVRECON: automated integration of genome-and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks

J Li, S Waldherr, W Weckwerth

Bioinformatics 39 (7), btad397