Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism ***,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields as substrate ...
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Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism ***,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields as substrate uptake rates *** divergence from experimental measurements prompted the creation of enzyme-constrained models(ecModels)for various species,successfully enhancing chemical *** upon studies that allocate macromolecule resources,we developed a Python-based workflow(ECMpy)that constructs an enzyme-constrained *** involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in ***,this procedure de-mands manual collection of enzyme kinetic parameter information and subunit composition details,making it rather *** this work,we’ve enhanced the ECMpy toolbox to version 2.0,broadening its scope to automatically generate ecGEMs for a wider array of *** 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters,which significantly enhances parameter ***,ECMpy 2.0 introduces common analytical and visualization features for ecModels,rendering computational results more user ***,ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic *** 2.0 is available at https://***/tibbdc/ECMpy or as a pip package(https://***/project/ECMpy/).
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