Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism behaviors.However,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields...Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism behaviors.However,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields as substrate uptake rates rise.This divergence from experimental measurements prompted the creation of enzyme-constrained models(ecModels)for various species,successfully enhancing chemical pro-duction.Building upon studies that allocate macromolecule resources,we developed a Python-based workflow(ECMpy)that constructs an enzyme-constrained model.This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions.However,this procedure de-mands manual collection of enzyme kinetic parameter information and subunit composition details,making it rather user-unfriendly.In this work,we’ve enhanced the ECMpy toolbox to version 2.0,broadening its scope to automatically generate ecGEMs for a wider array of organisms.ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters,which significantly enhances parameter coverage.Additionally,ECMpy 2.0 introduces common analytical and visualization features for ecModels,rendering computational results more user accessible.Furthermore,ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering.ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package(https://pypi.org/project/ECMpy/).展开更多
基金the National Key Research and Development Program of China(2021YFC2100700)National Natural Science Foundation of China(32300529,32201242,12326611)+2 种基金Tianjin Synthetic Biotechnology Innovation Capacity Improvement Projects(TSBICIPPTJS-001,TSBICIP-PTJS-002,TSBICIP-PTJJ-007)Major Program of Haihe Laboratory of Synthetic Biology(22HHSWSS00021)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0480000).
文摘Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism behaviors.However,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields as substrate uptake rates rise.This divergence from experimental measurements prompted the creation of enzyme-constrained models(ecModels)for various species,successfully enhancing chemical pro-duction.Building upon studies that allocate macromolecule resources,we developed a Python-based workflow(ECMpy)that constructs an enzyme-constrained model.This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions.However,this procedure de-mands manual collection of enzyme kinetic parameter information and subunit composition details,making it rather user-unfriendly.In this work,we’ve enhanced the ECMpy toolbox to version 2.0,broadening its scope to automatically generate ecGEMs for a wider array of organisms.ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters,which significantly enhances parameter coverage.Additionally,ECMpy 2.0 introduces common analytical and visualization features for ecModels,rendering computational results more user accessible.Furthermore,ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering.ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package(https://pypi.org/project/ECMpy/).
