SUMMARY: Metabolic Engineering aims to favor the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, that covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. AVAILABILITY AND IMPLEMENTATION: MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Metabolic Engineering aims to favor the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, that covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. AVAILABILITY AND IMPLEMENTATION:MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Rachel H Ng; Jihoon W Lee; Priyanka Baloni; Christian Diener; James R Heath; Yapeng Su Journal: Front Oncol Date: 2022-07-07 Impact factor: 5.738