ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models

Zhitao Mao; Jinhui Niu; Jianxiao Zhao; Yuanyuan Huang; Ke Wu; Liyuan Yun; Jirun Guan; Qianqian Yuan; Xiaoping Liao; Zhiwen Wang; Hongwu Ma

doi:10.1016/j.synbio.2024.04.005

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models

Synth Syst Biotechnol. 2024 Apr 10;9(3):494-502. doi: 10.1016/j.synbio.2024.04.005. eCollection 2024 Sep.

Authors

Zhitao Mao^{1

2}, Jinhui Niu^{1

2}, Jianxiao Zhao^{1

2

3}, Yuanyuan Huang^{1

2

4}, Ke Wu⁵, Liyuan Yun⁶, Jirun Guan^{1

2}, Qianqian Yuan^{1

2}, Xiaoping Liao^{1

2

7}, Zhiwen Wang³, Hongwu Ma^{1

2}

Affiliations

¹ Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
² National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China.
³ Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China.
⁴ College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China.
⁵ Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
⁶ Tianjin Agricultural College, Tianjin, 300384, China.
⁷ Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China.

Abstract

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 production. 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 demands 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/).