ArticleMicrobiology spectrum2023
Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.
Article in Microbiology spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.PLoS computational biology · 2025Article
- Implementation of aComputational and structural biotechnology journal · 2025Article
- ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.Synthetic and systems biotechnology · 2024Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
importanceThe metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.