kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters
- 1Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
- 2Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
Abstract
The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters (kcat) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these uncertainty-aware parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the framework’s capability to predict growth rates and fluxes that are consistent with experimental observations and measured proteome fractions, reduce median flux variability by several folds, and yield better-constrained metabolic models by explicitly accounting for the uncertainty inherent in ML-predicted kinetic parameters. kinGEMs successfully generated ecGEMs for 93 models spanning a phylogenetically diverse set of organisms including Gram-negative and Gram-positive bacteria, mycobacteria, parasitic protists, fungi, and mammalian cell lines. These innovations breakdown the barrier to application of ecGEMs to non-model organisms and open new avenues for metabolic engineering and synthetic biology in more industrially relevant hosts. Author summary: Living cells are chemical factories, and computer models of their metabolism can predict how they grow, what they consume, and what they produce, useful for everything from engineering biofuel-producing microbes to understanding disease. The more realistic versions of these models require knowing how fast each cellular enzyme works, but this information is missing for the vast majority of enzymes, especially outside of a handful of well-studied organisms like E. coli. We built kinGEMs, an automated pipeline that fills these gaps using a machine learning model to predict enzyme speeds directly from protein sequence, then automatically fine-tunes those predictions so the resulting model’s growth matches real experimental measurements. We show that models built this way better match independent, real-world data they were never tuned to match, including detailed protein-abundance measurements and a classic metabolic behavior (bacteria switching to fermentation at high growth rates) that conventional models cannot reproduce without manual intervention. We also show the approach works reasonably well across 93 different organisms and successfully reconstructs known metabolic trade-offs in an oil-producing yeast unrelated to E. coli. This provides a scalable way to build usable, enzyme-aware metabolic models for organisms that currently lack the detailed experimental data such models normally require.
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BibTeX
@article{barghout2026kingems,
title = {{kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters}},
author = {Rana A. Barghout and Lya Chiñas Serrano and Benjamin Sanchez-Lengeling and Radhakrishnan Mahadevan},
journal = {PLOS Computational Biology},
year = {2026},
month = nov,
doi = {10.1371/journal.pcbi.1014881},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014881},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters
AU - Rana A. Barghout
AU - Lya Chiñas Serrano
AU - Benjamin Sanchez-Lengeling
AU - Radhakrishnan Mahadevan
PY - 2026
DA - 2026/11/06
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014881
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014881
AB - The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters (kcat) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these uncertainty-aware parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the framework’s capability to predict growth rates and fluxes that are consistent with experimental observations and measured proteome fractions, reduce median flux variability by several folds, and yield better-constrained metabolic models by explicitly accounting for the uncertainty inherent in ML-predicted kinetic parameters. kinGEMs successfully generated ecGEMs for 93 models spanning a phylogenetically diverse set of organisms including Gram-negative and Gram-positive bacteria, mycobacteria, parasitic protists, fungi, and mammalian cell lines. These innovations breakdown the barrier to application of ecGEMs to non-model organisms and open new avenues for metabolic engineering and synthetic biology in more industrially relevant hosts. Author summary: Living cells are chemical factories, and computer models of their metabolism can predict how they grow, what they consume, and what they produce, useful for everything from engineering biofuel-producing microbes to understanding disease. The more realistic versions of these models require knowing how fast each cellular enzyme works, but this information is missing for the vast majority of enzymes, especially outside of a handful of well-studied organisms like E. coli. We built kinGEMs, an automated pipeline that fills these gaps using a machine learning model to predict enzyme speeds directly from protein sequence, then automatically fine-tunes those predictions so the resulting model’s growth matches real experimental measurements. We show that models built this way better match independent, real-world data they were never tuned to match, including detailed protein-abundance measurements and a classic metabolic behavior (bacteria switching to fermentation at high growth rates) that conventional models cannot reproduce without manual intervention. We also show the approach works reasonably well across 93 different organisms and successfully reconstructs known metabolic trade-offs in an oil-producing yeast unrelated to E. coli. This provides a scalable way to build usable, enzyme-aware metabolic models for organisms that currently lack the detailed experimental data such models normally require.
SN - 1553-7358
ER -