Coarse-graining metabolic networks via feature learning reveals cross-species growth predictors
- 1Westlake University
Abstract
Bacterial growth and the underlying metabolic networks are highly dissimilar across species, posing a fundamental challenge for various bioengineering tasks. For a given species across nutrient environments, growth is regulated via proteome allocation, which gives rise to linear relationships between growth and the fractions of coarse-grained proteome sectors. However, it remains unclear whether there are coarse-grained predictors that correlate with growth across species monocultures in a given environment. Here, simulating genome-scale metabolic models, we discover two conserved growth predictors: the number of nutrients utilized, and a set of metabolic sectors that we identify by coarse-graining metabolic networks via feature learning. We find evidence supporting both predictors in recent experiments; notably, the proteome fractions of the sectors correlate with growth across species in a gene expression dataset. We further analyze coarse-grained networks to show that interactions between sectors improve growth predictions. Our work provides a framework to investigate how coarse-grained growth predictors emerge across diverse metabolic networks.
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BibTeX
@misc{zhu2026coarsegraining,
title = {{Coarse-graining metabolic networks via feature learning reveals cross-species growth predictors}},
author = {A. Zhu and P.-Y. Ho},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.13.725055},
url = {https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Coarse-graining metabolic networks via feature learning reveals cross-species growth predictors
AU - A. Zhu
AU - P.-Y. Ho
PY - 2026
DA - 2026/05/13
PB - bioRxiv
DO - 10.64898/2026.05.13.725055
UR - https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2
AB - Bacterial growth and the underlying metabolic networks are highly dissimilar across species, posing a fundamental challenge for various bioengineering tasks. For a given species across nutrient environments, growth is regulated via proteome allocation, which gives rise to linear relationships between growth and the fractions of coarse-grained proteome sectors. However, it remains unclear whether there are coarse-grained predictors that correlate with growth across species monocultures in a given environment. Here, simulating genome-scale metabolic models, we discover two conserved growth predictors: the number of nutrients utilized, and a set of metabolic sectors that we identify by coarse-graining metabolic networks via feature learning. We find evidence supporting both predictors in recent experiments; notably, the proteome fractions of the sectors correlate with growth across species in a gene expression dataset. We further analyze coarse-grained networks to show that interactions between sectors improve growth predictions. Our work provides a framework to investigate how coarse-grained growth predictors emerge across diverse metabolic networks.
ER -