In silico prediction of metabolic trait robustness in microbial cells
- 1Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland
- 2Department of Computer Science, Department of Organismal and Evolutionary Biology, University of Helsinki, Helsinki, Finland
Abstract
Microbial strains undergo notable biomass expansion in industrial applications subjecting populations to Darwinian selection. The consequent adaptive evolution may threaten the often tediously developed desired metabolic traits of the strains, such as flavor or platform chemical production. Yet, it remains unresolved how to predict the evolutionary trait robustness. Here, we propose TRAEV (Trait Robustness Against EVolution), a computational framework for in silico prediction of evolutionary robustness of metabolic traits and trajectories of desired traits in application environments. TRAEV uses constraint-based metabolic models for predicting environment-dependent trait-fitness dependencies and accounts for the stochasticity of adaptive evolution by using Monte Carlo-based perturbation analysis. First, TRAEV predicts the immediate metabolic phenotype in the new environment, and then, the subsequent evolutionary trajectories of fitness and desired trait. Metabolic trait robustness is quantified from the evolutionary trajectories as two scores: Robustness Score (RS) and Trade-off Score (TS). RS represents trait robustness and the overall trait level during evolutionary adaptation, whereas TS captures trait–fitness trade-offs. We validated TRAEV by predicting relative evolutionary robustness of heterologous pigmentation of two engineered Saccharomyces cerevisiae strains and two desired aroma generation traits by multiple wine strains of S. cerevisiae if they were developed via a laboratory selection process. Thus, we showed how TRAEV predictions could guide strain development. TRAEV can be integrated into computational strain design workflows across microbial strains, desired metabolic traits, and application environments. Ultimately, model-predicted evolutionary robustness of desired metabolic traits can guide both strain and process development and help enhancing the economic attractiveness of industrial applications using microbial cells. Author summary: Microbial cells developed to express desired traits by genetic engineering or selection processes are used in a wide range of applications, from biotechnological chemical production to food and beverage fermentations. However, when the cells replicate in the application environment the traits are subject to Darwinian selection and consequent adaptive evolution. As a result, the desired traits can be compromised. To in silico predict the evolutionary adaptation of desired traits we developed TRAEV (Trait Robustness Against EVolution), a computational framework for assessing evolutionary trajectories and robustness of desired metabolic traits in application environments. TRAEV integrates constraint-based modeling and Monte Carlo-based perturbation analysis. We validated and demonstrated the usability of TRAEV by predicting the evolutionary robustness of heterologous pigmentation of two engineered Saccharomyces cerevisiae strains, and aroma generation by multiple S. cerevisiae wine strains if they were developed by laboratory selection. Usable across microbial strains, metabolic traits, and application environments, we believe that TRAEV can contribute to the development of economically attractive industrial applications using robust traits expressing microbial cells.
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BibTeX
@article{gu2026silico,
title = {{In silico prediction of metabolic trait robustness in microbial cells}},
author = {Chunhao Gu and Ville Mustonen and Paula Jouhten},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1014864},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014864},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - In silico prediction of metabolic trait robustness in microbial cells
AU - Chunhao Gu
AU - Ville Mustonen
AU - Paula Jouhten
PY - 2026
DA - 2026/10/27
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014864
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014864
AB - Microbial strains undergo notable biomass expansion in industrial applications subjecting populations to Darwinian selection. The consequent adaptive evolution may threaten the often tediously developed desired metabolic traits of the strains, such as flavor or platform chemical production. Yet, it remains unresolved how to predict the evolutionary trait robustness. Here, we propose TRAEV (Trait Robustness Against EVolution), a computational framework for in silico prediction of evolutionary robustness of metabolic traits and trajectories of desired traits in application environments. TRAEV uses constraint-based metabolic models for predicting environment-dependent trait-fitness dependencies and accounts for the stochasticity of adaptive evolution by using Monte Carlo-based perturbation analysis. First, TRAEV predicts the immediate metabolic phenotype in the new environment, and then, the subsequent evolutionary trajectories of fitness and desired trait. Metabolic trait robustness is quantified from the evolutionary trajectories as two scores: Robustness Score (RS) and Trade-off Score (TS). RS represents trait robustness and the overall trait level during evolutionary adaptation, whereas TS captures trait–fitness trade-offs. We validated TRAEV by predicting relative evolutionary robustness of heterologous pigmentation of two engineered Saccharomyces cerevisiae strains and two desired aroma generation traits by multiple wine strains of S. cerevisiae if they were developed via a laboratory selection process. Thus, we showed how TRAEV predictions could guide strain development. TRAEV can be integrated into computational strain design workflows across microbial strains, desired metabolic traits, and application environments. Ultimately, model-predicted evolutionary robustness of desired metabolic traits can guide both strain and process development and help enhancing the economic attractiveness of industrial applications using microbial cells. Author summary: Microbial cells developed to express desired traits by genetic engineering or selection processes are used in a wide range of applications, from biotechnological chemical production to food and beverage fermentations. However, when the cells replicate in the application environment the traits are subject to Darwinian selection and consequent adaptive evolution. As a result, the desired traits can be compromised. To in silico predict the evolutionary adaptation of desired traits we developed TRAEV (Trait Robustness Against EVolution), a computational framework for assessing evolutionary trajectories and robustness of desired metabolic traits in application environments. TRAEV integrates constraint-based modeling and Monte Carlo-based perturbation analysis. We validated and demonstrated the usability of TRAEV by predicting the evolutionary robustness of heterologous pigmentation of two engineered Saccharomyces cerevisiae strains, and aroma generation by multiple S. cerevisiae wine strains if they were developed by laboratory selection. Usable across microbial strains, metabolic traits, and application environments, we believe that TRAEV can contribute to the development of economically attractive industrial applications using robust traits expressing microbial cells.
SN - 1553-7358
SN - 1553-734X
ER -