ArticleMolecular systems biology2022
Predictive evolution of metabolic phenotypes using model-designed environments.
Article in Molecular systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Model-guided chemical environment and metabolic network design to couple pathways with cell fitness.Metabolic engineering communications · 2025Article
- COmmunity and Single Microbe Optimisation System (COSMOS).NPJ systems biology and applications · 2025Article
- Ecology and Evolutionary Biology as Frameworks to Study Wine Fermentations.Microbial biotechnology · 2025Review
- Steering and controlling evolution - from bioengineering to fighting pathogens.Nature reviews. Genetics · 2023Review
- Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts.BMC bioinformatics · 2023Article
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Plant and microbial sciences as key drivers in the development of metabolomics research.Proceedings of the National Academy of Sciences of the United States of America · 2023Review
- Predictive evolution of metabolic phenotypes using model-designed environments.Molecular systems biology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
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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 OpenQuestion graph.