ArticleCell systems2024
A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.
Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evolutionary causes and consequences of gene duplication.Nature reviews. Genetics · 2026Review
- Systematic CRISPRi screening reveals genetic modulators ofbioRxiv : the preprint server for biology · 2026Article
- Counting-based inference of mutant growth rates from pooled sequencing across growth regimes.bioRxiv : the preprint server for biology · 2026Article
- Phenotypic dominance emerges from activity fitness functions and molecular interactions.Genetics · 2026Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- Dissecting the effects of single amino acid substitutions in SARS-CoV-2 Mpro.Protein science : a publication of the Protein Society · 2025Article
- The rise and future of CRISPR-based approaches for high-throughput genomics.FEMS microbiology reviews · 2024Review
- Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery.The Biochemical journal · 2024Review
- CRISPRi functional genomics in bacteria and its application to medical and industrial research.Microbiology and molecular biology reviews : MMBR · 2024Review
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4 authors.
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Abstract
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
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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.