ArticleNature chemical biology2020
A quantitative method for proteome reallocation using minimal regulatory interventions.
Article in Nature chemical biology, 2020. 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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Toward a systems metabolic engineering approach to improve thermotolerance of Escherichia coli.Microbial cell factories · 2026Review
- Biomanufacturing Potential of Streamlined Cells.Biotechnology and bioengineering · 2025Article
- Gene Expression Depends on the Interplay Among Growth, Resource Biogenesis, and Nutrient Quality.ACS synthetic biology · 2025Article
- CFSA: Comparative flux sampling analysis as a guide for strain design.Metabolic engineering communications · 2024Article
- Impact of fleQ Deficiency on Resource Allocation and Heterologous Gene Expression in Pseudomonas putida Across Various Growth Media.Microbial biotechnology · 2024Article
- Enhancing Escherichia coli abiotic stress resistance through ornithine lipid formation.Applied microbiology and biotechnology · 2024Article
- Resource-aware construct design in mammalian cells.Nature communications · 2023Article
- Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy?Microbial biotechnology · 2023Article
- Stability, robustness, and containment: preparing synthetic biology for real-world deployment.Current opinion in biotechnology · 2023Review
- Glucose consumption rate-dependent transcriptome profiling of Escherichia coli provides insight on performance as microbial factories.Microbial cell factories · 2022Article
- Regulatory perturbations of ribosome allocation in bacteria reshape the growth proteome with a trade-off in adaptation capacity.iScience · 2022Article
- Global pleiotropic effects in adaptively evolvedOpen biology · 2022Article
- System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks.Frontiers in bioengineering and biotechnology · 2022Article
- Review
- Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity.International microbiology : the official journal of the Spanish Society for Microbiology · 2021Article
- Intelligent host engineering for metabolic flux optimisation in biotechnology.The Biochemical journal · 2021Review
- Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis.BMC bioinformatics · 2021Review
- Article
- Quantitative proteomics characterization of cancer biomarkers and treatment.Molecular therapy oncolytics · 2021Review
- Plasmid DNA Production in Proteome-ReducedMicroorganisms · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Engineering resource allocation in biological systems is an ongoing challenge. Organisms allocate resources for ensuring survival, reducing the productivity of synthetic biology functions. Here we present a new approach for engineering the resource allocation of Escherichia coli by rationally modifying its transcriptional regulatory network. Our method (ReProMin) identifies the minimal set of genetic interventions that maximizes the savings in cell resources. To this end, we categorized transcription factors according to the essentiality of its targets and we used proteomic data to rank them. We designed the combinatorial removal of transcription factors that maximize the release of resources. Our resulting strain containing only three mutations, theoretically releasing 0.5% of its proteome, had higher proteome budget, increased production of an engineered metabolic pathway and showed that the regulatory interventions are highly specific. This approach shows that combining proteomic and regulatory data is an effective way of optimizing strains using conventional molecular methods.
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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.