ArticleNature communications2023
A blueprint for a synthetic genetic feedback optimizer.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Tuning evolvability via plasmid copy number and regulatory architecture.Nature communications · 2025Article
- Optimisation of gene expression noise for cellular persistence against lethal events.Journal of theoretical biology · 2025Article
- Toward predictable universal genetic circuit design.Quantitative biology (Beijing, China) · 2024Article
- The Blueprint of Logical Decisions in a NF-κB Signaling System.ACS omega · 2024Article
- Competition and evolutionary selection among core regulatory motifs in gene expression control.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.
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Registered trials
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