ArticleNature biotechnology2014
A load driver device for engineering modularity in biological networks.
Article in Nature biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Predictive genetic circuit design for phenotype reprogramming in plants.Nature communications · 2025Article
- Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.Nucleic acids research · 2024Article
- Context-dependent redesign of robust synthetic gene circuits.Trends in biotechnology · 2024Review
- Customizing cellular signal processing by synthetic multi-level regulatory circuits.Nature communications · 2023Review
- Competition and evolutionary selection among core regulatory motifs in gene expression control.Nature communications · 2023Article
- Engineering synthetic phosphorylation signaling networks in human cells.bioRxiv : the preprint server for biology · 2023Article
- A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria.Nature communications · 2023Article
- Design of Oscillatory Networks through Post-Translational Control of Network Components.Synthetic biology and engineering · 2023Article
- Optimization of CRISPR-Cas system for clinical cancer therapy.Bioengineering & translational medicine · 2023Review
- A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria.bioRxiv : the preprint server for biology · 2023Article
- Design, Mechanical Properties, and Dynamics of Synthetic DNA Filaments.Bioconjugate chemistry · 2023Review
- Synthetic neuromorphic computing in living cells.Nature communications · 2022Article
- Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles.Nature communications · 2022Article
- Accurate characterization of dynamic microbial gene expression and growth rate profiles.Synthetic biology (Oxford, England) · 2022Article
- Competitive dCas9 binding as a mechanism for transcriptional control.Molecular systems biology · 2021Article
- Gene-circuit therapy on the horizon: synthetic biology tools for engineered therapeutics.Acta biochimica Polonica · 2021Review
- An engineered protein-phosphorylation toggle network with implications for endogenous network discovery.Science (New York, N.Y.) · 2021Article
- Context-aware synthetic biology by controller design: Engineering the mammalian cell.Cell systems · 2021Review
- A call for caution in analysing mammalian co-transfection experiments and implications of resource competition in data misinterpretation.Nature communications · 2021Article
- Automated design of synthetic microbial communities.Nature communications · 2021Article
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5 authors.
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Abstract
The behavior of gene modules in complex synthetic circuits is often unpredictable. After joining modules to create a circuit, downstream elements (such as binding sites for a regulatory protein) apply a load to upstream modules that can negatively affect circuit function. Here we devised a genetic device named a load driver that mitigates the impact of load on circuit function, and we demonstrate its behavior in Saccharomyces cerevisiae. The load driver implements the design principle of timescale separation: inclusion of the load driver's fast phosphotransfer processes restores the capability of a slower transcriptional circuit to respond to time-varying input signals even in the presence of substantial load. Without the load driver, we observed circuit behavior that suffered from a 76% delay in response time and a 25% decrease in system bandwidth due to load. With the addition of a load driver, circuit performance was almost completely restored. Load drivers will serve as fundamental building blocks in the creation of complex, higher-level genetic circuits.
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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.