ArticleNature communications2024
Deep model predictive control of gene expression in thousands of single cells.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Harnessing microfluidics for microbiology: from bacteria-host interactions to emerging cancer therapies.Communications biology · 2026Review
- Engineering microbial consortia for distributed signal processing.Nature communications · 2026Article
- Programmable microbial therapeutics: advances in engineered bacteria for targeted in vivo delivery and precision medicine.Journal of advanced research · 2026Review
- Closed-Loop Optogenetic Control in a Microplate Reader.ACS synthetic biology · 2026Article
- Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance.Cell reports · 2026Article
- Overriding Bioprocess Perturbations With a Cell-Machine Interface for Reliable Microbial Stress-Response Control.Microbial biotechnology · 2026Article
- Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Single-cell analysis and control of microbial systems using optogenetics.Current opinion in microbiology · 2026Review
- A stationary phase-specific bacterial green light sensor for enhancing metabolite production.Nature communications · 2025Article
- Closed-loop optogenetic control of cell biology enables outcome-driven microscopy.Nature communications · 2025Article
- Acousto-optogenetics bandpass stabilizer: A programmable platform for mapping single-cell phenotypic life trajectories.Materials today. Bio · 2025Article
- Actionable Forecasting as a Determinant of Biological Adaptation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Physical communication pathways in bacteria: an extra layer to quorum sensing.Biophysical reviews · 2025Review
- MyD88 inhibitor TJ-M2010-5 alleviates spleen impairment and inflammation by inhibiting the PI3K/miR-136-5p/AKT3 pathway in the early infection of Trichinella spiralis.Veterinary research · 2025Article
- LowTempGAL: a highly responsive low temperature-inducible GAL system in Saccharomyces cerevisiae.Nucleic acids research · 2024Article
- Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.Nature communications · 2023Article
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- An optogenetic toolkit for light-inducible antibiotic resistance.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.
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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.