ArticleProceedings of the National Academy of Sciences of the United States of America2012
Long-term model predictive control of gene expression at the population and single-cell levels.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed.
- Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability.eLife · 2024Article
- Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.Nature communications · 2023Article
- A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch.Scientific reports · 2023Article
- Determining growth rates from bright-field images of budding cells through identifying overlaps.eLife · 2023Article
- Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches.Nature communications · 2023Article
- Fungi-on-a-Chip: microfluidic platforms for single-cell studies on fungi.FEMS microbiology reviews · 2022Review
- Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC.BMC biology · 2022Article
- Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.ACS synthetic biology · 2022Article
- CyberSco.Py an open-source software for event-based, conditional microscopy.Scientific reports · 2022Article
- Enabling reactive microscopy with MicroMator.Nature communications · 2022Article
- A convolutional neural network for segmentation of yeast cells without manual training annotations.Bioinformatics (Oxford, England) · 2022Article
- Cybergenetic control of microbial community composition.Frontiers in bioengineering and biotechnology · 2022Review
- Exploiting Information and Control Theory for Directing Gene Expression in Cell Populations.Frontiers in microbiology · 2022Review
- Rapid prototyping and design of cybergenetic single-cell controllers.Nature communications · 2021Article
- Noise suppression in stochastic genetic circuits using PID controllers.PLoS computational biology · 2021Article
- Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control.Nature communications · 2021Article
- Dynamical Modeling of Optogenetic Circuits in Yeast for Metabolic Engineering Applications.ACS synthetic biology · 2021Article
- ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices.ACS omega · 2021Article
- The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments.Bioengineering (Basel, Switzerland) · 2020Review
- A convolutional neural network segments yeast microscopy images with high accuracy.Nature communications · 2020Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here, we show that, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmostress responsive promoter and playing with the osmolarity of the cells environment, we show that long-term control can, indeed, be achieved for both time-constant and time-varying target profiles at the population and even the single-cell levels. Importantly, we provide evidence that real-time control can dynamically limit the effects of gene expression stochasticity. We anticipate that our method will be useful to quantitatively probe the dynamic properties of cellular processes and drive complex, synthetically engineered networks.
Indexed as
Identifiers
What OpenQuestion holds
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.