ReviewCurrent opinion in microbiology2015
How to train your microbe: methods for dynamically characterizing gene networks.
Review in Current opinion in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 31 citations in OpenAlex.
- Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance.Cell reports · 2026Article
- Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation.PLoS computational biology · 2025Article
- A predictive model of gene expression reveals the role of network motifs in the mating response of yeast.Science signaling · 2021Article
- Exploiting natural chemical photosensitivity of anhydrotetracycline and tetracycline for dynamic and setpoint chemo-optogenetic control.Nature communications · 2020Article
- A yeast optogenetic toolkit (yOTK) for gene expression control in Saccharomyces cerevisiae.Biotechnology and bioengineering · 2020Article
- Optogenetic control of Bacillus subtilis gene expression.Nature communications · 2019Article
- Engineering Diagnostic and Therapeutic Gut Bacteria.Microbiology spectrum · 2017Review
- Oscillatory stimuli differentiate adapting circuit topologies.Nature methods · 2017Article
- Optogenetic Module for Dichromatic Control of c-di-GMP Signaling.Journal of bacteriology · 2017Article
- A photoconversion model for full spectral programming and multiplexing of optogenetic systems.Molecular systems biology · 2017Article
- Article
- Probe discovery: Disentangling gene networks.Nature chemical biology · 2016Article
- High-throughput microfluidics to control and measure signaling dynamics in single yeast cells.Nature protocols · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.
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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.