ReviewACS synthetic biology2012
A synthetic biology approach to understanding cellular information processing.
Review in ACS synthetic biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- A robust ultrasensitive transcriptional switch in noisy cellular environments.NPJ systems biology and applications · 2024Article
- Designed cell consortia as fragrance-programmable analog-to-digital converters.Nature chemical biology · 2017Article
- Engineering robust and tunable spatial structures with synthetic gene circuits.Nucleic acids research · 2017Article
- Chemical reaction networks for computing logarithm.Synthetic biology (Oxford, England) · 2017Article
- Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA.Journal of chemical information and modeling · 2016Article
- Coupling spatial segregation with synthetic circuits to control bacterial survival.Molecular systems biology · 2016Article
- Article
- Multi-Faceted Characterization of a Novel LuxR-Repressible Promoter Library for Escherichia coli.PloS one · 2015Article
- Synthetic biology in mammalian cells: next generation research tools and therapeutics.Nature reviews. Molecular cell biology · 2014Review
- Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions.Frontiers in plant science · 2014Review
- The biological microprocessor, or how to build a computer with biological parts.Computational and structural biotechnology journal · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The survival of cells and organisms requires proper responses to environmental signals. These responses are governed by cellular networks, which serve to process diverse environmental cues. Biological networks often contain recurring network topologies called "motifs". It has been recognized that the study of such motifs allows one to predict the response of a biological network and thus cellular behavior. However, studying a single motif in complete isolation of all other network motifs in a natural setting is difficult. Synthetic biology has emerged as a powerful approach to understanding the dynamic properties of network motifs. In addition to testing existing theoretical predictions, construction and analysis of synthetic gene circuits has led to the discovery of novel motif dynamics, such as how the combination of simple motifs can lead to autonomous dynamics or how noise in transcription and translation can affect the dynamics of a motif. Here, we review developments in synthetic biology as they pertain to increasing our understanding of cellular information processing. We highlight several types of dynamic behaviors that diverse motifs can generate, including the control of input/output responses, the generation of autonomous spatial and temporal dynamics, as well as the influence of noise in motif dynamics and cellular behavior.
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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.