ArticleIET systems biology2009
Quantifying evolvability in small biological networks.
Article in IET systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
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Who cites it
4 citing papers in PubMed.
- Parameter Evolvability in Gene Expression Models Drives Phenotypic Adaptation.ALIFE : proceedings of the artificial life conference. International Conference on Artificial Life · 2025Article
- Building Qualitative Models of Plant Regulatory Networks with SQUAD.Frontiers in plant science · 2012Article
- Statistical method for revealing form-function relations in biological networks.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- Spectral solutions to stochastic models of gene expression with bursts and regulation.Physical review. E, Statistical, nonlinear, and soft matter physics · 2009Article
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 authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.
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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.