ReviewCurrent opinion in biotechnology2011
The role of cellular objectives and selective pressures in metabolic pathway evolution.
Review in Current opinion in biotechnology, 2011. 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.
- Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping.Nature communications · 2023Article
- Application of Microalgal Stress Responses in Industrial Microalgal Production Systems.Marine drugs · 2021Review
- Induction of resistance mechanisms in Rhodotorula toruloides for growth in sugarcane hydrolysate with high inhibitor content.Applied microbiology and biotechnology · 2021Article
- Microalgae for the production of lipid and carotenoids: a review with focus on stress regulation and adaptation.Biotechnology for biofuels · 2018Review
- Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.BMC bioinformatics · 2017Article
- Dynamic interactive events in gene regulation using E. coli dehydrogenase as a model.Functional & integrative genomics · 2015Article
- Limited influence of oxygen on the evolution of chemical diversity in metabolic networks.Metabolites · 2013Article
- Adaptive laboratory evolution -- principles and applications for biotechnology.Microbial cell factories · 2013Review
- Engineering furfural tolerance in Escherichia coli improves the fermentation of lignocellulosic sugars into renewable chemicals.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Network context and selection in the evolution to enzyme specificity.Science (New York, N.Y.) · 2012Article
- Current understanding of the formation and adaptation of metabolic systems based on network theory.Metabolites · 2012Article
- Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.Nature reviews. Microbiology · 2012Review
- Article
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.
Funding
Abstract
Evolution results from molecular-level changes in an organism, thereby producing novel phenotypes and, eventually novel species. However, changes in a single gene can lead to significant changes in biomolecular networks through the gain and loss of many molecular interactions. Thus, significant insights into microbial evolution have been gained through the analysis and comparison of reconstructed metabolic networks. However, challenges remain from reconstruction incompleteness and the inability to experiment with evolution on the timescale necessary for new species to arise. Despite these challenges, experimental laboratory evolution of microbes has provided some insights into the cellular objectives underlying evolution, under the constraints of nutrient availability and the use of mechanisms that protect cells from extreme conditions.
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.