ReviewThe Biochemical journal2013
Experimental evolution of protein-protein interaction networks.
Review in The Biochemical journal, 2013. 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
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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
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.
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Who cites it
11 citing papers in PubMed.
- Cellular assays identify barriers impeding iron-sulfur enzyme activity in a non-native prokaryotic host.eLife · 2022Article
- Resurrecting ancestral genes in bacteria to interpret ancient biosignatures.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017Article
- Constraining the timing of the Great Oxidation Event within the Rubisco phylogenetic tree.Geobiology · 2017Article
- Functional Constraints on Replacing an Essential Gene with Its Ancient and Modern Homologs.mBio · 2017Article
- Protein interaction evolution from promiscuity to specificity with reduced flexibility in an increasingly complex network.Scientific reports · 2017Article
- Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein.Journal of molecular evolution · 2017Article
- In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.BMC systems biology · 2016Article
- Selective pressure against horizontally acquired prokaryotic genes as a driving force of plastid evolution.Scientific reports · 2016Article
- A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.Frontiers in genetics · 2016Article
- Global multiple protein-protein interaction network alignment by combining pairwise network alignments.BMC bioinformatics · 2015Article
- Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria.Frontiers in genetics · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.
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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.