Evidence map›Paper›PMID 35307408›Full record

ArticleJournal of theoretical biology2022

Genetic networks encode secrets of their past.

Peter Crawford-Kahrl, Robert R Nerem, Bree Cummins, Tomas Gedeon

Abstract read
In one paragraph

Article in Journal of theoretical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peter Crawford-KahrlCourant Institute of Mathematical Sciences, New York University, New York, NY, USA.
Robert R NeremInstitute for Quantum Science and Technology, University of Calgary, Alberta T2N 1N4, Canada.
Bree CumminsDepartment of Mathematical Sciences, Montana State University, Bozeman, MT, USA.
Tomas GedeonDepartment of Mathematical Sciences, Montana State University, Bozeman, MT, USA.

Funding

Needle in a haystack: effective search for dynamics across the space of networksR01GM126555 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI MISCHAIKOW, KONSTANTIN · 2017 to 2021
$1.8M
NIGMS NIH HHS R01 GM126555
6 · The paper itself

Abstract

Research shows that gene duplication followed by either repurposing or removal of duplicated genes is an important contributor to evolution of gene and protein interaction networks. We aim to identify which characteristics of a network can arise through this process, and which must have been produced in a different way. To model the network evolution, we postulate vertex duplication and edge deletion as evolutionary operations on graphs. Using the novel concept of an ancestrally distinguished subgraph, we show how features of present-day networks require certain features of their ancestors. In particular, ancestrally distinguished subgraphs cannot be introduced by vertex duplication. Additionally, if vertex duplication and edge deletion are the only evolutionary mechanisms, then a graph's ancestrally distinguished subgraphs must be contained in all of the graph's ancestors. We analyze two experimentally derived genetic networks and show that our results accurately predict lack of large ancestrally distinguished subgraphs, despite this feature being statistically improbable in associated random networks. This observation is consistent with the hypothesis that these networks evolved primarily via vertex duplication. The tools we provide open the door for analyzing ancestral networks using current networks. Our results apply to edge-labeled (e.g. signed) graphs which are either undirected or directed.

Indexed as

Gene Regulatory NetworksProtein Interaction MapsGene DuplicationGenetic networksGraph similarityMolecular evolutionNetwork models

Identifiers

PMID35307408
PMCPMC9037300

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Registered trials

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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.