Evidence map›Paper›PMID 40146859›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

The emergence of eukaryotes as an evolutionary algorithmic phase transition.

Enrique M Muro, Fernando J Ballesteros, Bartolo Luque, Jordi Bascompte

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. An algorithmic constraint at the transition to complex life.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Enrique M MuroInstitute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz DE-55128, Germany.
Fernando J BallesterosAstronomical Observatory, University of Valencia, Paterna, Valencia E-46980, Spain.ORCID 0000-0003-1053-8384
Bartolo LuqueDepartment of Applied Mathematics and Statistics, Escuela Técnica Superior de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Madrid E-28040, Spain.
Jordi BascompteDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich CH-8057, Switzerland.ORCID 0000-0002-0108-6411

Funding

Conselleria d'Educacio, Universitats i Ocupacio de la Generalitat Valenciana Excellence Prometeo/2020/085European Union NextGenerationEU 922 and Generalitat Valenciana ASFAE/2022/025Ministerio de Ciencia, Innovación y Universidades (MICIU) PID2019-109592GBI00Ministerio de Ciencia, Innovación y Universidades (MICIU) PID2020-113737GB-I00Swiss National Science Foundation 310030_197201
6 · The paper itself

Abstract

The origin of eukaryotes represents one of the most significant events in evolution since it allowed the posterior emergence of multicellular organisms. Yet, it remains unclear how existing regulatory mechanisms of gene activity were transformed to allow this increase in complexity. Here, we address this question by analyzing the length distribution of proteins and their corresponding genes for 6,519 species across the tree of life. We find a scale-invariant relationship between gene mean length and variance maintained across the entire evolutionary history. Using a simple model, we show that this scale-invariant relationship naturally originates through a simple multiplicative process of gene growth. During the first phase of this process, corresponding to prokaryotes, protein length follows gene growth. At the onset of the eukaryotic cell, however, mean protein length stabilizes around 500 amino acids. While genes continued growing at the same rate as before, this growth primarily involved noncoding sequences that complemented proteins in regulating gene activity. Our analysis indicates that this shift at the origin of the eukaryotic cell was due to an algorithmic phase transition equivalent to that of certain search algorithms triggered by the constraints in finding increasingly larger proteins.

Indexed as

AlgorithmsBiological EvolutionEukaryotaEukaryotic CellsEvolution, MolecularModels, GeneticProteinsProteinseukaryotic cellgene lengthprotein lengthscaling law

Identifiers

PMID40146859
PMCPMC12002324

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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.