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Enrique M. Muro

@emmuro.bsky.social
7 followers 6 following 2 posts

Computational biologist at the Johannes Gutenberg University in Mainz, Germany cbdm-01.zdv.uni-mainz.de/~muro github.com/emuro

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Reposted by Enrique M. Muro
Proceedings of the National Academy of Sciences @pnas.org · 14/04/2026
Join us in celebrating our 2025 Cozzarelli Prize–winning paper for Class II: Biological Sciences, “The emergence of eukaryotes as an evolutionary algorithmic phase transition.” Read the article here: ow.ly/wNur50YImie
2025 Cozzarelli Prize Class II Winner: “The emergence of eukaryotes as an evolutionary algorithmic phase transition.” Authors: Enrique M. Muro, Fernando J. Ballesteros, Bartolo Luque, and Jordi Bascompte
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Enrique M. Muro @emmuro.bsky.social · 11/03/2026
Our work The emergence of eukaryotes as an evolutionary algorithmic phase transition has won the 2025 Cozzarelli Prize in Biological Sciences, awarded by the National Academy of Sciences. More details: www.nasonline.org/news/pnas-20... Reference to the work: www.pnas.org/doi/10.1073/...
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Reposted by Enrique M. Muro
Proceedings of the National Academy of Sciences @pnas.org · 29/04/2025
One of the most-viewed PNAS articles in the last week is “The emergence of eukaryotes as an evolutionary algorithmic phase transition.” Explore the article here: www.pnas.org/doi/10.1073/... For more trending articles, visit ow.ly/Me2U50SkLRZ.
Gene and protein length distributions are lognormal. Length distributions for genes (blue, measured in number of base pairs) and their corresponding proteins (orange, measured in number of amino acids) for Danio rerio (zebrafish). Note that lengths are represented on a logarithmic scale. The red curves are fits to lognormal distributions.
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Reposted by Enrique M. Muro
Science X / Phys.org @sciencex.bsky.social · 17/04/2025
A phase transition in gene length distribution around 2.6 billion years ago is identified as a key event marking the emergence of eukaryotic cells and a major increase in biological complexity. doi.org/g9fxfk
phys.org
An evolutionary algorithmic phase transition 2.6 billion years ago may have sparked the emergence of eukaryotic cells
An international collaboration between four scientists from Mainz, Valencia, Madrid, and Zurich has published new research in the Proceedings of the National Academy of Sciences, shedding light on the most significant increase in complexity in the history of life's evolution on Earth: the origin of the eukaryotic cell.
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Reposted by Enrique M. Muro
Mainz University @mainzuniversity.bsky.social · 17/04/2025
#EukaryoticCells emerged as evolutionary algorithmic phase transition / #Eukaryogenesis happened abruptly, led by tension of increasing gene length and constraints on producing longer #proteins 👉 press.uni-mainz.de/eukaryotic-c... #EvolutionaryBiology #ComputerBiology #eukaryotic #genes #evolution
The image shows the evolution of protein-coding gene length distributions across evolutionary time (ill./©: Fernando J. Ballesteros)
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Reposted by Enrique M. Muro
Johannes Gutenberg-Universität Mainz @unimainz.bsky.social · 17/04/2025
Entstehung komplexen Lebens: Wissenschaftler haben herausgefunden, warum #Eukaryoten entstanden sind / Entwicklung der eukaryotischen Zelle stellt größte Komplexitätssteigerung in der Geschichte des Lebens auf der Erde dar 👉 presse.uni-mainz.de/die-entstehu... #Evolutionsbiologie #Computerbiologie
Die Grafik zeigt die Entwicklung der Längenverteilung proteincodierender Gene im Laufe der Evolution. (Abb./©: Fernando J. Ballesteros)
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Enrique M. Muro @emmuro.bsky.social · 31/03/2025
Our new study on the emergence of the eukaryotic cell as an evolutionary algorithmic phase transition. The transition was continuous yet abrupt... www.pnas.org/doi/10.1073/... See more results, such as the next scaling law in the entire tree of life:
Gene and protein length distributions are lognormal.Scale-invariant relationship between mean gene length and variance. Each dot in this log–log plot represents the genome of a single species (33,627), with H. sapiens highlighted by the circle. Different colors identify major phyletic groups.
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