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  Interacting cells driving the evolution of multicellular life cycles

Gao, Y., Traulsen, A., & Pichugin, Y. (2019). Interacting cells driving the evolution of multicellular life cycles. PLoS Computational Biology, 15(5): e1006987. doi:10.1371/journal.pcbi.1006987.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-D293-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-D294-8
Genre: Journal Article

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Gao, Yuanxiao1, Author              
Traulsen, Arne1, Author              
Pichugin, Yuriy1, Author              
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1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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 Abstract: Author summary Multicellular organisms are ubiquitous. But how did the first multicellular organisms arise? It is typically argued that this occurred due to benefits coming from interactions between cells. One example of such interactions is the division of labour. For instance, colonial cyanobacteria delegate photosynthesis and nitrogen fixation to different cells within the colony. In this way, the colony gains a growth advantage over unicellular cyanobacteria. However, not all cell interactions favour multicellular life. Cheater cells residing in a colony without any contribution will outgrow other cells. Then, the growing burden of cheaters may eventually destroy the colony. Here, we ask what kinds of interactions promote the evolution of multicellularity? We investigated all interactions captured by pairwise games and for each of them, we look for the evolutionarily optimal life cycle: How big should the colony grow and how should it split into offspring cells or colonies? We found that multicellularity can evolve with interactions far beyond cooperation or division of labour scenarios. More surprisingly, most of the life cycles found fall into either of two categories: A parent colony splits into two multicellular parts, or it splits into multiple independent cells.

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Language(s): eng - English
 Dates: 2019-01-182019-03-292019-05-142019-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1371/journal.pcbi.1006987
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (5) Sequence Number: e1006987 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1