dc.contributor.author
Devalle, F.
dc.contributor.author
Roxin, A.
dc.date.accessioned
2025-03-11T11:41:34Z
dc.date.available
2025-03-11T11:41:34Z
dc.date.issued
2024-12-11
dc.identifier.uri
http://hdl.handle.net/2072/482420
dc.description.abstract
The position of cells during development is constantly subject to noise, i.e. cell-level noise. We do not yet fully understand how cell-level noise coming from processes such as cell division or movement leads to morphological noise, i.e. morphological differences between genetically identical individuals developing in the same environment. To address this question we constructed a large ensemble of random genetic networks regulating cell behaviors (contraction, adhesion, etc.) and cell signaling. We simulated them with a general computational model of development, EmbryoMaker. We identified and studied the dynamics, under cell-level noise, of those networks that lead to the development of animal-like morphologies from simple blastula-like initial conditions. We found that growth by cell division is a major contributor to morphological noise. Self-activating gene network loops also amplified cell-level noise into morphological noise while long-range signaling and epithelial stiffness tended to reduce morphological noise.
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dc.description.sponsorship
AR acknowledges “Retos” project RTI2018-097570-B-100 from the Ministry of Science and Innovation of the Spanish Government, Flag-Era project from the EU for the Human Brain Project HIPPOPLAST (Era-ICT code PCI2018-093095), “Red de Investigación” RED2018-102323-T from the Ministry of Science and Innovation of the Spanish overnment. This work is supported by the Spanish State Research Agency, through the Severo Ochoa and Maria de Maeztu program for Centers and Units of Excellence in R&D (CEX2020-001084-M). We thank CERCA Program/Generalitat de Catalunya for institutional support. We acknowledge very helpful discussions with Marina Vegué, Toni Guillamon and Ernest Montrbrió.
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dc.format.extent
31 p.
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dc.relation.ispartof
Journal of Computational Neuroscience
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dc.rights
Attribution 4.0 International
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject.other
Synaptic plasticity
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dc.subject.other
Fluctuations
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dc.subject.other
Network connectivity
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dc.subject.other
Perceptual learning
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dc.title
Fluctuation-driven plasticity allows for flexible rewiring of neuronal assemblies
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dc.type
info:eu-repo/semantics/article
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dc.description.version
info:eu-repo/semantics/acceptedVersion
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dc.identifier.doi
10.1007/s10827-024-00885-z
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dc.rights.accessLevel
info:eu-repo/semantics/openAccess