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Optimal control of gene regulatory networks for morphogen-driven tissue patterning

Pezzotta, Alberto; Briscoe, James; (2023) Optimal control of gene regulatory networks for morphogen-driven tissue patterning. Cell Systems , 14 (11) 940-952.e11. 10.1016/j.cels.2023.10.004. Green open access

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Abstract

The generation of distinct cell types in developing tissues depends on establishing spatial patterns of gene expression. Often, this is directed by spatially graded chemical signals—known as morphogens. In the “French Flag model,” morphogen concentration instructs cells to acquire specific fates. How this mechanism produces timely and organized cell-fate decisions, despite the presence of changing morphogen levels, molecular noise, and individual variability, is unclear. Moreover, feedback is present at various levels in developing tissues, breaking the link between morphogen concentration, signaling activity, and position. Here, we develop an alternative framework using optimal control theory to tackle the problem of morphogen-driven patterning: intracellular signaling is derived as the control strategy that guides cells to the correct fate while minimizing a combination of signaling levels and time. This approach recovers experimentally observed properties of patterning strategies and offers insight into design principles that produce timely, precise, and reproducible morphogen patterning.

Type: Article
Title: Optimal control of gene regulatory networks for morphogen-driven tissue patterning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.cels.2023.10.004
Publisher version: https://doi.org/10.1016/j.cels.2023.10.004
Language: English
Additional information: Copyright © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Control theory; morphogen; optimal control; French Flag; dynamical systems; embryonic development
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10182391
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