Evolution of extrema features reveals optimal stimuli for biological state transitions
UMass Chan Affiliations
Department of NeurologyDocument Type
Journal ArticlePublication Date
2018-02-21Keywords
Computational modelsComputational neuroscience
Dynamical systems
Biology
Computational Biology
Computational Neuroscience
Computer Sciences
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Show full item recordAbstract
The ability to define the unique features of an input stimulus needed to control switch-like behavior in biological systems is an important problem in computational biology and medicine. We show in this study how highly complex and intractable optimization problems can be simplified by restricting the search to the signal's extrema as key feature points, and evolving the extrema features towards optimal solutions that closely match solutions derived from gradient-based methods. Our results suggest a model-independent approach for solving a class of optimization problems related to controlling switch-like state transitions.Source
Sci Rep. 2018 Feb 21;8(1):3403. doi: 10.1038/s41598-018-21761-8. Link to article on publisher's site
DOI
10.1038/s41598-018-21761-8Permanent Link to this Item
http://hdl.handle.net/20.500.14038/40587PubMed ID
29467377Related Resources
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Copyright © The Author(s) 2018. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Distribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.1038/s41598-018-21761-8
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Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2018. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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