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Biomolecular mechanisms for signal differentiation

Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of ap...

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Autores principales: Alexis, Emmanouil, Schulte, Carolin C.M., Cardelli, Luca, Papachristodoulou, Antonis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649740/
https://www.ncbi.nlm.nih.gov/pubmed/34927021
http://dx.doi.org/10.1016/j.isci.2021.103462
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author Alexis, Emmanouil
Schulte, Carolin C.M.
Cardelli, Luca
Papachristodoulou, Antonis
author_facet Alexis, Emmanouil
Schulte, Carolin C.M.
Cardelli, Luca
Papachristodoulou, Antonis
author_sort Alexis, Emmanouil
collection PubMed
description Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.
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spelling pubmed-86497402021-12-17 Biomolecular mechanisms for signal differentiation Alexis, Emmanouil Schulte, Carolin C.M. Cardelli, Luca Papachristodoulou, Antonis iScience Article Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology. Elsevier 2021-11-17 /pmc/articles/PMC8649740/ /pubmed/34927021 http://dx.doi.org/10.1016/j.isci.2021.103462 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alexis, Emmanouil
Schulte, Carolin C.M.
Cardelli, Luca
Papachristodoulou, Antonis
Biomolecular mechanisms for signal differentiation
title Biomolecular mechanisms for signal differentiation
title_full Biomolecular mechanisms for signal differentiation
title_fullStr Biomolecular mechanisms for signal differentiation
title_full_unstemmed Biomolecular mechanisms for signal differentiation
title_short Biomolecular mechanisms for signal differentiation
title_sort biomolecular mechanisms for signal differentiation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649740/
https://www.ncbi.nlm.nih.gov/pubmed/34927021
http://dx.doi.org/10.1016/j.isci.2021.103462
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