Cargando…

Chemical reaction networks for computing logarithm

Living cells constantly process information from their living environment. It has recently been shown that a number of cell signaling mechanisms (e.g. G protein-coupled receptor and epidermal growth factor) can be interpreted as computing the logarithm of the ligand concentration. This suggests that...

Descripción completa

Detalles Bibliográficos
Autor principal: Chou, Chun Tung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513738/
https://www.ncbi.nlm.nih.gov/pubmed/32995503
http://dx.doi.org/10.1093/synbio/ysx002
_version_ 1783586441565044736
author Chou, Chun Tung
author_facet Chou, Chun Tung
author_sort Chou, Chun Tung
collection PubMed
description Living cells constantly process information from their living environment. It has recently been shown that a number of cell signaling mechanisms (e.g. G protein-coupled receptor and epidermal growth factor) can be interpreted as computing the logarithm of the ligand concentration. This suggests that logarithm is a fundamental computation primitive in cells. There is also an increasing interest in the synthetic biology community to implement analog computation and computing the logarithm is one such example. The aim of this article is to study how the computation of logarithm can be realized using chemical reaction networks (CRNs). CRNs cannot compute logarithm exactly. A standard method is to use power series or rational function approximation to compute logarithm approximately. Although CRNs can realize these polynomial or rational function computations in a straightforward manner, the issue is that in order to be able to compute logarithm accurately over a large input range, it is necessary to use high-order approximation that results in CRNs with a large number of reactions. This article proposes a novel method to compute logarithm accurately in CRNs while keeping the number of reactions in CRNs low. The proposed method can create CRNs that can compute logarithm to different levels of accuracy by adjusting two design parameters. In this article, we present the chemical reactions required to realize the CRNs for computing logarithm. The key contribution of this article is a novel method to create CRNs that can compute logarithm accurately over a wide input range using only a small number of chemical reactions.
format Online
Article
Text
id pubmed-7513738
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-75137382020-09-28 Chemical reaction networks for computing logarithm Chou, Chun Tung Synth Biol (Oxf) Research Article Living cells constantly process information from their living environment. It has recently been shown that a number of cell signaling mechanisms (e.g. G protein-coupled receptor and epidermal growth factor) can be interpreted as computing the logarithm of the ligand concentration. This suggests that logarithm is a fundamental computation primitive in cells. There is also an increasing interest in the synthetic biology community to implement analog computation and computing the logarithm is one such example. The aim of this article is to study how the computation of logarithm can be realized using chemical reaction networks (CRNs). CRNs cannot compute logarithm exactly. A standard method is to use power series or rational function approximation to compute logarithm approximately. Although CRNs can realize these polynomial or rational function computations in a straightforward manner, the issue is that in order to be able to compute logarithm accurately over a large input range, it is necessary to use high-order approximation that results in CRNs with a large number of reactions. This article proposes a novel method to compute logarithm accurately in CRNs while keeping the number of reactions in CRNs low. The proposed method can create CRNs that can compute logarithm to different levels of accuracy by adjusting two design parameters. In this article, we present the chemical reactions required to realize the CRNs for computing logarithm. The key contribution of this article is a novel method to create CRNs that can compute logarithm accurately over a wide input range using only a small number of chemical reactions. Oxford University Press 2017-04-28 /pmc/articles/PMC7513738/ /pubmed/32995503 http://dx.doi.org/10.1093/synbio/ysx002 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research Article
Chou, Chun Tung
Chemical reaction networks for computing logarithm
title Chemical reaction networks for computing logarithm
title_full Chemical reaction networks for computing logarithm
title_fullStr Chemical reaction networks for computing logarithm
title_full_unstemmed Chemical reaction networks for computing logarithm
title_short Chemical reaction networks for computing logarithm
title_sort chemical reaction networks for computing logarithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513738/
https://www.ncbi.nlm.nih.gov/pubmed/32995503
http://dx.doi.org/10.1093/synbio/ysx002
work_keys_str_mv AT chouchuntung chemicalreactionnetworksforcomputinglogarithm