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Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological model...

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Detalles Bibliográficos
Autores principales: Wang, Shuqiang, Shen, Yanyan, Shi, Changhong, Wang, Tao, Wei, Zhiming, Li, Hanxiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4068073/
https://www.ncbi.nlm.nih.gov/pubmed/24995358
http://dx.doi.org/10.1155/2014/625754
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author Wang, Shuqiang
Shen, Yanyan
Shi, Changhong
Wang, Tao
Wei, Zhiming
Li, Hanxiong
author_facet Wang, Shuqiang
Shen, Yanyan
Shi, Changhong
Wang, Tao
Wei, Zhiming
Li, Hanxiong
author_sort Wang, Shuqiang
collection PubMed
description Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.
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spelling pubmed-40680732014-07-03 Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation Wang, Shuqiang Shen, Yanyan Shi, Changhong Wang, Tao Wei, Zhiming Li, Hanxiong ScientificWorldJournal Research Article Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes. Hindawi Publishing Corporation 2014 2014-06-05 /pmc/articles/PMC4068073/ /pubmed/24995358 http://dx.doi.org/10.1155/2014/625754 Text en Copyright © 2014 Shuqiang Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Shuqiang
Shen, Yanyan
Shi, Changhong
Wang, Tao
Wei, Zhiming
Li, Hanxiong
Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title_full Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title_fullStr Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title_full_unstemmed Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title_short Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
title_sort defining biological networks for noise buffering and signaling sensitivity using approximate bayesian computation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4068073/
https://www.ncbi.nlm.nih.gov/pubmed/24995358
http://dx.doi.org/10.1155/2014/625754
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