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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-4068073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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