Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation
Joint Authors
Wang, S.-Q.
Shen, Yanyan
Shi, Changhong
Wang, Tao
Wei, Zhiming
Li, Hanxiong
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Wang, S.-Q.& Shen, Yanyan& Shi, Changhong& Wang, Tao& Wei, Zhiming& Li, Hanxiong. 2014. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050399
Modern Language Association (MLA)
Wang, S.-Q.…[et al.]. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1050399
American Medical Association (AMA)
Wang, S.-Q.& Shen, Yanyan& Shi, Changhong& Wang, Tao& Wei, Zhiming& Li, Hanxiong. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050399
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050399