title: Defining biological networks for noise buffering and signaling sensitivity using approximate Bayesian computation.
abstract: 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.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China ; Department of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong.
authors.forename: Shuqiang
authors.identifier: 0000-0003-3356-9423
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Shen
authors.affiliation: School of Public Health, Guangzhou Medical University, Guangzhou 510000, China.
authors.forename: Changhong
authors.initials: C
authors.lastname: Shi
authors.affiliation: Specialist Union Co., Ltd, Shenzhen 518000, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.affiliation: Shandong Academy of Agricultural Machinery Sciences, Jinan 250000, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Wei
authors.affiliation: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong.
authors.forename: Hanxiong
authors.initials: H
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D053858:Metabolic Networks and Pathways / Q000502:physiology; D008954:Models, Biological