Steady-state analysis of probabilistic Boolean networks

Steady-state analysis of probabilistic Boolean networks

This paper investigates steady-state distributions of probabilistic Boolean networks via cascading aggregation. Under this approach, the problem is converted to computing least square solutions to several corresponding equations. Two necessary and sufficient conditions for the existence of the stead...

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Main Authors: Pan, Jinfeng, Feng, Jun-e, Meng, Min
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Engineering::Electrical and electronic engineering
Genetic Networks
Controllability
Online Access:https://hdl.handle.net/10356/151172
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1511722021-06-17T02:50:32Z Steady-state analysis of probabilistic Boolean networks Pan, Jinfeng Feng, Jun-e Meng, Min School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Genetic Networks Controllability This paper investigates steady-state distributions of probabilistic Boolean networks via cascading aggregation. Under this approach, the problem is converted to computing least square solutions to several corresponding equations. Two necessary and sufficient conditions for the existence of the steady-state distributions for probabilistic Boolean networks are given firstly. Secondly, an algorithm for finding the steady-state distributions of probabilistic probabilistic Boolean networks is given. Finally, a numerical example is given to show the effectiveness of the proposed method. This work was supported by National Natural Science Foundation under Grants 61773371 and 61877036, and China Postdoctoral Science Foundation under Grant 2016M-602143. 2021-06-17T02:50:32Z 2021-06-17T02:50:32Z 2019 Journal Article Pan, J., Feng, J. & Meng, M. (2019). Steady-state analysis of probabilistic Boolean networks. Journal of the Franklin Institute, 356(5), 2994-3009. https://dx.doi.org/10.1016/j.jfranklin.2019.01.039 0016-0032 https://hdl.handle.net/10356/151172 10.1016/j.jfranklin.2019.01.039 2-s2.0-85061155208 5 356 2994 3009 en Journal of the Franklin Institute © 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Genetic Networks
Controllability
spellingShingle Engineering::Electrical and electronic engineering
Genetic Networks
Controllability
Pan, Jinfeng
Feng, Jun-e
Meng, Min
Steady-state analysis of probabilistic Boolean networks
description This paper investigates steady-state distributions of probabilistic Boolean networks via cascading aggregation. Under this approach, the problem is converted to computing least square solutions to several corresponding equations. Two necessary and sufficient conditions for the existence of the steady-state distributions for probabilistic Boolean networks are given firstly. Secondly, an algorithm for finding the steady-state distributions of probabilistic probabilistic Boolean networks is given. Finally, a numerical example is given to show the effectiveness of the proposed method.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Pan, Jinfeng
Feng, Jun-e
Meng, Min
format Article
author Pan, Jinfeng
Feng, Jun-e
Meng, Min
author_sort Pan, Jinfeng
title Steady-state analysis of probabilistic Boolean networks
title_short Steady-state analysis of probabilistic Boolean networks
title_full Steady-state analysis of probabilistic Boolean networks
title_fullStr Steady-state analysis of probabilistic Boolean networks
title_full_unstemmed Steady-state analysis of probabilistic Boolean networks
title_sort steady-state analysis of probabilistic boolean networks
publishDate 2021
url https://hdl.handle.net/10356/151172
_version_ 1703971152220651520

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