Download the full-sized PDF of Stochastic Computational Models for Gene Regulatory Networks and Dynamic Fault Tree AnalysisDownload the full-sized PDF



Permanent link (DOI):


Export to: EndNote  |  Zotero  |  Mendeley


This file is in the following communities:

Graduate Studies and Research, Faculty of


This file is in the following collections:

Theses and Dissertations

Stochastic Computational Models for Gene Regulatory Networks and Dynamic Fault Tree Analysis Open Access


Other title
stochastic computing
Dynamic fault tree
gene regulatory network
Type of item
Degree grantor
University of Alberta
Author or creator
Zhu, PeiCan
Supervisor and department
Jie,Han(Electrical and Computer Engineering)
Examining committee member and department
FangXiang,Wu( Mechanical Engineering)
Jie,Han(Electrical and Computer Engineering)
Guohui,Lin(Computing Science)
Jie,Chen(Electrical and Computer Engineering)
Lukasz,Kurgan(Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Intergrated Circuits and Systems
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Originally proposed in the 1960s, stochastic computation uses random binary bit streams to encode signal probabilities. Stochastic computation enables the implementation of basic arithmetic functions using simple logic elements. Here, the application of stochastic computation is extended to the domain of gene network models and the fault-tree analysis of system reliability. Initially, context-sensitive stochastic Boolean networks (CSSBNs) are developed to model the effect of context sensitivity in a genetic network. A CSSBN allows for a tunable tradeoff between accuracy and efficiency in a simulation. Studies of a simple p53-Mdm2 network reveal that random gene perturbation has a greater effect on the steady state distribution (SSD) compared to context switching activities. Secondly, stochastic multiple-valued networks (SMNs) are investigated to evaluate the effect of noise in a WNT5A network. Lastly, asynchronous stochastic Boolean networks (ASBNs) are proposed for investigating various asynchronous state updating strategies in a gene regulatory network (GRN). The dynamic behavior of a T helper network is investigated and the SSDs found by using ASBNs show the robustness of attractors of the network. In a long term, these results may help to accelerate drug discovery and develop effective drug intervention strategies for some genetic diseases. As another application of stochastic computation, the reliability analysis of dynamic fault trees (DFTs) is further pursued. Stochastic computational models are proposed for the priority AND (PAND) gate, the spare gate and probabilistic common cause failures (PCCFs). Subsequently, a phased-mission system (PMS) is analyzed by using a DFT to model each phase’s failure conditions. The accuracy of a stochastic analysis increases with the length of random binary bit streams in stochastic computation. In addition, non-exponential failure distributions and repeated events are readily handled by the stochastic computational approach. The accuracy, efficiency and scalability of the stochastic approach are demonstrated by several case studies of DFT analysis.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
P. Zhu, J. Liang, and J. Han.(2014).BMC System Biology.P. Zhu, and J. Han.(2014) Journal of Computational Biology.P. Zhu, and J. Han.(2014).IEEE Transactions on Biomedical Circuits and Systems.P. Zhu, J. Han, L. Liu, and M. J. Zuo.(2014).IEEE Transactions on Reliability.P. Zhu, J. Han, L. Liu, and F. Lombardi.(2015).IEEE Transactions on Reliability.J. Han, H. Chen, J. Liang, P. Zhu, Z. Yang, and F. Lombardi.(2014). IEEE Transactions on Computers.

File Details

Date Uploaded
Date Modified
Audit Status
Audits have not yet been run on this file.
File format: pdf (PDF/A)
Mime type: application/pdf
File size: 7657098
Last modified: 2016:06:24 17:38:58-06:00
Filename: Zhu_Peican_201508_PhD.pdf
Original checksum: 3aa85e633dc96be2ff662b041435e24d
Activity of users you follow
User Activity Date