TY - JOUR
T1 - ASSA-PBN
T2 - A Toolbox for Probabilistic Boolean Networks
AU - Mizera, Andrzej
AU - Pang, Jun
AU - Su, Cui
AU - Yuan, Qixia
N1 - Funding Information:
Qixia Yuan was supported by the National Research Fund, Luxembourg (grant 7814267). Cui Su was supported by the research project SEC-PBN funded by the University of Luxembourg. This work was also partially supported by ANR-FNR project AlgoReCell (INTER/ANR/15/ 11191283). Experiments presented in this paper were carried out using the HPC facilities at the University of Luxembourg [31] (http://hpc.uni.lu). Andrzej Mizera contributed to this work while holding a postdoctoral researcher position at the Computer Science and Communications Research Unit, University of Luxembourg.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ASSA-PBN, a software toolbox for modelling, simulation, and analysis of PBNs. ASSA-PBN provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ASSA-PBN and to demonstrate the capabilities of ASSA-PBN to effectively analyse biological systems modelled as PBNs.
AB - As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ASSA-PBN, a software toolbox for modelling, simulation, and analysis of PBNs. ASSA-PBN provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ASSA-PBN and to demonstrate the capabilities of ASSA-PBN to effectively analyse biological systems modelled as PBNs.
KW - discrete-time Markov chains
KW - long-run analysis
KW - modelling
KW - parameter estimation
KW - Probabilistic Boolean networks
KW - simulation and analysis of biological networks
KW - steady-state analysis
UR - http://www.scopus.com/inward/record.url?scp=85035087821&partnerID=8YFLogxK
UR - https://www.ncbi.nlm.nih.gov/pubmed/29990128
U2 - 10.1109/TCBB.2017.2773477
DO - 10.1109/TCBB.2017.2773477
M3 - Conference article
C2 - 29990128
AN - SCOPUS:85035087821
SN - 1545-5963
VL - 15
SP - 1203
EP - 1216
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 4
M1 - 8107541
ER -