On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation

DOI10.1016/j.cam.2012.02.022zbMath1255.65027OpenAlexW2044734875MaRDI QIDQ433954

Lu-Bin Cui, Wen Li, Michael Kwok-Po Ng

Publication date: 9 July 2012

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2012.02.022

zbMATH Keywords

algorithmnumerical resultslinear systemscondition numberiterative methodstochastic matricessingular valuesgeneralized minimal residual methodpower methodperturbation boundprobabilistic Boolean networkssteady-state probability distribution

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Numerical analysis or methods applied to Markov chains (65C40) Iterative numerical methods for linear systems (65F10)

Related Items (5)

Deep Neural Networks for Solving Large Linear Systems Arising from High-Dimensional Problems ⋮ On Generating Optimal Sparse Probabilistic Boolean Networks with Maximum Entropy from a Positive Stationary Distribution ⋮ On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models ⋮ Dimension reduction of large sparse AND-NOT network models ⋮ Observability of switched Boolean control networks using algebraic forms

Cites Work

• Unnamed Item
• A matrix perturbation method for computing the steady-state probability distributions of probabilistic Boolean networks with gene perturbations
• Markov chains: models, algorithms and applications
• Steady-state probabilities for attractors in probabilistic Boolean networks
• Control of Boolean networks: hardness results and algorithms for tree structured networks
• Theorems on M-splittings of a singular M-Matrix which depend on graph structure
• Perturbation analysis of singular linear systems with index one^∗
• CONTROL OF STATIONARY BEHAVIOR IN PROBABILISTIC BOOLEAN NETWORKS BY MEANS OF STRUCTURAL INTERVENTION

This page was built for publication: On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation