scholarly journals Steady-State Analysis of Genetic Regulatory Networks Modelled by Probabilistic Boolean Networks

2003 ◽  
Vol 4 (6) ◽  
pp. 601-608 ◽  
Author(s):  
Ilya Shmulevich ◽  
Ilya Gluhovsky ◽  
Ronaldo F. Hashimoto ◽  
Edward R. Dougherty ◽  
Wei Zhang
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Markov Chains ◽  
Regulatory Networks ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Long Run ◽  
Probabilistic Boolean Networks

Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.

Construction of Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix: A Maximum Entropy Rate Approach

2011 ◽  
Vol 1 (2) ◽  
pp. 132-154 ◽  
Author(s):  
Xi Chen ◽  
Wai-Ki Ching ◽  
Xiao-Shan Chen ◽  
Yang Cong ◽  
Nam-Kiu Tsing
Keyword(s):  
Inverse Problem ◽  
Maximum Entropy ◽  
Regulatory Networks ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Genomic Research ◽  
Long Run ◽  
Probabilistic Boolean Networks

AbstractModeling genetic regulatory networks is an important problem in genomic research. Boolean Networks (BNs) and their extensions Probabilistic Boolean Networks (PBNs) have been proposed for modeling genetic regulatory interactions. In a PBN, its steady-state distribution gives very important information about the long-run behavior of the whole network. However, one is also interested in system synthesis which requires the construction of networks. The inverse problem is ill-posed and challenging, as there may be many networks or no network having the given properties, and the size of the problem is huge. The construction of PBNs from a given transition-probability matrix and a given set of BNs is an inverse problem of huge size. We propose a maximum entropy approach for the above problem. Newton's method in conjunction with the Conjugate Gradient (CG) method is then applied to solving the inverse problem. We investigate the convergence rate of the proposed method. Numerical examples are also given to demonstrate the effectiveness of our proposed method.

CONTROL OF STATIONARY BEHAVIOR IN PROBABILISTIC BOOLEAN NETWORKS BY MEANS OF STRUCTURAL INTERVENTION

2002 ◽  
Vol 10 (04) ◽  
pp. 431-445 ◽  
Author(s):  
ILYA SHMULEVICH ◽  
EDWARD R. DOUGHERTY ◽  
WEI ZHANG
Keyword(s):  
Steady State ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Optimal Solution ◽  
Boolean Networks ◽  
Underlying Structure ◽  
State Behavior ◽  
Efficient Manner ◽  
Long Run ◽  
Probabilistic Boolean Networks

Probabilistic Boolean Networks (PBNs) were recently introduced as models of gene regulatory networks. The dynamical behavior of PBNs, which are probabilistic generalizations of Boolean networks, can be studied using Markov chain theory. In particular, the steady-state or long-run behavior of PBNs may reflect the phenotype or functional state of the cell. Approaches to alter the steady-state behavior in a specific prescribed manner, in cases of aberrant cellular states, such as tumorigenesis, would be highly beneficial. This paper develops a methodology for altering the steady-state probabilities of certain states or sets of states with minimal modifications to the underlying rule-based structure. This approach is framed as an optimization problem that we propose to solve using genetic algorithms, which are well suited for capturing the underlying structure of PBNs and are able to locate the optimal solution in a highly efficient manner. Several computer simulation experiments support the proposed methodology.

The Robustness and Mutual Information Entropy of Random Modular Boolean Networks

2014 ◽  
Vol 989-994 ◽  
pp. 4417-4420 ◽  
Author(s):  
Nan Zhao ◽  
Bing Hui Guo ◽  
Fan Chao Meng
Keyword(s):  
Mutual Information ◽  
Regulatory Networks ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Information Propagation ◽  
Entropy Analysis ◽  
Random Boolean Networks ◽  
Basic Model ◽  
Different Types

Random Boolean networks have been proposed as a basic model of genetic regulatory networks for more than four decades. Attractors have been considered as the best way to represent the long-term behaviors of random Boolean networks. Most studies on attractors are made with random topologies. However, the real regulatory networks have been found to be modular or more complex topologies. In this work, we extend classical robustness and entropy analysis of random Boolean networks to random modular Boolean networks. We firstly focus on the robustness of the attractor to perturbations with different parameters. Then, we investigate and calculate how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information. The results can be used to study the capability of genetic information propagation of different types of genetic regulatory networks.

Exact and ordinary lumpability in finite Markov chains

1994 ◽  
Vol 31 (1) ◽  
pp. 59-75 ◽  
Author(s):  
Peter Buchholz
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Exact Determination ◽  
Finite Markov Chains

Exact and ordinary lumpability in finite Markov chains is considered. Both concepts naturally define an aggregation of the Markov chain yielding an aggregated chain that allows the exact determination of several stationary and transient results for the original chain. We show which quantities can be determined without an error from the aggregated process and describe methods to calculate bounds on the remaining results. Furthermore, the concept of lumpability is extended to near lumpability yielding approximative aggregation.

scholarly journals On the long-run sensitivity of probabilistic Boolean networks

2009 ◽  
Vol 257 (4) ◽  
pp. 560-577 ◽  
Author(s):  
Xiaoning Qian ◽  
Edward R. Dougherty
Keyword(s):  
Boolean Networks ◽  
Long Run ◽  
Probabilistic Boolean Networks
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