scholarly journals Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray Data

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Wentao Zhao ◽  
Erchin Serpedin ◽  
Edward R. Dougherty
Keyword(s):  
Steady State ◽  
Microarray Data ◽  
Chromatin Immunoprecipitation ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks

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.

Revealing temporal genetic regulatory networks from steady-state distributions

2009 ◽  
Author(s):  
David C. Martins ◽  
Evaldo A. de Oliveira ◽  
Paulo J. S. Silva ◽  
Ronaldo F. Hashimoto ◽  
Roberto M. Cesar
Keyword(s):  
Steady State ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks

scholarly journals Problems for Structure Learning Aggregation and Computational Complexity

2012 ◽  
pp. 1699-1720
Author(s):  
Frank Wimberly ◽  
David Danks ◽  
Clark Glymour ◽  
Tianjiao Chu
Keyword(s):  
Computational Complexity ◽  
Graphical Models ◽  
Microarray Data ◽  
Regulatory Network ◽  
Conditional Independence ◽  
Regulatory Networks ◽  
Structure Learning ◽  
Genetic Regulatory Networks ◽  
Complexity Of Algorithms ◽  
Realistic Data

Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem.

Microarray Data Mining

2011 ◽  
pp. 728-733
Author(s):  
Li M. Fu
Keyword(s):  
Data Mining ◽  
Signal Transduction ◽  
Dna Microarray ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Tissue Sample ◽  
Genetic Regulatory Networks ◽  
Molecular Network ◽  
Biological Complexity ◽  
Mathematical Problems

Based on the concept of simultaneously studying the expression of a large number of genes, a DNA microarray is a chip on which numerous probes are placed for hybridization with a tissue sample. Biological complexity encoded by a deluge of microarray data is being translated into all sorts of computational, statistical, or mathematical problems bearing on biological issues ranging from genetic control to signal transduction to metabolism. Microarray data mining is aimed to identify biologically significant genes and find patterns that reveal molecular network dynamics for reconstruction of genetic regulatory networks and pertinent metabolic pathways.

Microarray Data Mining

2011 ◽  
pp. 1224-1230
Author(s):  
Li-Min Fu
Keyword(s):  
Data Mining ◽  
Signal Transduction ◽  
Dna Microarray ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Tissue Sample ◽  
Genetic Regulatory Networks ◽  
Molecular Network ◽  
Biological Complexity ◽  
Mathematical Problems

Based on the concept of simultaneously studying the expression of a large number of genes, a DNA microarray is a chip on which numerous probes are placed for hybridization with a tissue sample. Biological complexity encoded by a deluge of microarray data is being translated into all sorts of computational, statistical or mathematical problems bearing on biological issues ranging from genetic control to signal transduction to metabolism. Microarray data mining is aimed to identify biologically significant genes and find patterns that reveal molecular network dynamics for reconstruction of genetic regulatory networks and pertinent metabolic pathways.

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