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.

Problems for Structure Learning

2010 ◽  
pp. 310-333 ◽  
Author(s):  
Frank Wimberly ◽  
David Danks ◽  
Clark Glymour ◽  
Tianjiao Chu
Keyword(s):  
Graphical Models ◽  
Microarray Data ◽  
Regulatory Network ◽  
Conditional Independence ◽  
Regulatory Networks ◽  
Structure Learning ◽  
Genetic Regulatory Networks ◽  
Genetic Regulatory Network ◽  
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.

scholarly journals Effect of Leakage Delay on Stability of Neutral-Type Genetic Regulatory Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Li Li ◽  
Yongqing Yang ◽  
Chuanzhi Bai
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Genetic Regulatory Networks ◽  
Genetic Regulatory Network ◽  
Leakage Delay ◽  
Leakage Delays ◽  
The Stability

The stability of neutral-type genetic regulatory networks with leakage delays is considered. Firstly, we describe the model of genetic regulatory network with neutral delays and leakage delays. Then some sufficient conditions are derived to ensure the asymptotic stability of the genetic regulatory network by the Lyapunov functional method. Further, the effect of leakage delay on stability is discussed. Finally, a numerical example is given to show the effectiveness of the results.

scholarly journals State Observer Design for Delayed Genetic Regulatory Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Li-Ping Tian ◽  
Zhi-Jun Wang ◽  
Amin Mohammadbagheri ◽  
Fang-Xiang Wu
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Estimation Error ◽  
State Observer ◽  
Genetic Regulatory Networks ◽  
Genetic Regulatory Network ◽  
Observer Design ◽  
Linear Matrix ◽  
Globally Asymptotically Stable ◽  
Internal States

Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.

Abstract 386: Identification of Genetic Regulatory Networks for Insulin Resistance in Multiple Populations of Diverse Ethnicities

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Aldons J Lusis ◽  
Ke Hao ◽  
Thomas Quertermous ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Graphical Models ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Association Studies ◽  
Network Models ◽  
Mapk Signaling ◽  
Genetic Regulatory Networks ◽  
Specific Gene ◽  
Genome Wide Association Studies

Insulin resistance (IR) is a critical pathogenic factor for highly prevalent modern cardiometabolic diseases, including coronary artery disease (CAD) and type 2 diabetes (T2D). However, the molecular circuitries underlying IR remain to be elucidated. The GENEticS of Insulin Sensitivity Consortium (GENESIS) conducted genome-wide association studies (GWAS) for direct measures of IR using euglycemic clamp or insulin suppression test. We sought to identify gene networks and their key intervening drivers for IR by performing a comprehensive integrative analysis leveraging GWAS data from seven GENESIS cohorts representing three ethnic groups - Europeans, Asians and Hispanics, along with expression quantitative trait loci, ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from IR relevant tissues. Integration of the multi-ethnic GWAS with diverse functional genomics information captured shared IR pathways and networks across ethnicities that are independent of body mass index, including GLUT4 translocation regulation, insulin signaling, MAPK signaling, interleukin signaling, extracellular matrix, branched-chain amino acids metabolisms, cell cycle, and oxidative phosphorylation. Further integration of these GWAS-informed IR processes with graphical gene networks uncovered potential key regulators including HADH, COX5A, VCAN and TOP2A , whose network neighbors are consistently enriched for the genetic association signals of IR across ethnicities, and show significant correlation with IR, fasting glucose and insulin levels in the transcriptomic-wide association data from a Hybrid Mouse Diversity Panel comprised of >100 strains fed with high-fat diet. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide new understanding of the pathways, gene networks and potential regulators contributing to IR. These results will also facilitate future functional investigations to unveil how DNA variations translate into IR.

scholarly journals Stability Analysis of Delayed Genetic Regulatory Networks via a Relaxed Double Integral Inequality

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Fu-Dong Li ◽  
Qi Zhu ◽  
Hao-Tian Xu ◽  
Lin Jiang
Keyword(s):  
Stability Analysis ◽  
Integral Inequality ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Genetic Regulatory Network ◽  
Double Integral ◽  
Integral Term ◽  
Double Inequality ◽  
The Stability

Time delay arising in a genetic regulatory network may cause the instability. This paper is concerned with the stability analysis of genetic regulatory networks with interval time-varying delays. Firstly, a relaxed double integral inequality, named as Wirtinger-type double integral inequality (WTDII), is established to estimate the double integral term appearing in the derivative of Lyapunov-Krasovskii functional with a triple integral term. And it is proved theoretically that the proposed WTDII is tighter than the widely used Jensen-based double inequality and the recently developed Wiringter-based double inequality. Then, by applying the WTDII to the stability analysis of a delayed genetic regulatory network, together with the usage of useful information of regulatory functions, several delay-range- and delay-rate-dependent (or delay-rate-independent) criteria are derived in terms of linear matrix inequalities. Finally, an example is carried out to verify the effectiveness of the proposed method and also to show the advantages of the established stability criteria through the comparison with some literature.

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.

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