Identification of genetic networks.

In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.

[1]  T. Haavelmo.  The Statistical Implications of a System of Simultaneous Equations , 1943 .

[2]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[3]  O. D. Duncan,et al.  Introduction to Structural Equation Models. , 1977 .

[4]  D. A. Kenny,et al.  Correlation and Causation , 1937, Wilmott.

[5]  F. Graybill,et al.  Matrices with Applications in Statistics. , 1984 .

[6]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[7]  S. Holmberg,et al.  Serine and threonine catabolism in Saccharomyces cerevisiae: the CHA1 polypeptide is homologous with other serine and threonine dehydratases. , 1992, Genetics.

[8]  C. Peebles,et al.  PTA1, an essential gene of Saccharomyces cerevisiae affecting pre-tRNA processing , 1992, Molecular and cellular biology.

[9]  K. Nasmyth,et al.  Cell cycle regulated transcription in yeast. , 1994, Current opinion in cell biology.

[10]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[11]  M. Kukuruzinska,et al.  Proliferation-dependent differential regulation of the dolichol pathway genes in Saccharomyces cerevisiae. , 1995, Glycobiology.

[12]  S. Narayana,et al.  Complement factor D, a novel serine protease , 1996, Protein science : a publication of the Protein Society.

[13]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Geoffrey M. Maruyama,et al.  Basics of structural equation modeling , 1997 .

[15]  L. Breeden,et al.  A novel Mcm1-dependent element in the SWI4, CLN3, CDC6, and CDC47 promoters activates M/G1-specific transcription. , 1997, Genes & development.

[16]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[17]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[18]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[19]  M. Q. Zhang.  Large-scale gene expression data analysis: a new challenge to computational biologists. , 1999, Genome research.

[20]  V. Arluison,et al.  RNA:pseudouridine synthetase Pus1 from Saccharomyces cerevisiae: oligomerization property and stoichiometry of the complex with yeast tRNA(Phe). , 1999, Biochimie.

[21]  D. Botstein,et al.  Exploring the new world of the genome with DNA microarrays , 1999, Nature Genetics.

[22]  S. P. Fodor,et al.  High density synthetic oligonucleotide arrays , 1999, Nature Genetics.

[23]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[24]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

[25]  Dieter E. Jenne,et al.  Changes in chromatin organization at the neutrophil elastase locus associated with myeloid cell differentiation. , 1999 .

[26]  M Wahde,et al.  Coarse-grained reverse engineering of genetic regulatory networks. , 2000, Bio Systems.

[27]  Bill Shipley,et al.  Cause and Correlation in Biology: A User''s Guide to Path Analysis , 2016 .

[28]  E. Winzeler,et al.  Genomics, gene expression and DNA arrays , 2000, Nature.

[29]  N. Assy,et al.  Increased GABAergic activity inhibits alpha-fetoprotein mRNA expression and the proliferative activity of the HepG2 human hepatocellular carcinoma cell line. , 2000, Journal of hepatology.

[30]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[31]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[32]  Satoru Miyano,et al.  Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint Function , 2000, J. Comput. Biol..

[33]  G. Odell,et al.  The segment polarity network is a robust developmental module , 2000, Nature.

[34]  L. Avivi,et al.  Modification in the inherent mode of allelic replication in lymphocytes of patients suffering from renal cell carcinoma: A novel genetic alteration associated with malignancy , 2000, Genes, chromosomes & cancer.

[35]  R. Young,et al.  Biomedical Discovery with DNA Arrays , 2000, Cell.

[36]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[37]  C. Brown,et al.  Determination of X-chromosome inactivation status using X-linked expressed polymorphisms identified by database searching. , 2000, Genomics.

[38]  Tommi S. Jaakkola,et al.  Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks , 2000, Pacific Symposium on Biocomputing.

[39]  C. Stefanis,et al.  GABA-A receptor β3 and α5 subunit gene cluster on chromosome 15q11-q13 and bipolar disorder : A Genetic Association Study , 2001 .

[40]  S. A. McLuckey,et al.  Mass analysis at the advent of the 21st century. , 2001, Chemical reviews.

[41]  Roger E Bumgarner,et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. , 2001, Science.

[42]  T. Hughes,et al.  DNA microarrays for expression profiling. , 2001, Current opinion in chemical biology.

[43]  D. Figeys,et al.  Proteomics on a chip: Promising developments , 2001, Electrophoresis.

[44]  M Dietel,et al.  Caveolin-1 is down-regulated in human ovarian carcinoma and acts as a candidate tumor suppressor gene. , 2001, The American journal of pathology.

[45]  G. Fiucci,et al.  Caveolin-1 inhibits anchorage-independent growth, anoikis and invasiveness in MCF-7 human breast cancer cells , 2002, Oncogene.

[46]  G. Daley,et al.  A functional screen identifies hDRIL1 as an oncogene that rescues RAS-induced senescence , 2002, Nature Cell Biology.

[47]  M. Mrksich,et al.  Peptide chips for the quantitative evaluation of protein kinase activity , 2002, Nature Biotechnology.

[48]  Jeff Hasty,et al.  Engineered gene circuits , 2002, Nature.

[49]  E. Dougherty,et al.  Gene perturbation and intervention in probabilistic Boolean networks. , 2002, Bioinformatics.

[50]  J. Arnold,et al.  An ensemble method for identifying regulatory circuits with special reference to the qa gene cluster of Neurospora crassa , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[51]  U. Alon,et al.  Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Steven Petrou,et al.  Altered kinetics and benzodiazepine sensitivity of a GABAA receptor subunit mutation [γ2(R43Q)] found in human epilepsy , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[53]  G. Schmitz,et al.  ABCA2: a candidate regulator of neural transmembrane lipid transport , 2002, Cellular and Molecular Life Sciences CMLS.

[54]  F. Entschladen,et al.  The Neurotransmitter γ-Aminobutyric Acid Is an Inhibitory Regulator for the Migration of SW 480 Colon Carcinoma Cells , 2002 .

[55]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[56]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[57]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[58]  S. Shioda,et al.  ATP‐binding cassette transporter ABCA2 (ABC2) expression in the developing spinal cord and PNS during myelination , 2002, The Journal of comparative neurology.

[59]  J. Xiang,et al.  Analysis of the gene expression profiles of immature versus mature bone marrow-derived dendritic cells using DNA arrays. , 2002, Biochemical and biophysical research communications.

[60]  Satoru Miyano,et al.  Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression , 2001, Pacific Symposium on Biocomputing.

[61]  David S. Park,et al.  Caveolin-1 mutations (P132L and null) and the pathogenesis of breast cancer: caveolin-1 (P132L) behaves in a dominant-negative manner and caveolin-1 (-/-) null mice show mammary epithelial cell hyperplasia. , 2002, The American journal of pathology.

[62]  K. Zänker,et al.  Influence of non‐volatile anesthetics on the migration behavior of the human breast cancer cell line 
MDA‐MB‐468 , 2002, Acta anaesthesiologica Scandinavica.

[63]  John Crowley,et al.  Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. , 2002, Blood.

[64]  T. Gojobori,et al.  Identification of neurotransmitter receptor genes under significantly relaxed selective constraint by orthologous gene comparisons between humans and rodents. , 2002, Molecular biology and evolution.

[65]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.