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From gene-expressions to pathways

By Ritesh V. Krishna


Rapid advancements in experimental techniques have benefited molecular biology in many ways. The experiments once considered impossible due to the lack of resources can now be performed with relative ease in an acceptable time-span; monitoring simultaneous expressions of thousands of genes at a given time point is one of them. Microarray technology is the most popular method in biological sciences to observe the simultaneous expression levels of a large number of genes.\ud The large amount of data produced by a microarray experiment requires considerable computational analysis before some biologically meaningful hypothesis can\ud be drawn. In contrast to a single time-point microarray experiment, the temporal microarray experiments enable us to understand the dynamics of the underlying system. Such information, if properly utilized, can provide vital clues about the structure and functioning of the system under study. This dissertation introduces some new computational techniques to process temporal microarray data. We focus on three broad stages of microarray data analysis - normalization, clustering and inference of gene-regulatory networks. We explain our methods using various\ud synthesized datasets and a real biological dataset, produced in-house, to monitor the leaf senescence process in Arabidopsis thaliana

Topics: QH426
OAI identifier: oai:wrap.warwick.ac.uk:3159

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  1. (2008). A partial granger causality approach to explore causal networks derived from multi-parameter data. doi
  2. (2003). An automated method for molecular complexes in large protein interaction networks.
  3. (2007). An introduction to Systems Biology : Design Principles of Biological Circuits. doi
  4. (2008). Analysis of Microarray Data: A Network-Based Approach. doi
  5. (2007). Analysis of timeseries gene expression data: Methods, challenges and opportunities. doi
  6. Analysis of variance for gene expression microarray data. doi
  7. (2005). Analyzing gene expression time-courses. doi
  8. (1988). Applied multivariate statistical analysis. doi
  9. (2006). Applying dynamic bayesian networks to perturbed gene expression data. doi
  10. (2005). Arabidopsis ethylene signaling pathway. doi
  11. (2003). Assessing experimentally derived interactions in a small world. doi
  12. (2001). Assessing gene signi from cdna microarray expression data via mixed models. doi
  13. (2008). Bats: a bayesian user-friendly software for analyzing time series microarray experiments. doi
  14. (2005). Bingo: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. doi
  15. (2009). Buchanan-Wollaston.Bibliography 163 Listen to genes: Dealing with microarray data in the frequency domain. PLos ONE, doi
  16. (2007). Causality and pathway search in microarray time series experiment. doi
  17. (2001). Circadian regulation of gene expression systems in the drosophila head. doi
  18. (1998). Cluster analysis and display of genome-wide expression patterns. doi
  19. (2002). Cluster analysis of gene expression dynamics. doi
  20. (2002). Clustering gene expression data using a graphtheoretic approach: an application of minimum spanning trees. doi
  21. (2005). Clustering of gene expression data using a local shape-based similarity measure. doi
  22. (2006). Clustering the periodic pattern of gene expression using fourier series approximations. doi
  23. (1998). Collective dynamics of 'small-world' networks.
  24. (2008). Comment on causality and pathway search in microarray time series experiment. doi
  25. (2006). Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. doi
  26. (2003). Comparing the continuous representation of time-series expression pro to identify dierentially expressed genes. doi
  27. (1998). Comprehensive identi of cell cycle-regulated genes of the yeast saccharomycesBibliography 171 cerevisiae by microarray hybridization ,. doi
  28. (2001). Computational analysis of microarray data. doi
  29. (2002). Computational system biology. doi
  30. (1992). Constitutive expression of cd69 in in-Bibliography 161 terspecies t-cell hybrids and locus assignment to human chromosome 12. doi
  31. (2003). Continuous representations of time series gene expression data. doi
  32. Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. doi
  33. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing.
  34. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks.
  35. (1987). Digital Spectral Analysis with Applications. doi
  36. (2002). Dip, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. doi
  37. (1989). Discrete-time signal processing.
  38. (2003). Dna microarray data clustering based on temporal variation: Fcv with tsd preclustering.
  39. (2001). Dna microarrays: raising the pro doi
  40. (2004). Dominant spectral component analysis for transcriptional regulations using microarray time-series data. doi
  41. (1999). editors. Identifying gene regulatory networks from experimental data, doi
  42. (2007). Elf4 is required for oscillatory properties of the circadian clock. doi
  43. (1999). Emergence of scaling in random networks.
  44. (2002). Enhanced signaling through the il-2 receptor in cd8+ t cells regulated by antigen recognition results in preferential proliferation and expansion of responding cd8+ t cells rather than promotion of cell death. doi
  45. (2000). Ethylene signaling: from mutants to molecules. doi
  46. (2003). Evaluation of normalization methods for microarray data.
  47. (2006). Experimental validation of a predicted feedback loop in the multi-oscillator clock of arabidopsis thaliana. doi
  48. (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. doi
  49. (2007). Extracting biology from high-dimensional biological data. doi
  50. (1984). Finding a maximum density subgraph.
  51. (1969). Fitting autoregressive models for regression. doi
  52. (1986). Forecasting Economic Time Series. doi
  53. (1999). Functional analysis of lat in tcr-mediated signaling pathways using a latde jurkat cell line. doi
  54. (2003). Gene expression dynamics inspector (gedi): for integrative analysis of expression pro doi
  55. (1994). Gene expresssion during leaf senescence. doi
  56. (2009). Genespring gx software version 10,
  57. (2002). Genetic network modeling. doi
  58. Genomic expression programs in the response of yeast cells to environmental changes. doi
  59. (1933). Grundbegrie der Wahrscheinlichkeitsrechnung. doi
  60. (2005). Handbook of photosynthesis. doi
  61. (2002). Hierarchical organization of modularity in metabolic networks. doi
  62. (1999). Housekeeping genesBibliography 172 as internal standards: use and limits. doi
  63. (2005). How does gene expression clustering work?
  64. (2004). Identifying periodically expressed transcripts in microarray time series data. doi
  65. Identifying time-lagged gene clusters using gene expression data. doi
  66. (2003). Inference of transcriptional regulation relationships from gene expression data. doi
  67. (2003). Inferring gene networks from time series microarray data using dynamic bayesian networks. doi
  68. (2006). Inferring gene regulatory networks from time series data using the minimum description length principle. doi
  69. Inferring subnetworks from preturbed expression pro Bioinformatics, 17:S215{ S224, 2001.Bibliography 169 doi
  70. (2009). Interaction based functional clustering of genomic data. doi
  71. (1969). Investigating causal relations by econometric models and cross-spectral methods. doi
  72. (2001). Issues in cdna microarray analysis: quality channel normalization, models of variations and assessment of gene eects. doi
  73. (1999). Lat is required for tyrosine phosphorylation of phospholipase cgamma2 and platelet activation by the collagen receptor gpvi.
  74. (2001). Lethality and centrality in protein networks.
  75. (2002). Linked: The New Science of Networks. Basic Books,
  76. (2003). Living by the calendar: how plants know when to ower. doi
  77. (2009). Main page | wikimedia commons, doi
  78. (2003). Matlab version 6.5.1. natick, massachusetts: The mathworks inc., doi
  79. (1977). Maximum likelihood from incomplete data via the em algorithm.
  80. (1982). Measurement of linear dependence and feedback between multiple time series. doi
  81. (2009). Mged open soruce projects. http://mged.sourceforge.net/ index.php,
  82. (2002). Microarray data normalization and transformation. doi
  83. (1969). Minimum spanning trees and single linkage analysis. doi
  84. (2002). Model-based cluster analysis of microarray geneexpression data.
  85. (2004). Model-based methods for identifying periodically regulated genes based on the time course microarray gene expression data. doi
  86. (2004). Modeling t-cell activation using gene expression pro and state-space models. doi
  87. (1956). Modern Mathermatics for Engineers.
  88. (1984). Multivariate Observations. doi
  89. (2003). Non-linear normalization and background correction in one-channel cdna microarray studies. doi
  90. (2006). Nonlinear parametric model for granger causality of time series. Physical Review E, 73:066216, doi
  91. (2004). Normalization and analysis of cdna micro-arrays using within-array replications applied to neuroblastoma cell response to a cytokine. doi
  92. (2002). Normalization and analysis of dna microarray data by self-consistency and local regression. Genome Biology,
  93. (2002). Normalization for cdna microarray data: a robust composite method addressing single and multiple slide systematic variation. doi
  94. (2001). Normalization for cdna microarray data. SPIE BioE, doi
  95. (2003). Normalization methods for analysis of microarray geneexpression data. doi
  96. (2003). Normalization of cdna microarray data. doi
  97. (2002). Nuclear factor-kappa b activation in the nasal polypepithelium: relationship to local cytokine gene expression. doi
  98. (2002). On spectral clustering: Analysis and an algorithm.
  99. (2000). Orchestrated transcription of key pathways in arabidopsis by the circadian clock. doi
  100. (2007). Overexpression of a chromatin architecture-controlling at-hook protein extends leaf longevity and increases the post-harvest storage life of plants. doi
  101. Partial directed coherence: a new concept in neural structure determination. doi
  102. (2008). Partial mixture model for tight clustering of gene expression time-course. doi
  103. (0562). Radial basis function approach to nonlinear granger causality of time series. Physical Review E, doi
  104. (1997). Ratio-based decisions and the quantitative analysis of cdna microarray images. doi
  105. (1991). Regression Analysis by Example. doi
  106. (2004). Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. doi
  107. Reverse engineering gene networks using singular value decomposition and robust regression. doi
  108. (1979). Robust locally weighted regression and smoothing scatterplots. doi
  109. (2000). Role of b7-cd28/ctla-4 costimulation and nf-kappa b in allergen-induced t cell chemotaxis by il-16 and rantes. doi
  110. (2005). Semilinear high-dimensional model for normalization of microarray data: a theoretical analysis and partial consistency. doi
  111. (2009). Shortest path analysis using partial correlations for classifying gene functions from gene expression data. doi
  112. (2003). Statistical Analysis of Gene Expression Microarray Data. Chapman and Hall/CRC, doi
  113. (2001). Statistical design and the analysis of gene expression microarray data. doi
  114. (2003). Statistical design of reverse dye microarrays. doi
  115. (2001). Statistical modeling of large microarray data sets to identify stimulus response pro doi
  116. (2004). Statistical resynchronization and bayesian detection of periodically expressed genes. doi
  117. Stem: a tool for the analysis of short time series gene expression data. doi
  118. (1999). Systematic determination of genetic network architecture.
  119. (2002). Systematic determination of patterns of gene expression during drosophila embryogenesis. doi
  120. (2006). Systems biology owering in the plant clock doi
  121. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application. doi
  122. (1979). Tests of causality: The message in the innovations. doi
  123. (1999). The central dogma of molecular biology,
  124. (2002). The elf4 gene controls circadian rhythms and owering time in arabidopsis thaliana. doi
  125. (2004). The ethylene signaling pathway: New insights. doi
  126. (2009). The open biomedical ontologies. http://www.obofoundry.org/, doi
  127. (1993). The Origins of Order.
  128. (2000). tool for the uni of biology.
  129. (2004). Transcript pro ing of early lateral root initiation. doi
  130. (2008). Uncovering interactions in the frequency domain. doi
  131. (2000). Using bayesian networks to analyze expression data. doi
  132. (2003). Using hidden markov models to analyze gene expression time course data. doi
  133. (2003). Variance and bias to compare normalization methods for high density oligonucleotide array data. doi

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