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Computational methods for analysis and modeling of time -course gene expression data.

机译：分析和建模时程基因表达数据的计算方法。

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摘要

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the author's doctoral study.;Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.
机译：基因编码蛋白质，其中一些反过来调节其他基因。这种相互作用构成基因调控关系或（动态）基因调控网络。随着基因表达测量技术和基因组测序技术的进步，在特定生物学过程中的一系列时间点同时测量细胞中数千个基因的表达水平已成为可能。这样的时程基因表达数据可以提供大多数（如果不是全部）有趣基因的快照，并且可以导致对基因调节关系和网络的更好理解。但是，推断基因调控关系或网络对功能强大的计算方法提出了很高的要求，这些方法能够充分挖掘大量时程基因表达数据，同时降低数据的复杂性以使其易于理解。本文提出了几种从时程基因表达中推断基因调控关系和基因调控网络的计算方法。这些方法是作者博士研究的结果。聚类分析在推断基因调控关系中起着重要作用，例如，揭示新的调节子（共调控基因的集合）及其推定的顺式调控元件。针对时程基因表达数据，开发了两种基于动态模型的聚类方法，即基于马尔可夫链模型（MCM）的聚类和基于自回归模型（ARM）的聚类。但是，基于聚类分析的基因调控关系是静态的，因此无法描述观察期内基因表达的动态演变。该基因调节网络被认为是随时间变化的系统。因此，根据时程基因表达数据，建立了动态​​基因调控网络的状态空间模型。为了解决基因调控网络中复杂的时延关系，将状态空间模型扩展为具有时间延迟的模型。最后，开发了一种基于遗传算法的方法来推断基因调控网络中的时间延迟关系。所有这些开发方法的验证均基于从公开引用的公共数据库中获得的实验数据。

著录项

  • 作者

    Wu, Fangxiang.;

  • 作者单位

    The University of Saskatchewan (Canada).;

  • 授予单位 The University of Saskatchewan (Canada).;
  • 学科 Biomedical engineering.;Biostatistics.;Molecular biology.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 168 p.
  • 总页数 168
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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