Computational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.<p>In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.

<p>Switching networks are a common model for biological systems, especially</p><p>for genetic transcription networks. Stuart Kaufman originally proposed</p><p>the usefulness of the Boolean framework, but much of the dynamical </p><p>features there are not realizable in a continuous analogue. We introduce the notion</p><p>of braid-like dynamics as a bridge between Boolean and continuous dynamics and</p><p>study its importance in the local dynamics of ring and ring-like networks. We discuss</p><p>a near-theorem on the global dynamics of general feedback networks, and in the final</p><p>chapter study the main ideas of this thesis in models of a yeast cell transcription network.</p> / Dissertation

No description available.

The development of cDNA microarray technology has made it possible to simultaneously monitor the expression status of thousands of genes. A natural use for this vast amount of information would be to try and &#64257;gure out inter-gene relationships by studying the gene expression patterns across different experimental conditions and to build Gene Regulatory Networks from these data. In this thesis, we study some of the issues involved in Genetic Regulatory Networks. One of them is to discover and elucidate multivariate logical predictive relations among gene expressions and to demonstrate how these logical relations based on coarse quantization closely re&#64258;ect corresponding relations in the continuous data. The other issue involves construction of synthetic Probabilistic Boolean Networks with particular attractor structures. These synthetic networks help in testing of various algorithms like Bayesian Connectivity based approach for design of Probabilistic Boolean Networks.

As the size of networks increases continuously, dealing with networks with faulty nodes becomes unavoidable. In this dissertation, we introduce a new measure for network fault tolerance, the strong fault tolerance (or strong Menger-connectivity)in multicomputer networks, and study the strong fault tolerance for popular multicomputer network structures. Let G be a network in which all nodes have degree d. We say that G is strongly fault tolerant if it has the following property: Let Gf be a copy of G with at most d - 2 faulty nodes. Then for any pair of non-faulty nodes u and v in Gf , there are min{degf (u), degf (v)} node-disjoint paths in Gf from u to v, where degf (u) and degf (v) are the degrees of the nodes u and v in Gf, respectively. First we study the strong fault tolerance for the popular network structures such as star networks and hypercube networks. We show that the star networks and the hypercube networks are strongly fault tolerant and develop efficient algorithms that construct the maximum number of node-disjoint paths of nearly optimal or optimal length in these networks when they contain faulty nodes. Our algorithms are optimal in terms of their time complexity. In addition to studying the strong fault tolerance, we also investigate a more realistic concept to describe the ability of networks for tolerating faults. The traditional definition of fault tolerance, sustaining at most d - 1faulty nodes for a regular graph G of degree d, reflects a very rare situation. In many cases, there is a chance that a routing path between two given nodes can be constructed though the network may have more faulty nodes than its degree. In this dissertation, we study the fault tolerance of hypercube networks under a probability model. When each node of the n-dimensional hypercube network has an independent failure probability p, we develop algorithms that, with very high probability, can construct a fault-free path when the hypercube network can sustain up to 2np faulty nodes.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 349-382). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Computer Science." Includes bibliographical references (p. 113-121).

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on June 27, 2009). Includes bibliographical references (p. 59-60).

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.

Thesis (M.S. in Information Systems)--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Tung Xuan Bui. Second Reader: Smith, Henry H. "September 1990." Description based on title screen as viewed on December 16, 2009. DTIC Identifier(s): Local area networks, security, control systems, thesis. Author(s) subject terms: Computer security, local area network (LAN), security and control. Includes bibliographical references (p. 87-90). Also available in print.