Thesis (M.S.)--University of Wisconsin--Madison, 1970. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 148-187).

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 121-129). Also available on the World Wide Web.

Thesis (M.S.)--Drexel University, 2005. / Includes abstract. Includes bibliographical references (leaves 46-49).

Thesis (Ph. D.)--Washington State University, May 2006. / Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 136-157). Also available in electronic version.

Recent advances in biology have revolutionized our understanding of living systems. For the first time, it is possible to study the behavior of individual cells. This has led to the discovery of many amazing phenomena. For example, cells have developed intelligent mechanisms for foraging, communicating, and responding to environmental changes. These diverse functions in cells are controlled through biochemical networks consisting of many different proteins and signaling molecules. These molecules interact and affect gene expression, which in turn affects protein production. This results in a complex mesh of feedback and feedforward interactions. These complex networks are generally highly nonlinear and stochastic, making them difficult to study quantitatively. Recent studies have shown that biochemical networks are also highly modular, meaning that different parts of the network do not interact strongly with each other. These modules tend to be conserved across species and serve specific biological functions. However, detect- ing modules and identifying their function tends to be a very difficult task. To overcome some of these complexities, I present an alternative modeling approach that builds quantitative models using coarse-grained biological processes. These coarse-grained models are often stochastic (probabilistic) and highly non-linear. In this thesis, I focus on modeling biochemical networks in two distinct biological systems: Dictyostelium discoideum and microRNAs. Chapters 2 and 3 focus on cellular communication in the social amoebae Dictyostelium discoideum. Using universality, I propose a stochastic nonlinear model that describes the behavior of individual cells and cellular populations. In chapter 4 I study the interaction between messenger RNAs and noncoding RNAs, using Langevin equations.

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The gene for bovine prothrombin is 15.6 Kbp in length which encodes a mRNA of 2025 nucleotides plus a poly(A) tail. The prothrombin gene is composed of 14 exons separated by 13 introns, all of which vary in size. The positions of the introns found within the prothrombin gene provides some insight into the evolution of prothrombin and provide evidence on the origin of introns. Within the activation peptide and leader sequence of precursor prothrombin, some of the introns appear to separate structural and functional protein domains. Introns are found to separate certain domains, including the pre-peptide, the propeptide and Gla region, and each of the kringles. This organization of exons may reflect the evolution of the prothrombin gene as the result of the fusion of exon(s) containing protein domains by exon shuffling. The activation peptide appears to be constructed from four domains: a pre-peptide, a pro-peptide and Gla region, and two kringles. On comparison of the exon organization of the serine protease domain of prothrombin and other serine protease genes, it was found that none of the introns of the prothrombin gene are shared with any of the other serine protease genes. This absence of shared introns is in contrast to the shared introns found for the shared domains of the activation peptide and leader. The positions of the introns of the serine protease domain of serine proteases genes does not appear to reflect the evolution of the serine protease from protein domains, but rather the result of intron insertion into the serine protease coding regions. Intron insertion would also explain the origin of the few introns of the activation peptide that do not appear to separate protein domains. In conclusion, the organization of the exons and introns of the gene for prothrombin reflect both the origin of introns by insertion events, and the use of introns in exon shuffling. The insertion of introns, and the subsequent possibility of exon shuffling appear to have been essential for the evolution of the multidomainal proteins, such as prothrombin, which are essential for vertebrate life. / Medicine, Faculty of / Biochemistry and Molecular Biology, Department of / Graduate