Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 166-180). Also available on the World Wide Web.

Thesis (Ph. D.)--Rutgers University, 2010. / "Graduate Program in Cell and Developmental Biology." Includes bibliographical references (p. 292-304).

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Cell and Developmental Biology." Includes bibliographical references (p. 154-174).

Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Cell and Developmental Biology." Includes bibliographical references (p. 272-286).

Thesis (M. S.)--Biology, Georgia Institute of Technology, 2009. / Committee Chair: Boyan Barbara; Committee Member: Guldberg Robert; Committee Member: Lovachev Kiril; Committee Member: Schwartz Zvi. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Cell and Developmental Biology." Includes bibliographical references (p. 96-110).

Thesis (Ph. D.)--University of Chicago, Dept. of Organismal Biology and Anatomy. / Includes bibliographical references. Also available on the Internet.

Thesis (M.S.) in Biochemistry--University of Maine, 2003. / Includes vita. Includes bibliographical references (leaves 87-94).

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<p> Stochastic models of biochemical interactions elucidate essential properties of the network which are not accessible to deterministic modeling. In this thesis it is described how a network motif, the proportional-reversibility interaction with active intermediate states, gives rise to the ability for the variance of biochemical signals to be controlled without changing the mean, a property designated as mean-independent noise control (MINC). This noise control is demonstrated to be essential for macro-scale biological processes via spatial models of the zebrafish hindbrain boundary sharpening. Additionally, the ability to deduce noise origin from the aggregate noise properties is shown. </p><p> However, these large-scale stochastic models of developmental processes required significant advances in the methodology and tooling for solving stochastic differential equations. Two improvements to stochastic integration methods, an efficient method for time stepping adaptivity on high order stochastic Runge-Kutta methods termed Rejection Sampling with Memory (RSwM) and optimal-stability stochastic Runge-Kutta methods, are combined to give over 1000 times speedups on biological models over previously used methodologies. In addition, a new software for solving differential equations in the Julia programming language is detailed. Its unique features for handling complex biological models, along with its high performance (routinely benchmarking as faster than classic C++ and Fortran integrators of similar implementations) and new methods, give rise to an accessible tool for simulation of large-scale stochastic biological models.</p><p>