Browsing by Subject "Gene Network"

Now showing 1 - 2 of 2
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    Network-based mixture models for genomic data.
    (2009-06) Wei, Peng
    A common task in genomic studies is to identify genes satisfying certain conditions, such as differentially expressed genes between normal and tumor tissues or regulatory target genes of a transcription factor (TF). Standard approaches treat all the genes identically and independently a priori and ignore the fact that genes work coordinately in biological processes as dictated by gene networks, leading to inefficient analysis and reduced power. We propose incorporating gene network information as prior biological knowledge into statistical modeling of genomic data to maximize the power for biological discoveries. We propose a spatially correlated mixture model based on the use of latent Gaussian Markov random fields (GMRF) to smooth gene specific prior probabilities in a mixture model over a network, assuming that neighboring genes in a network are functionally more similar to each other. In addition, we propose a Bayesian implementation of a discrete Markov random field (DMRF)-based mixture model for incorporating gene network information, and compare its performance with that based on Gaussian Markov random fields. We also extend the network-based mixture models to ones that are able to integrate multiple gene networks and diverse types of genomic data, such as protein- DNA binding, gene expression and DNA sequence data, to accurately identify regulatory target genes of a TF. Applications to high-throughput microarray data, along with simulations, demonstrate the utility of the new methods and the statistical efficiency gains over other methods.
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    Stochastic-Kinetic modeling of synthetic gene regulatory networks
    (2009-07) Tomshine, Jonathan Robert
    This dissertation focuses on the development and application of new computational methods and tools for the in silico simulation of the behavior of networks of biochemical interactions with a particular focus on synthetic gene regulatory networks. In gene regulatory networks the protein product of one gene regulates the expression of one or more genes. With simple positive or negative feedback or feedforward regulatory relationships, biological phenotypes of astonishing complexity naturally emerge. Gene regulatory networks are also of particular interest in the field of synthetic biology. This is a new discipline that is influenced by both engineering and biological sciences. Synthetic biology efforts focus on the construction of new gene regulatory networks that give rise to controllable phenotypic behavior. Synthetic biology is fueled by an ever-increasing body of genetic knowledge and the technologies to read and write DNA sequences quickly and inexpensively. As in any forward-engineering discipline, modeling can play a catalytic role in the rational design of synthetic gene regulatory networks. The construction of mathematical models of gene networks can provide far more than a simple summary of experimental data. The models constructed in this dissertation are highly detailed and follow from first-principles. That is, a "reductionist" approach is followed, whereby a potentially complex network of gene expression (including all of the fundamental steps of transcription, translation, regulation, etc. of each gene) is reduced to a large series of elementary chemical reactions. These mechanistic models of gene networks are simulated using an accurate stochastic algorithm that accounts for the small size of a biological cell and the dilute nature of critical reacting species. These tools -- quantitative, mechanistic, kinetic models simulated stochastically -- are used to study several gene network paradigms including genetic oscillators and logic gates.