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Voit,
Eberhard O.
Voit,
Eberhard O.
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ItemCanonical Modeling as a Tool in Metabolic Engineering(Georgia Institute of Technology, 2008-11-12) Voit, Eberhard O.A growing branch of metabolic engineering uses mathematical pathway models for the development of strategies for optimizing yield in microbes. The use of such models is necessary because the production pathways are often complex, both in structure and in regulation. For reasons of simplicity, many metabolic engineers use stoichiometric and flux balance models. However, these models ignore cellular regulation. As an alternative, I will discuss canonical models within the modeling framework of Biochemical Systems Theory (BST) as good default representations of fully regulated pathway systems. The presentation will begin with a general introduction to BST, provide some representative examples, and then focus on two questions of optimization. The first concerns the actual optimization of BST models toward yield improvements, which can be formulated as a single linear program or as a series of linear programs. The second type of optimization addresses the de novo design and estimation of BST models from biological data. Of special interest here is the use of in vivo NMR data that characterize time trends in microbial metabolic profiles in a non-invasive fashion. As a specific example I will discuss the production of lactate and other compounds in the bacterium Lactococcus lactis, which is widely used in the food and dairy industry.
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ItemCollective decision making in bacterial viruses(Georgia Institute of Technology, 2008-09) Weitz, Joshua S. ; Mileyko, Yuriy ; Joh, Richard I. ; Voit, Eberhard O.For many bacterial viruses, the choice of whether to kill host cells or enter a latent state depends on the multiplicity of coinfection. Here, we present a mathematical theory of how bacterial viruses can make collective decisions concerning the fate of infected cells. We base our theory on mechanistic models of gene regulatory dynamics. Unlike most previous work, we treat the copy number of viral genes as variable. Increasing the viral copy number increases the rate of transcription of viral mRNAs. When viral regulation of cell fate includes nonlinear feedback loops, very small changes in transcriptional rates can lead to dramatic changes in steady-state gene expression. Hence, we prove that deterministic decisions can be reached, e.g., lysis or latency, depending on the cellular multiplicity of infection within a broad class of gene regulatory models of viral decision-making. Comparisons of a parameterized version of the model with molecular studies of the decision structure in the temperate bacteriophage l are consistent with our conclusions. Because the model is general, it suggests that bacterial viruses can respond adaptively to changes in population dynamics, and that features of collective decision-making in viruses are evolvable life history traits.
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ItemComputational systems analysis of dopamine metabolism(Georgia Institute of Technology, 2008-06) Qi, Zhen ; Miller, Gary W. ; Voit, Eberhard O.A prominent feature of Parkinson’s disease (PD) is the loss of dopamine in the striatum, and many therapeutic interventions for the disease are aimed at restoring dopamine signaling. Dopamine signaling includes the synthesis, storage, release, and recycling of dopamine in the presynaptic terminal and activation of pre- and post-synaptic receptors and various downstream signaling cascades. As an aid that might facilitate our understanding of dopamine dynamics in the pathogenesis and treatment in PD, we have begun to merge currently available information and expert knowledge regarding presynaptic dopamine homeostasis into a computational model, following the guidelines of biochemical systems theory. After subjecting our model to mathematical diagnosis and analysis, we made direct comparisons between model predictions and experimental observations and found that the model exhibited a high degree of predictive capacity with respect to genetic and pharmacological changes in gene expression or function. Our results suggest potential approaches to restoring the dopamine imbalance and the associated generation of oxidative stress. While the proposed model of dopamine metabolism is preliminary, future extensions and refinements may eventually serve as an in silico platform for prescreening potential therapeutics, identifying immediate side effects, screening for biomarkers, and assessing the impact of risk factors of the disease.
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ItemParameter optimization in S-system models(Georgia Institute of Technology, 2008-04) Vilela, Marco ; Chou, I-Chun ; Vinga, Susana ; Vasconcelos, Ana Tereza R. ; Voit, Eberhard O. ; Almeida, Jonas S.Background: The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential Ssystem equations, which results in a set of algebraic equations. Results: A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion: A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
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ItemSystems Biology and its Role in Predictive Health and Personalized Medicine(Georgia Institute of Technology, 2008-02-05) Voit, Eberhard O.
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ItemSystems Biology—What’s All the Buzz About?(Georgia Institute of Technology, 2008) Voit, Eberhard O.