2026-09-20T13:45:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/792082022-01-13T07:53:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Bruce Tidor.
Cui, Yuanyuan, Ph.D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Computational and Systems Biology Program.
Massachusetts Institute of Technology. Computational and Systems Biology Program
2013-06-17T19:47:39Z
2013-06-17T19:47:39Z
2013
2013
http://hdl.handle.net/1721.1/79208
844752744
Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references.
The rapid development and applications of high throughput measurement techniques bring the biological sciences into a 'big data' era. The vast available data for enzyme and metabolite concentrations, fluxes, and kinetics under normal or perturbed conditions in biological networks provide unprecedented opportunities to understand the cell functions. On the other hand, it brings new challenges of handling, integrating, and interpreting the large amount of data to acquire novel biological knowledge. In this thesis, we address this problem with a new ordinary differential equation (ODE) model based on the mass-action rate law (MRL) of the biochemical reactions. It describes the detailed biochemical mechanisms of the enzyme reactions, and therefore reflects closely of how the enzymes work in the systems. Because the MRL models are constructed with elementary enzyme reaction steps, it is also much more flexible than the aggregated rate law (ARL) model to incorporate new enzyme interactions and regulations. Two versions of the MRL model ensembles for the central carbon metabolic network, which generates most of the precursors for the secondary metabolite, were constructed. The E. coli version contains the basic reactions in this network and was applied to optimize the aromatic amino acid production which requires fine-tuned flux partition between glycolysis pathway and the pentose phosphate pathway. The S. cerevisiae version is more sophisticated with the incorporated dynamics of the NAD/NADH and NADP/NADPH, as well as the automatic switch from aerobic to anaerobic condition. It was applied to maximize the ethanol production yield, for which the NAD/NADH ratio is a crucial regulating factor. In order to develop methodologies to understand the intrinsic network properties and optimize the network behavior, we further explored approaches for the identification of pathway bottlenecks. Four computational assays were studied, including metabolite accumulation, conditional Vmax, increased glucose input, and decreased E₀, which were applied to the ethanol model ensemble to discover their effectiveness in bottleneck identification in this network. The TDH reaction was detected as a major bottleneck restricting carbon flow towards the ethanol pathway and affecting NADH availability. To manipulate the network for desired production rates of target metabolites, we developed an optimization technique for mass-action rate law ODE models that allows parallel or sequential combinations of enzyme knock-out and over-/under-expression strategies to be conducted on the model. Many strategies were suggested to improve the aromatic amino acid production and help identify the two-direction flux feature of the pentose phosphate pathway. Strategies were also found to enhance the ethanol production yield above 95% of the theoretical yield. Although the two applications studied here are both in the field of metabolic engineering, it is anticipated that the mass-action rate law models for the central carbon metabolism can be extended to study the cancer metabolism. Preliminary studies show promising results for designing cancer clinical trial simulations with a combined model incorporating high level cancer progression and detailed cancer biochemical metabolism.
by Yuanyuan Cui.
Ph.D.
156 p.
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Computational and Systems Biology Program.
Computational modeling techniques for biological network productivity increases : optimization and rate-limiting reaction detection
Thesis
application/pdf
Publication
unknown
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<Abstract>The rapid development and applications of high throughput measurement techniques bring the biological sciences into a 'big data' era. The vast available data for enzyme and metabolite concentrations, fluxes, and kinetics under normal or perturbed conditions in biological networks provide unprecedented opportunities to understand the cell functions. On the other hand, it brings new challenges of handling, integrating, and interpreting the large amount of data to acquire novel biological knowledge. In this thesis, we address this problem with a new ordinary differential equation (ODE) model based on the mass-action rate law (MRL) of the biochemical reactions. It describes the detailed biochemical mechanisms of the enzyme reactions, and therefore reflects closely of how the enzymes work in the systems. Because the MRL models are constructed with elementary enzyme reaction steps, it is also much more flexible than the aggregated rate law (ARL) model to incorporate new enzyme interactions and regulations. Two versions of the MRL model ensembles for the central carbon metabolic network, which generates most of the precursors for the secondary metabolite, were constructed. The E. coli version contains the basic reactions in this network and was applied to optimize the aromatic amino acid production which requires fine-tuned flux partition between glycolysis pathway and the pentose phosphate pathway. The S. cerevisiae version is more sophisticated with the incorporated dynamics of the NAD/NADH and NADP/NADPH, as well as the automatic switch from aerobic to anaerobic condition. It was applied to maximize the ethanol production yield, for which the NAD/NADH ratio is a crucial regulating factor. In order to develop methodologies to understand the intrinsic network properties and optimize the network behavior, we further explored approaches for the identification of pathway bottlenecks. Four computational assays were studied, including metabolite accumulation, conditional Vmax, increased glucose input, and decreased E₀, which were applied to the ethanol model ensemble to discover their effectiveness in bottleneck identification in this network. The TDH reaction was detected as a major bottleneck restricting carbon flow towards the ethanol pathway and affecting NADH availability. To manipulate the network for desired production rates of target metabolites, we developed an optimization technique for mass-action rate law ODE models that allows parallel or sequential combinations of enzyme knock-out and over-/under-expression strategies to be conducted on the model. Many strategies were suggested to improve the aromatic amino acid production and help identify the two-direction flux feature of the pentose phosphate pathway. Strategies were also found to enhance the ethanol production yield above 95% of the theoretical yield. Although the two applications studied here are both in the field of metabolic engineering, it is anticipated that the mass-action rate law models for the central carbon metabolism can be extended to study the cancer metabolism. Preliminary studies show promising results for designing cancer clinical trial simulations with a combined model incorporating high level cancer progression and detailed cancer biochemical metabolism.</Abstract>
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