2026-09-25T00:28:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/82032022-01-13T07:54:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Gregory Stephanopoulos.
Klapa, Maria I
Massachusetts Institute of Technology. Dept. of Chemical Engineering.
Massachusetts Institute of Technology. Department of Chemical Engineering
2005-08-23T18:16:15Z
2005-08-23T18:16:15Z
2001
2001
http://hdl.handle.net/1721.1/8203
50104897
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2001.
Page 313 blank.
Includes bibliographical references.
Cellular physiology is a combination of many different functions that have to be accurately probed individually and then precisely correlated to each other, in order to reveal the language used by the cell to communicate changes from the environment to gene expression and vice versa. Genes are transcribed to proteins, which catalyze metabolic pathways, whose activity may in return affect gene expression. DNA microarrays allowed the measurement of the full gene expression profile under a particular set of environmental conditions and genetic backgrounds. To understand, however, the correlation between gene expression and the actual metabolic state of the cell, the latter needs to be also determined with high accuracy. This requires that a comprehensive set of variables is defined to describe metabolic activity and reliable methodologies are developed for the accurate determination of such variables. Defining flux as the rate at which material is processed through a metabolic pathway, the fluxes of a metabolic bioreaction network can be employed to provide an overall measure of metabolic activity. A complete and accurate flux map is the phenotypic equivalent of the gene expression profile. In addition, metabolic fluxes, and especially their changes in response to genetic or environmental perturbations, provide insightful information about the distribution of kinetic and regulatory controls in metabolism.
(cont.) In this context, my Ph.D. thesis focused in the development of methods for high-resolution metabolic flux determination using stable isotopes, mass spectrometry and bioreaction network analysis. Metabolic fluxes cannot be measured directly, but they are rather estimated from measurements of extracellular metabolite consumption and production rates along with data of isotopic-tracer distribution at various network metabolites after the introduction of labeled substrates. This indirect estimation is possible because the unknown fluxes are mapped into the measurements through mass and isotopomer balances. I applied observability analysis techniques into metabolic systems to determine which is the maximum resolution of the in vivo metabolic flux network that can be obtained from potential or provided experimental data. My research focused primarily in examining whether mass spectrometric measurements can be used as sensors of the metabolic fluxes. An experimental protocol for the acquisition of mass spectrometric measuremets of biomass hydrolysates using GC-(ion-trap) MS was developed. Finally, the developed computational and experimental methodology for flux quantification was applied in the elucidation of lysine biosynthesis flux network of Corynebacterium glutamicum under glucose limitation.
by Maria Ioanni Klapa.
Ph.D.
313 p.
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25283854 bytes
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application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Chemical Engineering.
High resolution metabolic flux determination using stable isotopes and mass spectrometry
Thesis
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<Abstract>Cellular physiology is a combination of many different functions that have to be accurately probed individually and then precisely correlated to each other, in order to reveal the language used by the cell to communicate changes from the environment to gene expression and vice versa. Genes are transcribed to proteins, which catalyze metabolic pathways, whose activity may in return affect gene expression. DNA microarrays allowed the measurement of the full gene expression profile under a particular set of environmental conditions and genetic backgrounds. To understand, however, the correlation between gene expression and the actual metabolic state of the cell, the latter needs to be also determined with high accuracy. This requires that a comprehensive set of variables is defined to describe metabolic activity and reliable methodologies are developed for the accurate determination of such variables. Defining flux as the rate at which material is processed through a metabolic pathway, the fluxes of a metabolic bioreaction network can be employed to provide an overall measure of metabolic activity. A complete and accurate flux map is the phenotypic equivalent of the gene expression profile. In addition, metabolic fluxes, and especially their changes in response to genetic or environmental perturbations, provide insightful information about the distribution of kinetic and regulatory controls in metabolism.</Abstract>
<Abstract>(cont.) In this context, my Ph.D. thesis focused in the development of methods for high-resolution metabolic flux determination using stable isotopes, mass spectrometry and bioreaction network analysis. Metabolic fluxes cannot be measured directly, but they are rather estimated from measurements of extracellular metabolite consumption and production rates along with data of isotopic-tracer distribution at various network metabolites after the introduction of labeled substrates. This indirect estimation is possible because the unknown fluxes are mapped into the measurements through mass and isotopomer balances. I applied observability analysis techniques into metabolic systems to determine which is the maximum resolution of the in vivo metabolic flux network that can be obtained from potential or provided experimental data. My research focused primarily in examining whether mass spectrometric measurements can be used as sensors of the metabolic fluxes. An experimental protocol for the acquisition of mass spectrometric measuremets of biomass hydrolysates using GC-(ion-trap) MS was developed. Finally, the developed computational and experimental methodology for flux quantification was applied in the elucidation of lysine biosynthesis flux network of Corynebacterium glutamicum under glucose limitation.</Abstract>
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