Modifications to flux balance analysis to enable large scale integrated modeling
Abstract/Contents
- Abstract
- Metabolism is the cellular system that converts surrounding material and energy gradients into the molecules needed to sustain life. All the processes of cell maintenance and replication rely on metabolic products to function. For this reason, quantitative metabolic models contribute greatly to our understanding of biological systems, and underlie many of our predictive capabilities for microbial cells. Therefore, to increase understanding and expand predictions, we must advance quantitative models of metabolism and how cell processes use metabolic products. The metabolic modeling method Flux Balance Analysis (FBA) facilitated the first genome-scale microbial models. FBA consists of a detailed stoichiometric mass balance over hundreds of small molecule reactions. Classic FBA presents a cell-as-metabolism perspective so far as the interface with and function of all other cellular processes are contained in a single summary biomass reaction. A screen for host-dependencies of bacteriophage --- bacterial virus --- prompted my investigation of how infecting pathogens exploit host metabolic resources. Viruses lack metabolic infrastructure, so new viral particles are necessarily built using macromolecular precursors and energy from the host cell. To computationally model viral use of host metabolites, a previously developed model of bacteriophage T7 macromolecular replication was combined with an FBA model of host E. coli metabolism. To enable solution of the combined system, I developed an algorithm to simulate the differential equation and linear optimization models simultaneously with mutual constraint. However, difficulties with FBA in integrated models arise from the biomass reaction. Previously the biomass reaction gave FBA power by abstracting cell process interaction and regulation detail, but in order to do so it assumed a long-time, steady-state, and population average function of metabolism. These assumptions are inconsistent with dynamic and single-cell applications. By relaxing the rigid biomass reaction and its assumptions, I developed FBA methods suited to integrated and whole-cell modeling applications. The first component of this solution was my introduction of a flexible (flexFBA) objective to encourage, rather than require, proportional production of cell process reactants. The second update is to link the processes' use of reactants and return of byproducts across simulation time steps (tFBA). In combination, these modifications make FBA relevant on a much shorter timescale. For example, flexFBA and tFBA can simulate transitions between metabolic steady states, and avoid FBA artifacts due to small copy number enzymes.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Birch, Elsa Wren |
|---|---|
| Associated with | Stanford University, Department of Chemical Engineering. |
| Primary advisor | Covert, Markus |
| Primary advisor | Spormann, Alfred M |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Spormann, Alfred M |
| Thesis advisor | Spakowitz, Andrew James |
| Advisor | Spakowitz, Andrew James |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Elsa Wren Birch. |
|---|---|
| Note | Submitted to the Department of Chemical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Elsa Wren Birch
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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