search query: @instructor Czeizler, Elena / total: 3
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| Author: | Ilievska, Maja |
| Title: | Identification of metabolic fluxes leading to the production of industrially relevant products |
| Publication type: | Master's thesis |
| Publication year: | 2016 |
| Pages: | 54 s. + liitt. 4 Language: eng |
| Department/School: | Perustieteiden korkeakoulu |
| Main subject: | Bioinformatics (T3012) |
| Supervisor: | Rousu, Juho |
| Instructor: | Czeizler, Elena ; Blomberg, Peter |
| Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201606172447 |
| Location: | P1 Ark Aalto 4046 | Archive |
| Keywords: | metabolic pathway analysis flux vectors right null space stoichiometric modelling industrial products |
| Abstract (eng): | In metabolic pathway analysis the focus is on identifying the complete range of paths within a biochemical network. However, most current methods characterizing all potential paths between the selected substrates and product are based either on the enumeration of all elementary flux modes or all extreme pathways. This becomes computationally unfeasible for large reaction matrices. In this work, we propose an alternative approach that identifies a set of potential paths while avoiding an exhaustive enumeration. More specifically, we identify a set of (minimal) flux vectors that produce the desired product and do not accumulate any intermediates while consuming at least one of the specified substrates. Our k-best approach uses linear programming to identify the first k solutions, according to a predefined objective function. Furthermore, in order to determine biologically more meaningful flux vectors we define an augmented solution space, where in addition to the flux distribution we incorporate the net consumption/production of external metabolites and the contribution of the null space basis vectors to the given flux distribution. One of the main aims of this research was to computationally determine the best substrate-path-product combination for industrial scale production. In fact, we were interested in identifying the best carbon source (or the best combination of different carbon sources) that will lead to the highest productivity for a specific product, as well as the best metabolic pathway from the identified sources to the product. A special focus within this work was the identification of an objective function for the enumerated paths, which would return a good set of candidate paths. The results demonstrate that our k-best method is able to identify a set of candidate pathways for genome-scale metabolic models, where elementary modes and extreme pathway analysis fail to provide a resulting set of pathways. Among the pathways proposed by our enumeration approach there are novel ones with the potential to improve the production processes of the specific product in terms of energetic efficiency. |
| ED: | 2016-07-17 |
INSSI record number: 53879
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