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Project properties |
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| Title | Incorporation of ferredoxin in E. coli genome scale metabolic models. |
| Group | Systems and Synthetic Biology |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Claudia de Buck, Prof. Dr Maria Suarez Diez |
| Examiner(s) | Prof. Maria Suarez Diez (SSB) Dr. Rob Smith |
| Contact info | robert1.smith@wur.nl |
| Begin date | 2024/02/09 |
| End date | |
| Description | To understand and study microbial metabolism there have been many efforts to represent microbial reaction networks as computational models. Escherichia coli is one of the organisms for which these efforts have been most intensive, since E. coli is one of the model organisms in the field of biotechnology and metabolic engineering. One way to represent the E. coli (core) metabolism is with a genome-scale metabolic model (GEM), which is based on sets of reaction equations represented in a stoichiometric matrix (Zhang & Hua, 2015). The most recently published E. coli GEM (iML1515, (Monk et al., 2017)) contains 1877 metabolites and 2712 reactions. While this is an extensive representation of E. coli’s metabolism, it is not complete. It is also not necessary to obtain a ‘complete’ model of E. coli, as a larger model does not necessarily lead to a more accurate result: the content of a model should be tailored to the biological question one is trying to answer.
During my PhD project I am interested in the energy metabolism of E. coli and the role of redox cofactors. The cofactor ferredoxin in particular has been reported to play a role in several metabolic pathways ((Li et al., 2021)), but this is not reflected in this latest E. coli model. This thesis project will therefore focus on the role of ferredoxin in E. coli metabolism and its incorporation in the latest E. coli GEM. The first step will consist of an in depth analysis of the reactions in which ferredoxin is involved, so that it becomes clear which ferredoxin-related reactions are missing from the model. This will involve bioinformatical analyses using databases such as Uniprot and Ecocyc and a literature search. Following this, the latest E. coli GEM can be modified according to the previous findings. Resulting versions of the model can be simulated using packages available in python (Ebrahim et al., 2013). During this part of the thesis you will develop python scripts that form a small pipeline which will carry out the model modifications and simulations, followed by reporting the results. You will also learn to work with git as a version control tool, and you get some basic experience with linux as you will run the model simulations on the linux-server hosted by SSB. References Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013). COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Systems Biology, 7. https://doi.org/10.1186/1752-0509-7-74 Li, S., Ye, Z., Moreb, E. A., Hennigan, J. N., Castellanos, D. B., Yang, T., & Lynch, M. D. (2021). Dynamic control over feedback regulatory mechanisms improves NADPH flux and xylitol biosynthesis in engineered E. coli. Metabolic Engineering, 64, 1096–7176. https://doi.org/10.1016/j.ymben.2021.01.005 Monk, J. M., Lloyd, C. J., Brunk, E., Mih, N., Sastry, A., King, Z., Takeuchi, R., Nomura, W., Zhang, Z., Mori, H., Feist, A. M., & Palsson, B. O. (2017). iML1515, a knowledgebase that computes Escherichia coli traits. Nature Biotechnology, 35(10), 904. https://doi.org/10.1038/NBT.3956 Zhang, C., & Hua, Q. (2015). Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine. Frontiers in Physiology, 6(JAN), 413. https://doi.org/10.3389/FPHYS.2015.00413 |
| Used skills | Metabolic modelling with genome scale metabolic models; in depth understanding of the role of cofactors in microbial metabolism. |
| Requirements | Python, some background in systems biology (BPE-34306, SSB-31806 or SSB-50806). |