I will develop a simulation tool to determine how E. coli manages protein synthesis stability.
This project will act as an example of how simulations can be used as an alternative way to answer scientific questions that would traditionally require a lab experiment. In terms of skills, I hope that this project will improve my ability to create simulations and use stochastic modeling. I intend to use computational techniques including Monte Carlo simulations, Hill functions, and Markov Chains. This project will also help me gain experience with using GLMs and Bayesian hierarchical models, which I will use to quantify the E. Colis’ synthesis rate of stability across codon categories.
The project will involve developing a python-based simulation which can model the synthesis rate changes of robust and sensitive codons in E. coli under stress, specifically changing nutrient conditions. It will primarily focus on the codon degeneracy lifting mechanism, which will provide a computational framework to compute the synthesis rate. As mentioned, the simulation will make use of computational techniques and statistical modeling, both of which will be used to model the process, interpret data, and compute the result. I will accumulate this process into a report which will follow a traditional experimental format.
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08635-0\
https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1010641
https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.1042675/full
https://link.springer.com/article/10.1134/S0006297923140109
https://www.jbc.org/article/S0021-9258%2823%2902191-9/fulltext
https://microbialcellfactories.biomedcentral.com/articles/10.1186/s12934-023-02230-y
https://www.biorxiv.org/content/10.1101/2024.03.21.586065v1.full.pdf
https://arxiv.org/abs/1212.1537?utm