Team:UFSCar-Brasil/modelling.html
Modelling
The mathematical explanation of our results, and how we can provide desired information
OverviewOverview
Our main purpose with modeling was to determine how deeply connected the collected data are and starting from this to make some previsions and take decisions. For this purpose, we have used several mathematical tools, since: analytic geometry, complex systems calculations to final tridimensional solutions. We hope that our previsions and mathematical models help the next teams and groups work better and harder, despite of course answer important questions of our current work.
Plasmolysis
This section proposes to determine what would be the longest time of bacterial maintenance with lowest possible percentage of PEG 6000. Plasmolysis experiments were performed in microbiology laboratory for some weeks. Collected data were processed and analyzed for best-fit model to those points. After that, using this model, it was built a simulated surface and specific values were found. Finally, we found tangent lines to the surface where our point of interest could be found. This study was of great significance when we realize that our project belongs to manufacturing tracking. Since it results in a product, and it should be found in stores, this analysis makes possible to predict the validity of the product and of its storage process.
Protein solubilization toolkit
Desenvolvemos uma abordagem estatística para o estudo da eficiência das chaperonas no enovelamento da limoneno sintase. Com o intuito de criar uma método que melhor descreva a combinação de chaperonas com base nos dados de rendimento, criamos um dendograma que ilustra o agrupamento delas com base na distancia estatística dos seus rendimentos. Depois foi resolvido um sistema linear com todas as relações e encontrou-se constantes estatísticas de cada componente.
Kill Switch
One of our prime objective is to describe the activity of uspA promoter ( Universal Stress Protein A promoter) when exposed to osmotic chock compared with J23101 promoter. To quantify more precisely this behavior we adjust experimental points with general exponential functions and also related PEG concentration’s date with osmotic pressure. With the proper fitted curves, we modeled the concentration’s fall of Zn 2+ from external environment by import the metal to intracellular environment and gradually build up the smtA protein aiming estimate the approximate time for begin the death cell process, that initiate with the release of our killswitch’s promoter region due to the absence of Zn in cellular environmental to maintain the zur factor repressing ufscarA promoter. With the unblocked promoter, the transcription of death genes starts, metabolism and cell integrity is compromised leading to cell death.
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