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At a certain moment, antigen levels are set at a concentration of 1. In the new scoring function, the model is simulated in absence of antigen. Additionally, I altered the speed objective score. Thus, I simplified the setup 2 and compared this to the behavior of setup 2 and 3. Looking critically at the setups 2 and 3, I saw these could be simplified. September Week 18 (29th of August - 4th of September) Go/No-Go presentation! In addition I extended the sensitivity analysis to include varying concentrations of CpxR and CpxA, as these are the key players in producing the YFP signal. Week 17 (22nd of August - 28th of August) I thought of several ways of visualizing the influence of the antigen concentration on the objective scores. This week I set up the draft script in which the model is simulated under different concentrations of antigen. Week 16 (15th of August - 21st of August) New scoring function taking background GFP was developed.
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Setup 2 was adapted to conform with setup 3, as this does take into account background GFP formation. Together with Bart and the supervisors, possible future experiments were discussed to combine wet-lab with the model.Īugust Week 14 (1st of August - 7th of August) Log-uniform distributed sets were generated.Īnalysis of setup 2 was finished. Start setting up Setup 1, in which dimerization of phosphorylated CpxR leads to GFP maturation. Some conclusions were made on the characteristics of good Setup 2 parameter sets. This week was spent on figuring out and selecting the pareto sets of the setup 2 parameter set scores.Īs the speed score itself is not very characteristic for the efficiency of the parameter set, a new score takes both time and increased GFP concentration into account. The log-uniform distribution and uniform distributed sets were compared based on speed and GFP scores. This is to run processes continuously and parallel to each other.ĭeveloped second objective score script, aimed at saving the time it takes to reach half of the final GFP concentration.
MATLAB LATIN HYPERCUBE SAMPLING LOGNORMAL HOW TO
Learned about servers, terminal, screens and how to generate parameters outside the Mac. Included parameterset distributed by log-uniform distribution. Retried with 500,000 parameter sets and learned about lognormal distributions. Generated 100,000 parameters sets, still found too much inconsistency when analyzing the best 10%. Learned how to introduce pulses to the system. The objective used for selecting the best parameter sets was a maximal final GFP concentration that could be formed with those parameters. Here, random parameters were generated using Latin Hypercube sampling. The model gives the concentration of each compound over time, given a set op reaction speeds (parameters).Īs the parameters of most reactions of the system are not known, parameter optimisation can be used. Improved the reaction equations and started writing in Matlab.įirst runs of model. Started on the second model layout (Setup 2), which involves GFPc bound to CpxA and GFPn to CpxR. More system design and learning Matlab (and getting used to my Mac). Aim of the thesis is to compare the systems kinetics, sensitivities and their maximum output.
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Discussed with receptor and BiFC students on system layout, finally decided on three different ways in which the system could work. Learning Matlab and Modelling basics through a short Maths lecture and multiple exercises.įinished thesis proposal.