Team:Wageningen UR/Notebook/Modelling
Quorum sensing
May
Week 1
literature research about quorum sensing systems. Drawing quorum sensing system.
Week 2
Writing quorum sensing equations. Writing scripts in Matlab. Testing first scripts for a single cell, simulate the model response vs time.
Week 3
First results on single cell scripts. Expanding model for multi cell population. Testing multi cell, simulate the model response vs time.
Week 4
Look at correlations of different parameters in the model. Simulate the model for the GFP response with different parameter sets, low, medium and high set. Implement positive and negative feedback of LuxR in the system. test whether the mRNA transcription of LuxR feedback protein has an influence on the system.
June
Week 1
looked at the correlations of the different feedback in the system. To see which parameter has the biggest influence on the system and which parameters could be changed to create subpopulations. Fixed bugs in the system with help of Ronald.
Week 2
With changing the parameters with the biggest correlation different populations can be generated. The LuxR production rate is changed to do so. However, this does not correlate to reality yet. Subpopulations are simulated, but further research is needed to create a system that can be tested in the lab.
July
Week 1
Went to iGEM meetup in Paris
Week 3
Expand the old quorum sensing model to 100.000 parameter sets. This needed to run on the server because it took too long on a single computer. Because the server did not save the work properly it needed to be done a few times. So it took a full week to get results.
Week 4
make the subpopulation system, write the equations and try to get the best parameter sets by searching for a statistical method that does so. For the quorum sensing system. look at the quorum sensing system with and without feedback.
August
Week 1
Test changes in highest parameter from the quorum sensing system to see if there is a change in GFP production. This is both done for the system with and without negative feedback.
Week 2
Set up the subpopulation system and run it with a few parameter sets. When it worked I changed the concentrations in Arabinose and Glucose in the system. Had some bugs with putting different arabinose and glucose concentrations in the system. Remco helped me solve this.
Week 3
To set up a normal distribution for the quorum sensing part different methods are tried. fixing bugs when making the normal distributions. Plot the amount of GFP vs time. The subpopulation system ran on the server for a couple of days with 100.000 parameter sets. There is searched for the parameter set with the highest RFP, and also for the set with the lowest RFP.
Week 4
Parameter analysis with the best sets, got from the confidence interval. Put the models, quorum and subpopulations together. This did not work out the first time, but the second it did.
September
Week 1
plot normal distributions of the separate systems.
Week 2
look for differences in production of GFP by tweaking the parameters. Chose the best sets to work with for the combined system. Found four sets that can give the responses we want.
Week 3
Calculate growth rate for the e. coli used by Thomas.
Week 4
Tried the Event function in Matlab to generate oscillations in the system. This failed due to the fact that it could be only used for single cells and not for multi cell populations. We did not know that this was a limitation of the function. Try to look at the effect of the total volume for a single cell and multicell, but the multicell was not possible.
October
Week 1
I got a code for generating oscillations from my supervisor, but it needed to be debugged. So I spended the week on doing this myself and together with my supervisor. Make a heatmap for the subpopulation system about the ratio of lambda-cl and 434 compared to the RFP production.
Week 2
look at ratios lambda-cl and 434 to compare with the data Thomas got from the lab. He used a library for the promoter from the subpopulation system. Tried to simulate the combined model with total RFP.
Beehave
July
Week 4
Orientation literature research, found articles on BEEHAVE model. Proceeded to read the modeling articles BEEHAVE is based on. Also installed NetLogo and ran a few standard simulations.
August
Week 1
Started looking into BEEHAVE model and figuring out how to integrate BeeT into it. Identified ways of adding mite mortality to the model. Made a very rough first approximation of mite mortality and fixed resultant bugs.
Week 2
Start looking into how to have transport BeeT into the hive. Initial idea is to add an additional 'BeeT' patch, turns out patches are hard coded into the model and adding in a new one is difficult. Got started with a new BeeT module 'BeeTproc' since the changing the patch was a dead end.
Week 3
Started using a local github depository since changes were getting difficult to keep track of. Added comments to all the code I added and put everything into github. Also coupled BeeT in the hive to mite mortality. Added period at which BeeT patch is present.
Week 4
Worked on getting initial results by increasing year round mite mortality, worked on understanding RNetLogo and getting some statistics for results. More background literature research as many aspects of system still unclear
September
Week 1
Had a call with a beekeeper (Johan Calis) and based on that call found articles regarding sugar water transport in the hive. Started working on alternative application method using bee bread.
Week 2
Added in degradation for BeeT in the patch and the hive. Started adding in code to handle bee bread. Unclear how to determine how much ends up at the larvae.
Week 3
Realised that consumption of pollen and sugar water is included in BEEHAVE. Proceed to couple BeeT transport in the hive to consumption patterns of bees and larvae. Fixing more bugs.
Week 4
Finish up final version of BeeT module, include reporters to make sure everything works correctly. General cleanup of code. Get access to server and start figuring out how parallel works.
October
Week 1
Find bugs in my RNetLogo code related to the server. Waste time tracking them down and fixing them. Mess up parallel so that it repeats the same code 20 times instead of breaking it up into 20 pieces. Improving R code so I can save results while parallel is running. Start running code on server.
Week 2
Start writing wiki related material. Get results from the server and start analyses. Run more in depth runs to figure out what is going on exactly.
