Model of honey bee receptor responses and resulting antennal lobe (AL) activity.
This model is described in
Ho Ka Chan, Fabian Hersperger, Emiliano Marachlian, Brian H Smith, Fernando Locatelli, Paul Szyszka, Thomas Nowotny, Odorant mixtures elicit less variable and faster responses than pure odorants. PLoS Computational Biology (2018)
Corresponding authors: Ho Ka Chan <firstname.lastname@example.org>, Thomas Nowotny <email@example.com>
To run the model:
1. Compile all .cpp files with your favourite compiler, e.g.
>> g++ -o 1highconc_response_no-chemsim 1highconc_response_no-chemsim.cpp
Open in Visual Studio, or use command line tools:
>> CL /Fe 1highconc_response_no-chemsim.exe 1highconc_response_no-chemsim.cpp
2. Run the files in the order of the numbers in teh file name, i.e.
>> ./2finding chem sim in data
>> 2finding chem sim in data.exe
3. The results are stored in ASCII files that can then be used to plot various aspects of the model (in the order how they are generated):
- ORNdata.txt: contains the experimental data from 28 glomeruli of honey bee in response to 16 odors, used as input
- responseinter.txt: contains the receptor steady-state response (at saturating concentration) generated by the model, which has taken the correlation between ORN response into account but without considering the chemical similarity of odorants.
- chemsimresponse.txt: same as above, but also considers the chemcial similarity of odorants (calculated based on ORNdata.txt)
- files with names having numbers 0 to -6 at the end corresponds to the steady state responses of receptor (receptor activation), ORN and PN. The number is the concentration/dilution of the stimuli.
- nprime: stores the value of n'=n*log(10) for different receptors, where n is the Hill coefficient.
- kone, knegone, ktwo, knegtwo: store the value of k_1, k_-1, k_2, k_-2 for different receptor-odorant combinations