file decompilers. We use readelf. It makes it faster to read the ELF file by turning it into human readable output. But you must have seen one byte-by-byte example first, and think how readelf output maps to the standard.
Given the view of the Standard Model where the electron and quarks are just completely separate matter fields, there is at first sight no clear theoretical requirement for that.
The Docker setup from README does just work. The image download is abit tedius, as it requires you to create aGitHub API key as described in the README, but there must be reasons for that.
Once the image is downloaded, you really want to run is from the root of the source tree:
sudo docker run --name=wcm -it -v "$(pwd):/wcEcoli" docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full
This mounts the host source under /wcEcoli, so you can easily edit and view output images from your host. Once inside Docker we can compile, run the simulation, and analyze results with:
Asa Docker refresher, after you stop the container, e.g. by restarting your computer or running sudo docker stop wcm, you can get back into it with:
sudo docker start wcm
sudo docker run -it wcm bash
runscripts/manual/runFitter.py takes about 15 minutes, and it generates files such as reconstruction/ecoli/dataclasses/process/two_component_system.py (related) which is required to run the simulation, it is basically a part of the build.
runSim.py does the main simulation, progress output contains lines of type:
Time (s) Dry mass Dry mass Protein RNA Small mol Expected
(fg) fold change fold change fold change fold change fold change
======== ======== =========== =========== =========== =========== ===========
0.00 403.09 1.000 1.000 1.000 1.000 1.000
0.20 403.18 1.000 1.000 1.000 1.000 1.000
when the cell had almost doubled, and presumably divided in 42 minutes of simulated time, which could make sense compared to the 20 under optimal conditions.
Some of the output data is stored as .cpickle files. To observe those files, you need the original Python classes, and therefore you have to be inside Docker, from the host it won't work.
We can list all the plots that have been produced under out/ with
find -name '*.png'
Plots are also available in SVG and PDF formats, e.g.:
wildtype_000000: variant conditions. wildtype is ahuman readable label, and 000000 is an index amongst the possible wildtype conditions. For example, we can have different simulations with different nutrients, or different DNAsequences. An example of this is shown at run variants.
genereation_000000: this will increase with generations if we simulate multiple cells, which is supported by the model
000000: this will presumably contain the cell index within a generation
We also understand that some of the top level directories contain summaries over all cells, e.g. the massFractionSummary.pdf plot exists at several levels of the hierarchy:
Each of thoes four levels of plotOut is generated by a different one of the analysis scripts:
./out/manual/plotOut: generated by python runscripts/manual/analysisVariant.py. Contains comparisons of different variant conditions. We confirm this by looking at the results of run variants.
./out/manual/wildtype_000000/plotOut: generated by python runscripts/manual/analysisCohort.py --variant_index 0. TODO not sure how to differentiate between two different labels e.g. wildtype_000000 and somethingElse_000000. If -v is not given, a it just picks the first one alphabetically. TODO not sure how to automatically generate all of those plots without inspecting the directories.
./out/manual/wildtype_000000/000000/plotOut: generated by python runscripts/manual/analysisMultigen.py --variant_index 0 --seed 0
./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated by python runscripts/manual/analysisSingle.py --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specificcell.
It would be boring if we could only simulate the same condition all the time, so let's have a look at the different boundary conditions that we can apply to the cell!
We are able to alter things like the composition of the external medium, and the genome of the bacteria, which will make the bacteria behave differently.
The variant selection is abit cumbersome aswe have to use indexes instead of names, but one you know what you are doing, it is fine.
which select row indices from reconstruction/ecoli/flat/condition/condition_defs.tsv. The above 1 1 would mean the second line of that file which starts with:
