Ciro Santilli $£ Sponsor €¥ 中国独裁统治 China Dictatorship 新疆改造中心、六四事件、法轮功、郝海东、709大抓捕、2015巴拿马文件 邓家贵、低端人口、西藏骚乱 is a whole cell simulation model created by Covert Lab and other collaborators.
The project is written in Python, hurray! But according to te README, it seems to be the use a code drop model with on-request access to master, very meh, asked rationale on GitHub discussion, and they confirmed as expected that it is to:
  • to prevent their publication ideas from being stolen. Who would steal publication ideas with public proof in an issue tracker without crediting original authors?
  • to prevent noise from non collaborators. They do only get like 2 issues as year though, people forget that it is legal to ignore other people :-)
Oh well.
The project is a followup to the earlier M. genitalium whole cell model by Covert lab which modelled Mycoplasma genitalium. E. Coli has 8x more genes (500 vs 4k), but it the undisputed bacterial model organism and as such has been studied much more thoroughly. It also reproduces faster than Mycoplasma (20 minutes vs a few hours), which is a huge advantages for validation/exploratory experiments.
The project has a partial dependency on the proprietary optimization software CPLEX which is freeware, for students, not sure what it is used for exactly, from the comment in the requirements.txt the dependency is only partial.
This project makes Ciro Santilli think of the E. Coli as an optimization problem. Given such external nutrient/temperature condition, which DNA sequence makes the cell grow the fastest? Balancing metabolites feels like designing a Factorio speedrun.
Everything in this section refers to version 7e4cc9e57de76752df0f4e32eca95fb653ea64e4, the code drop from November 2020, and was tested on Ubuntu 21.04 with a docker install of with image id 502c3e604265, unless otherwise noted.
At 7e4cc9e57de76752df0f4e32eca95fb653ea64e4 you basically need to use the Docker image on Ubuntu 21.04 due to pip breaking changes... (not their fault). Perhaps pyenv would solve things, but who has the patience for that?!?!
The Docker setup from README does just work. The image download is a bit tedius, as it requires you to create a GitHub 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"
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:
make clean compile &&
python runscripts/manual/ &&
python runscripts/manual/ &&
python runscripts/manual/ &&
python runscripts/manual/ &&
python runscripts/manual/ &&
python runscripts/manual/
The meaning of each of the analysis commands is described at Section 2. "Output overview".
As a 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/ takes about 15 minutes, and it generates files such as reconstruction/ecoli/dataclasses/process/ (related) which is required to run the simulation, it is basically a part of the build. 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
and then it ended on the Lenovo ThinkPad P51 (2017) at:
 2569.18    783.09        1.943        1.910        2.005        1.950        1.963

Simulation finished:
 - Length: 0:42:49
 - Runtime: 0:09:13
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.

2. Output overview

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Run output is placed under out/:
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.:
  • PNG: ./out/manual/plotOut/low_res_plots/massFractionSummary.png
  • SVG: ./out/manual/plotOut/svg_plots/massFractionSummary.svg The SVGs write text as polygons, see also: SVG fonts.
  • PDF: ./out/manual/plotOut/massFractionSummary.pdf
The output directory has a hierarchical structure of type:
  • wildtype_000000: variant conditions. wildtype is a human readable label, and 000000 is an index amongst the possible wildtype conditions. For example, we can have different simulations with different nutrients, or different DNA sequences. An example of this is shown at run variants.
  • 000000: initial random seed for the initial cell, likely fed to NumPy's np.random.seed
  • 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/ 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/ --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/ --variant_index 0 --seed 0
  • ./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated by python runscripts/manual/ --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specific cell.
Let's look into a sample plot, out/manual/plotOut/svg_plots/massFractionSummary.svg, and try to understand as much as we can about what it means and how it was generated.
This plot contains how much of each type of mass is present in all cells. Since we simulated just one cell, it will be the same as the results for that cell.
We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential.
  • total dry mass (mass excluding water)
  • protein mass
  • rRNA mass
  • mRNA mass
  • DNA mass. The last label is not very visible on the plots, but we can deduce it from the source code.
By grepping the title "Cell mass fractions" in the source code, we see the files:
which must correspond to the different massFractionSummary plots throughout different levels of the hierarchy.
By reading models/ecoli/analysis/variant/ a little bit, we see that:
  • the plotting is done with Matplotlib, hurray
  • it is reading its data from files under ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/, more precisely ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/columns/<column-name>/data. They are binary files however.
    Looking at the source for wholecell/io/ shows that those are just a standard NumPy serialization mechanism. Maybe they should have used the Hierarchical Data Format instead.
    We can also take this opportunity to try and find where the data is coming from. Mass from the ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/ looks like an ID, so we grep that and we reach models/ecoli/listeners/
    From this we understand that all data that is to be saved from a simulation must be coming from listeners: likely nothing, or not much, is dumped by default, because otherwise it would take up too much disk space. You have to explicitly say what it is that you want to save via a listener that acts on each time step.
Figure 1. Minimal condition mass fraction plot. Source. File name: out/manual/plotOut/svg_plots/massFractionSummary.svg
More plot types will be explored at time series run variant, where we will contrast two runs with different growth mediums.

3. Run variants

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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 a bit cumbersome as we have to use indexes instead of names, but one you know what you are doing, it is fine.
Of course, genetic modification is limited only to experimentally known protein interactions due to the intractability of computational protein folding and computational chemistry in general, solving those would bsai.
The default run variant, if you don't pass any options, just has the minimal growth conditions set. What this means can be seen at condition.
Notably, this implies a growth medium that includes glucose and salt. It also includes oxygen, which is not strictly required, but greatly benefits cell growth, and is of course easier to have than not have as it is part of the atmosphere!
But the medium does not include amino acids, which the bacteria will have to produce by itself.
To modify the nutrients as a function of time, with To select a time series we can use something like:
python runscripts/manual/ --variant nutrientTimeSeries 25 25
As mentioned in python runscripts/manual/ --help, nutrientTimeSeries is one of the choices from
25 25 means to start from index 25 and also end at 25, so running just one simulation. 25 27 would run 25 then 26 and then 27 for example.
The timeseries with index 25 is reconstruction/ecoli/flat/condition/timeseries/000025_cut_aa.tsv and contains
"time (units.s)" "nutrients"
0 "minimal_plus_amino_acids"
1200 "minimal"
so we understand that it starts with extra amino acids in the medium, which benefit the cell, and half way through those are removed at time 1200s = 20 minutes. We would therefore expect the cell to start expressing amino acid production genes exactly at that point.
nutrients likely means condition in that file however, see bug report with 1 1 failing:
When we do this the simulation ends in:
Simulation finished:
 - Length: 0:34:23
 - Runtime: 0:08:03
so we see that the doubling time was faster than the one with minimal conditions of 0:42:49, which makes sense, since during the first 20 minutes the cell had extra amino acid nutrients at its disposal.
The output directory now contains simulation output data under out/manual/nutrientTimeSeries_000025/. Let's run analysis and plots for that:
python runscripts/manual/ &&
python runscripts/manual/ --variant 25 &&
python runscripts/manual/ --variant 25 &&
python runscripts/manual/ --variant 25
We can now compare the outputs of this run to the default wildtype_000000 run from Section 1. "Install and first run".
The following plots from under out/manual/wildtype_000000/000000/{generation_000000,nutrientTimeSeries_000025}/000000/plotOut/svg_plots have been manually joined side-by-side with:
for f in out/manual/wildtype_000000/000000/generation_000000/000000/plotOut/svg_plots/*; do
  echo $f \
    --direction h \
    out/manual/wildtype_000000/000000/generation_000000/000000/plotOut/svg_plots/$(basename $f) \
    out/manual/nutrientTimeSeries_000025/000000/generation_000000/000000/plotOut/svg_plots/$(basename $f) \
    > tmp/$(basename $f)
Figure 3. Amino acid counts. Source. aaCounts.svg:
  • default: quantities just increase
  • amino acid cut: there is an abrupt fall at 20 minutes when we cut off external supply, presumably because it takes some time for the cell to start producing its own
Figure 4. External exchange fluxes of amino acids. Source. aaExchangeFluxes.svg:
  • default: no exchanges
  • amino acid cut: for all graphs except phenylalanine (PHE), either the cell was intaking the AA (negative flux), and that intake goes to 0 when the supply is cut, or the flux is always 0.
    For PHE however, the flux is at all times, except shortly after the cut. Why? And why there was no excretion on the default conditions?
Figure 5. Evaluation time. Source. evaluationTime.svg: this has nothing to do with biology, but it is rather a profile of the program runtime. We can see that the simulation gets slower and slower as time passes, presumably because there are more and more molecules to simulate.
Figure 6. mRNA count of highly expressed mRNAs. Source. From file expression_rna_03_high.svg. Each of the entries is a gene using the conventional gene naming convention of xyzW, e.g. here's the BioCyc for the first entry, tufA:, which comments
Elongation factor Tu (EF-Tu) is the most abundant protein in E. coli.
In E. coli, EF-Tu is encoded by two genes, tufA and tufB
. What they seem to mean is that tufA and tufB are two similar molecules, either of which can make up the EF-Tu of the E. Coli, which is an important part of translation.
Figure 7. External exchange fluxes. Source.
mediaExcange.svg: this one is similar to aaExchangeFluxes.svg, but it also tracks other substances. The color version makes it easier to squeeze more substances in a given space, but you lose the shape of curves a bit. The title seems reversed: red must be excretion, since that's where glucose (GLC) is.
The substances are different between the default and amino acid cut graphs, they seem to be the most exchanged substances. On the amino cut graph, first we see the cell intaking most (except phenylalanine, which is excreted for some reason). When we cut amino acids, the uptake of course stops.
Besides time series run variants, conditions can also be selected directly without a time series as in:
python runscripts/manual/ --variant condition 1 1
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:
"condition" "nutrients" "genotype perturbations" "doubling time (units.min)" "active TFs"
"basal" "minimal" {} 44.0 []
"no_oxygen" "minimal_minus_oxygen" {} 100.0 []
"with_aa" "minimal_plus_amino_acids" {} 25.0 ["CPLX-125", "MONOMER0-162", "CPLX0-7671", "CPLX0-228", "MONOMER0-155"]
so 1 means no_oxygen.

5. Source code overview

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The key model database is located in the source code at reconstruction/ecoli/flat.
Let's try to understand some interesting looking, with a special focus on our understanding of the tiny E. Coli K-12 MG1655 operon thrLABC part of the metabolism, which we have well understood at Section "E. Coli K-12 MG1655 operon thrLABC".
We'll realize that a lot of data and IDs come from/match BioCyc quite closely.
Before we start, there is one major thing missing thing in the current model: promoters/transcription factor interactions are not modelled due to lack/low quality of experimental data: They just have a magic direct "transcription factor to gene" relationship, encoded at reconstruction/ecoli/flat/foldChanges.tsv in terms of type "if this is present, such protein is expressed 10x more". Transcription units are not implemented at all it appears.

5.1. Condition

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TODO compare with actual datasetes.

7. Publications

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Unfortunately, due to lack of one page to rule them all, the on-Git tree publication list is meager, some of the most relevant ones seems to be:


  1. E. Coli whole cell simulation
  2. Escherichia coli
  3. List of bacteria
  4. Bacteria
  5. Species
  6. Taxonomy
  7. Biology
  8. Natural science
  9. Science
  10. Ciro Santilli's Homepage