Ciro Santilli (@cirosantilli) - New articles

E. Coli Whole Cell Model by Covert Lab / Default run variant Updated 2025-07-16

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.

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E. Coli Whole Cell Model by Covert Lab / Time series run variant Updated 2025-07-16

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To modify the nutrients as a function of time, with To select a time series we can use something like:

python runscripts/manual/runSim.py --variant nutrientTimeSeries 25 25

As mentioned in python runscripts/manual/runSim.py --help, nutrientTimeSeries is one of the choices from github.com/CovertLab/WholeCellEcoliRelease/blob/7e4cc9e57de76752df0f4e32eca95fb653ea64e4/models/ecoli/sim/variants/__init__.py#L57

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: github.com/CovertLab/WholeCellEcoliRelease/issues/24

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/analysisVariant.py &&
python runscripts/manual/analysisCohort.py --variant 25 &&
python runscripts/manual/analysisMultigen.py --variant 25 &&
python runscripts/manual/analysisSingle.py --variant 25

We can now compare the outputs of this run to the default wildtype_000000 run from Section "Install and first run".

out/manual/plotOut/svg_plots/massFractionSummary.svg: because we now have two variants in the same out/ folder, wildtype_000000 and nutrientTimeSeries_000025, we now see a side by side comparision of both on the same graph!
The run variant where we started with amino acids initially grows faster as expected, because the cell didn't have to make it's own amino acids, so growth is a bit more efficient.
Then, at 20 minutes, which is about 0.3 hours, we see that the cell starts growing a bit less fast as the slope of the curve decreases a bit, because we removed that free amino acid supply.
Figure 1.
Minimal condition vs amino acid cut mass fraction plot
. Source. From file out/manual/plotOut/svg_plots/massFractionSummary.svg.

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 $f
  svg_stack.py \
    --direction h \
    out/manual/wildtype_000000/000000/generation_000000/000000/plotOut/svg_plots/$(basename $f) \
    out/manual/nutrientTimeSeries_000025/000000/generation_000000/000000/plotOut/svg_plots/$(basename $f) \
    > tmp/$(basename $f)
done

Figure 2.
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 3.
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 4.
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 5.
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`: biocyc.org/gene?orgid=ECOLI&id=EG11036, which comments
Elongation factor Tu (EF-Tu) is the most abundant protein in E. coli.
and
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 6.
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.

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E. Coli K-12 MG1655 origin of replication Updated 2025-07-16

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biocyc.org/ECOLI/NEW-IMAGE?type=EXTRAGENIC-SITE&object=G0-10506:

Note that this is not the conventional starting point for gene numbering: Section "E. Coli genome starting point".

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E. Coli K-12 MG1655 gene Updated 2025-07-16

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E. Coli K-12 MG1655 gene of unknown function Updated 2025-07-16

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All gene names that start with an Y such as:

appear to be proteins of unknown function.

UniProt for example describes YaaX as "Uncharacterized protein YaaX".

As function is discovered, they then change it to a better name, e.g. to names such as the E. Coli K-12 MG1655 transcription unit thrLABC proteins all of which have a clear name due to threonine.

There are many other y??? as of 2021! Though they do tend to be smaller molecules.

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E. Coli K-12 MG1655 promoter Updated 2025-07-16

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biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI

From this we see that there is a convention of naming promoters as protein name + p, e.g. the first gene in E. Coli K-12 MG1655 promoter thrLp encodes protein thrL.

It is also possible to add numbers after the p, e.g. at biocyc.org/ECOLI/NEW-IMAGE?type=OPERON&object=PM0-45989 we see that the protein zur has two promoters:

zurp6
zurp7

TODO why 6 and 7? There don't appear to be 1, 2, etc.

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Pluto Updated 2025-07-16

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gothinkster/django-realworld-example-app Updated 2025-07-16

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As of 2021, last updated 2016, and python 3.5 appears to be mandatory or else:

RuntimeError: __class__ not set defining 'AbstractBaseUser' as <class 'django.contrib.auth.base_user.AbstractBaseUser'>. Was __classcell__ propagated to type.__new__?

which apparently broke in 3.6: stackoverflow.com/questions/41343263/provide-classcell-example-for-python-3-6-metaclass and pyenv install fails on Ubuntu 20.10, so... fuck. Workarounds at:

but am I in the mood considering that the ancient Django version would require an immediate port anyways? Repo is at Django 1.0, while newest is now already Django 3. The Rails one is broken for the same reason. Fuck 2.

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Markus W. Covert Updated 2025-07-16

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Covert Lab lead.

Ciro Santilli really likes this dude, because Ciro really likes simulation.

Video 1.

How to build a computer model of a cell by Markus Covert (2020)

Source.

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Newton supported the corpuscular theory of light Updated 2025-07-16

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Cool data embedded in the Bitcoin blockchain / ILoveYouMore.jpg Updated 2026-04-05

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This is the first of many love declarations and mentions EMBII makes of his partner Chiharu! This came just one day afte the very first uploads of the system.