Ciro Santilli (@cirosantilli) - undefined articles

Artificial gene synthesis  Updated 2024-12-15  +Created 1970-01-01

Using de novo DNA synthesis to synthesize a genes to later insert somewhere.

Note that this is a specific application of de novo DNA synthesis, e.g. polymerase chain reaction primers is another major application that does not imply creating genes.

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E. Coli K-12 MG1655  Updated 2024-12-15  +Created 1970-01-01

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NCBI taxonomy entry: www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=511145 This links to:

genome: www.ncbi.nlm.nih.gov/genome/?term=txid511145 From there there are links to either:
- Download the FASTA: "Download sequences in FASTA format for genome, protein"
  For the genome, you get a compressed FASTA file with extension .fna called GCF_000005845.2_ASM584v2_genomic.fna that starts with:
  >NC_000913.3 Escherichia coli str. K-12 substr. MG1655, complete genome AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGCTTCTGAACTG
  Using wc as in wc GCF_000005845.2_ASM584v2_genomic.fna gives 58022 lines, in Vim we see that each line is 80 characters, except for the final one which is 52. So we have 58020 * 80 + 52 = 4641652 =~ 4.6 Mbp
- Interactively browse the sequence on the browser viewer: "Reference genome: Escherichia coli str. K-12 substr. MG1655" which eventually leads to: www.ncbi.nlm.nih.gov/nuccore/556503834?report=graph
  If we zoom into the start, we hover over the very first gene/protein: the famous (just kidding) e. Coli K-12 MG1655 gene thrL, at position 190-255.
  The second one is the much more interesting e. Coli K-12 MG1655 gene thrA.
- Gene list, with a total of 4,629 as of 2021: www.ncbi.nlm.nih.gov/gene/?term=txid511145

KEGG entry: www.genome.jp/pathway/eco01100+M00022

BioCyc promoter database query URL: biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI

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E. Coli K-12 MG1655 gene of unknown function  Updated 2024-12-15  +Created 1970-01-01

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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 gene thrA  Updated 2024-12-15  +Created 1970-01-01

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UniProt entry: www.uniprot.org/uniprot/P00561.

NCBI entry: www.ncbi.nlm.nih.gov/gene/945803.

The second gene in the E. Coli K-12 MG1655 genome. Part of the E. Coli K-12 MG1655 operon thrLABC.

Part of a reaction that produces threonine.

This protein is an enzyme. The UniProt entry clearly shows the chemical reactions that it catalyses. In this case, there are actually two! It can either transforming the metabolite:

"L-homoserine" into "L-aspartate 4-semialdehyde"
"L-aspartate" into "4-phospho-L-aspartate"

Also interestingly, we see that both of those reaction require some extra energy to catalyse, one needing adenosine triphosphate and the other nADP+.

TODO: any mention of how much faster it makes the reaction, numerically?

Since this is an enzyme, it would also be interesting to have a quick search for it in the KEGG entry starting from the organism: www.genome.jp/pathway/eco01100+M00022 We type in the search bar "thrA", it gives a long list, but the last entry is our "thrA". Selecting it highlights two pathways in the large graph, so we understand that it catalyzes two different reactions, as suggested by the protein name itself (fused blah blah). We can now hover over:

the edge: it shows all the enzymes that catalyze the given reaction. Both edges actually have multiple enzymes, e.g. the L-Homoserine path is also catalyzed by another enzyme called metL.
the node: they are the metabolites, e.g. one of the paths contains "L-homoserine" on one node and "L-aspartate 4-semialdehyde"

Note that common cofactor are omitted, since we've learnt from the UniProt entry that this reaction uses ATP.

If we can now click on the L-Homoserine edge, it takes us to: www.genome.jp/entry/eco:b0002+eco:b3940. Under "Pathway" we see an interesting looking pathway "Glycine, serine and threonine metabolism": www.genome.jp/pathway/eco00260+b0002 which contains a small manually selected and extremely clearly named subset of the larger graph!

But looking at the bottom of this subgraph (the UI is not great, can't Ctrl+F and enzyme names not shown, but the selected enzyme is slightly highlighted in red because it is in the URL www.genome.jp/pathway/eco00260+b0002 vs www.genome.jp/pathway/eco00260) we clearly see that thrA, thrB and thrC for a sequence that directly transforms "L-aspartate 4-semialdehyde" into "Homoserine" to "O-Phospho-L-homoserine" and finally tothreonine. This makes it crystal clear that they are not just located adjacently in the genome by chance: they are actually functionally related, and likely controlled by the same transcription factor: when you want one of them, you basically always want the three, because you must be are lacking threonine. TODO find transcription factor!

The UniProt entry also shows an interactive browser of the tertiary structure of the protein. We note that there are currently two sources available: X-ray crystallography and AlphaFold. To be honest, the AlphaFold one looks quite off!!!

By inspecting the FASTA for the entire genome, or by using the NCBI open reading frame tool, we see that this gene lies entirely in its own open reading frame, so it is quite boring

From the FASTA we see that the very first three Codons at position 337 are

ATG CGA GTG

where ATG is the start codon, and CGA GTG should be the first two that actually go into the protein:

CGA: arginine
GTG: valine

ecocyc.org/gene?orgid=ECOLI&id=ASPKINIHOMOSERDEHYDROGI-MONOMER mentions that the enzime is most active as protein complex with four copies of the same protein:

Aspartate kinase I / homoserine dehydrogenase I comprises a dimer of ThrA dimers. Although the dimeric form is catalytically active, the binding equilibrium dramatically favors the tetrameric form. The aspartate kinase and homoserine dehydrogenase activities of each ThrA monomer are catalyzed by independent domains connected by a linker region.

TODO image?

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E. Coli K-12 MG1655 gene thrL  Updated 2024-12-15  +Created 1970-01-01

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UniProt entry: www.uniprot.org/uniprot/P0AD86.

NCBI gene entry: www.ncbi.nlm.nih.gov/gene/944742.

The first gene in the E. Coli K-12 MG1655 genome. Remember however that bacterial chromosome is circular, so being the first doesn't mean much, how the choice was made: Section "E. Coli genome starting point".

Part of E. Coli K-12 MG1655 operon thrLABC.

At only 65 bp, this gene is quite small and boring. For a more interesting gene, have a look at the next gene, e. Coli K-12 MG1655 gene thrA.

Does something to do with threonine.

This is the first in the sequence thrL, thrA, thrB, thrC. This type of naming convention is quite common on related adjacent proteins, all of which must be getting transcribed into a single RNA by the same promoter. As mentioned in the analysis of the KEGG entry for e. Coli K-12 MG1655 gene thrA, those A, B and C are actually directly functionally linked in a direct metabolic pathway.

We can see that thrL, A, B, and C are in the same transcription unit by browsing the list of promoter at: biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI. By finding the first one by position we reach; biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486.

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E. Coli K-12 MG1655 operon thrLABC  Updated 2024-12-15  +Created 1970-01-01

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Contains the genes: e. Coli K-12 MG1655 gene thrL, e. Coli K-12 MG1655 gene thrA, e. Coli K-12 MG1655 gene thrB and e. Coli K-12 MG1655 gene thrC, all of which have directly linked functionality.

We can find it by searching for the species in the BioCyc promoter database. This leads to: biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI.

By finding the first operon by position we reach: biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486.

That page lists several components of the promoter, which we should try to understand!

Some of the transcription factors are proteins:

After the first gene in the codon, thrL, there is a rho-independent termination. By comparing:

we understand that the presence of threonine or isoleucine variants, L-threonyl and L-isoleucyl, makes the rho-independent termination become more efficient, so the control loop is quite direct! Not sure why it cares about isoleucine as well though.

TODO which factor is actually specific to that DNA region?

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E. Coli K-12 MG1655 promoter  Updated 2024-12-15  +Created 1970-01-01

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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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E. Coli K-12 MG1655 transcription unit thrL  Updated 2024-12-15  +Created 1970-01-01

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Contains the gene: e. Coli K-12 MG1655 gene thrL.

biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486

Subset of the longer E. Coli K-12 MG1655 transcription unit thrLABC.

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E. Coli K-12 MG1655 transcription unit thrLABC  Updated 2024-12-15  +Created 1970-01-01

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Contains the genes: e. Coli K-12 MG1655 gene thrL, e. Coli K-12 MG1655 gene thrA, e. Coli K-12 MG1655 gene thrB and e. Coli K-12 MG1655 gene thrC.

biocyc.org/ECOLI/NEW-IMAGE?object=TU00178

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E. Coli Whole Cell Model by Covert Lab  Updated 2024-12-15  +Created 1970-01-01

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github.com/CovertLab/WholeCellEcoliRelease 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.

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: github.com/CovertLab/WholeCellEcoliRelease/issues/21. 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.

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 docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full with image id 502c3e604265, unless otherwise noted.

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Time series run variant  Updated 2024-12-15  +Created 1970-01-01

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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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Escherichia coli  Updated 2024-12-15  +Created 1970-01-01

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www.cell.com/cell/fulltext/S0092-8674(15)00568-1 2015. Using Genome-scale Models to Predict Biological Capabilities. Edward J. O'Brien, Jonathan M. Monk, Bernhard O. Palsson.
www.quora.com/What-are-some-good-books-on-Escherichia-Coli-E-Coli

Size: 1-2 micrometers long and about 0.25 micrometer in diameter, so: 2 * 0.5 * 0.5 * 10e-18 and thus 0.5 micrometer square.

Reference strain: E. Coli K-12 MG1655.

Genome:

4k genes
5 Mbps
www.ncbi.nlm.nih.gov/genome/167
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/005/845/GCF_000005845.2_ASM584v2/GCF_000005845.2_ASM584v2_genomic.fna.gz
wget -O NC_000913.3.fasta 'https://www.ncbi.nlm.nih.gov/search/api/sequence/NC_000913.3/?report=fasta'

Synthesis project: www.sciencemag.org/news/2016/08/biologists-are-close-reinventing-genetic-code-life

Omics modeling: www.ncbi.nlm.nih.gov/pmc/articles/PMC5611438/ Tools for Genomic and Transcriptomic Analysis of Microbes at Single-Cell Level Zixi Chen, Lei Chen, Weiwen Zhang.

Bibliography: