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Electronic Proceedings in Theoretical Computer Science by Wikipedia Bot 0
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Electronic Proceedings in Theoretical Computer Science refers to the online publication of research papers, articles, and other scholarly contributions presented at conferences and workshops within the field of theoretical computer science. These proceedings serve as a medium to disseminate research findings quickly and widely, allowing researchers to access and cite the latest developments in the domain.
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Mobile automaton by Wikipedia Bot 0
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A **mobile automaton** (often abbreviated as "MA") is a theoretical computational model used primarily in the study of automata theory and cellular automata. Unlike traditional automata, such as finite state machines or pushdown automata, a mobile automaton consists of a collection of independent agents (or "particles") that can move across a discrete space (often represented as a grid or lattice).
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E. Coli Whole Cell Model by Covert Lab / Install and first run by Ciro Santilli 40 Updated 2025-07-16
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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" 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:
make clean compile &&
python runscripts/manual/runFitter.py &&
python runscripts/manual/runSim.py &&
python runscripts/manual/analysisVariant.py &&
python runscripts/manual/analysisCohort.py &&
python runscripts/manual/analysisMultigen.py &&
python runscripts/manual/analysisSingle.py
The meaning of each of the analysis commands is described at Section "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/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
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.
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E. Coli Whole Cell Model by Covert Lab / Run variants by Ciro Santilli 40 Updated 2025-07-16
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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.
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E. Coli Whole Cell Model by Covert Lab / Default run variant by Ciro Santilli 40 Updated 2025-07-16
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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 by Ciro Santilli 40 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 Whole Cell Model by Covert Lab / Other run variants by Ciro Santilli 40 Updated 2025-07-16
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Besides time series run variants, conditions can also be selected directly without a time series as in:
python runscripts/manual/runSim.py --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.
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E. Coli Whole Cell Model by Covert Lab / Source code overview by Ciro Santilli 40 Updated 2025-07-16
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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.
  • reconstruction/ecoli/flat/compartments.tsv contains cellular compartment information:
    "abbrev" "id"
"n" "CCO-BAC-NUCLEOID"
"j" "CCO-CELL-PROJECTION"
"w" "CCO-CW-BAC-NEG"
"c" "CCO-CYTOSOL"
"e" "CCO-EXTRACELLULAR"
"m" "CCO-MEMBRANE"
"o" "CCO-OUTER-MEM"
"p" "CCO-PERI-BAC"
"l" "CCO-PILUS"
"i" "CCO-PM-BAC-NEG"
  • reconstruction/ecoli/flat/promoters.tsv contains promoter information. Simple file, sample lines:
    "position" "direction" "id" "name"
148 "+" "PM00249" "thrLp"
    corresponds to E. Coli K-12 MG1655 promoter thrLp, which starts as position 148.
  • reconstruction/ecoli/flat/proteins.tsv contains protein information. Sample line corresponding to e. Coli K-12 MG1655 gene thrA:
    "aaCount" "name" "seq" "comments" "codingRnaSeq" "mw" "location" "rnaId" "id" "geneId"
[91, 46, 38, 44, 12, 53, 30, 63, 14, 46, 89, 34, 23, 30, 29, 51, 34, 4, 20, 0, 69] "ThrA" "MRVL..." "Location information from Ecocyc dump." "AUGCGAGUGUUG..." [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 89103.51099999998, 0.0, 0.0, 0.0, 0.0] ["c"] "EG10998_RNA" "ASPKINIHOMOSERDEHYDROGI-MONOMER" "EG10998"
    so we understand that:
  • reconstruction/ecoli/flat/rnas.tsv: TODO vs transcriptionUnits.tsv. Sample lines:
    "halfLife" "name" "seq" "type" "modifiedForms" "monomerId" "comments" "mw" "location" "ntCount" "id" "geneId" "microarray expression"
174.0 "ThrA [RNA]" "AUGCGAGUGUUG..." "mRNA" [] "ASPKINIHOMOSERDEHYDROGI-MONOMER" "" [0.0, 0.0, 0.0, 0.0, 790935.00399999996, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] ["c"] [553, 615, 692, 603] "EG10998_RNA" "EG10998" 0.0005264904
  • reconstruction/ecoli/flat/sequence.fasta: FASTA DNA sequence, first two lines:
    >E. coli K-12 MG1655 U00096.2 (1 to 4639675 = 4639675 bp)
AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGCTTCTG
  • reconstruction/ecoli/flat/transcriptionUnits.tsv: transcription units. We can observe for example the two different transcription units of the E. Coli K-12 MG1655 operon thrLABC in the lines:
    "expression_rate" "direction" "right" "terminator_id"  "name"    "promoter_id" "degradation_rate" "id"       "gene_id"                                   "left"
0.0               "f"         310     ["TERM0-1059"]   "thrL"    "PM00249"     0.198905992329492 "TU0-42486" ["EG11277"]                                  148
657.057317358791  "f"         5022    ["TERM_WC-2174"] "thrLABC" "PM00249"     0.231049060186648 "TU00178"   ["EG10998", "EG10999", "EG11000", "EG11277"] 148
  • reconstruction/ecoli/flat/genes.tsv
    "length" "name"                      "seq"             "rnaId"      "coordinate" "direction" "symbol" "type" "id"      "monomerId"
66       "thr operon leader peptide" "ATGAAACGCATT..." "EG11277_RNA" 189         "+"         "thrL"   "mRNA" "EG11277" "EG11277-MONOMER"
2463     "ThrA"                      "ATGCGAGTGTTG"    "EG10998_RNA" 336         "+"         "thrA"   "mRNA" "EG10998" "ASPKINIHOMOSERDEHYDROGI-MONOMER"
  • reconstruction/ecoli/flat/metabolites.tsv contains metabolite information. Sample lines:
    "id"                       "mw7.2" "location"
"HOMO-SER"                 119.12  ["n", "j", "w", "c", "e", "m", "o", "p", "l", "i"]
"L-ASPARTATE-SEMIALDEHYDE" 117.104 ["n", "j", "w", "c", "e", "m", "o", "p", "l", "i"]
    In the case of the enzyme thrA, one of the two reactions it catalyzes is "L-aspartate 4-semialdehyde" into "Homoserine".
    Starting from the enzyme page: biocyc.org/gene?orgid=ECOLI&id=EG10998 we reach the reaction page: biocyc.org/ECOLI/NEW-IMAGE?type=REACTION&object=HOMOSERDEHYDROG-RXN which has reaction ID HOMOSERDEHYDROG-RXN, and that page which clarifies the IDs:
    so these are the compounds that we care about.
  • reconstruction/ecoli/flat/reactions.tsv contains chemical reaction information. Sample lines:
    "reaction id" "stoichiometry" "is reversible" "catalyzed by"

"HOMOSERDEHYDROG-RXN-HOMO-SER/NAD//L-ASPARTATE-SEMIALDEHYDE/NADH/PROTON.51."
  {"NADH[c]": -1, "PROTON[c]": -1, "HOMO-SER[c]": 1, "L-ASPARTATE-SEMIALDEHYDE[c]": -1, "NAD[c]": 1}
  false
  ["ASPKINIIHOMOSERDEHYDROGII-CPLX", "ASPKINIHOMOSERDEHYDROGI-CPLX"]

"HOMOSERDEHYDROG-RXN-HOMO-SER/NADP//L-ASPARTATE-SEMIALDEHYDE/NADPH/PROTON.53."
  {"NADPH[c]": -1, "NADP[c]": 1, "PROTON[c]": -1, "L-ASPARTATE-SEMIALDEHYDE[c]": -1, "HOMO-SER[c]": 1
  false
  ["ASPKINIIHOMOSERDEHYDROGII-CPLX", "ASPKINIHOMOSERDEHYDROGI-CPLX"]
    • catalized by: here we see ASPKINIHOMOSERDEHYDROGI-CPLX, which we can guess is a protein complex made out of ASPKINIHOMOSERDEHYDROGI-MONOMER, which is the ID for the thrA we care about! This is confirmed in complexationReactions.tsv.
  • reconstruction/ecoli/flat/complexationReactions.tsv contains information about chemical reactions that produce protein complexes:
    "process" "stoichiometry" "id" "dir"
"complexation"
  [
    {
      "molecule": "ASPKINIHOMOSERDEHYDROGI-CPLX",
      "coeff": 1,
      "type": "proteincomplex",
      "location": "c",
      "form": "mature"
    },
    {
      "molecule": "ASPKINIHOMOSERDEHYDROGI-MONOMER",
      "coeff": -4,
      "type": "proteinmonomer",
      "location": "c",
      "form": "mature"
    }
  ]
"ASPKINIHOMOSERDEHYDROGI-CPLX_RXN"
1
    The coeff is how many monomers need to get together for form the final complex. This can be seen from the Summary section of ecocyc.org/gene?orgid=ECOLI&id=ASPKINIHOMOSERDEHYDROGI-MONOMER:
    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.
    Fantastic literature summary! Can't find that in database form there however.
  • reconstruction/ecoli/flat/proteinComplexes.tsv contains protein complex information:
    "name" "comments" "mw" "location" "reactionId" "id"
"aspartate kinase / homoserine dehydrogenase"
""
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 356414.04399999994, 0.0, 0.0, 0.0, 0.0]
["c"]
"ASPKINIHOMOSERDEHYDROGI-CPLX_RXN"
"ASPKINIHOMOSERDEHYDROGI-CPLX"
  • reconstruction/ecoli/flat/protein_half_lives.tsv contains the half-life of proteins. Very few proteins are listed however for some reason.
  • reconstruction/ecoli/flat/tfIds.csv: transcription factors information:
    "TF"   "geneId"  "oneComponentId"  "twoComponentId" "nonMetaboliteBindingId" "activeId" "notes"
"arcA" "EG10061" "PHOSPHO-ARCA"    "PHOSPHO-ARCA"
"fnr"  "EG10325" "FNR-4FE-4S-CPLX" "FNR-4FE-4S-CPLX"
"dksA" "EG10230"
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E. Coli Whole Cell Model by Covert Lab / Condition by Ciro Santilli 40 Updated 2025-07-16
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  • reconstruction/ecoli/flat/condition/nutrient/minimal.tsv contains the nutrients in a minimal environment in which the cell survives:
    "molecule id" "lower bound (units.mmol / units.g / units.h)" "upper bound (units.mmol / units.g / units.h)"
"ADP[c]" 3.15 3.15
"PI[c]" 3.15 3.15
"PROTON[c]" 3.15 3.15
"GLC[p]" NaN 20
"OXYGEN-MOLECULE[p]" NaN NaN
"AMMONIUM[c]" NaN NaN
"PI[p]" NaN NaN
"K+[p]" NaN NaN
"SULFATE[p]" NaN NaN
"FE+2[p]" NaN NaN
"CA+2[p]" NaN NaN
"CL-[p]" NaN NaN
"CO+2[p]" NaN NaN
"MG+2[p]" NaN NaN
"MN+2[p]" NaN NaN
"NI+2[p]" NaN NaN
"ZN+2[p]" NaN NaN
"WATER[p]" NaN NaN
"CARBON-DIOXIDE[p]" NaN NaN
"CPD0-1958[p]" NaN NaN
"L-SELENOCYSTEINE[c]" NaN NaN
"GLC-D-LACTONE[c]" NaN NaN
"CYTOSINE[c]" NaN NaN
    If we compare that to reconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsv, we see that it adds the 20 amino acids on top of the minimal condition:
    "L-ALPHA-ALANINE[p]" NaN NaN
"ARG[p]" NaN NaN
"ASN[p]" NaN NaN
"L-ASPARTATE[p]" NaN NaN
"CYS[p]" NaN NaN
"GLT[p]" NaN NaN
"GLN[p]" NaN NaN
"GLY[p]" NaN NaN
"HIS[p]" NaN NaN
"ILE[p]" NaN NaN
"LEU[p]" NaN NaN
"LYS[p]" NaN NaN
"MET[p]" NaN NaN
"PHE[p]" NaN NaN
"PRO[p]" NaN NaN
"SER[p]" NaN NaN
"THR[p]" NaN NaN
"TRP[p]" NaN NaN
"TYR[p]" NaN NaN
"L-SELENOCYSTEINE[c]" NaN NaN
"VAL[p]" NaN NaN
    so we guess that NaN in the upper mound likely means infinite.
    We can try to understand the less obvious ones:
    • ADP: TODO
    • PI: TODO
    • PROTON[c]: presumably a measure of pH
    • GLC[p]: glucose, this can be seen by comparing minimal.tsv with minimal_no_glucose.tsv
    • AMMONIUM: ammonium. This appears to be the primary source of nitrogen atoms for producing amino acids.
    • CYTOSINE[c]: hmmm, why is external cytosine needed? Weird.
  • reconstruction/ecoli/flat/reconstruction/ecoli/flat/condition/timeseries/ contains sequences of conditions for each time. For example:
    • reconstruction/ecoli/flat/reconstruction/ecoli/flat/condition/timeseries/000000_basal.tsv contains:
      "time (units.s)" "nutrients"
0 "minimal"
      which means just using reconstruction/ecoli/flat/condition/nutrient/minimal.tsv until infinity. That is the default one used by runSim.py, as can be seen from ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Environment/attributes/nutrientTimeSeriesLabel which contains just 000000_basal.
    • reconstruction/ecoli/flat/reconstruction/ecoli/flat/condition/timeseries/000001_cut_glucose.tsv is more interesting and contains:
      "time (units.s)" "nutrients"
0 "minimal"
1200 "minimal_no_glucose"
      so we see that this will shift the conditions half-way to a condition that will eventually kill the bacteria because it will run out of glucose and thus energy!
    Timeseries can be selected with --variant nutrientTimeSeries X Y, see also: run variants.
    We can use that variant with:
    VARIANT="condition" FIRST_VARIANT_INDEX=1 LAST_VARIANT_INDEX=1 python runscripts/manual/runSim.py
  • reconstruction/ecoli/flat/condition/condition_defs.tsv contains lines of form:
    "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"]
    • condition refers to entries in reconstruction/ecoli/flat/condition/condition_defs.tsv
    • nutrients refers to entries under reconstruction/ecoli/flat/condition/nutrient/, e.g. reconstruction/ecoli/flat/condition/nutrient/minimal.tsv or reconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsv
    • genotype perturbations: there aren't any in the file, but this suggests that genotype modifications can also be incorporated here
    • doubling time: TODO experimental data? Because this should be a simulation output, right? Or do they cheat and fix doubling by time?
    • active TFs: this suggests that they are cheating transcription factors here, as those would ideally be functions of other more basic inputs
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E. Coli Whole Cell Model by Covert Lab / Model validation by Ciro Santilli 40 Updated 2025-07-16
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TODO compare with actual datasetes.
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E. Coli Whole Cell Model by Covert Lab / Publications by Ciro Santilli 40 Updated 2025-07-16
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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:
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PromethION by Ciro Santilli 40 Updated 2025-07-16
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Multi-Omics Model and Analytics by Ciro Santilli 40 Updated 2025-07-16
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By Tagkopoulos lab at University of California, Davies.
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E. Coli Metabolome Database by Ciro Santilli 40 Updated 2025-07-16
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E. Coli strain by Ciro Santilli 40 Updated 2025-07-16
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E. Coli K-12 MG1655 by Ciro Santilli 40 Updated 2025-07-16
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NCBI taxonomy entry: www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=511145 This links to:
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E. Coli K-12 MG1655 gene by Ciro Santilli 40 Updated 2025-07-16
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How to use an Oxford Nanopore MinION to extract DNA from river water and determine which bacteria live in it / Sample collection by Ciro Santilli 40 Updated 2025-07-16
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As you would expect, not much secret here, we just dumped a 1 liter glass bottle with a rope attached around the neck in a few different locations of the river, and pulled it out with the rope.
And, in the name of science, we even wore gloves to not contaminate the samples!
Figure 1.
Swans swimming in the river when during sample collection
. Source. Swam poo bacteria?
Figure 2.
Tying rope to bootle for river water sample collection
. Source.
Figure 3.
Dumping the bottle into the river to collect the water sample
. Source.
Figure 4.
Measuring the river water sample temperature with a mercury thermometer
. Source.
Figure 5.
Measuring the river water sample pH with a pH strip
. Source. The strip is compared with the color of a mobile app that gives the pH for a given strip color.
Figure 6.
Noting sample collection location on the water bottle
. Source.
Video 1.
Dumping the bottle into the river to collect the water sample
. Source. That was fun.
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E. Coli K-12 MG1655 gene thrL by Ciro Santilli 40 Updated 2025-07-16
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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".
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 gene thrB by Ciro Santilli 40 Updated 2025-07-16
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Immediately follows e. Coli K-12 MG1655 gene thrA,
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Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Video 1.
Intro to OurBigBook
. Source.
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
    Video 2.
    OurBigBook Web topics demo
    . Source.
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 2.
    You can publish local OurBigBook lightweight markup files to either https://OurBigBook.com or as a static website
    .
    Figure 3.
    Visual Studio Code extension installation
    .
    Figure 4.
    Visual Studio Code extension tree navigation
    .
    Figure 5.
    Web editor
    . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
    Video 4.
    OurBigBook Visual Studio Code extension editing and navigation demo
    . Source.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact
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