Source code overview Updated 2024-12-15 +Created 1970-01-01
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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:
    • aaCount: amino acid count, how many of each of the 20 proteinogenic amino acid are there
    • seq: full sequence, using the single letter abbreviation of the proteinogenic amino acids
    • mw; molecular weight? The 11 components appear to be given at reconstruction/ecoli/flat/scripts/unifyBulkFiles.py:
      molecular_weight_keys = [
  '23srRNA',
  '16srRNA',
  '5srRNA',
  'tRNA',
  'mRNA',
  'miscRNA',
  'protein',
  'metabolite',
  'water',
  'DNA',
  'RNA' # nonspecific RNA
  ]
      so they simply classify the weight? Presumably this exists for complexes that have multiple classes?
    • location: cell compartment where the protein is present, c defined at reconstruction/ecoli/flat/compartments.tsv as cytoplasm, as expected for something that will make an amino acid
  • 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
    • halfLife: half-life
    • mw: molecular weight, same as in reconstruction/ecoli/flat/proteins.tsv. This molecule only have weight in the mRNA class, as expected, as it just codes for a protein
    • location: same as in reconstruction/ecoli/flat/proteins.tsv
    • ntCount: nucleotide count for each of the ATGC
    • microarray expression: presumably refers to DNA microarray for gene expression profiling, but what measure exactly?
  • 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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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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KEGG Updated 2024-12-15 +Created 1970-01-01
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For a commented initial example, see: e. Coli K-12 MG1655 gene thrA.
KEGG does the visual maps well.
But BioCyc is generally better otherwise.
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