The De Gerlache Seamounts are a group of underwater volcanic mountains located in the Southern Ocean, specifically to the south of the Antarctic Peninsula. Named after the Belgian explorer Adrien de Gerlache, these seamounts are part of a larger geological province that includes various submerged volcanic features. The seamounts are significant for both geological research and marine ecology.
The Dubinin Trough is a geological feature located in the Arctic Ocean, specifically within the Laptev Sea, northeast of Siberia, Russia. It is named after the Russian scientist V.A. Dubinin. The trough is characterized by a significant depression in the seafloor that is part of the continental margin of the Eurasian continent.
Enderby Plain is a large ice-covered region in Antarctica, specifically part of the East Antarctic Ice Sheet. It is located within Queen Mary Land and is bordered by several prominent locations, including the Enderby Land coastline. The plain has been named after the Enderby family, who were significant figures in the exploration of the Antarctic region during the 19th century. Enderby Plain is of interest to scientists studying climate change, glaciology, and Antarctic ecosystems.
The Glomar Challenger Basin is not a widely recognized or established term in geological or oceanographic contexts. However, it may refer to the geological findings obtained from the Glomar Challenger, a deep-sea drilling vessel used during the Deep Sea Drilling Project (DSDP) from 1968 to 1983. The Glomar Challenger played a significant role in advancing the understanding of oceanic and sedimentary processes, plate tectonics, and the geological history of the ocean floor.
Kammu Seamount is a submerged volcano located in the Pacific Ocean, specifically in an area to the east of Japan. It is part of the Mariana Arc, which consists of a series of volcanic islands and underwater features formed by tectonic activity associated with the subduction of the Pacific Plate beneath the North American Plate. Seamounts like Kammu are often characterized by their steep sides and flat tops, and they can create unique ecosystems that support a variety of marine life.
Kimmei Seamount is an underwater volcanic feature located in the Pacific Ocean, part of the Hawaiian-Emperor seamount chain. This chain of seamounts and islands extends from the Hawaiian Islands to the Aleutian Trench and features a series of volcanoes created by the movement of the Pacific Plate over the Hawaiian hotspot. Kimmei Seamount is notable for its geological characteristics typical of such underwater volcanoes, which may include steep-sided basaltic formations.
Isla Pérez is a small uninhabited island located in the southwestern part of the Caribbean Sea. It is part of the Archipelago of Los Jardines de la Reina, which is situated off the southern coast of Cuba. This archipelago is known for its rich biodiversity, coral reefs, and pristine marine environments, making it a popular destination for eco-tourism and diving.
North American Pacific Fjordland refers to a geographical region along the Pacific coast of North America characterized by its deep inlets, steep cliffs, and rugged landscapes, typical of fjord formations. The term "fjord" generally describes long, narrow sea or lake inlets that are often flanked by steep landforms, which were typically formed by glacial erosion.
The Kara Strait, also known as the Kara Sea Strait, is a body of water located in the Arctic region, specifically serving as the passage between the Kara Sea to the west and the Barents Sea to the southeast. It is situated to the north of Russia, separating the northern coast of the Russian mainland from the northern islands, such as the Severnaya Zemlya archipelago.
Moai is a seamount located in the South Pacific Ocean. It is part of the underwater volcanic formations known as seamounts, which are submerged mountains that rise from the ocean floor but do not reach the surface of the water. Specifically, Moai is situated near the island of Rapa Iti, which is part of French Polynesia.
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. Ciro Santilli asked at 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? Academia is broken. Academia should be the most open form of knowledge sharing. But instead we get this silly competition for publication points.
- to prevent noise from non-collaborators. But they only get like 2 issues as year on such a meganiche subject... Did you know that you can ignore people, and even block them if they are particularly annoying? Much more likely is that no one will every hear about your project and that it will die with its last graduate student slave.
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. E. Coli Whole Cell Model by Covert Lab Install and first run by 
Ciro Santilli 40 Updated 2025-07-16
Ciro Santilli 40 Updated 2025-07-16At 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:This mounts the host source under The meaning of each of the analysis commands is described at Section "Output overview".
sudo docker run --name=wcm -it -v "$(pwd):/wcEcoli" docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full/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.pyAs 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 bashrunscripts/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 2569.18 783.09 1.943 1.910 2.005 1.950 1.963
Simulation finished:
- Length: 0:42:49
- Runtime: 0:09:13It 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.
E. Coli Whole Cell Model by Covert Lab Time series run variant by Ciro Santilli 40 Updated 2025-07-16
Ciro Santilli 40 Updated 2025-07-16To modify the nutrients as a function of time, with To select a time series we can use something like:As mentioned in
python runscripts/manual/runSim.py --variant nutrientTimeSeries 25 25python runscripts/manual/runSim.py --help, nutrientTimeSeries is one of the choices from github.com/CovertLab/WholeCellEcoliRelease/blob/7e4cc9e57de76752df0f4e32eca95fb653ea64e4/models/ecoli/sim/variants/__init__.py#L5725 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 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.
reconstruction/ecoli/flat/condition/timeseries/000025_cut_aa.tsv and contains"time (units.s)" "nutrients"
0 "minimal_plus_amino_acids"
1200 "minimal"nutrients likely means condition in that file however, see bug report with 1 1 failing: github.com/CovertLab/WholeCellEcoliRelease/issues/24When we do this the simulation ends in:so we see that the doubling time was faster than the one with minimal conditions of
Simulation finished:
- Length: 0:34:23
- Runtime: 0:08:030:42:49, 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 25We 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 sameout/folder,wildtype_000000andnutrientTimeSeries_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.
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
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
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.
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.
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:which select row indices from so
python runscripts/manual/runSim.py --variant condition 1 1reconstruction/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"]1 means no_oxygen.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".
reconstruction/ecoli/flat/compartments.tsvcontains 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"CCO: "Celular COmpartment"BAC-NUCLEOID: nucleoidCELL-PROJECTION: cell projectionCW-BAC-NEG: TODO confirm: cell wall (of a Gram-negative bacteria)CYTOSOL: cytosolEXTRACELLULAR: outside the cellMEMBRANE: cell membraneOUTER-MEM: bacterial outer membranePERI-BAC: periplasmPILUS: pilusPM-BAC-NEG: TODO: plasma membrane, but that is the same as cell membrane no?
reconstruction/ecoli/flat/promoters.tsvcontains promoter information. Simple file, sample lines:corresponds to E. Coli K-12 MG1655 promoter thrLp, which starts as position 148."position" "direction" "id" "name" 148 "+" "PM00249" "thrLp"reconstruction/ecoli/flat/proteins.tsvcontains protein information. Sample line corresponding to e. Coli K-12 MG1655 gene thrA:so we understand that:"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"aaCount: amino acid count, how many of each of the 20 proteinogenic amino acid are thereseq: full sequence, using the single letter abbreviation of the proteinogenic amino acidsmw; molecular weight? The 11 components appear to be given atreconstruction/ecoli/flat/scripts/unifyBulkFiles.py:so they simply classify the weight? Presumably this exists for complexes that have multiple classes?molecular_weight_keys = [ '23srRNA', '16srRNA', '5srRNA', 'tRNA', 'mRNA', 'miscRNA', 'protein', 'metabolite', 'water', 'DNA', 'RNA' # nonspecific RNA ]23srRNA,16srRNA,5srRNAare the three structural RNAs present in the ribosome: 23S ribosomal RNA, 16S ribosomal RNA, 5S ribosomal RNA, all others are obvious:- tRNA
- mRNA
- protein. This is the seventh class, and this enzyme only contains mass in this class as expected.
- metabolite
- water
- DNA
- RNA: TODO
rnavsmiscRNA
location: cell compartment where the protein is present,cdefined atreconstruction/ecoli/flat/compartments.tsvas cytoplasm, as expected for something that will make an amino acid
reconstruction/ecoli/flat/rnas.tsv: TODO vstranscriptionUnits.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" 0.0005264904halfLife: half-lifemw: molecular weight, same as inreconstruction/ecoli/flat/proteins.tsv. This molecule only have weight in themRNAclass, as expected, as it just codes for a proteinlocation: same as inreconstruction/ecoli/flat/proteins.tsvntCount: nucleotide count for each of the ATGCmicroarray 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) AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGCTTCTGreconstruction/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"] 148promoter_id: matches promoter id inreconstruction/ecoli/flat/promoters.tsvgene_id: matches id inreconstruction/ecoli/flat/genes.tsvid: matches exactly those used in BioCyc, which is quite nice, might be more or less standardized:
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.tsvcontains metabolite information. Sample lines:In the case of the enzyme thrA, one of the two reactions it catalyzes is "L-aspartate 4-semialdehyde" into "Homoserine"."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"]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 IDHOMOSERDEHYDROG-RXN, and that page which clarifies the IDs:so these are the compounds that we care about.- biocyc.org/compound?orgid=ECOLI&id=L-ASPARTATE-SEMIALDEHYDE: "L-aspartate 4-semialdehyde" has ID
L-ASPARTATE-SEMIALDEHYDE - biocyc.org/compound?orgid=ECOLI&id=HOMO-SER: "Homoserine" has ID
HOMO-SER
- biocyc.org/compound?orgid=ECOLI&id=L-ASPARTATE-SEMIALDEHYDE: "L-aspartate 4-semialdehyde" has ID
reconstruction/ecoli/flat/reactions.tsvcontains 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 seeASPKINIHOMOSERDEHYDROGI-CPLX, which we can guess is a protein complex made out ofASPKINIHOMOSERDEHYDROGI-MONOMER, which is the ID for thethrAwe care about! This is confirmed incomplexationReactions.tsv.
reconstruction/ecoli/flat/complexationReactions.tsvcontains information about chemical reactions that produce protein complexes:The"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" 1coeffis 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:Fantastic literature summary! Can't find that in database form there however.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.
reconstruction/ecoli/flat/proteinComplexes.tsvcontains 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.tsvcontains 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"
reconstruction/ecoli/flat/condition/nutrient/minimal.tsvcontains the nutrients in a minimal environment in which the cell survives:If we compare that to"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 NaNreconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsv, we see that it adds the 20 amino acids on top of the minimal condition:so we guess that"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 NaNNaNin theupper moundlikely means infinite.We can try to understand the less obvious ones:ADP: TODOPI: TODOPROTON[c]: presumably a measure of pHGLC[p]: glucose, this can be seen by comparingminimal.tsvwithminimal_no_glucose.tsvAMMONIUM: 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.tsvcontains:which means just using"time (units.s)" "nutrients" 0 "minimal"reconstruction/ecoli/flat/condition/nutrient/minimal.tsvuntil infinity. That is the default one used byrunSim.py, as can be seen from./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Environment/attributes/nutrientTimeSeriesLabelwhich contains just000000_basal.reconstruction/ecoli/flat/reconstruction/ecoli/flat/condition/timeseries/000001_cut_glucose.tsvis more interesting and contains: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!"time (units.s)" "nutrients" 0 "minimal" 1200 "minimal_no_glucose"
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.pyreconstruction/ecoli/flat/condition/condition_defs.tsvcontains 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"]conditionrefers to entries inreconstruction/ecoli/flat/condition/condition_defs.tsvnutrientsrefers to entries underreconstruction/ecoli/flat/condition/nutrient/, e.g.reconstruction/ecoli/flat/condition/nutrient/minimal.tsvorreconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsvgenotype perturbations: there aren't any in the file, but this suggests that genotype modifications can also be incorporated heredoubling 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
TODO compare with actual datasetes.
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!
Intro to OurBigBook
. Source. We have two killer features:
- 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-calculusArticles 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/derivativeVideo 2. OurBigBook Web topics demo. Source. - 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.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
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. 
- Infinitely deep tables of contents:
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