Dirac delta function Updated 2025-07-16
There's not way to describe it as a classical function, making it the most important example of a distribution.
Applications:
- position operator in quantum mechanics. It's not a coincidence that the function is named after Paul Dirac.
History of condensed matter physics Updated 2025-07-16
Kilian Jornet Burgada Updated 2025-07-16
Micronation Updated 2025-07-16
Normal distribution Updated 2025-07-16
Partial differential equation Updated 2025-07-16
Resistor Updated 2025-07-16
Science, technology, engineering, and mathematics Updated 2025-07-16
Student culture Updated 2025-07-16
Why it takes several days to enter inflammatory phase in COVID-19? Updated 2025-07-16
Why is it there such a clear separation of phases?
Why do people with mild symptoms go on to die? It is a great mystery.
Ciro Santilli's theory is that COVID is extremely effective at avoiding immune response. Then, in people where this is effective, things reach a point where there is so much virus, that the body notices and moves on to take a more drastic approach. This is compatible with the virus killing older people more, as they have weaker immunes systems. This is however incompatible with the fact that people don't seem to be contagious after the viral phase is over...
E. Coli K-12 MG1655 Updated 2025-07-16
NCBI taxonomy entry: www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=511145 This links to:
- 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=graphIf 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
E. Coli K-12 MG1655 gene dksA Updated 2025-07-16
E. Coli K-12 MG1655 gene fnr Updated 2025-07-16
E. Coli K-12 MG1655 gene thrL Updated 2025-07-16
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.
E. Coli K-12 MG1655 gene yaaX Updated 2025-07-16
E. Coli K-12 MG1655 promoter Updated 2025-07-16
E. Coli replication time Updated 2025-07-16
20 minutes in optimal conditions, with a crazy multiple start sites mechanism: E. Coli starts DNA replication before the previous one finished.
Otherwise, naively, would take 60-90 minutes just to replicate and segregate the full DNA otherwise. So it starts copying multiple times.
- biology.stackexchange.com/questions/30080/how-can-e-coli-proliferate-so-rapidly
- stochasticscientist.blogspot.co.uk/2012/02/how-e-coli-grows-so-fast.html
- www.ncbi.nlm.nih.gov/pmc/articles/PMC2063475/ Organization of sister origins and replisomes during multifork DNA replication in Escherichia coli by Fossum et al (2007)
E. Coli Whole Cell Model by Covert Lab Updated 2025-07-16
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 Other run variants Updated 2025-07-16
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. E. Coli Whole Cell Model by Covert Lab Output overview Updated 2025-07-16
Run output is placed under
out/:Some of the output data is stored as
.cpickle files. To observe those files, you need the original Python classes, and therefore you have to be inside Docker, from the host it won't work.We can list all the plots that have been produced under Plots are also available in SVG and PDF formats, e.g.:
out/ withfind -name '*.png'The output directory has a hierarchical structure of type:where:
./out/manual/wildtype_000000/000000/generation_000000/000000/wildtype_000000: variant conditions.wildtypeis a human readable label, and000000is an index amongst the possiblewildtypeconditions. For example, we can have different simulations with different nutrients, or different DNA sequences. An example of this is shown at run variants.000000: initial random seed for the initial cell, likely fed to NumPy'snp.random.seedgenereation_000000: this will increase with generations if we simulate multiple cells, which is supported by the model000000: this will presumably contain the cell index within a generation
We also understand that some of the top level directories contain summaries over all cells, e.g. the
massFractionSummary.pdf plot exists at several levels of the hierarchy:./out/manual/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/000000/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut/massFractionSummary.pdfEach of thoes four levels of
plotOut is generated by a different one of the analysis scripts:./out/manual/plotOut: generated bypython runscripts/manual/analysisVariant.py. Contains comparisons of different variant conditions. We confirm this by looking at the results of run variants../out/manual/wildtype_000000/plotOut: generated bypython runscripts/manual/analysisCohort.py --variant_index 0. TODO not sure how to differentiate between two different labels e.g.wildtype_000000andsomethingElse_000000. If-vis not given, a it just picks the first one alphabetically. TODO not sure how to automatically generate all of those plots without inspecting the directories../out/manual/wildtype_000000/000000/plotOut: generated bypython runscripts/manual/analysisMultigen.py --variant_index 0 --seed 0./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated bypython runscripts/manual/analysisSingle.py --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specific cell.
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