Ciro Santilli @cirosantilli 40

This can be used to detect if a given species of microorganism is present in a sample, and is therefore a widely used diagnostics technique to see if someone is infected with a virus.

You could of course do full DNA Sequencing to see everything that is there, but since it is as a more generic procedure, sequencing is more expensive and slow.

The alternative is to use a DNA amplification technique.

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Discounts that happen more often than not Updated 2025-07-16

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These are obviously just a manipulative lie sales practice to make you want to buy at regular price.

Shame on you.

Similarly, recurrent Internet payments that give you one year's discount, and make you put up on your calendar to call them one year later threatening to give more discounts to be as cheap as competitors or I'm out.

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Dirac equation solution for the hydrogen atom Updated 2025-07-16

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Predicts fine structure.

Bibliography:

Video "Quantum Mechanics 12b - Dirac Equation II by ViaScience (2015)" youtu.be/tR6UebCvFqE?t=549

Video 1.

Dirac equation for the electron and hydrogen Hamiltonian by Barton Zwiebach (2019)

Source. Uses perturbation theory to get to the relativistic corrections of fine structure! Part of MIT 8.06 Quantum Physics III, Spring 2018 by Barton Zwiebach

Video 2.

How To Solve The Dirac Equation For The Hydrogen Atom | Relativistic Quantum Mechanics by Dietterich Labs (2018)

Source.

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Lie algebra Updated 2025-07-16

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Like everything else in Lie groups, first start with the matrix as discussed at Section "Lie algebra of a matrix Lie group".

Intuitively, a Lie algebra is a simpler object than a Lie group. Without any extra structure, groups can be very complicated non-linear objects. But a Lie algebra is just an algebra over a field, and one with a restricted bilinear map called the Lie bracket, that has to also be alternating and satisfy the Jacobi identity.

Another important way to think about Lie algebras, is as infinitesimal generators.

Because of the Lie group-Lie algebra correspondence, we know that there is almost a bijection between each Lie group and the corresponding Lie algebra. So it makes sense to try and study the algebra instead of the group itself whenever possible, to try and get insight and proofs in that simpler framework. This is the key reason why people study Lie algebras. One is philosophically reminded of how normal subgroups are a simpler representation of group homomorphisms.

To make things even simpler, because all vector spaces of the same dimension on a given field are isomorphic, the only things we need to specify a Lie group through a Lie algebra are:

the dimension
the Lie bracket

Note that the Lie bracket can look different under different basis of the Lie algebra however. This is shown for example at Physics from Symmetry by Jakob Schwichtenberg (2015) page 71 for the Lorentz group.

As mentioned at Lie Groups, Physics, and Geometry by Robert Gilmore (2008) Chapter 4 "Lie Algebras", taking the Lie algebra around the identity is mostly a convention, we could treat any other point, and things are more or less equivalent.

Bibliography:

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Lie algebra of

G L (n)

Updated 2025-07-16

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Is the set of all n-by-y square matrices.

Because

G L (n)

is a Lie group we can use Section "Lie algebra of a matrix Lie group".

For every matrix

x

in the set of all n-by-y square matrices

M_{n}

e^{x}

has inverse

e^{-} x

Note that this works even if

x

is not invertible, and therefore not in

G L (n)

Therefore, the Lie algebra of

G L (n)

is the entire

M_{n}

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Deuterium Updated 2025-07-16

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Applications:

because it has an even number of nucleons it is transparent to NMR, and therefore is useful in solvents for NMR spectroscopy

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E. Coli Whole Cell Model by Covert Lab Updated 2025-07-16

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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. 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.

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DeepMind project Updated 2025-07-16

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DeepMind Lab Updated 2025-07-16

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github.com/deepmind/lab

github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 has some good games with video demos on YouTube, though for some weird reason they are unlisted.

TODO get one of the games running. Instructions: github.com/deepmind/lab/blob/master/docs/users/build.md. This may helpgithub.com/deepmind/lab/issues/242: "Complete installation script for Ubuntu 20.04".

It is interesting how much overlap some of those have with Ciro's 2D reinforcement learning games

The games are 3D, but most of them are purely flat, and the 3D is just a waste of resources.

Video 1.

Human player test of DMLab-30 Collect Good Objects task by DeepMind (2018)

Source.

Video 2.

Human player test of DMLab-30 Exploit Deferred Effects task by DeepMind (2018)

Source.

Video 3.

Human player test of DMLab-30 Select Described Object task by DeepMind (2018)

Source. Some of their games involve language instructions from the use to determine the desired task, cool concept.

Video 4.

Human player test of DMLab-30 Fixed Large Map task by DeepMind (2018)

Source. They also have some maps with more natural environments.

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Deep learning Updated 2025-07-16

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Deep learning is the name artificial neural networks basically converged to in the 2010s/2020s.

It is a bit of an unfortunate as it suggests something like "deep understanding" and even reminds one of AGI, which it almost certainly will not attain on its own. But at least it sounds good.

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Data Updated 2025-07-16

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Czochralski method Updated 2025-07-16

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Cycle of an element of a group Updated 2025-07-16

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Take the element and apply it to itself. Then again. And so on.

In the case of a finite group, you have to eventually reach the identity element again sooner or later, giving you the order of an element of a group.

The continuous analogue for the cycle of a group are the one parameter subgroups. In the continuous case, you sometimes reach identity again and to around infinitely many times (which always happens in the finite case), but sometimes you don't.

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E. Coli Whole Cell Model by Covert Lab / Source code overview 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"
```
- CCO: "Celular COmpartment"
- BAC-NUCLEOID: nucleoid
- CELL-PROJECTION: cell projection
- CW-BAC-NEG: TODO confirm: cell wall (of a Gram-negative bacteria)
- CYTOSOL: cytosol
- EXTRACELLULAR: outside the cell
- MEMBRANE: cell membrane
- OUTER-MEM: bacterial outer membrane
- PERI-BAC: periplasm
- PILUS: pilus
- PM-BAC-NEG: TODO: plasma membrane, but that is the same as cell membrane no?
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?
  - 23srRNA, 16srRNA, 5srRNA are 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 rna vs miscRNA
- 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
```
- promoter_id: matches promoter id in reconstruction/ecoli/flat/promoters.tsv
- gene_id: matches id in reconstruction/ecoli/flat/genes.tsv
- id: matches exactly those used in BioCyc, which is quite nice, might be more or less standardized:
  - biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486
  - biocyc.org/ECOLI/NEW-IMAGE?type=OPERON&object=TU00178

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:
- 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
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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James Somers Updated 2025-07-16

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Huge interest overlap with Ciro Santilli, e.g. he's into

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