100 Greatest Discoveries by the Discovery Channel (2004-2005) Updated 2025-01-10 +Created 1970-01-01
View more
Hosted by Bill Nye.
Physics topics:
biology topics:
  • Leeuwenhoek microscope and the discovery of microorganisms, and how pond water is not dead, but teeming with life. No sample of course.
  • 1831 Robert Brown cell nucleus in plants, and later Theodor Schwann in tadpoles. This prepared the path for the idea that "all cells come from other cells", and the there seemed to be an unifying theme to all life: the precursor to DNA discoveries. Re-enactment, yay.
  • 1971 Carl Woese and the discovery of archaea
Genetics:
Medicine:
  • blood circulation
  • anesthesia
  • X-ray
  • germ theory of disease, with examples from Ignaz Semmelweis and Pasteur
  • 1796 Edward Jenner discovery of vaccination by noticing that cowpox cowpox infected subjects were immune
  • vitamin by observing scurvy and beriberi in sailors, confirmed by Frederick Gowland Hopkins on mice experiments
  • Fleming, Florey and Chain and the discovery of penicillin
  • Prontosil
  • diabetes and insulin
Read the full article
Animation of molecular biology processes Updated 2025-01-10 +Created 1970-01-01
View more
Nothing makes the fact that your life is an illusion clearer than animations of molecular biology processes. You just have no idea what is going on inside your own body right now!
And don't get Ciro Santilli started on the brain and the impossibility of free will.
And yet, we live, oblivious to all of it.
Video 1.
ATP synthase in action by HarvardX (2017)
Source.
Video 2.
Electron transport chain by HarvardX (2017)
Source. This actually explains how mitochondrions use sugar derivatives and oxygen to transform ADP into ATP.
Video 3.
The Inner Life of the Cell by XVIVO Scientific Animation (2011)
Source. Also created for BioVisions from Harvard University apparently like other amazing videos. It also has the best music.
Video 4.
DNA animations by wehi.tv for Science-Art exhibition by WEHImovies (2018)
Source.
Video 5.
Dengue virus Invades a Cell by XVIVO Scientific Animation (2008)
Source. Reupload by the MRC Laboratory of Molecular Biology, which was reuploaded from www.pbslearningmedia.org/resource/den08.sci.life.stru.dengue/dengue-virus-invades-a-cell/ which was reuploaded from wherever crazy place XVIVO put it.
Read the full article
E. Coli Whole Cell Model by Covert Lab Updated 2025-01-10 +Created 1970-01-01
View more
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.
Read the full article
Mass fraction summary plot analysis Updated 2025-01-10 +Created 2024-12-04
View more
Let's look into a sample plot, out/manual/plotOut/svg_plots/massFractionSummary.svg, and try to understand as much as we can about what it means and how it was generated.
This plot contains how much of each type of mass is present in all cells. Since we simulated just one cell, it will be the same as the results for that cell.
We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential.
  • total dry mass (mass excluding water)
  • protein mass
  • rRNA mass
  • mRNA mass
  • DNA mass. The last label is not very visible on the plots, but we can deduce it from the source code.
By grepping the title "Cell mass fractions" in the source code, we see the files:
models/ecoli/analysis/cohort/massFractionSummary.py
models/ecoli/analysis/multigen/massFractionSummary.py
models/ecoli/analysis/variant/massFractionSummary.py
which must correspond to the different massFractionSummary plots throughout different levels of the hierarchy.
By reading models/ecoli/analysis/variant/massFractionSummary.py a little bit, we see that:
  • the plotting is done with Matplotlib, hurray
  • it is reading its data from files under ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/, more precisely ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/columns/<column-name>/data. They are binary files however.
    Looking at the source for wholecell/io/tablereader.py shows that those are just a standard NumPy serialization mechanism. Maybe they should have used the Hierarchical Data Format instead.
    We can also take this opportunity to try and find where the data is coming from. Mass from the ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/ looks like an ID, so we grep that and we reach models/ecoli/listeners/mass.py.
    From this we understand that all data that is to be saved from a simulation must be coming from listeners: likely nothing, or not much, is dumped by default, because otherwise it would take up too much disk space. You have to explicitly say what it is that you want to save via a listener that acts on each time step.
Figure 1.
Minimal condition mass fraction plot
. Source. File name: out/manual/plotOut/svg_plots/massFractionSummary.svg
More plot types will be explored at time series run variant, where we will contrast two runs with different growth mediums.
Read the full article
Output overview Updated 2025-01-10 +Created 1970-01-01
View more
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 out/ with
find -name '*.png'
Plots are also available in SVG and PDF formats, e.g.:
  • PNG: ./out/manual/plotOut/low_res_plots/massFractionSummary.png
  • SVG: ./out/manual/plotOut/svg_plots/massFractionSummary.svg The SVGs write text as polygons, see also: SVG fonts.
  • PDF: ./out/manual/plotOut/massFractionSummary.pdf
The output directory has a hierarchical structure of type:
./out/manual/wildtype_000000/000000/generation_000000/000000/
where:
  • wildtype_000000: variant conditions. wildtype is a human readable label, and 000000 is an index amongst the possible wildtype conditions. 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's np.random.seed
  • genereation_000000: this will increase with generations if we simulate multiple cells, which is supported by the model
  • 000000: 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.pdf
Each of thoes four levels of plotOut is generated by a different one of the analysis scripts:
  • ./out/manual/plotOut: generated by python 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 by python runscripts/manual/analysisCohort.py --variant_index 0. TODO not sure how to differentiate between two different labels e.g. wildtype_000000 and somethingElse_000000. If -v is 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 by python runscripts/manual/analysisMultigen.py --variant_index 0 --seed 0
  • ./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated by python runscripts/manual/analysisSingle.py --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specific cell.
Read the full article
Source code overview Updated 2025-01-10 +Created 1970-01-01
View more
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"
Read the full article
Genetics Updated 2025-01-10 +Created 1970-01-01
View more
High level DNA studies? :-)
Read the full article
Human mitochondrion Updated 2025-01-10 +Created 1970-01-01
View more
DNA stuff at: human mtDNA.
Read the full article
Molecular biology feels like systems programming Updated 2025-01-10 +Created 1970-01-01
View more
Whenever Ciro Santilli learns about molecular biology, he can't help but to feel that it feels like programming, and notably systems programming and computer hardware design.
In some sense, the comparison is obvious: DNA is clearly a programmable medium like any assembly language, but still, systems programming did give Ciro some further feelings.
  • The most important analogy perhaps is observability, or more precisely the lack of it. For the computer, this is described at: The lower level you go into a computer, the harder it is to observe things.
    And then, when Ciro started learning a bit about biology techniques, he started to feel the exact same thing.
    For example when he played with E. Coli Whole Cell Model by Covert Lab, the main thing Ciro felt was: it is going to be hard to verify any of this data, because it is hard/impossible to know the concentration of each element in a cell as a function of time.
    More generally of course, this is exactly why making any biology discovery is so hard: we can't easily see what's going on inside the cell, and have to resort to indirect ways of doing so..
    This exact idea was highlighted by I should have loved biology by James Somers:
    For a computer scientist, a biologist's methods can seem insane; the trouble comes from the fact that cells are too small, too numerous, too complex to analyze the way a programmer would, say in a step-by-step debugger.
    And then just like in software, some of the methods biologists use to overcome the lack of visibility have direct software analogues:
  • The boot process is another one. E.g. in x86 the way that you start in 16-bit mode, largely compatible into the 70's, then move to 32-bit and finally 64, does feel a lot the way a earlier stages of embryo development looks more and more like more ancient animals.
Ciro likes to think that maybe that is why a hardcore systems programmer like Bert Hubert got into molecular biology.
Some other people who mention similar things:
Read the full article
Molecular biology technologies Updated 2025-01-10 +Created 1970-01-01
View more
As of 2019, the silicon industry is ending, and molecular biology technology is one of the most promising and growing field of engineering.
Figure 1.
42 years of microprocessor trend data by Karl Rupp
. Source. Only transistor count increases, which also pushes core counts up. But what you gonna do when atomic limits are reached? The separation between two silicon atoms is 0.23nm and 2019 technology is at 5nm scale.
Such advances could one day lead to both biological super-AGI and immortality.
Ciro Santilli is especially excited about DNA-related technologies, because DNA is the centerpiece of biology, and it is programmable.
First, during the 2000's, the cost of DNA sequencing fell to about 1000 USD per genome in the end of the 2010's: Figure 2. "Cost per genome vs Moore's law from 2000 to 2019", largely due to "Illumina's" technology.
The medical consequences of this revolution are still trickling down towards medical applications of 2019, inevitably, but somewhat slowly due to tight privacy control of medical records.
Figure 2.
Cost per genome vs Moore's law from 2000 to 2019
. Source.
Ciro Santilli predicts that when the 100 dollar mark is reached, every person of the First world will have their genome sequenced, and then medical applications will be closer at hand than ever.
But even 100 dollars is not enough. Sequencing power is like computing power: humankind can never have enough. Sequencing is not a one per person thing. For example, as of 2019 tumors are already being sequenced to help understand and treat them, and scientists/doctors will sequence as many tumor cells as budget allows.
Then, in the 2010's, CRISPR/Cas9 gene editing started opening up the way to actually modifying the genome that we could now see through sequencing.
What's next?
Ciro believes that the next step in the revolution could be could be: de novo DNA synthesis.
This technology could be the key to the one of the ultimate dream of biologists: cheap programmable biology with push-button organism bootstrap!
Just imagine this: at the comfort of your own garage, you take some model organism of interest, maybe start humble with Escherichia coli. Then you modify its DNA to your liking, and upload it to a 3D printer sized machine on your workbench, which automatically synthesizes the DNA, and injects into a bootstrapped cell.
You then make experiments to check if the modified cell achieves your desired new properties, e.g. production of some protein, and if not reiterate, just like a software engineer.
Of course, even if we were able to do the bootstrap, the debugging process then becomes key, as visibility is the key limitation of biology, maybe we need other cheap technologies to come in at that point.
This a place point we see the beauty of evolution the brightest: evolution does not require observability. But it also implies that if your changes to the organism make it less fit, then your mutation will also likely be lost. This has to be one of the considerations done when designing your organism.
Other cool topic include:
It's weird, cells feel a lot like embedded systems: small, complex, hard to observe, and profound.
Ciro is sad that by the time he dies, humanity won't have understood the human brain, maybe not even a measly Escherichia coli... Heck, even key molecular biology events are not yet fully understood, see e.g. transcription regulation.
One of the most exciting aspects of molecular biology technologies is their relatively low entry cost, compared for example to other areas such as fusion energy and quantum computing.
Read the full article
OpenWorm Updated 2025-01-10 +Created 1970-01-01
View more
High level simulation only, no way to get from DNA to worm! :-) Includes:
3D body viewer at: browser.openworm.org/ TODO can you click on a cell to get its name?
Video 1.
OpenWorm Sibernetic demo by Mike Vella (2013)
Source. Sibernetic adds a fluid dynamics solver for brain-in-the-loop simulation of C. elegans.
Read the full article
Overview of the experiment Updated 2025-01-10 +Created 1970-01-01
View more
For those that know biology and just want to do the thing, see: Section "Protocols used".
The PuntSeq team uses an Oxford Nanopore MinION DNA sequencer made by Oxford Nanopore Technologies to sequence the 16S region of bacterial DNA, which is about 1500 nucleotides long.
This kind of "decode everything from the sample to see what species are present approach" is called "metagenomics".
This is how the MinION looks like: Figure 1. "Oxford Nanopore MinION top".
Figure 1.
Oxford Nanopore MinION top
. Source.
Figure 2.
Oxford Nanopore MinION side
. Source.
Figure 3.
Oxford Nanopore MinION top open
. Source.
Figure 4.
Oxford Nanopore MinION side USB
. Source.
The 16S region codes for one of the RNA pieces that makes the bacterial ribosome.
Before sequencing the DNA, we will do a PCR with primers that fit just before and just after the 16S DNA, in well conserved regions expected to be present in all bacteria.
The PCR replicates only the DNA region between our two selected primers a gazillion times so that only those regions will actually get picked up by the sequencing step in practice.
Eukaryotes also have an analogous ribosome part, the 18S region, but the PCR primers are selected for targets around the 16S region which are only present in prokaryotes.
This way, we amplify only the 16S region of bacteria, excluding other parts of bacterial genome, and excluding eukaryotes entirely.
Despite coding such a fundamental piece of RNA, there is still surprisingly variability in the 16S region across different bacteria, and it is those differences will allow us to identify which bacteria are present in the river.
The variability exists because certain base pairs are not fundamental for the function of the 16S region. This variability happens mostly on RNA loops as opposed to stems, i.e. parts of the RNA that don't base pair with other RNA in the RNA secondary structure as shown at: Code 1. "RNA stem-loop structure".
                A-U
               /   \
A-U-C-G-A-U-C-G     C
| | | | | | | |     |
U-A-G-C-U-A-G-C     G
               \   /
                U-A
|             ||    |
+-------------++----+
    stem        loop
Code 1.
RNA stem-loop structure
.
This is how the 16S RNA secondary structure looks like in its full glory: Figure 5. "16S RNA secondary structure".
height=800
Figure 5.
16S RNA secondary structure
. Source.
Since loops don't base pair, they are less crucial in the determination of the secondary structure of the RNA.
The variability is such that it is possible to identify individual species apart if full sequences are known with certainty.
With the experimental limitations of experiment however, we would only be able to obtain family or genus level breakdowns.
Read the full article
Physics and the illusion of life Updated 2025-01-10 +Created 1970-01-01
View more
The natural sciences are not just a tool to predict the future.
They are a reminder that the lives that we live daily are mere illusions, religious concepts such as Maya and Samsara come to mind.
We as individuals perceive nothing about the materials that we touch every day really work, nor more importantly how our brain and cell work.
Everything is magic out of our control.
The natural sciences allow us peek, with huge concentrated effort, into tiny little bits a little of those unknowns, and blow our minds as we notice that we don't know anything.
For all practical purposes in life, there is a huge macro micro gap. We are only able to directly perceive and influence the macro events. And through those we try to affect micro events. Because for good or bad, micro events reflect in the macro world.
It is as if we live in a different plane of existence above molecules, and below galaxies. The hierarchy of Figure "xkcd 435: Fields arranged by purity" puts that nicely into perspective, shame it only starts at the economical level, not going up to astronomy.
The great beauty of science is that it allows us to puncture through some of the layers of reality, either up or down, away from our daily experience.
And the great beauty of artificial intelligence research is that it allows to peer deeper into exactly our layer of existence.
Every one or two weeks Ciro Santilli remembers that he and everything he touches are just a bunch of atoms, and that is an amazing feeling. This is Ciro's preferred source of Great doubt. Another concept that comes to mind is when you see it, you'll shit bricks.
Perhaps, the feeling of physics and the illusion of life reaches its peak in molecular biology.
Just look at your fucking hand right now.
Do you have any idea of each of the cells in it work? Isn't is at least 100 times more complex than the materials of the table you hand is currently resting on?
This is the non-science fiction version of the lotus-Eater Machine.
Alan Watts's "Philosopher" talk mentions related ideas:
The origin of a person who is defined as a philosopher, is one who finds that existence itself is exceedingly odd.
The toddler of a friend of Ciro Santilli's wife asked her mum:
Why doesn't my tiger doll close its eyes when we sleep?
Our perception of the macroscopic world is so magic that children have to learn the difference between living and non-living things.
James Somers put it very well as well in his article I should have loved biology by James Somers, this quote was brought to Ciro's attention by Bert Hubert's website[ref].
Quote 1
I should have loved biology but I found it to be a lifeless recitation of names: the Golgi apparatus and the Krebs cycle; mitosis, meiosis; DNA, RNA, mRNA, tRNA.
In the textbooks, astonishing facts were presented without astonishment. Someone probably told me that every cell in my body has the same DNA. But no one shook me by the shoulders, saying how crazy that was. I needed Lewis Thomas, who wrote in The Medusa and the Snail:
For the real amazement, if you wish to be amazed, is this process. You start out as a single cell derived from the coupling of a sperm and an egg; this divides in two, then four, then eight, and so on, and at a certain stage there emerges a single cell which has as all its progeny the human brain. The mere existence of such a cell should be one of the great astonishments of the earth. People ought to be walking around all day, all through their waking hours calling to each other in endless wonderment, talking of nothing except that cell.
The same applies to other natural sciences.
Video 1.
Alan Watts' "Philosopher" talk (1973)
Source. Lecture given at UCLA on 1973-02-21. Some key quotes from the talk:
The origin of a person who is defined as a philosopher, is one who finds that existence itself is exceedingly odd.
Read the full article
Promoter (genetics) Updated 2025-01-10 +Created 1970-01-01
View more
A DNA sequence that marks the start of a transcription area.
Read the full article
Protein tag Updated 2025-01-10 +Created 1970-01-01
View more
You modify the DNA of a cell and stick a fluorescent protein right before or after another protein. Then when it gets translated, the GFP is stuck to the protein of interest, which hopefully hasn't lost its function as a result, then you can just see the protein of interest.
Read the full article
Reverse transcriptase Updated 2025-01-10 +Created 1970-01-01
View more
Converts RNA to DNA, i.e. the inverse of transcription. Found in viruses such as Retrovirus, which includes e.g. HIV.
Read the full article
RNA-dependent RNA polymerase Updated 2025-01-10 +Created 1970-01-01
View more
Makes RNA from RNA.
Used in Positive-strand RNA virus to replicate.
I don't think it's present outside viruses. Well regulated organisms just transcribe more DNA instead.
Read the full article