100 Greatest Discoveries by the Discovery Channel (2004-2005) Updated 2024-12-15 +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
E. Coli K-12 MG1655 Updated 2024-12-15 +Created 1970-01-01
View more
NCBI taxonomy entry: www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=511145 This links to:
  • genome: www.ncbi.nlm.nih.gov/genome/?term=txid511145 From there there are links to either:
    • Download the FASTA: "Download sequences in FASTA format for genome, protein"
      For the genome, you get a compressed FASTA file with extension .fna called GCF_000005845.2_ASM584v2_genomic.fna that starts with:
      >NC_000913.3 Escherichia coli str. K-12 substr. MG1655, complete genome
AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGCTTCTGAACTG
      Using wc as in wc GCF_000005845.2_ASM584v2_genomic.fna gives 58022 lines, in Vim we see that each line is 80 characters, except for the final one which is 52. So we have 58020 * 80 + 52 = 4641652 =~ 4.6 Mbp
Read the full article
E. Coli K-12 MG1655 gene thrA Updated 2024-12-15 +Created 1970-01-01
View more
The second gene in the E. Coli K-12 MG1655 genome. Part of the E. Coli K-12 MG1655 operon thrLABC.
Part of a reaction that produces threonine.
This protein is an enzyme. The UniProt entry clearly shows the chemical reactions that it catalyses. In this case, there are actually two! It can either transforming the metabolite:
  • "L-homoserine" into "L-aspartate 4-semialdehyde"
  • "L-aspartate" into "4-phospho-L-aspartate"
Also interestingly, we see that both of those reaction require some extra energy to catalyse, one needing adenosine triphosphate and the other nADP+.
TODO: any mention of how much faster it makes the reaction, numerically?
Since this is an enzyme, it would also be interesting to have a quick search for it in the KEGG entry starting from the organism: www.genome.jp/pathway/eco01100+M00022 We type in the search bar "thrA", it gives a long list, but the last entry is our "thrA". Selecting it highlights two pathways in the large graph, so we understand that it catalyzes two different reactions, as suggested by the protein name itself (fused blah blah). We can now hover over:
  • the edge: it shows all the enzymes that catalyze the given reaction. Both edges actually have multiple enzymes, e.g. the L-Homoserine path is also catalyzed by another enzyme called metL.
  • the node: they are the metabolites, e.g. one of the paths contains "L-homoserine" on one node and "L-aspartate 4-semialdehyde"
Note that common cofactor are omitted, since we've learnt from the UniProt entry that this reaction uses ATP.
If we can now click on the L-Homoserine edge, it takes us to: www.genome.jp/entry/eco:b0002+eco:b3940. Under "Pathway" we see an interesting looking pathway "Glycine, serine and threonine metabolism": www.genome.jp/pathway/eco00260+b0002 which contains a small manually selected and extremely clearly named subset of the larger graph!
But looking at the bottom of this subgraph (the UI is not great, can't Ctrl+F and enzyme names not shown, but the selected enzyme is slightly highlighted in red because it is in the URL www.genome.jp/pathway/eco00260+b0002 vs www.genome.jp/pathway/eco00260) we clearly see that thrA, thrB and thrC for a sequence that directly transforms "L-aspartate 4-semialdehyde" into "Homoserine" to "O-Phospho-L-homoserine" and finally tothreonine. This makes it crystal clear that they are not just located adjacently in the genome by chance: they are actually functionally related, and likely controlled by the same transcription factor: when you want one of them, you basically always want the three, because you must be are lacking threonine. TODO find transcription factor!
The UniProt entry also shows an interactive browser of the tertiary structure of the protein. We note that there are currently two sources available: X-ray crystallography and AlphaFold. To be honest, the AlphaFold one looks quite off!!!
By inspecting the FASTA for the entire genome, or by using the NCBI open reading frame tool, we see that this gene lies entirely in its own open reading frame, so it is quite boring
From the FASTA we see that the very first three Codons at position 337 are
ATG CGA GTG
where ATG is the start codon, and CGA GTG should be the first two that actually go into the protein:
ecocyc.org/gene?orgid=ECOLI&id=ASPKINIHOMOSERDEHYDROGI-MONOMER mentions that the enzime is most active as protein complex with four copies of the same protein:
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.
TODO image?
Read the full article
E. Coli K-12 MG1655 operon thrLABC Updated 2024-12-15 +Created 1970-01-01
View more
We can find it by searching for the species in the BioCyc promoter database. This leads to: biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI.
By finding the first operon by position we reach: biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486.
That page lists several components of the promoter, which we should try to understand!
After the first gene in the codon, thrL, there is a rho-independent termination. By comparing:
we understand that the presence of threonine or isoleucine variants, L-threonyl and L-isoleucyl, makes the rho-independent termination become more efficient, so the control loop is quite direct! Not sure why it cares about isoleucine as well though.
TODO which factor is actually specific to that DNA region?
Read the full article
E. Coli K-12 MG1655 promoter Updated 2024-12-15 +Created 1970-01-01
View more
From this we see that there is a convention of naming promoters as protein name + p, e.g. the first gene in E. Coli K-12 MG1655 promoter thrLp encodes protein thrL.
It is also possible to add numbers after the p, e.g. at biocyc.org/ECOLI/NEW-IMAGE?type=OPERON&object=PM0-45989 we see that the protein zur has two promoters:
  • zurp6
  • zurp7
TODO why 6 and 7? There don't appear to be 1, 2, etc.
Read the full article
Mass fraction summary plot analysis Updated 2024-12-15 +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
Source code overview Updated 2024-12-15 +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
Enzyme Updated 2024-12-15 +Created 1970-01-01
View more
A protein that is a catalyst for some chemical reaction.
For an initial concrete example, consider e. Coli K-12 MG1655 gene thrA.
Video 1.
How Enzymes Work by RCSBProteinDataBank (2017)
Source. Shows in detail how aconitase catalyses the citrate to isocitrate reaction in the citric acid cycle.
Read the full article
History of X-ray crystallography Updated 2024-12-15 +Created 1970-01-01
View more
Read the full article
Lysozyme structure resolution (1965) Updated 2024-12-15 +Created 1970-01-01
View more
With X-ray crystallography by David Chilton Phillips. The second protein to be resolved fter after myoglobin, and the first enzyme.
Phillips also published a lower resolution (6angstrom) of the enzyme-inhibitor complexes at about the same time: Structure of Some Crystalline Lysozyme-Inhibitor Complexes Determined by X-Ray Analysis At 6 Å Resolution (1965). The point of doing this is that it points out the active site of the enzyme.
Read the full article
Molecular biology technologies Updated 2024-12-15 +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
Polycistronic mRNA Updated 2024-12-15 +Created 1970-01-01
View more
Read the full article
Protein degradation Updated 2024-12-15 +Created 1970-01-01
View more
proteins also have a half-life, much like RNA. But it tends to be longer.
Read the full article
RNA secondary structure Updated 2024-12-15 +Created 1970-01-01
View more
Analogous problem to the secondary structure of proteins. Likely a bit simpler due to the strong tendency for complementary pairs to bind.
Read the full article
Saccharomyces cerevisiae Updated 2024-12-15 +Created 1970-01-01
View more
Size: 10 micrometers.
Division time: 100 minutes.
Read the full article
Sequence alignment Updated 2024-12-15 +Created 1970-01-01
View more
Sequence alignment is trying to match a DNA or amino acid sequence, even though the sequences might not be exactly the same, otherwise it would be a straight up string-search algorithm.
This is fundamental in bioinformatics for two reasons:
Read the full article
UniProt Updated 2024-12-15 +Created 1970-01-01
View more
For an example with context, have a look at E. Coli K-12 MG1655 and the second protein of the genome, e. Coli K-12 MG1655 gene thrA.
Read the full article