Aminoacyl tRNA synthetase Updated 2025-02-22 +Created 1970-01-01
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Binds an amino acid to the correct corresponding tRNA sequence. Wikipedia mentions that humans have 20 of them, one for each proteinogenic amino acid.
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The best articles by Ciro Santilli Updated 2025-02-22 +Created 1970-01-01
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These are the best articles ever authored by Ciro Santilli, most of them in the format of Stack Overflow answers.
Ciro posts update about new articles on his Twitter accounts.
A chronological list of all articles is also kept at: Section "Updates".
Some random generally less technical in-tree essays will be present at: Section "Essays by Ciro Santilli".
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Condition Updated 2025-02-22 +Created 1970-01-01
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  • reconstruction/ecoli/flat/condition/nutrient/minimal.tsv contains the nutrients in a minimal environment in which the cell survives:
    "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 NaN
    If we compare that to reconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsv, we see that it adds the 20 amino acids on top of the minimal condition:
    "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 NaN
    so we guess that NaN in the upper mound likely means infinite.
    We can try to understand the less obvious ones:
    • ADP: TODO
    • PI: TODO
    • PROTON[c]: presumably a measure of pH
    • GLC[p]: glucose, this can be seen by comparing minimal.tsv with minimal_no_glucose.tsv
    • AMMONIUM: 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.tsv contains: "time (units.s)" "nutrients" 0 "minimal" which means just using reconstruction/ecoli/flat/condition/nutrient/minimal.tsv until infinity. That is the default one used by runSim.py, as can be seen from ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Environment/attributes/nutrientTimeSeriesLabel which contains just 000000_basal. * reconstruction/ecoli/flat/reconstruction/ecoli/flat/condition/timeseries/000001_cut_glucose.tsv is more interesting and contains:
      "time (units.s)" "nutrients"
  0 "minimal"
  1200 "minimal_no_glucose"
    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!
    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.py
  • reconstruction/ecoli/flat/condition/condition_defs.tsv contains 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"]
    • condition refers to entries in reconstruction/ecoli/flat/condition/condition_defs.tsv
    • nutrients refers to entries under reconstruction/ecoli/flat/condition/nutrient/, e.g. reconstruction/ecoli/flat/condition/nutrient/minimal.tsv or reconstruction/ecoli/flat/condition/nutrient/minimal_plus_amino_acids.tsv
    • genotype perturbations: there aren't any in the file, but this suggests that genotype modifications can also be incorporated here
    • doubling 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
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Default run variant Updated 2025-02-22 +Created 1970-01-01
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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.
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Source code overview Updated 2025-02-22 +Created 1970-01-01
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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"
  • 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"
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Time series run variant Updated 2025-02-22 +Created 1970-01-01
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To modify the nutrients as a function of time, with To select a time series we can use something like:
python runscripts/manual/runSim.py --variant nutrientTimeSeries 25 25
As mentioned in python runscripts/manual/runSim.py --help, nutrientTimeSeries is one of the choices from github.com/CovertLab/WholeCellEcoliRelease/blob/7e4cc9e57de76752df0f4e32eca95fb653ea64e4/models/ecoli/sim/variants/__init__.py#L57
25 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 reconstruction/ecoli/flat/condition/timeseries/000025_cut_aa.tsv and contains
"time (units.s)" "nutrients"
0 "minimal_plus_amino_acids"
1200 "minimal"
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.
nutrients likely means condition in that file however, see bug report with 1 1 failing: github.com/CovertLab/WholeCellEcoliRelease/issues/24
When we do this the simulation ends in:
Simulation finished:
 - Length: 0:34:23
 - Runtime: 0:08:03
so we see that the doubling time was faster than the one with minimal conditions of 0:42:49, 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 25
We 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 same out/ folder, wildtype_000000 and nutrientTimeSeries_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.
    Then, at 20 minutes, which is about 0.3 hours, we see that the cell starts growing a bit less fast as the slope of the curve decreases a bit, because we removed that free amino acid supply.
    Figure 1.
    Minimal condition vs amino acid cut mass fraction plot
    . Source. From file out/manual/plotOut/svg_plots/massFractionSummary.svg.
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
Figure 2.
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
Figure 3.
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.
    For PHE however, the flux is at all times, except shortly after the cut. Why? And why there was no excretion on the default conditions?
Figure 4.
Evaluation time
. Source. evaluationTime.svg: this has nothing to do with biology, but it is rather a profile of the program runtime. We can see that the simulation gets slower and slower as time passes, presumably because there are more and more molecules to simulate.
Figure 5.
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.
and
In E. coli, EF-Tu is encoded by two genes, tufA and tufB
. What they seem to mean is that tufA and tufB are two similar molecules, either of which can make up the EF-Tu of the E. Coli, which is an important part of translation.
Figure 6.
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.
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Key mitochondrial proteins aren't necessarily in mtDNA Updated 2025-02-22 +Created 1970-01-01
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E.g. in humans the adenine nucleotide translocator is present in chromosome 4, not in mtDNA.
These have almost certainly been transferred to nuclear DNA in the course of evolution.
This isn't completely surprising, since when mitochondria die, their DNA is kind of left in the cell, so it is not hard to imagine how genes end up getting uptaken by the nucleus. This is suggested at Power, Sex, Suicide by Nick Lane (2006) page 196.
A limiting factor appears to be that you can't just past those genes in the nucleus, further mutations are necessary for mitochondrial protein import to work, apparenty some kind of tagging with extra amino acids.
However, you likely don't want to remove all genes from the mitochondria because mitochondria have DNA because they need to be controlled individually.
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Mitochondrial protein import Updated 2025-02-22 +Created 1970-01-01
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The process that imports proteins encoded in the nuclear DNA and made in the cytosol into the mitochondria.
The term is mentioned e.g. in this article: www.nature.com/articles/nrm2959.
Power, Sex, Suicide by Nick Lane (2006) suggests that proteins are somehow tagged with extra amino acids for this.
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Mycoplasma genitalium Updated 2025-02-22 +Created 1970-01-01
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Size: 300 x 600 nm
Has one of the smallest genomes known, and JCVI made a minimized strain with 473 genes: JCVI-syn3.0.
The reason why genitalium has such a small genome is that parasites tend to have smaller DNAs. So it must be highlighted that genitalium can only survive in highly enriched environments, it can't even make its own amino acids, which it normally obtains fromthe host cells! And because it cannot do cellular respiration, it very likely replicates slower than say E. Coli. It's easy to be small in such scenarios!
Power, Sex, Suicide by Nick Lane (2006) section "How to lose the cell wall without dying" page 184 has some related mentions puts it well very:
One group, the Mycoplasma, comprises mostly parasites, many of which live inside other cells. Mycoplasma cells are tiny, with very small genomes. M. genitalium, discovered in 1981, has the smallest known genome of any bacterial cell, encoding fewer than  genes. Despite its simplicity, it ranks among the most common of sexually transmitted diseases, producing symptoms similar to Chlamydia infection. It is so small (less than a third of a micron in diameter, or an order of magnitude smaller than most bacteria) that it must normally be viewed under the electron microscope; and difficulties culturing it meant its significance was not appreciated until the important advances in gene sequencing in the early 1990s. Like Rickettsia, Mycoplasma have lost virtually all the genes required for making nucleotides, amino acids, and so forth. Unlike Rickettsia, however, Mycoplasma have also lost all the genes for oxygen respiration, or indeed any other form of membrane respiration: they have no cytochromes, and so must rely on fermentation for energy.
Downsides mentioned at youtu.be/PSDd3oHj548?t=293:
  • too small to see on light microscope
  • difficult to genetically manipulate. TODO why?
  • less literature than E. Coli.
Data:
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Parasites tend to have smaller DNAs Updated 2025-02-22 +Created 1970-01-01
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If you live in the relatively food abundant environment of another cell, then you don't have to be able to digest every single food source in existence, of defend against a wide range of predators.
So because DNA replication is a key limiting factor of bacterial replication time, you just reduce your genome to a minimum.
And likely you also want to be as small as possible to evade the host's immune system.
Power, Sex, Suicide by Nick Lane (2006) section "Gene loss as an evolutionary trajectory" puts it well:
One of the most extreme examples of gene loss is Rickettsia prowazekii, the cause of typhus. [...] Over evolutionary time Rickettsia has lost most of its genes, and now has a mere  protein-coding genes left. [...] Rickettsia is a tiny bacterium, almost as small as a virus, which lives as a parasite inside other cells. It is so well adapted to this lifestyle that it can no longer survive outside its host cells. [...] It was able to lose most of its genes in this way simply because they were not needed: life inside other cells, if you can survive there at all, is a spoonfed existence.
and also section "How to lose the cell wall without dying" page 184 has some related mentions:
While many types of bacteria do lose their cell wall during parts of their life cycle only two groups of prokaryotes have succeeded in losing their cell walls permanently, yet lived to tell the tale. It's interesting to consider the extenuating circumstances that permitted them to do so.
[...]
One group, the Mycoplasma, comprises mostly parasites, many of which live inside other cells. Mycoplasma cells are tiny, with very small genomes. M. genitalium, discovered in 1981, has the smallest known genome of any bacterial cell, encoding fewer than 500 genes. M. genitalium, discovered in 1981, has the smallest known genome of any bacterial cell, encoding fewer than 500 genes. [...] Like Rickettsia, Mycoplasma have lost virtually all the genes required for making nucleotides, amino acids, and so forth.
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Sequence alignment Updated 2025-02-22 +Created 1970-01-01
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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:
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