Abstract
Epigraph: The Naming of Euplotid “The cell is a system that is capable of creating and conserving its own code” ‐‐Marcello Barbieri[1]
The Cambrian explosion is responsible for the cementing of animal life on this planet; phylogenetic analysis appears to indicate that all metazoans originated from a single common flagellated organism, something resembling a Euplotid. Darwin has noted that this event does not seem to follow with a traditional evolutionary view, the speed and emergence of metazoans accelerated orders of magnitude when compared to the life that preceded them. There have been many attempts to explain this event, from Oxygen concentration to complexity thresholds, but no-one has considered that clues may lie within the genetic code itself, after all, this code is fundamental for the evolution of life as we know it.
A number of patterns have been observed within the codons, but a particularly interesting combination of two rules allows for the writing of the Euplotid genetic code in a contracted manner. By combining Rumer’s Bisection (a)[2] with Hisagawa-Minata’s[3] ordering by codon redundancy/mass (b) and applying them to the Euplotid genetic code we are able to contract and arrange the codons as first shown in Makukov et al[4].
Makukov et al discovered the protacted Euplotid codon arrangement and go well into the arithmetic interpretation, that is to say the part dealing with arithmatic. Below is an ideographical interpretation:
1. GGTCGACC: How to number DNA, 0=T, C=1, G=2, A=3
2. GCCCTCTG: What radix number system to use. CCC = 111 (base 4) = 21 (base 10) = number of amino acids + stop codon. TCT = 010 (base 4) = 4 (base 10) = number of nucleotides.
3. NNNNNNNN: Is able to complement “GGTCGACC”
4. TATATAGCAGCTATAT: Euplotids remove Internal Eliminated Sequences (IESs) in the process of generating the large vegetative Macronucleus (MAC) from the small sexually produced Micronucleus (MIC). The Euplote Crassus consensus sequence is 5′-TATrGCRN-3′[5].
5. GTAGTAAAAAATGATG: Translating from left to right starting w/ TAG would produce: Stop-Stop-Lys-Start-Start using the canonical genetic code, but in Euplotids +1 Programmed Ribosomal Frameshifting (+1 PRF) occurs at AAA sites preceding stop codons due to a tRNA recognizing UAAA instead of UAA[6] [7]. So if we perform a +1 PRF and read starting at AGT we get: Ser-Lys-Lys-Cys (brief movie showing the process). This is only in Euplotids due to TGA coding for Cys
6. instead of stop. +1 PRF in Euplotids is used to generate functional proteins from fusing different reading frames in over 3,700 proteins, including the Reverse Transcriptase of LINE-elements, ORF2 [8]HHRYRYYRRRYYRYGG: Is able to complement “TATATAGCAGCTATAT”
Due to this unique genetic signature, I decided to name this quantized geometric model of the eukaryotic cell, “Euplotid”.
2.0.1 http://dborgesr.github.io/Euplotid/Life as we know it has continued to shock and amaze us, consistently reminding us that truth is far stranger than fiction. Euplotid is a quantized geometric model of the eukaryotic cell, a first attempt at quantifying, using planck’s constant geometric shape as its base, the incredible combinatorial complexity that gives rise to a living cell. By beginning from the very bottom we are able to build the pieces, which when hierarchically and combinatorially combined, can produce the emergent complex behavior that even a single celled organism can show. Euplotid is composed of a set of quantized geometric 3D building blocks and constantly evolving bioinformatic pipelines encapsulated and running in Docker containers enabling a user to build and annotate the local regulatory structure of every gene starting from raw sequencing reads of DNA-interactions, chromatin accessibility, and RNA-sequencing. Reads are quantified using the latest computational tools and the results are normalized, quality-checked, and stored. The local regulatory neighborhood of each gene is built using a Louvain based graph partitioning algorithm parameterized by the chromatin extrusion model and CTCF-CTCF interactions. Cis-Regulatory Elements are defined using chromatin accessibility peaks which are then mapped to Transcription Start Sites based on inclusion within the same neighborhood. Deep Neural Networks are trained in order to provide a statistical model mimicking transcription factor binding, giving the ability to identify all TFs within a given chromatin accessibility peak. By in-silico mutating and re-applying the neural network we are able to gauge the impact of a transition mutation on the binding of any human transcription factor. The annotated output can be visualized in a variety of 1D, 2D, 3D and 4D ways overlaid with existing bodies of knowledge, such as GWAS results or PDB structures. Once a particular CRE of interest has been identified by a biologist the difficulty of a Base Editor mediated transition mutation can be quantitatively observed and induced in a model organism.
