Abstract
Epigraph: The Naming of Euplotid “The cell is a system that is capable of creating and conserving its own code” –Marcello Barbieri1
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 Hisagawa-Minata’s2 grouping by codon redundancy and sorting by mass with Rumer’s Bisection3 by transitions and transversions and applying them to the Euplotid genetic code we are able to contract and arrange the codons as first shown in Makukov et al4.
Makukov et al discovered the protacted Euplotid codon arrangement and go well into the arithmetic interpretation. Below is an ideographical interpretation:
Due to this unique genetic signature, I decided to name this quantized geometric model of the eukaryotic cell, “Euplotid”.
Introduction to Euplotid’s playground 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 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 combinatorically combined 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 Transcriptional 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 Transcription Factors 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 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.