Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Euplotid: A quantized geometric model of the eukaryotic cell

Diego Borges-Rivera
doi: https://doi.org/10.1101/170159
Diego Borges-Rivera
Massachusetts Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dborgesrmit@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

1 Abstract

Life continues to shock and amaze us, reminding us that truth is far stranger than fiction. http://Euplotid.io is a quantized geometric model of the eukaryotic cell, an attempt at quantifying the incredible complexity that gives rise to a living cell by beginning from the smallest unit, a quanta. Starting from the very bottom we are able to build the pieces which when hierarchically and combinatorially 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 dockerized bioinformatic pipelines enabling a user to build and interact with the local regulatory architecture of every gene starting from 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 architecture 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 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 a Base Editor mediated transition mutation can then be performed in a relevant model for further study.

Figure 0.1:
  • Download figure
  • Open in new tab
Figure 0.1:

Graphical Abstract

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added G4, tweaked clarity of epigraph

  • http://Euplotid.io

  • https://dborgesr.github.io/Euplotid/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted October 26, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Euplotid: A quantized geometric model of the eukaryotic cell
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Euplotid: A quantized geometric model of the eukaryotic cell
Diego Borges-Rivera
bioRxiv 170159; doi: https://doi.org/10.1101/170159
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Euplotid: A quantized geometric model of the eukaryotic cell
Diego Borges-Rivera
bioRxiv 170159; doi: https://doi.org/10.1101/170159

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biophysics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4667)
  • Biochemistry (10332)
  • Bioengineering (7653)
  • Bioinformatics (26277)
  • Biophysics (13497)
  • Cancer Biology (10663)
  • Cell Biology (15388)
  • Clinical Trials (138)
  • Developmental Biology (8480)
  • Ecology (12800)
  • Epidemiology (2067)
  • Evolutionary Biology (16817)
  • Genetics (11378)
  • Genomics (15451)
  • Immunology (10591)
  • Microbiology (25140)
  • Molecular Biology (10187)
  • Neuroscience (54317)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2889)
  • Physiology (4331)
  • Plant Biology (9223)
  • Scientific Communication and Education (1585)
  • Synthetic Biology (2551)
  • Systems Biology (6769)
  • Zoology (1459)