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

Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA

View ORCID ProfileBradly Alicea
doi: https://doi.org/10.1101/006775
Bradly Alicea
1Orthogonal Research, Champaign, IL 61821
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Bradly Alicea
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

How does the regulatory machinery of an animal cell ensure its survival during large-scale biochemical and phenotypic transitions? When a cell is strongly perturbed by an environmental stimulus, it can either die or persist with compensatory changes. But what do the dynamics of individual genes look like during this process of adaptation? In a previous technical paper, two approaches (drug treatments and polysome isolation) were used in tandem to demonstrate the effects of perturbation on cellular phenotype. In this paper, we can use these data in tandem with a discrete, first-order feedback model that incorporates leaky components to better characterize adaptive responses of mRNA regulation related to information processing in the cell. By evaluating the dynamic relationship between mRNA associated with transcription (translatome) and mRNA associated with the polysome (transcriptome) at multiple timepoints, hypothetical conditions for decay and aggregation are found and discussed. Our feedback model allows for the approximation of fluctuations and other aspects of cellular information processing, in addition to the derivation of three information processing principles. These results will lead us to a better understanding of how mRNA provides variable information over time to the complex intracellular environment, particularly in the context of large-scale phenotypic change.

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 July 02, 2014.
Download PDF
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.
Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA
(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
Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA
Bradly Alicea
bioRxiv 006775; doi: https://doi.org/10.1101/006775
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA
Bradly Alicea
bioRxiv 006775; doi: https://doi.org/10.1101/006775

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

  • Systems Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3609)
  • Biochemistry (7590)
  • Bioengineering (5533)
  • Bioinformatics (20833)
  • Biophysics (10347)
  • Cancer Biology (7998)
  • Cell Biology (11663)
  • Clinical Trials (138)
  • Developmental Biology (6619)
  • Ecology (10227)
  • Epidemiology (2065)
  • Evolutionary Biology (13648)
  • Genetics (9557)
  • Genomics (12860)
  • Immunology (7932)
  • Microbiology (19576)
  • Molecular Biology (7678)
  • Neuroscience (42194)
  • Paleontology (309)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3272)
  • Plant Biology (7064)
  • Scientific Communication and Education (1296)
  • Synthetic Biology (1953)
  • Systems Biology (5435)
  • Zoology (1119)