Skip to main content
  • Submit
  • About
    • Editorial Board
    • PNAS Staff
    • FAQ
    • Accessibility Statement
    • Rights and Permissions
    • Site Map
  • Contact
  • Journal Club
  • Subscribe
    • Subscription Rates
    • Subscriptions FAQ
    • Open Access
    • Recommend PNAS to Your Librarian
  • Log in
  • My Cart

Main menu

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
  • Front Matter
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses
  • Submit
  • About
    • Editorial Board
    • PNAS Staff
    • FAQ
    • Accessibility Statement
    • Rights and Permissions
    • Site Map
  • Contact
  • Journal Club
  • Subscribe
    • Subscription Rates
    • Subscriptions FAQ
    • Open Access
    • Recommend PNAS to Your Librarian

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Home
Home

Advanced Search

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
  • Front Matter
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses

New Research In

Physical Sciences

Featured Portals

  • Physics
  • Chemistry
  • Sustainability Science

Articles by Topic

  • Applied Mathematics
  • Applied Physical Sciences
  • Astronomy
  • Computer Sciences
  • Earth, Atmospheric, and Planetary Sciences
  • Engineering
  • Environmental Sciences
  • Mathematics
  • Statistics

Social Sciences

Featured Portals

  • Anthropology
  • Sustainability Science

Articles by Topic

  • Economic Sciences
  • Environmental Sciences
  • Political Sciences
  • Psychological and Cognitive Sciences
  • Social Sciences

Biological Sciences

Featured Portals

  • Sustainability Science

Articles by Topic

  • Agricultural Sciences
  • Anthropology
  • Applied Biological Sciences
  • Biochemistry
  • Biophysics and Computational Biology
  • Cell Biology
  • Developmental Biology
  • Ecology
  • Environmental Sciences
  • Evolution
  • Genetics
  • Immunology and Inflammation
  • Medical Sciences
  • Microbiology
  • Neuroscience
  • Pharmacology
  • Physiology
  • Plant Biology
  • Population Biology
  • Psychological and Cognitive Sciences
  • Sustainability Science
  • Systems Biology
Research Article

Distribution shapes govern the discovery of predictive models for gene regulation

View ORCID ProfileBrian Munsky, Guoliang Li, Zachary R. Fox, Douglas P. Shepherd, and Gregor Neuert
PNAS July 17, 2018 115 (29) 7533-7538; first published June 29, 2018; https://doi.org/10.1073/pnas.1804060115
Brian Munsky
aDepartment of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523;
bKeck Scholars, School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Brian Munsky
  • For correspondence: munsky@colostate.edu gregor.neuert@vanderbilt.edu
Guoliang Li
cDepartment of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zachary R. Fox
bKeck Scholars, School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Douglas P. Shepherd
dDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gregor Neuert
cDepartment of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232;
eDepartment of Biomedical Engineering, School of Engineering, Vanderbilt University, Nashville, TN 37232;
fDepartment of Pharmacology, School of Medicine, Vanderbilt University, Nashville, TN 37232
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: munsky@colostate.edu gregor.neuert@vanderbilt.edu
  1. Edited by Herbert Levine, Rice University, Houston, TX, and approved May 31, 2018 (received for review March 10, 2018)

  • Article
  • Figures & SI
  • Info & Metrics
  • PDF
Loading

Significance

Systems biology seeks to combine experiments with computation to predict biological behaviors. However, despite tremendous data and knowledge, biological models make less-accurate predictions compared with other fields. By analyzing single-cell, single-molecule measurements of mRNA during yeast stress response, we explore why and how the shapes of experimental distributions control prediction accuracy. We show how asymmetric data distributions with long tails cause standard modeling approaches to yield excellent fits but make meaningless predictions. We show how these biases arise from the violation of fundamental assumptions in standard modeling approaches. We demonstrate how advanced computational tools solve this dilemma and achieve predictive understanding of spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport, and decay.

Abstract

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

  • single cell
  • transcription
  • quantitative
  • prediction
  • modeling

Footnotes

  • ↵1To whom correspondence may be addressed. Email: munsky{at}colostate.edu or gregor.neuert{at}vanderbilt.edu.
  • Author contributions: B.M. and G.N. designed research; B.M., G.L., Z.R.F., D.P.S., and G.N. performed research; B.M., Z.R.F., and G.N. contributed new reagents/analytic tools; B.M. and G.N. analyzed data; and B.M. and G.N. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1804060115/-/DCSupplemental.

  • Copyright © 2018 the Author(s). Published by PNAS.

This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

View Full Text
PreviousNext
Back to top
Article Alerts
Email Article

Thank you for your interest in spreading the word on PNAS.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Distribution shapes govern the discovery of predictive models for gene regulation
(Your Name) has sent you a message from PNAS
(Your Name) thought you would like to see the PNAS web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Distribution shapes govern the discovery of predictive models for gene regulation
Brian Munsky, Guoliang Li, Zachary R. Fox, Douglas P. Shepherd, Gregor Neuert
Proceedings of the National Academy of Sciences Jul 2018, 115 (29) 7533-7538; DOI: 10.1073/pnas.1804060115

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Request Permissions
Share
Distribution shapes govern the discovery of predictive models for gene regulation
Brian Munsky, Guoliang Li, Zachary R. Fox, Douglas P. Shepherd, Gregor Neuert
Proceedings of the National Academy of Sciences Jul 2018, 115 (29) 7533-7538; DOI: 10.1073/pnas.1804060115
Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Mendeley logo Mendeley
Proceedings of the National Academy of Sciences: 115 (29)
Table of Contents

Submit

Sign up for Article Alerts

Article Classifications

  • Biological Sciences
  • Biophysics and Computational Biology
  • Physical Sciences
  • Biophysics and Computational Biology

Jump to section

  • Article
    • Abstract
    • Results
    • Discussion
    • Methods
    • Acknowledgments
    • Footnotes
    • References
  • Figures & SI
  • Info & Metrics
  • PDF

You May Also be Interested in

Abstract depiction of a guitar and musical note
Science & Culture: At the nexus of music and medicine, some see disease treatments
Although the evidence is still limited, a growing body of research suggests music may have beneficial effects for diseases such as Parkinson’s.
Image credit: Shutterstock/agsandrew.
Scientist looking at an electronic tablet
Opinion: Standardizing gene product nomenclature—a call to action
Biomedical communities and journals need to standardize nomenclature of gene products to enhance accuracy in scientific and public communication.
Image credit: Shutterstock/greenbutterfly.
One red and one yellow modeled protein structures
Journal Club: Study reveals evolutionary origins of fold-switching protein
Shapeshifting designs could have wide-ranging pharmaceutical and biomedical applications in coming years.
Image credit: Acacia Dishman/Medical College of Wisconsin.
White and blue bird
Hazards of ozone pollution to birds
Amanda Rodewald, Ivan Rudik, and Catherine Kling talk about the hazards of ozone pollution to birds.
Listen
Past PodcastsSubscribe
Goats standing in a pin
Transplantation of sperm-producing stem cells
CRISPR-Cas9 gene editing can improve the effectiveness of spermatogonial stem cell transplantation in mice and livestock, a study finds.
Image credit: Jon M. Oatley.

Similar Articles

Site Logo
Powered by HighWire
  • Submit Manuscript
  • Twitter
  • Facebook
  • RSS Feeds
  • Email Alerts

Articles

  • Current Issue
  • Latest Articles
  • Archive

PNAS Portals

  • Anthropology
  • Chemistry
  • Classics
  • Front Matter
  • Physics
  • Sustainability Science
  • Teaching Resources

Information

  • Authors
  • Editorial Board
  • Reviewers
  • Librarians
  • Press
  • Site Map
  • PNAS Updates

Feedback    Privacy/Legal

Copyright © 2021 National Academy of Sciences. Online ISSN 1091-6490