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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;
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  • 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;
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Zachary R. Fox
bKeck Scholars, School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523;
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Douglas P. Shepherd
dDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045;
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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
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  • 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)

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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).

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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

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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
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