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Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Marian Dominguez-Mirazo1,2, Jeremy D Harris3, David Demory4

  • 1School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.

Mbio
|July 19, 2024
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Summary

One-step growth curves underestimate bacteriophage latent periods due to cellular variability. A new computational framework accurately estimates latent period mean and variance, improving viral trait analysis.

Keywords:
bacteriophage lysiscellular variabilityinferencelatent periodmathematical modelingphage ecologypopulation dynamicsviral traits

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Area of Science:

  • Microbiology
  • Virology
  • Computational Biology

Background:

  • Viral traits like latent period are crucial for understanding virus-host dynamics.
  • One-step growth curves are the conventional method for estimating viral latent periods.
  • These curves do not account for cellular-level variability, potentially biasing results.

Purpose of the Study:

  • To investigate how individual-level variation in latent period affects virus-host dynamics.
  • To develop a computational framework for estimating latent period variability.
  • To improve the accuracy of viral trait inference.

Main Methods:

  • Utilized nonlinear dynamical models to simulate virus-host interactions.
  • Developed a computational framework for estimating latent period from host and virus population data.
  • Incorporated realistic measurement noise into simulations.

Main Results:

  • One-step growth curves systematically underestimate the mean latent period.
  • Cellular variability in lysis timing creates an artifactual earlier mean release time.
  • The new framework accurately recovers both mean and variance of the latent period in simulations.

Conclusions:

  • Reframing the latent period as a distribution is essential for accurate viral trait analysis.
  • Improved estimation of viral traits will enhance predictive models of viral impacts.
  • This work provides a practical method to improve viral trait estimation in microbe-host systems.