Published on Fri Jul 16 2021

A flexible Bayesian approach to estimating size-structured matrix population models

Mattern, J. P., Glauninger, K., Britten, G. L., Casey, J., Hyun, S., Wu, Z., Armbrust, E. V., Harchaoui, Z., Ribalet, F.

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment. The invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. Here we present a flexible Bayesian extension of size-structured matrix population models.

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Abstract

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating rate parameters of microbial populations by mechanistically describing changes in microbial cell size distributions over time. And yet, the construction, analysis, and biological interpretation of these models are underdeveloped, as current implementations do not adequately constrain or assess the biological feasibility of parameter values, leading to inference which may provide a good fit to observed size distributions but does not necessarily reflect realistic physiological dynamics. Here we present a flexible Bayesian extension of size-structured MPMs for testing underlying assumptions describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework takes prior scientific knowledge into account and generates biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we herein demonstrate the performance improvements of our approach over current models and isolate previously ignored biological processes, such as respiratory and exudative carbon losses, as critical parameters for the modeling of microbial population dynamics. The results demonstrate that this modeling framework can provide deeper insights into microbial population dynamics provided by flow-cytometry time-series data.