Microbial Primer: Bayesian learning of traits from microbial time series data

Area of Science:

• Microbial Ecology
• Ecological Modeling
• Computational Biology

Background:

• Mathematical models are crucial for understanding microbial systems, but fitting them to data is challenging.
• Current methods often lack explicit principles for parameter estimation from time-series data.
• Prior knowledge and measurement noise complicate model fitting in microbial ecology.

Purpose of the Study:

• To introduce Bayesian inference as a principled approach for ecological modeling of microbial time series.
• To provide a practical guide and case studies for applying Bayesian methods in microbial ecology.
• To bridge the gap between ecological theory, computational tools, and empirical data in microbial research.

Main Methods:

• Bayesian inference applied to ordinary differential equation models.
• Parameter estimation for microbial traits and dynamics using time-series data.
• Case studies involving algal population dynamics modeled by birth-death processes.

Main Results:

• Demonstration of Bayesian inference for ecological models with microbial time-series data.
• Successful application to algal population dynamics, accounting for noise and prior knowledge.
• Development of an accessible primer with online tutorials for Bayesian inverse modeling.

Conclusions:

• Bayesian inference offers a robust framework for microbial ecological modeling.
• The provided resources facilitate the adoption of Bayesian methods in microbial ecology research.
• This work enhances the ability to infer microbial traits and dynamics from complex datasets.