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arXiv · 2410.03814

Graphical models for inference: A model comparison approach for analyzing bacterial conjugation

Nat Kendal-Freedman·Joseph Victor Fiorillo Meleshko·Aaron Yip·Brian Ingalls
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Abstract

We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics

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Nat Kendal-Freedman, Joseph Victor Fiorillo Meleshko, Aaron Yip, Brian Ingalls. 2024-10-04. Graphical models for inference: A model comparison approach for analyzing bacterial conjugation. https://arxiv.org/abs/2410.03814

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