🤖 AI Summary
This study addresses the potential bias introduced by using evolution accumulation models (EvAMs) that assume feature irreversibility when applied to scenarios where true evolutionary processes are reversible. Through comprehensive computer simulations and statistical inference, the authors systematically evaluate how neglecting reversibility affects the reliability of inferred evolutionary event orders and core dynamic structures. The findings reveal that while EvAMs exhibit robustness in reconstructing relative event sequences and backbone pathway architectures, their uncertainty quantification and analyses of feature interactions are notably susceptible to bias stemming from the irreversibility assumption. This work delineates the applicability boundaries and limitations of irreversible EvAMs under reversible evolutionary dynamics, thereby offering both theoretical grounding and practical guidance for modeling evolutionary trajectories.
📝 Abstract
Evolutionary accumulation models (EvAMs) are an emerging class of machine learning methods designed to infer the evolutionary pathways by which features are acquired. Applications include cancer evolution (accumulation of mutations), anti-microbial resistance (accumulation of drug resistances), genome evolution (organelle gene transfers), and more diverse themes in biology and beyond. Following these themes, many EvAMs assume that features are gained irreversibly -- no loss of features can occur. Reversible approaches do exist but are often computationally (much) more demanding and statistically less stable. Our goal here is to explore whether useful information about evolutionary dynamics which are in reality reversible can be obtained from modelling approaches with an assumption of irreversibility. We identify, and use simulation studies to quantify, errors involved in neglecting reversible dynamics, and show the situations in which approximate results from tractable models can be informative and reliable. In particular, EvAM inferences about the relative orderings of acquisitions, and the core dynamic structure of evolutionary pathways, are robust to reversibility in many cases, while estimations of uncertainty and feature interactions are more error-prone.