Modeling heterogeneity, commitment, and memory of bacterial spore germination

• Published in: mBio Vol. 16; no. 5; p. e0059625
• Main Authors: Li, William, Mednick, Steven, Setlow, Peter, Li, Yong-Qing
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 14.05.2025
• Subjects: bacterial spore; Bacteriology; cellular heterogeneity; mathematical modeling; Models, Biological; Neural Networks, Computer; Research Article; spore germination; spore memory; Spores, Bacterial - growth & development; Spores, Bacterial - physiology; mathematical modeling; spore germination; spore memory; cellular heterogeneity; bacterial spore
• ISSN: 2150-7511; 2150-7511
• DOI: 10.1128/mbio.00596-25

Spore germination is a crucial process through which spores of bacteria return to life when triggered by germinants, and some spore species cause food spoilage, human diseases, and bioterrorism. Understanding and theoretical predictions of spore germination could facilitate the development of “germinate to kill” strategies as spores lose their resistance upon germination. Here, we developed a novel mathematical model to describe the characteristics of spore germination including heterogeneity, commitment, memory, and kinetic CaDPA release using an artificial neural network. This model predicts new aspects of germination such as the retention and loss of memory and the effect of GRs’ distribution on germination rate and could be useful in data-driven discoveries to enhance our understanding of germination’s biophysical intricacies.