Abstract

Understanding how environmental conditions shape the evolution of bacteriophages (phages) is critical for designing correct evolutionary experiments that select specific phage traits. This study provides a general mathematical framework that integrates physics-informed neural networks, agent-based statistical simulations, and symbolic regression machine learning techniques to design evolutionary experiments targeting specific phage traits such as high variability in phage phenotypes.  In the study, we used agent-based statistical simulations to generate synthetic time series data for evolutionary scenarios with diverse phenotypic outcomes. Subsequently, we trained Physics-informed neural networks (PINNs) embedded in differential equations on the synthetic time series to reveal possible environments that select given phage traits and uncovered hidden interactions in the system [1].    Reference: [1] Grigorian, G., George, S.V. and Arridge, S., 2024. Learning Governing Equations of Unobserved States in Dynamical Systems. arXiv preprint arXiv:2404.18572.