A General Framework for Designing Evolutionary Experiments to Select Specific Phage Phenotypes Using Neural Networks, Statistical Simulations, and Symbolic Regression

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• Hassan Alam (University of Cambridge)
• Thursday 11 September 2025, 15:10-15:15
• External.

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TGM150 - 9th Edwards Symposium – Frontiers in Statistical Physics and Soft Matter

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. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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