Evolution is inherently a stochastic process, influenced by random mutations, genetic drift, and environmental fluctuations. This makes predicting evolutionary outcomes seem almost impossible. Yet, decades of experimental work, ranging from microbial evolution to protein engineering, consistently reveal strikingly repeatable patterns and parallel outcomes, suggesting a strong underlying determinism.
This project aims to explore the theoretical foundations that reconcile this apparent contradiction. How can a process driven by chance lead to such predictable outcomes? What factors constrain or shape evolutionary trajectories to make them repeatable?
Students will engage with mathematical models, simulations, and conceptual frameworks from statistical physics, systems biology, and evolutionary theory. The project will combine theory with real-world applications, and will uncover how deep principles of chance and order govern the evolution of complex systems. Interested students should be interested in theory, simulations, and data analysis.