Simulating Phase Transitions in Biological Systems Using the 2D Ising Model: A Statistical Physics Approach

This paper investigates how the Ising model, a foundational construct in statistical physics, can be employed to simulate phase transitions observed in biological systems such as protein folding and lipid membrane dynamics. Using the two-dimensional version of the model and the Metropolis-Hastings algorithm, the study explores binary state systems and analyzes temperature-dependent behaviors like spin clustering and energy variations. The findings demonstrate how these simulations mirror biological processes, offering valuable insights into mechanisms like molecular misfolding and cellular organization. This approach bridges theoretical physics with biological complexity, suggesting broader applications in fields such as neuroscience and genetic regulation.