Mathematical biology stands at the frontier where mathematics and the life sciences converge. It provides powerful tools to understand dynamic biological phenomena, many of which exhibit hidden or complex attributes that are difficult to observe directly. Differential equations have long served as central instruments for modeling such processes, including cellular interactions, epidemiology, and population dynamics.

Stochastic processes, by contrast, capture the inherent randomness of biological systems. They represent systems evolving probabilistically, where outcomes emerge from sequences of random events. This framework not only allows discovery of process properties but also enables prediction and classification based on past history.

Gene expression is a prime example. As the fundamental process through which cells transcribe and translate genetic information into proteins, gene expression determines cellular structure, function, and signaling. Its inherently noisy character means that variability itself shapes development, adaptation, and disease progression. Stochastic modeling has therefore become indispensable for uncovering the role of randomness in shaping biological outcomes.

Applications of these ideas extend across medicine, biology, genetics, biotechnology, statistical physics, and biophysics. Our special session will highlight recent innovations in stochastic processes, mathematical biology, and gene expression, aiming to advance our understanding of stability, adaptability, and progression in complex biological networks and systems.

Topics include, but are not limited to: