Speakers
Description
Modern cell and developmental biology increasingly relies on the integration of mechanistic modeling, data-driven inference, and advanced computational techniques to generate predictive insight and guide experimental design. State-of-the-art approaches now combine differential equations and stochastic models with modern data analysis and data science methodologies, including machine learning, to address the complexity, heterogeneity, and multiscale nature of biological systems. These tools enable parameter inference, model discovery, uncertainty quantification, and sensitivity analysis from high-dimensional experimental data.
This session will highlight recent advances in modeling and inference across cell and developmental biology, medicine, medical research, and bioengineering, and their role in cell growth, intracellular transport, cell differentiation, cell migration, and tissue development. Speakers will present methods that integrate experimental data with mechanistic and statistical models to study regulatory networks and emergent multicellular behaviors, as well as translational applications relevant to disease modeling and therapeutic design. The speakers will discuss current challenges and open directions in combining mathematical modeling, experimental data, stochastic processes, and machine learning to advance biological understanding and medical innovation.
