Systems Biology, Digital Twins and AI

Aim

Upon completion of the course, the student should be able to:

• Analyze properties of nonlinear dynamic systems.
• Formulate models for biological digital twins, using relevant modelling formalisms for applications such as drug development and clinical research.
• Formulate and solve optimization problems using available optimization solvers, and to evaluate the agreement between model and data.
• Compute model uncertainty and create predictions for experimental design.
• Combine AI and mechanistic models into hybrid models for risk calculations.
• Critically review systems biology models in scientific articles.

Content

The overall purpose of the course is to advance the understanding of methods and approaches within data-driven mechanistic systems biology and apply this understanding to real world problems within biomedicine. The course contains

• Properties of nonlinear dynamic systems: numerical methods for simulation, continuation, and bifurcation analysis; basin-of-attraction, transients, and stationary behaviors.
• Basic concepts and methods within data-driven mechanistic modelling: formation of cost functions, optimization, statistical tests.
• Methods of uncertainty analysis: profile-likelihood, core predictions, Markov Chain Monte Carlo.
• Parameter identifiability analysis: difference between structural and practical identifiability.
• Different modelling formats: ordinary differential equations, differential algebraic equations, Boolean models, nonlinear mixed-effects modelling, stochastic models.
• Multi-level models: flat modelling, modular modelling.
• Hybrid modelling: basics within bioinformatics, AI, and machine learning to calculate risks and generate perturbed images and responses; combinations of statistical and mechanistic models.
• Usage areas of modelling and digital twins: in basic research, for drug development, for replacement of animal experiments, and for clinical and everyday applications within eHealth.