Data-driven modelling

Parameter estimation

Formulation of estimation problem using cost and likelihood function

Local optimization algorithms

Global optimization

Model uncertainty

Structural identifiability analysis

Practical identifiability analysis

Core prediction analysis

Drawing conclusions

Statistical tests

Experiment design