  Systems biology relies on mathematical models that often involve complex and
intractable likelihood functions, posing challenges for efficient inference and
model selection. Generative models, such as normalizing flows, have shown
remarkable ability in approximating complex distributions in various domains.
However, their application in systems biology for approximating intractable
likelihood functions remains unexplored. Here, we elucidate a framework for
leveraging normalizing flows to approximate complex likelihood functions
inherent to systems biology models. By using normalizing flows in the
Simulation-based inference setting, we demonstrate a method that not only
approximates a likelihood function but also allows for model inference in the
model selection setting. We showcase the effectiveness of this approach on
real-world systems biology problems, providing practical guidance for
implementation and highlighting its advantages over traditional computational
methods.
