Structured stochastic models based on layered entities for biological and biochemical applications

The horizontal transfer of modelling techniques from other domains of science is a convenient choice. With already established formalism, computational tools with a strong community support are available right away. However, this low effort approach has some serious drawbacks representation-wise. Even when the final model is technically correct, correctness by itself is not enough to make a model "good". Other features like simplicity, meaningfulness and computability must be optimized as well to make the model relevant for engineering purposes.

In this talk, I will describe a modelling framework that supports features typical of biochemical systems, often missing in approaches transferred from other fields. I will discuss the framework main advantages, such as composable orthogonal components, and some high-level aspects of its design.