The hierarchical organisation of biological systems requires the adoption of multi-level modelling for capturing the interactions that occur among the different levels: according to the principles of downward and upward causation the behaviour of the parts (down) is determined by the behavior of the whole (up), and the emergent behaviour of the whole is determined by the behaviour of the parts.

Modelling and simulating such scenarios is an open problem in literature and a framework for supporting researchers in these investigations is still not fully available.
I devised a computational framework that permits the reproduction of multi-level and multi-compartments dynamics. It is based on (i) a high-level language to specify system structure and inner chemical behaviours, (ii) a simulation engine implementing an optimised version of Gillespie's SSA (stochastic simulation algorithm), which allows to capture different level dynamics, exactly foreseeing the chemical reactions of substances into the system's compartments and their diffusion across compartment's membrane, and (iii) a module for performing parameter tuning by metaheuristics.
The computational framework is applied at the morphogenesis of biological systems, investigating the phenomena responsible for the spatial pattern formation of gene expression in Drosophila Melanogaster.
More than that I propose an agent-based model of morphogenesis validated on the Drosophila Melanogaster regionalisation.
Taking inspiration from these phenomena we are exploiting our computational framework for the realisation of "self-organising morphogenetic systems", i.e. artificial systems which self-organise themselves through bio-inspired rules.