Programming languages for controlling adaptive and hybrid systems

There is a vast literature concerning modeling of dynamical systems which in the beginning was restricted to continuous systems  and later extended to hybrid-systems  incorporating also discrete events or automata. The stability of hybrid systems  has been investigated and special programming languages designed. Many programming languages concerning biological systems have been developed in the areas of Molecular Computing,  or with formal specifications as in π-calculus, Petri-nets and in the study of hybrid systems. These hybrid systems also have been applied to modeling biological systems. A promising concurrent language is Hybrid cc  but UML-RT (unified modeling language, real time ) diagrams promise to provide a more tractable programming access point. This has already been discussed in the context of Systems Biology  with SBML  (systems-biology-markup-language) and its spatial extensions as the communication platform of cellular models. Also the developments in the membrane molecular computing paradigm  deserve further consideration as a basis for a programming language for artificial cells and dynamically compartmentalized programmed chemical systems. What is missing form these formulations are collective self-organization and self-assembly processes and the notion of instruction scale feedback control loops (or ubiquitous regulation) as a basis for adaptivity. We intend to bridge this gap in ECCell. Similarly, developments in genetic algorithms and programming, classifier systems and in particular self-assembling microprocessors provide a powerful additional starting point for exploring embedded real time adaptive and evolutionary programming.