Aims of the Book

The interactions between different formal models and biology have a long-standing successful history. Results have been produced on both sides of this process while continuously new models are considered and new biological territories are uncovered. On one hand, biological systems, as cells, have been recognised as complex systems requesting various models to simulate, explain, and verify their multiple properties. A very popular notion of complex systems is that of a very large number of simple and identical elements interacting to produce complex emergent behaviour. Unlike complex systems of simple elements, in which functions emerge from the properties of the network they form rather than from any specific elements, functions of biological systems rely on a combination of the network and the specific elements involved. Molecular biology has uncovered a great deal of biological facts, each one being very important in isolation, but various biological entities (cells, tissues, organs, organisms, colonies , societies) reveal very complex interactions. This level of aggregation, generally called systems biology, requires a combination of approaches, from experiments to abstract formal models able to manage the huge complexity of the biological systems to accurately represent and simulate them. In order to capture the complexity and the dynamicity of these systems, various models have been developed ranging from abstract mathematical-based models (either continuous or discrete approaches) to different specific programming paradigms or sophisticated software systems. In the same time, natural sciences, and especially biology, represented a rich source of modelling paradigms. Well-defined areas of artificial intelligence (genetic algorithms, neural networks), mathematics, and theoretical computer science (L systems, DNA computing) are massively influenced by the behaviour of various biological entities and phenomena. In the last decades or so, new