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Previously, we proposed a coarse-grained, quantitative approach based on the basic Petri net formalism, to mimic the behaviour of the biological processes during multicellular differentiation. Here, we apply our modelling approach to the well-studied process of Caenorhabditis elegans vulval development. We show that our model correctly reproduces a large set of in vivo experiments with statistical accuracy. It also generates gene expression time series in accordance with recent biological evidence. Finally, we modelled the role of microRNA mir-61 during vulval development and predict its contribution in stabilizing cell pattern formation.

Abstract. Petri nets are a widely used formalism to qualitatively model concurrent systems such as a biological cell. We present techniques for modelling biological processes as Petri nets for further analyses and insilico experiments. Instead of extending the formalism with ,,colours" or rates, as is most often done, we focus on preserving the simplicity of the formalism and developing an execution semantics which resembles biology -we apply a principle of maximal parallelism and introduce the novel concept of bounded execution with overshooting. A number of modelling solutions are demonstrated using the example of the wellstudied C. elegans vulval development process. To date our model is still under development, but first results, based on Monte Carlo simulations, are promising.

Abstract. This position paper argues that the operational modelling approaches from the formal methods community can be applied fruitfully within the systems biology domain. The results can be complementary to the traditional mathematical descriptive modelling approaches used in systems biology. We discuss one example: a recent Petri net analysis of C. elegans vulval development. Systems BiologySystems biology studies complex interactions in biological systems, with the aim to understand better the entirety of processes that happen in such a system, as well as to grasp the emergent properties of such a system as a whole. This can for instance be at the level of metabolic or interaction networks, signal transduction, genetic regulatory networks, multi-cellular development, or social behaviour of insects.The last decade has seen a rapid and successful development in the collaboration between biologists and computer scientists in the area of systems biology and bioinformatics. It has turned out that formal modelling and analysis techniques that have been developed for distributed computer systems, are applicable to biological systems as well. Namely, both kinds of systems have a lot in common. Biological systems are built from separate components that communicate with each other and thus influence each other's behaviour. Notably, signal transduction within a cell consists of cascades of biochemical reactions, by which for instance genes are activated or down-regulated. The genes themselves produce the proteins that drive signal transduction, and cells can be connected in a multicellular organism, making this basically one large, complex distributed system. Another, very different, example at the organism level is how ants in one colony send stimuli to each other in the form of pheromones.Biological systems are reactive systems, as they continuously interact with their environment. In November 2002, David Harel [11] put forward a grand challenge to computer science, to build a fully animated model of a multi-cellular organism as a reactive system; specifically, he suggested to build such a model of the C. elegans nematode worm, which serves as a one of the model organisms in developmental biology.Open questions in biology that could be addressed in such a modelling framework include the following, listed in order from a detailed, molecular viewpoint to a more global view of whole organisms: -How complete is our knowledge of metabolic, signalling and regulatory processes at a molecular level?

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