This talk reports on our investigations to explore appropriate modelling and analysis techniques for processes evolving simultaneously over time and space, applied to biological systems. Current challenges for modelling in Systems Biology include those associated with issues of complexity and representing systems with multi-scale attributes. A drawback of current modelling approaches, including Petri nets, is their limitation to relatively small networks. We use Stochastic and Continuous Petri Nets to consider continuous time evolution as Markov process or system of Ordinary Differential Equations, and Coloured Petri Nets to statically encode finite discrete space. Combining both concepts yields Coloured Stochastic and Coloured Continuous Petri nets, which allow for directly executable models as well as computational experiments using standard analysis and simulation techniques over very large networks. We illustrate our approach by a couple of case studies, including gradient formation, multistrain bacterial colonies, and planar cell polarity signalling in Drosophila wing.