Mathematical modelling has a long tradition in the biological sciences . The development of early models was driven by the desire to develop a theoretical framework in which general phenomena could be understood, such as the occurrence of oscillations in wild animal populations or the activity of an enzyme in dependence of its substrate concentration . Thus, like in the physical sciences, mathematical descriptions were developed with the aim to derive underlying, fundamental principles. While in physics, a huge class of phenomena could be broken down to a small number of equations (the Lagrange equations of motion in Hamiltonian mechanics; the Maxwell equations in electrodynamics; Schrödinger's equation in Quantum Mechanics), similar concepts, such as variational principles explaining metabolic fluxes, gene expression patterns, or ecosystem compositions in dependence on environmental parametres, simply do not exist (or at least have not been discovered) in biology. The reason is most likely that biological systems are far more complex than inanimate systems and new emergent properties occur at every level of organisation. Still, life obeys the laws of physics, so why should it not be predictable in a similar fashion as a highly complex deterministic system, such as the model simulating the performance and combustion profile of an engine ? For example, to what extent can we predict the behaviour of an E. coli (or, in fact, any other) cell?