The aim of this review is twofold. Firstly, we present the state of the art in dynamic modelling and model-based design, optimisation and control of food systems. The need for nonlinear, dynamic, multi-physics and multi-scale representations of food systems is established. Current difficulties in building such models are reviewed: incomplete, piecewise available knowledge, spread out among different disciplines (physics, chemistry, biology and consumer science) and contributors (scientists, experts, process operators, process managers), scarcity, uncertainty and high cost of measured data, complexity of phenomena and intricacy of time and space scales. Secondly, we concentrate on the opportunities offered by the complex systems science to cope with the difficulties faced by food science and engineering. Newly developed techniques such as model-based viability analysis, optimisation, dynamic Bayesian networks etc. are shown to be relevant and promising for design and optimisation of foods and food processes based on consumer needs and expectations.