1. A multi-component model engineering system comprising: one or more processors arranged to provide a library of model components each comprising a function for describing the behavior of a sub-set of the state variables of a multi-component dynamical system, and having at least one parameter, the multicomponent dynamical system including a multi-component model wherein each component of the multi-component model is a model comprising one or more functions representing biological or physical processes and their interactions; a data access engine arranged to access empirical data measured from the dynamical system, the data being accessed from a plurality of different datasets; an inference engine arranged to learn one or more of the parameters of specified ones of the model components and to learn a probability distribution for each parameter which represents a degree of uncertainty of the parameter; a model-data association engine arranged to link a plurality of specified model components from the library of model components to form a multi-component model and to associate each model component with parameters to be learnt, with data from at least one of the datasets; the model-data association engine also being arranged to pass the specified model components to the inference engine in a format suitable for use by the inference engine to learn the parameters and probability distributions of the specified model components using the data associated with the specified model components; and a model engineering system arranged to obtain a first learnt probability distribution based on a model fitting process for each of the model component of the entire multi-component model and a second learnt probability distribution based on a model fitting process of an individual model component, and automatically revise the individual model component to produce a revised model component if the difference between the first and second learnt probability distributions exceeds a threshold value, the revised model component retaining one or more elements of the individual model component.