Plant defence model revisions through iterative minimisation of constraint violations.

Biologists have been investigating plant defence response to virus infections; however, a comprehensive mathematical model of this complex process has not been developed. One obstacle in developing a dynamic model, useful for simulation, is the lack of kinetic data from which the model parameters could be determined. We address this problem by proposing a methodology for iterative improvement of the model parameters until the simulation results come close to the expectation of biology experts. These expectations are formalised in the form of constraints to be satisfied by the model simulations. In three iterative steps the model converged to satisfy the biology experts. There are two results of our approach: individual simulations and optimised model parameters, which provide a deeper insight into the biological system. Our constraint-driven optimisation approach allows for an efficient exploration of the dynamic behaviour of biological models and, at the same time, increases their reliability.