People have to go around measuring things. There's no escape from that for most of that type of work. There's a deep relationship between the two. No one's going to come up with a model that works without going and comparing with experiment. But it is the intelligent use of experimental measurements that we're after there because that goes to this concept of Bayesian methods. I will perform the right number of experiments to make measurements of, say, the time series evolution of a given set of proteins. From those data, when things are varying in time, I can map that on to my deterministic Popperian model and infer what's the most likely value of all the parameters that would be Popperian ones that would fit into the model. It's an intelligent interaction between them that's necessary in many complicated situations. 

PETER COVENEY holds a chair in Physical Chemistry, and is director of the Centre for Computational Science at University College London and co-author, with Roger Highfield, of The Arrow of Time and Frontiers of Complexity. Peter Coveney's Edge Bio Page [2]