Events

Date: 25 February 2026   Time: 14:00 - 15:00     

Location: MB503 or teams link

Turing patterns have found huge success as a mechanism for explaining patterns in biology, chemistry and phyiscs. However, one of the problems of applying Turing's theory to biology is the "Robustness Problem". Namely, small changes to the input condition can lead to large changes in the output pattern. Beautiful work by Edmund Crampin showed that uniform domain growth could remove this problem. However, all of this understanding (and more) rests on the system being deterministic. What happens when we add biologically realistic, intrinsic noise? Well, everything breaks down and we once again have the Robustness Problem and we must question whether robustness is possible at all. Coupling techniques from weak-noise expansions and discrete Fourier transforms I demonstrate different methods of growth can support robust pattern development, as well show that additional noise is not the destructive force that we may first consider.