  In this paper, we consider the fast numerical solution of an optimal control
formulation of the Keller--Segel model for bacterial chemotaxis. Upon
discretization, this problem requires the solution of huge-scale saddle point
systems to guarantee accurate solutions. We consider the derivation of
effective preconditioners for these matrix systems, which may be embedded
within suitable iterative methods to accelerate their convergence. We also
construct low-rank tensor-train techniques which enable us to present efficient
and feasible algorithms for problems that are finely discretized in the space
and time variables. Numerical results demonstrate that the number of
preconditioned GMRES iterations depends mildly on the model parameters.
Moreover, the low-rank solver makes the computing time and memory costs
sublinear in the original problem size.
