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Chemotaxis describes the movement of an organism, such as single ormulti-cellular organisms and bacteria, in response to a chemical stimulus. Twowidely used models to describe the phenomenon are the celebrated Keller-Segelequation and a chemotaxis kinetic equation. These two equations describe theorganism movement at the macro- and mesoscopic level respectively, and areasymptotically equivalent in the parabolic regime. How the organism responds toa chemical stimulus is embedded in the diffusion/advection coefficients of theKeller-Segel equation or the turning kernel of the chemotaxis kinetic equation.Experiments are conducted to measure the time dynamics of the organisms'population level movement when reacting to certain stimulation. From this oneinfers the chemotaxis response, which constitutes an inverse problem. \\ Inthis paper we discuss the relation between both the macro- and mesoscopicinverse problems, each of which is associated to two different forward models.The discussion is presented in the Bayesian framework, where the posteriordistribution of the turning kernel of the organism population is sought after.We prove the asymptotic equivalence of the two posterior distributions.

Multiscale convergence of the inverse problem for chemotaxis in the Bayesian setting