A numerical evaluation of state reconstruction methods for heterogeneous cell populations

Heterogeneity among cells is a common characteristic of living systems. For mathematical modeling of heterogeneous cell populations, one typically has to reconstruct the underlying heterogeneity from measurements on the population level. Based on recent insights into the mathematical nature of this problem as an inverse problem of tomographic type, we evaluate numerical methods to perform such a reconstruction in basic case studies. We compare a kernel density based optimization approach, filtered back projection, and algebraic reconstruction techniques. The latter two are well established methods in computed tomography.