. A `homogeneous Gaussian’ distributed variable G(, 2) has imply and standard deviation 2. It really is homogeneous in that exactly the same parameterized Gaussian is utilized to represent all cells’ translational (or turn) values. In contrast, a `heterogeneous Gaussian’ distribution comprises a bespoke Gaussian Gi i ; s2 for each cell i within the dataset. The mean i and common deviation i s2 of Gi are themselves drawn from Gaussian distributions; this can be carried out once at Gi’s creation, i as well as the values are maintained throughout Gi’s use thereafter. Hence, a heterogeneous Gaussian is formulated as Gi i G M ; s2 s2 G S ; s2 , and has parameters M, s2 , S and s2 . M i S M S U() represents a uniformly distributed random variable over the variety (0, ]. The parameters describing every statistical distribution are shown in Table 3:PLOS Computational Biology | DOI:10.1371/journl.pcbi.1005082 September 2,19 /Leukocyte Motility Assessed by means of Simulation and Multi-objective Optimization-Based Model SelectionTable three. The parameters characterising each statistical distribution utilised within this manuscript. Statistical distribution Homogeneous Gaussian Heterogeneous Gaussian L y Uniform doi:ten.1371/journal.pcbi.1005082.t003 Parameters , two M, s2 , S, s2 M S ,To evaluate the capacity to get a offered statistical distribution, D, to reproduce an in vivo dataset’s translational data we produce an artificial dataset of similar structure. Values are drawn from D, and allocated into groups. There’s 1 group for every single track within the in vivo dataset, and in.