Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems

• DOI: 10.1007/s00285-023-01903-x
• Publication Date: 2023-04-05T09:58:38Z
• Authors: Sara Hamis; Panu Somervuo; J. Arvid Ågren; Dagim Shiferaw Ta...; Juha Kesseli; Jacob G. Scott; Matti Nykter; Philip Gerlee; Dmitri Finkelshtein; Otso Ovaskainen
• Keywords: Cumulant

Abstract Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation enables rigorous analysis these models. However, spatial cumulant (SCMs), which arisen from theoretical ecology, describe population dynamics generated specific family IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved formulated system differential equations approximate two STPP-generated summary statistics: first-order cumulants (densities), second-order (spatial covariances). We exemplify how can be used in oncology modelling cell populations comprising interacting growth factor-producing non-producing cells. To formulate model equations, we computational tools enable generation STPPs, mean-field (MFPMs) user-defined descriptions (Cornell et al. Nat Commun 10:4716, 2019). calculate compare STPP, SCM MFPM-generated statistics, develop an application-agnostic pipeline. Our results demonstrate capture density dynamics, even when MFPMs fail to do so. From both MFPM derive treatment-induced death rates required achieve non-growing populations. When testing treatment strategies populations, our SCM-informed outperform MFPM-informed terms inhibiting growths. thus provide new framework study cell-cell interactions, perturb dynamics. We, therefore, argue increase IBMs’ applicability research.