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dc.contributor.authorHamis, Sara
dc.contributor.authorSomervuo, Panu
dc.contributor.authorÅgren, J Arvid
dc.contributor.authorTadele, Dagim Shiferaw
dc.contributor.authorKesseli, Juha
dc.contributor.authorScott, Jacob G.
dc.contributor.authorNykter, Matti
dc.contributor.authorGerlee, Philip
dc.contributor.authorFinkelshtein, Dmitri
dc.contributor.authorOvaskainen, Otso Tapio
dc.date.accessioned2024-02-20T16:04:43Z
dc.date.available2024-02-20T16:04:43Z
dc.date.created2023-04-25T08:57:45Z
dc.date.issued2023
dc.identifier.citationJournal of Mathematical Biology. 2023, 86 (5), .en_US
dc.identifier.issn0303-6812
dc.identifier.urihttps://hdl.handle.net/11250/3118756
dc.description.abstractTheoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs’ applicability in cancer research.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSpatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systemsen_US
dc.title.alternativeSpatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systemsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber33en_US
dc.source.volume86en_US
dc.source.journalJournal of Mathematical Biologyen_US
dc.source.issue5en_US
dc.identifier.doi10.1007/s00285-023-01903-x
dc.identifier.cristin2143038
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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