BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sara Hamis (Tampere University) DTSTART:20231026T111500Z DTEND:20231026T120000Z DTSTAMP:20260422T155153Z UID:gbgstats/31 DESCRIPTION:Title: Spatial cumulant models for mathematical cancer research\nby Sara Ha mis (Tampere University) as part of Gothenburg statistics seminar\n\nLectu re held in MVL14.\n\nAbstract\nSpatial cumulant models (SCMs) are spatiall y resolved population models\, formulated by differential equations\, that describe population dynamics generated by spatio-temporal point processes (STPPs). Specifically\, SCMs approximate the dynamics of two STPP-generat ed summary statistics: first-order spatial cumulants (densities)\, and sec ond-order spatial cumulants (spatial covariances). \n\nIn this talk\, I’ ll exemplify how SCMs can be used in mathematical oncology by modelling th eoretical cancer cell populations comprising interacting subclones. Our re sults demonstrate that SCMs can capture STPP-generated population density dynamics\, even when mean-field population models (MFPMs) fail to do so. F rom both MFPM and SCM equations\, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treat ment strategies in STPP-generated cell populations\, our results demonstra te that SCM-informed strategies outperform MFPM-informed strategies in ter ms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions and treatments t hat take cell-cell interactions into account. \n\nJoint work with: Panu So mervuo\; J. Arvid Ågren\; Dagim S. Tadele\; Juha Kesseli\; Jacob G. Scott \; Matti Nykter\; Philip Gerlee\; Dmitri Finkelshtein\; Otso Ovaskainen.\n LOCATION:https://researchseminars.org/talk/gbgstats/31/ END:VEVENT END:VCALENDAR