BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sara Hamis (Uppsala University) DTSTART:20250423T111500Z DTEND:20250423T120000Z DTSTAMP:20260422T155025Z UID:gbgstats/79 DESCRIPTION:Title: Predicting and controlling cell systems that generate spatio-temporal po int patterns\nby Sara Hamis (Uppsala University) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nRecent technolo gical advances have resulted in a multitude of spatio-temporal cell imagin g data. These can be translated into spatio-temporal point patterns in whi ch points represent cells. Such data hold rich information about how cells act and interact\, much of which is not extractable through data analysis alone. Therefore\, to identify\, predict and control cell systems that ge nerate spatio-temporal patterns\, we propose using two unified classes of mathematical models: spatio-temporal point processes (STPPs) and spatial c umulant models (SCMs). SCMs are population models formulated by differenti al equations that approximate the dynamics of two STPP-generated summary s tatistics: first-order spatial cumulants (densities)\, and second-order sp atial cumulants (spatial covariances). In this talk\, I’ll demonstrate t hat (1) SCMs can capture STPP-generated density dynamics\, even when mean- field population models (MFPMs) fail to do so\, and (2) SCM-informed treat ment strategies outperform MFPM-informed strategies in terms of inhibiting population growths. Overall\, our work demonstrates that SCMs provide a p romising framework in which to study ecological systems that generate spat io-temporal point patterns in cell biology and beyond.\n LOCATION:https://researchseminars.org/talk/gbgstats/79/ END:VEVENT END:VCALENDAR