Predicting and controlling cell systems that generate spatio-temporal point patterns

Abstract: Recent technological advances have resulted in a multitude of spatio-temporal cell imaging data. These can be translated into spatio-temporal point patterns in which 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 generate spatio-temporal patterns, we propose using two unified classes of mathematical models: spatio-temporal point processes (STPPs) and spatial cumulant models (SCMs). SCMs are population models formulated by differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). In this talk, I’ll demonstrate that (1) SCMs can capture STPP-generated density dynamics, even when mean-field population models (MFPMs) fail to do so, and (2) SCM-informed treatment strategies outperform MFPM-informed strategies in terms of inhibiting population growths. Overall, our work demonstrates that SCMs provide a promising framework in which to study ecological systems that generate spatio-temporal point patterns in cell biology and beyond.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper )

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.