Statistical abstraction for multi-scale spatio-temporal systems

Michalis Michaelides, Jane Hillston, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review


Modelling spatio-temporal systems exhibiting multi-scale behaviour is a powerful tool in many branches of science, yet it still presents significant challenges. Here we consider a general two-layer (agent-environment) modelling framework, where spatially distributed agents behave according to external inputs and internal computation; this behaviour may include influencing their immediate environment, creating a medium over which agent-agent interaction signals can be transmitted. We propose a novel simulation strategy based on a statistical abstraction of the agent layer, which are typically the most detailed components of the model and can incur significant computational cost in simulation. The abstraction makes use of Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning, to estimate the agent's behaviour given the environmental input. We show on two biological case studies how this technique can be used to speed up simulations and provide further insights into model behaviour.
Original languageEnglish
Article number22
Pages (from-to)22:1-22:29
Number of pages29
JournalACM Transactions on Modeling and Computer Simulation
Issue number4
Publication statusPublished - 10 Dec 2019


  • Theory of computation
  • Applied computing
  • Abstraction
  • Systems biology
  • Multi-scale systems
  • spatio-temporal
  • agent-based
  • statistical abstraction
  • coarsening


Dive into the research topics of 'Statistical abstraction for multi-scale spatio-temporal systems'. Together they form a unique fingerprint.

Cite this