Parallelisation strategies for agent based simulation of immune systems

Abstract

BackgroundIn recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches.ResultsThis paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment.ConclusionsOur performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.

In Silico Studies to Support Vaccine Development

“…This is because the immune system and its multiple agents and components are linked to complex interactions, and the ABM methodology allows these complex behaviors to emerge during simulation. This makes ABM perfect for performing biological simulations (i.e., for studying the complex and dynamic interactions within the biological environment) [ 34 ]. The Universal Immune System Simulator (UISS) platform is a type of ABM model and has been successfully applied to a large number of disease-modeling scenarios, including COVID-19, and can simulate, for instance, infection dynamics and its interactions with the host immune system, making it possible as well to predict the immunogenicity response of compounds [ 20 , 23 ].…”

An agent-based approach for modelling and simulation of glycoprotein VI receptor diffusion, localisation and dimerisation in platelet lipid rafts

“…Moreover, the present still simple ABM can be further developed into a more complex system with more agents and conditions. Useful additions such as receptor interactions with the cytoskeleton can be added in by utilising a computational cluster 63,64 .…”

An Agent-Based Ship Firefighting Model

“…ABMS is recognized and applied in many scientific disciplines, not only in Science, Technology, Engineering, and Mathematics (STEM) but also in the natural and social sciences [21]. With regard to STEM, ABMS has been applied to several disciplines, such as the design of self-organizing systems [22], geographic information systems [23], epidemiology [24,25], ecology [26][27][28], transportation and logistics [29][30][31], manufacturing [32,33], design of critical systems [34][35][36][37], and cloud computing [38]. ABMS is also well suited to the social sciences, where an understanding of the individuals and how they interact is important for understanding the synergy effect and emerging behavior of systems.…”