Abstract
Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.
Highlights
Cellular signalling is essential in translating extrinsic and/or intrinsic chemical and physical stimuli into diverse cell responses such as proliferation, cell migration or cell death
The most commonly used approach is ordinary differential equation (ODE) modelling that uses a series of differential equations to define how the concentrations of reactants change over time
As the relatively poor performance of Agent-based modelling (ABM) simulations is the major drawback of ABM compared to other methods such as ODE, SDE or PDE models, we looked at whether the use of graphical processing units (GPUs) and central processing units (CPUs) parallelisation could improve the speed of ABM simulations
Summary
Cellular signalling is essential in translating extrinsic and/or intrinsic chemical and physical stimuli into diverse cell responses such as proliferation, cell migration or cell death. The most commonly used approach is ordinary differential equation (ODE) modelling that uses a series of differential equations to define how the concentrations of reactants change over time. This has been used effectively to describe a number of cell signalling pathways, including the NFκB pathway[3,4], the intrinsic apoptosis pathway[5,6,7] and the cell cycle[8]. Biological systems are characterised by, complex structural organisation, a great level of heterogeneity and physical phenomena, such as molecular crowding, that are not adequately included in ODE models. Improved methods are required for deeper understanding of how complex events at the individual molecule scale underlie system level effects in cell signalling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
