Parallelisation strategies for agent based simulation of immune systems

Background In 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. Results This 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. Conclusions Our 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

Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression

Cite as: Perrin, Dimitri (2008) Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression. PhD thesis, Dublin City University

Model refinement through high-performance computing: an agent-based HIV example

Background Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Stochastic computational modelling of complex drug delivery systems

As modern drug formulations become more advanced, pharmaceutical companies face the need for adequate tools to permit them to model complex requirements and to reduce unnecessary adsorption rates while increasing the dosage administered. The aim of the research presented here is the development and application of a general stochastic framework with agent-based elements for building drug dissolution models, with a particular focus on controlled release systems. The utilisation of three dimensional Cellular Automata and Monte Carlo methods, to describe structural compositions and the main physico-chemical mechanisms, is shown to have several key advantages: (i) the bottom up approach simplifies the definition of complex interactions between underlying phenomena such as diffusion,polymer degradation and hydration, and the dissolution media; (ii) permits straightforward extensibility for drug formulation variations in terms of supporting various geometries and exploring effects of polymer composition and layering; (iii) facilitates visualisation, affording insight on system structural evolution over time by capturing successive stages of dissolution. The framework has been used to build models simulating several distinct release scenarios from coated spheres covering single coated erosion and swelling dominated spheres as well as the influence of multiple heterogeneous coatings. High-performance computational optimisation enables precision simulations of the very thin coatings used and allows fast realisation of model state changes. Furthermore, theoretical analysis of the comparative impact of synchronous and asynchronous Cellular Automata and the suitability of their application to pharmaceutical systems is performed. Likely parameter distributions from noisy in vitro data are reconstructed using Inverse Monte Carlo methods and outcomes are reported

Dense agent-based HPC simulation of cell physics and signaling with real-time user interactions

Introduction: Distributed simulations of complex systems to date have focused on scalability and correctness rather than interactive visualization. Interactive visual simulations have particular advantages for exploring emergent behaviors of complex systems. Interpretation of simulations of complex systems such as cancer cell tumors is a challenge and can be greatly assisted by using “built-in” real-time user interaction and subsequent visualization.Methods: We explore this approach using a multi-scale model which couples a cell physics model with a cell signaling model. This paper presents a novel communication protocol for real-time user interaction and visualization with a large-scale distributed simulation with minimal impact on performance. Specifically, we explore how optimistic synchronization can be used to enable real-time user interaction and visualization in a densely packed parallel agent-based simulation, whilst maintaining scalability and determinism. We also describe the software framework created and the distribution strategy for the models utilized. The key features of the High-Performance Computing (HPC) simulation that were evaluated are scalability, deterministic verification, speed of real-time user interactions, and deadlock avoidance.Results: We use two commodity HPC systems, ARCHER (118,080 CPU cores) and ARCHER2 (750,080 CPU cores), where we simulate up to 256 million agents (one million cells) using up to 21,953 computational cores and record a response time overhead of ≃350 ms from the issued user events.Discussion: The approach is viable and can be used to underpin transformative technologies offering immersive simulations such as Digital Twins. The framework explained in this paper is not limited to the models used and can be adapted to systems biology models that use similar standards (physics models using agent-based interactions, and signaling pathways using SBML) and other interactive distributed simulations