Abstract: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.
Abstract-Concurrent process-oriented programming is a natural medium for simulating complex systems, particularly systems where many simple components interact in an environment (which may itself be complex). There is little guidance for engineering complex systems simulation. In the context of simulation work to support immunological research, we explore relevant approaches to modelling, and draw on concepts from dependable and high-integrity systems engineering, including the emphasis on the need to validate all aspects of the simulation.
In studying complex systems, agent-based simulations offer the possibility of directly modelling components in an environment. However, the scientific value of agent-based simulations has been limited by inadequate scientific rigour. The paper focuses on agent-based simulations that are used in biological and bio-medical research. Starting from a review of best practice in simulation engineering, the paper identifies some of the key activities in developing complex systems simulations that support scientific research, and how these contribute to the essential development of mutual trust among developers and scientists. Examples from the authors' own experience illustrate how a range of studies have manifested these key activities, and identifies some successes and problems encountered.
Abstract-The CoSMoS project aims to develop reusable tools and techniques for complex systems modelling and simulation. Using process-oriented software design techniques, we have built a concurrent model of continuous space, usable in a variety of complex systems simulations. In this paper, we describe how we refactored our space model to allow our simulations to run in an efficient and highly-scalable manner across clusters of commodity machines-and, in particular, to support distributed simulation and visualisation on the Tromsø Display Wall.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.