Mark Read

CoCoRo -- The Self-Aware Underwater Swarm

The EU-funded CoCoRo project studies heterogeneous swarms of AUVs used for the purposes of under water monitoring and search. The CoCoRo underwater swarm system will combine bio-inspired motion principles with biologically-derived collective cognition mechanisms to provide a novel robotic system that is scalable, reliable and flexible with respect its behavioural potential. We will investigate and develop swarm-level emergent self-awareness, taking biological inspiration from fish, honeybees, the immune system and neurons. Low-level, local information processing will give rise to collective-level memory and cognition. CoCoRo will develop a novel bio-inspired operating system whose default behaviour will be to provide AUV shoaling functionality and the maintenance of swarm coherence. Collective discrimination of environmental properties will be processed on an individual-or on a collective-level given the cognitive capabilities of the AUVs. We will investigate collective self-recognition through experiments inspired by ethology and psychology, allowing for the quantification of collective cognition.

Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis

For computational agent-based simulation, to become a serious tool for investigating biological systems requires the implications of simulation-derived results to be appreciated in terms of the original system. However, epistemic uncertainty regarding the exact nature of biological systems can complicate the calibration of models and simulations that attempt to capture their structure and behaviour, and can obscure the interpretation of simulation-derived experimental results with respect to the real domain. We present an approach to the calibration of an agent-based model of experimental autoimmune encephalomyelitis (EAE), a mouse proxy for multiple sclerosis (MS), which harnesses interaction between a modeller and domain expert in mitigating uncertainty in the data derived from the real domain. A novel uncertainty analysis technique is presented that, in conjunction with a latin hypercube-based global sensitivity analysis, can indicate the implications of epistemic uncertainty in the real domain. These analyses may be considered in the context of domain-specific knowledge to qualify the certainty placed on the results of in silico experimentation.

Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems

Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.

Collective-adaptive Lévy flight for underwater multi-robot exploration

This paper presents the use of Lévy flight, a bio-inspired algorithm, to efficiently and effectively locate targets in underwater search scenarios. We demonstrate how a novel adaptation strategy, building on the Firefly optimization algorithm, substantially improves Lévy flight performance. The adaptation strategy represents a swarm intelligence approach, the distribution patterns governing robot motion are optimized in accordance with the distribution of targets in the environment, as detected by and communicated between the robots themselves. Simulation experiments contrasting the performance of the present Lévy flight and two other search strategies in both sparse and clustered distributions of targets are conducted. We identify Lévy flight as exhibiting the best performance, and this is improved with our adaptation strategy, particularly when targets are clustered. Finally, Lévy flights superior performance over the alternative strategies examined here is empirically confirmed through deployment on real-world underwater swarm robotic platforms.

Using UML to Model EAE and Its Regulatory Network

Experimental Autoimmune Encephalomyelitis (EAE) is an autoimmune disease in mice which serves as a model for multiple sclerosis in humans [4,5]. The disease constitutes the direction of immunity towards myelin, an insulatory material that covers neurons. The consequential damage to the central nervous system (CNS) can lead to paralysis and death [6].

Agent‐Based Modeling in Systems Pharmacology

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.

Determining Disease Intervention Strategies Using Spatially Resolved Simulations

Predicting efficacy and optimal drug delivery strategies for small molecule and biological therapeutics is challenging due to the complex interactions between diverse cell types in different tissues that determine disease outcome. Here we present a new methodology to simulate inflammatory disease manifestation and test potential intervention strategies in silico using agent-based computational models. Simulations created using this methodology have explicit spatial and temporal representations, and capture the heterogeneous and stochastic cellular behaviours that lead to emergence of pathology or disease resolution. To demonstrate this methodology we have simulated the prototypic murine T cell-mediated autoimmune disease experimental autoimmune encephalomyelitis, a mouse model of multiple sclerosis. In the simulation immune cell dynamics, neuronal damage and tissue specific pathology emerge, closely resembling behaviour found in the murine model. Using the calibrated simulation we have analysed how changes in the timing and efficacy of T cell receptor signalling inhibition leads to either disease exacerbation or resolution. The technology described is a powerful new method to understand cellular behaviours in complex inflammatory disease, permits rational design of drug interventional strategies and has provided new insights into the role of TCR signalling in autoimmune disease progression.

In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis

BackgroundExperimental autoimmune encephalomyelitis has been used extensively as an animal model of T cell mediated autoimmunity. A down-regulatory pathway through which encephalitogenic CD4Th1 cells are killed by CD8 regulatory T cells (Treg) has recently been proposed. With the CD8Treg cells being primed by dendritic cells, regulation of recovery may be occuring around these antigen presenting cells. CD4Treg cells provide critical help within this process, by licensing dendritic cells to prime CD8Treg cells, however the spatial and temporal aspects of this help in the CTL response is currently unclear.ResultsWe have previously developed a simulator of experimental autoimmune encephalomyelitis (ARTIMMUS). We use ARTIMMUS to perform novel in silico experimentation regarding the priming of CD8Treg cells by dendritic cells, and the resulting CD8Treg mediated killing of encephalitogenic CD4Th1 cells. Simulations using dendritic cells that present antigenic peptides in a mutually exclusive manner (either MBP or TCR-derived, but not both) suggest that there is no significant reliance on dendritic cells that can prime both encephalitogenic CD4Th1 and Treg cells. Further, in silico experimentation suggests that dynamics of CD8Treg priming are significantly influenced through their spatial competition with CD4Treg cells and through the timing of Qa-1 expression by dendritic cells.ConclusionThere is no requirement for the encephalitogenic CD4Th1 cells and cytotoxic CD8Treg cells to be primed by the same dendritic cells. We conjecture that no significant portion of CD4Th1 regulation by Qa-1 restricted CD8Treg cells occurs around individual dendritic cells, and as such, that CD8Treg mediated killing of CD4Th1 cells occurring around dendritic cells is not critical for recovery from the murine autoimmune disease. Furthermore, the timing of the CD4Treg licensing of dendritic cells and the spatial competition between CD4Treg and CD8Treg cells around the dendritic cell is critical for the size of the cytotoxic T lymphocyte response, because dendritic cells have a limited lifespan. If treatments can be found to either speed up the licensing process, or increase the spatial competitiveness of CD8Treg cells, the magnitude of the cytotoxic T lymphocyte response can be increased.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UMLs ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UMLs lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UMLs activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

Profiling Underwater Swarm Robotic Shoaling Performance Using Simulation

Underwater exploration is important for mapping out the oceans, environmental monitoring, and search and rescue, yet water represents one of the most challenging of operational environments. The CoCoRo project proposes to address these challenges using cognitive swarm intelligent systems. We present here CoCoRoSim, an underwater swarm robotics simulation used in designing underwater swarm robotic systems. Collective coordination of robots represents principle challenge here, and use simulation in evaluating shoaling algorithm performance given the communication, localization and orientation challenges of underwater environments. We find communication to be essential for well-coordinated shoals, and provided communication is possible, inexact localization does not significantly impact performance. As a proof of concept simulation is employed in evaluating shoaling performance in turbulent waters.