Simulation is a favoured technique in robotics. It is, however, costly, in terms of development time, and its usability is limited by the lack of standardisation and portability of simulators. We present RoboSim, a diagrammatic tool-independent domain-specific language to model robotic platforms and their controllers. It can be regarded as a profile of UML/SysML enriched with time primitives, differential equations, and a mathematical semantics. Our previous work on RoboSim described a notation to specify control software. In this paper, we present a novel notation to describe physical models: block diagrams that can be linked to the platform-independent software model to characterise how services required by the software are realised by actuators and sensors. Behaviours are specified by differential equations, and simulations and mathematical models of the whole system can be generated automatically. Our main contributions are a modular and extensible diagrammatic notation that supports the explicit specification of physical behaviours; a set of validation rules that identify well-formed models; a model-to-model transformation from RoboSim to an input format accepted by several simulators; and a formal semantics for mathematical reasoning.

Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combine evolution with a learning algorithm that can either improve the inherited controllers of new offspring to fine tune them to the new body design or learn them from scratch. In this paper an approach is proposed in which a robot is specified indirectly by two compositional pattern producing networks (CPPN) encoded in a single genome, one which encodes the brain and the other the body. The body part of the genome is evolved using an evolutionary algorithm (EA), with an individual learning algorithm (also an EA) applied to the inherited controller to improve it. The goal of this paper is to determine how to utilise the results of learning process most effectively to improve task performance of the robot. Specifically, three variants are investigated: (1) evolution of the body+controller only; (2) a learning algorithm is applied to the inherited controller with the learned fitness assigned to the genome; (3) learning is applied and the genome is updated with the learned controller, as well as being assigned the learned fitness. Experiments are performed in three different scenarios chosen to favour different bodies and locomotion patterns. It is shown that better performance can be obtained using learning but only if the learned controller is inherited by the offspring.

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.

Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding.

Robots are becoming ubiquitous: from vacuum cleaners to driverless cars, there is a wide variety of applications, many with potential safety hazards. The work presented in this paper proposes a set of constructs suitable for both modelling robotic applications and supporting verification via model checking and theorem proving. Our goal is to support roboticists in writing models and applying modern verification techniques using a language familiar to them. To that end, we present RoboChart, a domain-specific modelling language based on UML, but with a restricted set of constructs to enable a simplified semantics and automated reasoning. We present the RoboChart metamodel, its well-formedness rules, and its process-algebraic semantics. We discuss verification based on these foundations using an implementation of RoboChart and its semantics as a set of Eclipse plug-ins called RoboTool.

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input-output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and γ -GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations.

The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task. In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots. We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.

Spiking Astrocyte-neuron Networks (SANNs) model the adaptive/repair feature of the human brain. They integrate astrocyte cells with spiking neurons to facilitate a distributed and fine-grained self-repair capability at the synapse level. SANNs are more complex with the addition of astrocyte cells and require longer simulation times, as they are dynamic over much longer time-scales than traditional neural networks. Therefore, dedicated FPGA accelerators offer reductions in simulation times. To support the acceleration of SANNs, the capability of fault injection to synapses and monitoring significant levels of neuron and astrocyte data for off-chip transmission to PC-based analysis, are required. This paper presents an FPGA-based monitoring platform (FMP) for injecting faults and capturing and analyzing data acquired from the SANN FPGA accelerator, Astrobyte. The FMP uses custom logic and a NIOS II based system to control fault injection and data monitoring on the FPGA. Results show accurate accelerated simulations of fault injection scenarios using FMP with speedups up to 65 times greater compared with equivalent Matlab implementations.

Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications.

The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. We have developed an agent-based model of the IL-1 stimulated NF-κB signalling pathway, which has been calibrated to wet-lab data at the single-cell level. Through rigorous software engineering, we believe our model provides an abstracted view of the underlying real-world system, and can be used in a predictive capacity through in silico experimentation. In this study, we have focused on the dynamics of the IKK complex and its activation of NF-κB. Our agent-based model suggests that the pathway is sensitive to: variations in the binding probability of IKK to the inhibited NF-κB-IκBα complex; and variations in the temporal rebinding delay of IKK.

We present a novel methodology that addresses the problem of faults in synapses of a spiking neural network using astrocyte regulation, inspired by recovery processes in the brain. Since Field Programmable Gate Arrays (FPGAs) are widely used for neural network applications, we aim to achieve fault tolerance in an astrocyte-neuron unit implemented on an FPGA. A fault is considered as a reduction in transmission probability of a synapse, leading to reduced spiking activity. Our novel repair mechanism exploits Dynamic Partial Reconfiguration (DPR) of the FPGA Clock Management Tiles (CMTs) to increase the clock frequency of neurons with reduced synaptic input, which restores the firing rate to pre-fault levels. The system maintains effective functional behavior with a loss of up to 90% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware footprints with the repair unit which consumes only 0.8215% of the complete design and therefore supports scalable implementations. Additionally, the impact on power consumption of the design is also minimal (1.371W). The work opens up a novel way to utilize the capabilities of modern hardware to mimic homeostatic self-repair behavior achieving fault recovery.

Robot software controllers are often concurrent and time critical, and requires modern engineering approaches for validation and verification. With this motivation, we have developed a tool and techniques for graphical modelling with support for automatic generation of underlying mathematical definitions for model checking. It is possible to check automatically both general properties, like absence of deadlock, and specific application properties. We cater both for timed and untimed modelling and verification. Our approach has been tried in examples used in a variety of robotic applications.

Robot interaction planning is a computationally expensive process which rarely makes use of previous experiences in a deliberative manner. This paper addresses this issue by examining dimensionality reduction techniques to allow comparison of objects in a robot's environment based on the way they react to robot manipulation. We compare a number of techniques which can map objects from an observation space, which may contain thousands of dimensions, to a lower dimensionality space - the embedding space - which allows objects to be compared in an efficient manner, making knowledge transfer between similar objects more computationally tractable.

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model's sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.

Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application.

The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal solutions are directly contrasted to identify models best capturing in vivo dynamics, a technique that can aid model selection more generally. Our technique robustly determines our cell populations' motility strategies, and paves the way for simulations that incorporate accurate immune cell motility dynamics.

Germplasm collections are an important strategy for conservation of diversity, a challenge in ecoinformatics. It is common to select a core to represent the genetic diversity of a germplasm collection, aiming to minimize the costs of conservation, while ensuring the maximization of genetic variation. For the problem of finding a core for a germplasm collection, we proposed the use of a constrained multi-objective artificial immune algorithm (MAIS), based on principles of systematic conservation planning (SCP), and incorporating heterozygosity information. Therefore, optimization takes genotypic diversity and variability patterns into account. As a case study, we used Dipteryx alata molecular marker information. We were able to identify within several accessions, the exact entries that should be chosen to preserve species diversity. MAIS presented better performance measure results when compared to NSGA-II. The proposed approach can be used to help construct cores with maximal genetic richness, and also be extended to in situ conservation. As far as we know, this is the first time that an AIS algorithm is applied to the problem of finding a core for a germplasm collection using heterozygosity information as well.

Computational and mathematical modelling approaches are increasingly being adopted in attempts to further our understanding of complex biological systems. This approach can be subjected to strong criticism as substantial aspects of the biological system being captured are not currently known, meaning assumptions need to be made that could have a critical impact on simulation response. We have utilised the CoSMoS process in the development of an agent-based simulation of the formation of Peyer's patches (PP), gut-associated lymphoid organs that have a key role in the initiation of adaptive immune responses to infection. Although the use of genetic tools, imaging technologies and ex vivo culture systems has provided significant insight into the cellular components and associated pathways involved in PP development, interesting questions remain that cannot be addressed using these approaches, and as such well justified assumptions have been introduced into our model to counter this. Here we focus not on the development of the model itself, but instead demonstrate how the resultant simulation can be used to assess how these assumptions impact the simulation response. For example, we consider the impact of our assumption that the migration rate of lymphoid tissue cells into the gut remains constant throughout PP development. We demonstrate that an analysis of the assumptions made in the construction of the domain model may either increase confidence in the model as a representation of the biological system it captures, or may suggest areas where further biological experimentation is required.

In attempts to further understand complex systems at an in-dividual level, the application of agent-based modeling is becoming prevalent across a range of academic disciplines. With the advantages of being multi-platform, requiring little programming experience, and supported by a large number of freely available case study examples, Netlogo has become a popular choice as the software tool to apply in the construc-tion of agent-based models. To utilize the constructed model as an informative or predictive tool, statistical analyses can be performed to reveal the influence that a parameter has on simulation behavior, offering an insight into the system un-der study. Here we demonstrate the integration of Netlogo’s parameter sweep function, Behavior Space, with an extended version of SPARTAN, our previously published open source statistical package for performing local and global sensitiv-ity analyses. With the addition of SPARTAN, the researcher can automatically create Netlogo experiment files for both local (individual parameter) and global (latin-hypercube and Fourier frequency) analyses, run these experiments in Net-logo, and receive detailed statistical information on the in-fluence a parameter has on simulation response: vital in-formation for translating a simulation result to a hypothesis grounded in the system being studied. To ensure our example work is reproducible, we demonstrate use of SPARTAN us-ing the Virus transmission and perpetuation model available in the Netlogo model library.

In this paper we present the Relay Chain: a new algorithm central to a novel strategy for exploring underwater environments using a swarm of autonomous underwater vehicles (AUVs). The Relay Chain provides a mobile, scalable and dynamic communication link between the water’s surface and a shoal of AUVs exploring the sea bed. The chain tracks the shoal as it explores, recruiting and returning AUVs from and to the shoal to modulate the length of the chain as required. The chain can instruct the shoal to reverse course when it travels too far from the starting point and there are no further AUVs to recruit. Given the challenging underwater environment, chain breakages are however inevitable, and as such we consider a number of recovery strategies to address chain breakages and minimise the time for which messages destined to the surface from the shoal are delayed. A simple ‘turn and search’ strategy is contrasted with strategies that depend on absolute positioning systems of varying accuracy. Implementing underwater absolute positioning systems is challenging, and our results highlight how accurate such a system must be to outperform more naive strategies, and hence be considered a worthwhile investment. We find the accuracy must be within 20cm, being 40% of AUV sensor range.

Collective identity helps swarms remain coherent in the presence of others. Building identity into artificial systems enables groups of agents to work in the same area as one another, without interference from other agents. By linking the firefly algorithm to the control logic of the agents, we present a method to form and maintain identity in swarms. By measuring swarm polarization, and swarm overlap, we show that the inclusion of an identity allows a swarm to remain coherent for an extended period of time, without interference from other swarms.

This paper presents a novel approach to the run-time detection of faults in autonomous mobile robots, based on simulated predictions of real robot behaviour. We show that although simulation can be used to predict real robot behaviour, drift between simulation and reality occurs over time due to the reality gap. This necessitates periodic reinitialisation of the simulation to reduce false positives. Using a simple obstacle avoidance controller afflicted with partial motor failure, we show that selecting the length of this reinitialisation time period is non-trivial, and that there exists a trade-off between minimising drift and the ability to detect the presence of faults.

BACKGROUND: Experimental 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. RESULTS: We 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. CONCLUSION: There 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.

Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete "granulomas" within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma.

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 RToolkit 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.

Testing a new network protocol experimentally in WSNs is an important step prior to deployment because theoretical models and assumptions made often differ between real environmental properties and performance. It is imperative to ensure that the results obtained from the test are reliable and the performance observed in simulation is a valid representation of the real world. Thus there is a need to perform extensive experimental analysis and evaluation to produce results with an acceptable level of confidence. In this paper, we outline experimental statistical and analysis techniques that allow us to have some confidence in the results obtained are at least relevant to physical deployment. Using the results from hardware and software experiments, we apply our proposed Experimental Verification Process (EVP) to evaluate the performance of the Multimodal Routing Protocol (MRP) against Adhoc On-demand Distance Vector (AODV) and Not So Tiny-AODV (NST-AODV). With the EVP, we have improved the credibility of MRP.

This paper describes the application of the receptor density algorithm, an artificial immune system, as used to detect chemicals from data provided by various spectrometers. The system creates chemical signatures which are matched to a library of known chemicals, allowing the positive identification of hazardous substances. The performance of the system is tested against a publicly available mass-spectrometry dataset, against which it has previously been demonstrated as an effective anomaly detection algorithm. An autonomous chemical-detection device is then discussed, in which the algorithm is running on hardware embedded in a Pioneer robot carrying a portable chemical agent monitor.

This paper details an extension to an architecture for robust bidirectional hetero-associative recall. Our proposed Protein Processor Associative Memory (PPAM) is fundamentally different from the traditional processing methods which use arithmetic operations and consequently Arithmetic and Logic Units (ALUs). In this paper, we improve on our initial work addressing concerns surrounding hardware implementation. We present the improved computational architecture, coupled with a corresponding hardware architecture for implementation. Results of applying the hardware implementation on a small dataset are included, along with reports from synthesis tools about hardware utilisation.

Swarm systems consist of a number of relatively simple agents that interact with each other to afford a complex behaviour. Such swarm systems are inherently parallel but as yet little work has focussed on the development of specific hardware platforms that might take advantage of such parallelism. This paper proposes a hardware platform for the implementation of swarm system simulations, using a case study of Reynolds' boids. Our platform provides a flexible decentralised intelligent bus communication architecture designed to provide effective communication between agents on a hardware platform.

In a number of areas, for example, sensor networks and systems of systems, complex networks are being used as part of applications that have to be dependable and safe. A common feature of these networks is they operate in a de-centralised manner and are formed in an ad-hoc manner and are often based on individual nodes that were not originally developed specifically for the situation that they are to be used. In addition, the nodes and their environment will have different behaviours over time, and there will be little knowledge during development of how they will interact. A key challenge is therefore how to understand what behaviour is normal from that which is abnormal so that the abnormal behaviour can be detected, and be prevented from affecting other parts of the system where appropriate recovery can then be performed. In this paper we review the state of the art in bio-inspired approaches, discuss how they can be used for error detection as part of providing a safe dependable sensor network, and then provide and evaluate an efficient and effective approach to error detection.

This paper presents a lightweight, immune-inspired system for the diagnosis of faults. We adapt the dendritic cell algorithm, to develop the Diagnostic Dendritic Cell Algorithm (D-DCA) to afford on-line diagnosis of stuck-at-fault conditions. Our work shows that the D-DCA is capable of successfully diagnosing simple faults within an acceptable time frame, with an acceptable accuracy.

The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Most parallel hardware architectures tend to combine Von Neumann style processors to make a multi-processor environment and computation is based on Arithmetic and Logic Units (ALU). This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations. The architecture proposed here is targeted towards robust artificial intelligence applications.

When deriving bio-inspired algorithms, the ''probes'' of Openness, Diversity, Interaction, Structure and Scale (ODISS) can form the basis of a systematic analysis. In our work, we apply ODISS to both the biological inspiration (the mammalian immune system) and the application area, Adaptive Information Filtering (AIF). This approach allows a principled analysis and development of a complex bio-inspired approach to AIF.

Granuloma is a medical term for a ball-like collection of immune cells that attempts to remove foreign substances from a host organism. This response is a special type of inflammatory reaction common to a wide variety of diseases. Granulomas are an organised collection of macrophages, whose formation involves the stimulation of macrophages as well as T-Cells. Fault tolerance in swarm robotic systems is essential to the continued operation of swarm robotic systems. Under certain conditions, a failing robot can have a detrimental effect on the overall swarm behaviour, causing stagnation in the swarm and affecting its ability to undertake its task. Our study is concerned specifically with modelling the trafficking of macrophages and T-cells in the development of granuloma formation, and using that as a basis to create a self-healing swarm robotic system, in the context of power system failure.

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.

Visceral leishmaniasis is a parasitic disease that is usually fatal if untreated. Host resistance is thought to involve the accumulation of inflammatory cells into structures called granulomas. To date, the possible processes underlying granuloma formation are not fully understood. The importance of modelling in immunology is increasing particularly for dynamic processes that are hard to study in vivo over extended periods of time. Appropriate modelling can provide novel insights that might help deepen the understanding of phenomena and/or help guide experimental work. This paper discusses initial studies on the regulation of granuloma using a combination of UML like modelling and agent based simulation.

Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.

The immune system is a key component in the maintenance of host homeostasis. Key actors in this process are cells known as dendritic cells (DCs). An artificial immune system based on DCs (known as the Dendritic Cell Algorithm: DCA) is well established in the literature and has been applied in a number of applications. Work in this paper is concerned with the development of an integrated homeostatic system for small, autonomous robotic systems, implemented on a resource limited micro-controller. As a first step, we have modified the DCA to operate in both simulated robotic units, and a resource constrained micro-controller that can operate in an on-line manner. Errors can be introduced into the robotic unit during operation, and these can be detected and then circumvented by the modified DCA.

MOTIVATION: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. RESULTS: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

We present a novel multi-layered unsupervised learning artificial immune system (MARIA). We have employed vector quantisation to augment MARIA (and Kohonen Networks) to allow for a comparison of performance between the two systems. Analysis shows that MARIA is competitive with Kohonen Networks on some clustering tasks.

Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been claimed that the complementary approach to RVNS, termed real-valued positive selection, is not a realistic solution. We investigate these claims and show that these claims can not be sufficiently supported.

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well-known PSP lattice models, the HP model in two-dimensional and three-dimensional square lattices', and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed IA is very competitive with the existing state-of-art algorithms for the PSP on lattice models

This paper advocates a problem-oriented approach for the design of artificial immune systems (AIS) for data mining. By problem-oriented approach we mean that, in real-world data mining applications the design of an AIS should take into account the characteristics of the data to be mined together with the application domain: the components of the AIS - such as its representation, affinity function, and immune process - should be tailored for the data and the application. This is in contrast with the majority of the literature, where a very generic AIS algorithm for data mining is developed and there is little or no concern in tailoring the components of the AIS for the data to be mined or the application domain. To support this problem-oriented approach, we provide an extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection. We discuss several important lessons to be taken from the natural immune system to design new AIS that are considerably more adaptive than current AIS. Finally, we conclude this paper with a summary of seven limitations of current AIS for data mining and ten suggested research directions.

This paper presents an immune-inspired adaptable error detection (AED) framework for automated teller machines (ATMs). This framework has two levels: one is local to a single ATM, while the other is network-wide. The framework employs vaccination and adaptability analogies of the immune system. For discriminating between normal and erroneous states, an immune-inspired one-class supervised algorithm was employed, which supports continual learning and adaptation. The effectiveness of the proposed approach was confirmed in terms of classification performance and impact on availability. The overall results are encouraging as the downtime of ATMs can de reduced by anticipating the occurrence of failures before they actually occur.

In the context of generating detectors using the r-contiguous matching rule, questions have been raised at the efficiency of the process. We show that the problem of generating r-contiguous detectors can be transformed in a k-CNF satisfiability problem. This insight allows for the wider understanding of the problem of generating r-contiguous detectors. Moreover, we apply this result to consider questions relating to the complexity of generating detectors, and when detectors are generable.

Imbalanced data are often encountered in real-world real-world real-world applications, they may incline the performance of classification to be biased. The immune-based algorithm Artificial Immune Recognition System (AIRS) is applied to Duke Energy distribution systems outage data and we investigate its capability to classify imbalanced data. The performance of AIRS is compared with an Artificial Neural Network (ANN). Two major distribution fault causes, tree and lightning strike, are used as prototypes and a tailor-made measure for imbalanced data, g-mean, is used as the major performance measure. The results indicate that AIRS is able to achieve a more balanced performance on imbalanced data than ANN.

Artificial immune systems have become popular in recent years as a new approach for intrusion detection systems. Indeed, the (natural) immune system applies very effective mechanisms to protect the body against foreign intruders. We present empirical and theoretical arguments, that the artificial immune system negative selection principle, which is primarily used for network intrusion detection systems, has been copied to naively and is not appropriate and not applicable for network intrusion detection systems.

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.

Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the B-cell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledged that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.

Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.

With the increase in information on the Internet, the strive to find more effective tools for distinguishing between interesting and non-interesting material is increasing. Drawing analogies from the biological immune system, this paper presents an immune-inspired algorithm called AISEC that is capable of continuously classifying electronic mail as interesting and non-interesting without the need for re-training. Comparisons are drawn with a naive Bayesian classifier and it is shown that the proposed system performs as well as the naive Bayesian system and has a great potential for augmentation.

This paper presents the adaptation of an immune network model, originally proposed to perform information compression and data clustering, to solve multimodal function optimization problems. The algorithm is described theoretically and empirically compared with similar approaches from the literature. The main features of the algorithm include: automatic determination of the population size, combination of local with global search (exploitation plus exploration of the fitness landscape), defined convergence criterion, and capability of locating and maintaining stable local optima solutions.

An investigation has been undertaken to repeat previous work on an artificial immune system for data analysis called AINE (Artificial Immune Network). The previous work was limited to testing the algorithm on relatively small data sets. The aim of this investigation is two fold, firstly to corroborate the results presented in previous work and secondly, to test the algorithm on a larger and more complex data set. A new re-implementation of AINE is then described and differences in behaviour are identified and explained. It is argued that the behaviour seen in the new implementation is more accurate than that seen in previous work and an in-depth analysis of the algorithm structure is undertaken in order to confirm these observations. The algorithm is also tested on new data and the results of this are presented. Comparisons are draw with other similar techniques for data mining and it is argued that AINE is an effective data-mining algorithm.

The human immune system can provide many metaphors that can be utilised effectively in the field of machine learning. These metaphors have been successfully applied to the complex real world problem of mortgage fraud detection, using a learning system known as Jisys. The Jisys system identifies patterns in mortgage fraud data by constructing an immune network, which is then evolved through the analysis of additional fraudulent and no-fraudulent applications. By viewing this network, a human expert can gain a better understanding of the fraudulent behavior. This paper describes significant developments over the original Jisys system. For example, the network is currently a flat structure with significant groupings within it. However, it can be difficult to identify these groups, to analyse them and then determine their significance. This paper describes some advances, which significantly improve the interpretation of the network. We also consider various statistical techniques which can be used to enhance the performance of the Jisys system as well as exploiting inherent properties of the network which enable significant performance improvements to be implemented (with not loss of information content). Finally, the paper presents some analysis of the structures generated by the Jisys system and relates them back to the known structures in the training data set.