Capturing Regular Human Activity through a Learning Context Memory

A learning context memory consisting of two main parts is presented. The first part performs lossy data compression, keeping the amount of stored data at a minimum by combining similar context attributes — the compression rate for the presented GPS data is 150:1 on average. The resulting data is stored in an appropriate data structure highlighting the level of compression. Elements with a high level of compression are used in the second part to form the start and end points of episodes capturing common activity consisting of consecutive events. The context memory is used to investigate how little context data can be stored containing still enough information to capture regular human activity

A new paradigm for SpeckNets:inspiration from fungal colonies

In this position paper, we propose the development of a new biologically inspired paradigm based on fungal colonies, for the application to pervasive adaptive systems. Fungal colonies have a number of properties that make them an excellent candidate for inspiration for engineered systems. Here we propose the application of such inspiration to a speckled computing platform. We argue that properties from fungal colonies map well to properties and requirements for controlling SpeckNets and suggest that an existing mathematical model of a fungal colony can developed into a new computational paradigm

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

The rise in computational systems biology approaches for understanding NF-κB signaling dynamics

A study by Cheng et al. in this issue of Science Signaling highlights the distinct single-cell signaling characteristics conferred by pathways mediated by the adaptor proteins MyD88 and TRIF in the TLR4-dependent activation of the transcription factor nuclear factor κB (NF-κB)

Computational models of the NF-κB signalling pathway

In this review article, we discuss the current state of computational modelling of the nuclear factor-kappa B (NF-κB) signalling pathway. NF-κB is a transcription factor, which is ubiquitous within cells and controls a number of immune responses, including inflammation and apoptosis. The NF-κB signalling pathway is tightly regulated, commencing with activation at the cell membrane, signal transduction through various components within the cytoplasm, translocation of NF-κB into the nucleus and, finally, the transcription of various genes relating to the innate and adaptive immune responses. There have been a number of computational (mathematical) models developed of the signalling pathway over the past decade. This review describes how these approaches have helped advance our understanding of NF-κB control

Macrophage transactivation for chemokine production identified as a negative regulator of granulomatous inflammation using agent-based modeling

Cellular activation in trans by interferons, cytokines and chemokines is a commonly recognized mechanism to amplify immune effector function and limit pathogen spread. However, an optimal host response also requires that collateral damage associated with inflammation is limited. This may be particularly so in the case of granulomatous inflammation, where an excessive number and / or excessively florid granulomas can have significant pathological consequences. Here, we have combined transcriptomics, agent-based modeling and in vivo experimental approaches to study constraints on hepatic granuloma formation in a murine model of experimental leishmaniasis. We demonstrate that chemokine production by non-infected Kupffer cells in the Leishmania donovani-infected liver promotes competition with infected KCs for available iNKT cells, ultimately inhibiting the extent of granulomatous inflammation. We propose trans-activation for chemokine production as a novel broadly applicable mechanism that may operate early in infection to limit excessive focal inflammation

Self-repairing mobile robotic car using astrocyte-neuron networks

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

GPCRTree: online hierarchical classification of GPCR function

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree

A Hormone-Inspired Arbitration System For Self Identifying Abilities Amongst A Heterogeneous Robot Swarm

Current exploration of adaptation in robot swarms requires the swarm or individuals within that swarm to have knowledge of their own capabilities. Across long term use a swarms understanding of its capabilities may become inaccurate due to wear or faults in the system. In addition to this, systems capable of self designing morphologies are becoming increasingly feasible. In these self designing examples it would be impossible to have accurate knowledge of capability before executing a task for the first time. We propose an arbitration system that requires no explicit knowledge of capability but instead uses hormone-inspired values to decide on an environmental preference. The robots in the swarm differ by wheel type and thus how quickly they are able to move across terrain. The goal of this system is to allow robots to identify their strengths within a swarm and allocate themselves to areas of an environment with a floor type that suits their ability. This work shows that the use of a hormone-inspired arbitration system can extrapolate robot capability and adapt the systems preference of terrain to suit said capability.</p