Jon Timmis

Artificial immune systems---today and tomorrow

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.

Mechanisms of hyperthermia in magnetic nanoparticles

We report on a theoretical framework for magnetic hyperthermia where the amount of heat generated by nanoparticles can be understood when both the physical and hydrodynamic size distributions are known accurately. The model is validated by studying the magnetic, colloidal and heating properties of magnetite/maghemite nanoparticles of different sizes dispersed in solvents of varying viscosity. We show that heating arising due to susceptibility losses can be neglected with hysteresis loss being the dominant mechanism. We show that it is crucial to measure the specific absorption rate of samples only when embedded in a solid matrix to avoid heating by stirring. However the data shows that distributions of both size and anisotropy must be included in theoretical models.

Differential RET Signaling Pathways Drive Development of the Enteric Lymphoid and Nervous Systems

Cis and trans signaling mechanisms direct different developmental responses to ligands for the receptor tyrosine kinase RET. RET Signaling in Cis and Trans Development of the enteric (gastrointestinal) organs requires coordinated growth of tissues from various embryonic layers. Evidence suggests that ligands of the receptor tyrosine kinase RET are used in different tissues to control distinct developmental end points. Lymphoid tissue initiator (LTin) cells are thought to function in the early development of Peyer’s patches (PPs), which are secondary lymphoid organs of the gut important for mucosal immunity. The formation of the enteric nervous system, which enervates the lymphoid tissue, depends on interactions between neural crest cells and stroma cells of the gut wall. RET signaling requires the presence of co-receptors, which bind to ligands, in the same cell (in cis), or RET co-receptors can be cleaved from cells, leading to the possibility of RET signaling in trans; however, the physiological relevance of such signaling is uncertain. Patel et al. investigated lymphoid tissue morphogenesis in mice and found that whereas development of the enteric nervous tissue depended on RET signaling in cis, aggregation of LTin cells and development of lymphoid tissue were driven by RET signaling in trans and depended on the local availability of RET co-receptors and ligands. During the early development of the gastrointestinal tract, signaling through the receptor tyrosine kinase RET is required for initiation of lymphoid organ (Peyer’s patch) formation and for intestinal innervation by enteric neurons. RET signaling occurs through glial cell line–derived neurotrophic factor (GDNF) family receptor α co-receptors present in the same cell (signaling in cis). It is unclear whether RET signaling in trans, which occurs in vitro through co-receptors from other cells, has a biological role. We showed that the initial aggregation of hematopoietic cells to form lymphoid clusters occurred in a RET-dependent, chemokine-independent manner through adhesion-mediated arrest of lymphoid tissue initiator (LTin) cells. Lymphoid tissue inducer cells were not necessary for this initiation phase. LTin cells responded to all RET ligands in trans, requiring factors from other cells, whereas RET was activated in enteric neurons exclusively by GDNF in cis. Furthermore, genetic and molecular approaches revealed that the versatile RET responses in LTin cells were determined by distinct patterns of expression of the genes encoding RET and its co-receptors. Our study shows that a trans RET response in LTin cells determines the initial phase of enteric lymphoid organ morphogenesis, and suggests that differential co-expression of Ret and Gfra can control the specificity of RET signaling.

Inspiration for the next generation of artificial immune systems

In this conceptual paper, we consider the state of artificial immune system (AIS) design today, and the nature of the immune theories on which they are based. We highlight the disagreement amongst many immunologists regarding the concept of self–non-self discriminations in the immune system, and go on describe on such model that removes altogether the requirement for self–non-self discrimination. We then identify the possible inspiration ideas for AIS that can be gained from such new, and often radical, models of the immune system. Next, we outline a possible approach to designing AIS that are inspired by new immune theories, following a suitable methodology and selecting appropriate modelling tools. Lastly, we follow our approach and present an example of how the AIS designer might take inspiration from a specific property of a new immune theory. This example highlights our proposed method for inspiring the design of the next generation of AIS.

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.

Immune-Inspired Adaptable Error Detection for Automated Teller Machines

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.

A Markov chain model of the b-cell algorithm

An exact Markov chain model of the B-cell algorithm (BCA) is constructed via a novel possible transit method. The model is used to formulate a proof that the BCA is convergent absolute under a very broad set of conditions. Results from a simple numerical example are presented, we use this to demonstrate how the model can be applied to increase understanding of the performance of the BCA in optimizing function landscapes as well as giving insight into the optimal parameter settings for the BCA.

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.

GPCRTree: online hierarchical classification of GPCR function

BackgroundG 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.FindingsUsing techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a proteins physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level.ConclusionA 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/.

On artificial immune systems and swarm intelligence

This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are many aspects of AIS that have direct parallels with SI and examine the role of AIS and SI in science and also in engineering, with the primary focus being on the immune system. We explore how in some ways, algorithms from each area are similar, but we also advocate, and explain, that rather than being competitors, AIS and SI are complementary tools and can be used effectively together to solve complex engineering problems.