Paul S. Andrews

An Investigation into Mutation Operators for Particle Swarm Optimization

The Particle Swarm Optimization (PSO) technique can be augmented with an additional mutation operator that helps prevent premature convergence on local optima. In this paper, different mutation operators for PSO are empirically investigated and compared. A review of previous mutation approaches is given and key factors concerning how mutation operators can be applied to PSO are identified. A PSO algorithm incorporating different mutation operators is applied to both mathematical and constrained optimization problems. Results shown that the addition of a mutation operator to PSO can enhance optimisation performance and insight is gained into how to design mutation operators dependent on the nature of the problem being optimized.

An interdisciplinary perspective on artificial immune systems

This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner.

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.

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.

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.

Functional complexity of the Leishmania granuloma and the potential of in silico modeling

In human and canine visceral leishmaniasis and in various experimental models of this disease, host resistance is strongly linked to efficient granuloma development. However, it is unknown exactly how the granuloma microenvironment executes an effective antileishmanial response. Recent studies, including using advanced imaging techniques, have improved our understanding of granuloma biology at the cellular level, highlighting heterogeneity in granuloma development and function, and hinting at complex cellular, temporal, and spatial dynamics. In this mini-review, we discuss the factors involved in the formation and function of Leishmania donovani-induced hepatic granulomas, as well as their importance in protecting against inflammation-associated tissue damage and the generation of immunity to rechallenge. Finally, we discuss the role that computational, agent-based models may play in answering outstanding questions within the field.

Pairing experimentation and computational modeling to understand the role of tissue inducer cells in the development of lymphoid organs

The use of genetic tools, imaging technologies and ex vivo culture systems has provided significant insights into the role of tissue inducer cells and associated signaling pathways in the formation and function of lymphoid organs. Despite advances in experimental technologies, the molecular and cellular process orchestrating the formation of a complex three-dimensional tissue is difficult to dissect using current approaches. Therefore, a robust set of simulation tools have been developed to model the processes involved in lymphoid tissue development. Specifically, the role of different tissue inducer cell populations in the dynamic formation of Peyer’s patches has been examined. Utilizing approaches from systems engineering, an unbiased model of lymphoid tissue inducer cell function has been developed that permits the development of emerging behaviors that are statistically not different from that observed in vivo. These results provide the confidence to utilize statistical methods to explore how the simulator predicts cellular behavior and outcomes under different physiological conditions. Such methods, known as sensitivity analysis techniques, can provide insight into when a component part of the system (such as a particular cell type, adhesion molecule, or chemokine) begins to have an influence on observed behavior, and quantifies the effect a component part has on the end result: the formation of lymphoid tissue. Through use of such a principled approach in the design, calibration, and analysis of a computer simulation, a robust in silico tool can be developed which can both further the understanding of a biological system being explored, and act as a tool for the generation of hypotheses which can be tested utilizing experimental approaches.

Special issue on artificial immune systems

The field of artificial immune systems (AIS) is a diverse area of research that bridges the disciplines of immunology and engineering. AIS algorithms are typically developed from the abstraction of immune system theories, processes and agents, and they have been applied to a wide variety of engineering applications including computer security, fault tolerance, data mining and optimisation. More recently there has been a growing trend within AIS to facilitate closer interaction between the domains of immunology and engineering through the use of various mathematical and computational modelling approaches. These have included dynamical systems analysis, agent-based modelling and cellular automata. The resulting models serve a dual purpose: to improve understanding of the biological domain, and to aid the development of more biologically inspired AIS for engineering problems. The field of swarm intelligence (SI) encompasses a wide range of scientific and engineering disciplines to explore and exploit the complex behaviours that arise from groupings of agents such as social insects or animals. Research in this field incorporates many decentralised and distributed systems that exploit the collective behaviour that emerges from the interaction of individual agents with each other and their environment. This perspective affords a natural link between SI and AIS: many immune algorithms operate in a very similar manner with populations of immune agents exhibiting similar high-level collective behaviours; it has furthermore been suggested by several authors that the natural immune