Paul S. Andrews

Bio: Paul S. Andrews is an academic researcher from University of York. The author has contributed to research in topics: Artificial immune system & Experimental autoimmune encephalomyelitis. The author has an hindex of 18, co-authored 53 publications receiving 1031 citations. Previous affiliations of Paul S. Andrews include York University.

An interdisciplinary perspective on artificial immune systems

Abstract: 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.

Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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 ...

On artificial immune systems and swarm intelligence

Abstract: 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.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Advances in artificial immune systems

Abstract: During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity