How Do We Evaluate Artificial Immune Systems?
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Editorial Computational and Bioinformatics Techniques for Immunology
Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 263189, 2 pages http://dx.doi.org/10.1155/2014/263189 Editorial Computational and Bioinformatics Techniques for Immunology Francesco Pappalardo,1 Vladimir Brusic,2 Filippo Castiglione,3 and Christian Schönbach2 1 Department of Drug Sciences, University of Catania, 95125 Catania, Italy 2School of Science and Technology, Nazarbayev University, Astana 010000, Kazakhstan 3Istituto Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy Correspondence should be addressed to Francesco Pappalardo; [email protected] Received 22 July 2014; Accepted 22 July 2014; Published 31 December 2014 Copyright © 2014 Francesco Pappalardo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational immunology and immunological bioinfor- biology spanning different scales: an open challenge”by matics are well-established and rapidly evolving research F. Castiglione et al. fields. Whereas the former aims to develop mathematical Exploring the connections between classical mathemat- and/or computational methods to study the dynamics of ical modeling (at different scales) and bioinformatics pre- cellular and molecular entities during the immune response dictions of omic scope along with specific aspects of the [1–4], the latter targets proposing methods to analyze large immune system in combination with concepts and methods genomic and proteomic immunological-related datasets and like computer simulations, mathematics and statistics for the derive (i.e., predict) new knowledge mainly by statistical discovery, design, and optimization of drugs, vaccines, and inference and machine learning algorithms. -
Toward Computational Modelling on Immune System Function Francesco Pappalardo1*, Marzio Pennisi2, Pedro A
Pappalardo et al. BMC Bioinformatics 2019, 20(Suppl 6):622 https://doi.org/10.1186/s12859-019-3239-x INTRODUCTION Open Access Toward computational modelling on immune system function Francesco Pappalardo1*, Marzio Pennisi2, Pedro A. Reche3 and Giulia Russo1 From 2nd International Workshop on Computational Methods for the Immune System Function Madrid, Spain. 3–6 December 2018 Abstract The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics. Introduction modeling of the Immune system function, along with their The constant and rapid increasing of computing power application in understanding the pathogenesis of specific has favoured the diffusion of computational methods diseases (e.g., infectious diseases, cancers, hypersensitivi- into immunology, giving the birth to computational ties, autoimmune disorders) has been in our minds for -
An Interdisciplinary Perspective on Artificial Immune Systems
Evol. Intel. (2008) 1:5–26 DOI 10.1007/s12065-007-0004-2 REVIEW ARTICLE An interdisciplinary perspective on artificial immune systems J. Timmis Æ P. Andrews Æ N. Owens Æ E. Clark Received: 7 September 2007 / Accepted: 11 October 2007 / Published online: 10 January 2008 Ó Springer-Verlag 2008 Abstract This review paper attempts to position the area 1 Introduction of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an Artificial Immune Systems (AIS) is a diverse area of established conceptual framework that encapsulates math- research that attempts to bridge the divide between ematical and computational modelling of immunology, immunology and engineering and are developed through abstraction and then development of engineered systems. the application of techniques such as mathematical and We argue that AIS are much more than engineered systems computational modelling of immunology, abstraction from inspired by the immune system and that there is a great those models into algorithm (and system) design and deal for both immunology and engineering to learn from implementation in the context of engineering. Over recent each other through working in an interdisciplinary manner. years there have been a number of review papers written on AIS with the first being [25] followed by a series of others Keywords Artificial immune systems Á that either review AIS in general, for example, [29, 30, 43, Immunological modelling Á Mathematical modelling Á 68, 103], or more specific aspects of AIS such as data Computational modelling Á mining [107], network security [71], applications of AIS Applications of artificial immune systems Á [58], theoretical aspects [103] and modelling in AIS [39]. -
Computational Immunology Workshop
Computational Immunology Workshop On the 16th of November, with the support of the Pole Rabelais, the SIRIC Montpellier Cancer, and the IRCM, we organize a 1-day workshop on the theme Computational Immunology. The meeting will take place at IRCM auditorium. We will discuss computational or bioinformatics aspects of immunology and the cancer microenvironment. Recent developments in DNA sequencing and functional proteomics have made possible remarkably successful data driven research in these fields. It requires bioinformatics and computational abilities to transform data into information. The workshop is a unique opportunity to learn from experts who applied such techniques to conduct meaningful biological and translational research. We will welcome prestigious international and French speakers: • Dr Andreas Pichlmair, Max Plank Institute, Munich, innate immunity ; • Dr Fatima Mechta-Grigoriou, Institut Curie, Paris, tumor microenvironment ; • Pr Zlatko Trajanoski, Biocenter, Innsbruck, oncoimmunology ; • Dr Monsef Benkirane, IGH, Montpellier, immunology ; • Pr Marie-Paule Lefranc, IGH, Montpellier, immunoinformatics. Registration is free but mandatory: Inscription In addition, YOU are invited to submit an abstract for an oral or poster presentation (send to [email protected]) and contribute to the success of this workshop. The organizers: Ula Hibner (IGMM), Sofia Kossida (IGH), Jacques Colinge (IRCM). Preliminary Program 08:45 Registration 09:00 Opening and introductory remarks Session 1 Innate and adaptive immunity 09:00 Keynote -
Artificial Immune System Based Intrusion Detection: Innate Immunity
Artificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach Farhoud Hosseinpour, Payam Vahdani Amoli, Fahimeh Farahnakian, Juha Plosila, Timo Hämäläinen Artificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach 1Farhoud Hosseinpour, 2Payam Vahdani Amoli, 3Fahimeh Farahnakian, 4Juha Plosila and 5 Timo Hämäläinen 1 Corresponding Author, 3 and 4 Department of Information Technology, University of Turku, Finland. {farhos;fahfar;juplos}@utu.fi 2 and 5Faculty of Information Technology, University of Jyväskylä, 40100, Jyväskylä, Finland. 2 [email protected] 5 [email protected] Abstract This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system. Different methods for unsupervised machine learning are investigated and DBSCAN (density-based spatial clustering of applications with noise) is selected to be utilized in this architecture. The adaptive immune system in our proposed architecture also takes advantage of the distributed structure, which has shown better self-improvement rate compare to centralized mode and provides primary and secondary immune response for unknown anomalies and zero-day attacks. The experimental results of proposed architecture is presented and discussed. Keywords: Distributed intrusion detection system, Artificial immune system, Innate immune system, Unsupervised learning 1. -
Quantitative Immunology for Physicists
bioRxiv preprint doi: https://doi.org/10.1101/696567; this version posted July 28, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Quantitative Immunology for Physicists Gr´egoireAltan-Bonnet,1, ∗ Thierry Mora,2, ∗ and Aleksandra M. Walczak2, ∗ 1Immunodynamics Section, Cancer & Inflammation Program, National Cancer Institute, Bethesda MD 20892, USA 2Laboratoire de physique de l'Ecole´ normale sup´erieure (PSL University), CNRS, Sorbonne Universit´e,Universit´ede Paris, 75005 Paris, France Abstract. The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS A. Cytokine signaling and the JAK-STAT pathway 14 I. Introduction 2 1. Cytokine binding and signaling at equilibrium 15 II. Physical chemistry of ligand-receptor 2. Tunability of cytokine responses. 15 interaction: specificity, sensitivity, kinetics. 4 3. Regulation by cytokine consumption 17 A. Diffusion-limited reaction rates 4 4. -
Good and Bad Memory Control in Artificial Immune System
Adaptive clusters formation in an Artificial Immune System Sławomir T. Wierzchoń1,2), Urszula Kużelewska2) 1) Institute of Computer Science, Polish Academy of Sciences 01-237 Warszawa, ul. Ordona 21 2) Department of Computer Science, Białystok Technical University 15-351 Białystok, ul. Wiejska 45a e-mail: [email protected] A new version of an artificial immune system designed for automated cluster formation in training data is presented. The algorithm fully exploits self-organizing properties of the vertebrate immune system and produces stable immune network. Keywords: immune network, clonal selection, somatic mutation, cluster formation 1. Introduction From a computer science perspective the immune system is a complex, self organizing and highly distributed system which has no centralized control and which uses learning and memory when solving particular tasks. The learning process does not require negative examples and the acquired knowledge is represented in explicit form. These features attract computer scientists offering new paradigm for information processing. Particularly, Jerne’s idea of the immune network, [7], has been used to develop new tools for data analysis. The first paper devoted to this subject was that of Hunt and Cooke, [6], where the idea of the immune network with cells represented by the binary strings has been applied to the recognizing promoters in DNA sequences. Next this idea was refined by Timmis, [10], who proposed an interesting algorithm for unsupervised learning, data analysis and data visualization. Here instead of binary representation of antigens and antibodies (see Section 2 for explanation of these terms) real vectors were used. De Castro and von Zuben, [3] developed another algorithm for data analysis and data reduction that refers to the meta- dynamics typical for the so-called second generation immune networks, [11]. -
Immunoinformatics - Computational Immunology Table of Contents
Immunoinformatics - Computational Immunology Table Of Contents • Immune system presentation • Immunoinformatics • Papers presentation – IMGT, the international ImMunoGeneTics information system – The futur for computational modelling and prediction systems in clinical immunology • Summary Immune System Presentation Immune System • Composed of many interdependent –cell types –organs • Collectively protect the body Self/Non-Self • Every cell display a marker based on the major histocompatibility complex (MHC) • MHC may be of three different classes – MHC I, MHC II, MHC III • Any cell not displaying this marker is treated as non-self and attacked. Main Organs • Bone Marrow – Immune system’s cells production •Thymus – Maturation of T cells • Spleen – Immunologic blood filter and RBC recycle unit •Lymph Nodes – Immunologic filter for the lymph Main Cells • Blood cells ar manufactured by stem cells in the bone marrow • The process is called hematopoiesis • Three different types of blood cells – Erythrocytes (RBC) –Leukocytes (WBC) • Granulocytes • Agranulocytes – Thrombocytes (platelets) Main Cells (2) Main Cells (3) • Lymphocytes –T • TH (CD4+ T) • TK (CD8+ T) –B –NK •Granulocytes • Macrophages •Dendritic Cells Antibody • Antigen: substance that elicits an immune response • Antibody: complex protein secreted by B cells – Tagging: Coat the foreign invaders to make them attractive to the circulating scavenger cells – Complement fixation: Combine with antigens and a circulating protein in the blood that cause lysis – Neutralization: Coat viruses and prevent them from entering cells – Agglutination: massively clump to the antigen – Precipitation: force insolubility and settling out of solution Antibody (2) •IgG-75% •IgA-15% •IgM-7.5% •IgD-1% •IgE-0.002% Activation of WBC Activation of helper T cells Activation of B cells to make Activation of cytotoxix T cells antibody VIRUS IMMUNITY BACTERIA IMMUNITY Cell-mediated/Hummoral Immunity • Cell-mediated immunity – T cells (lymphocytes) bind to the surface of other cells that display the antigen and trigger a response. -
Biochem: a New International and Interdisciplinary Journal
Editorial BioChem: A New International and Interdisciplinary Journal Buyong Ma School of Pharmacy, Shanghai Jiaotong University, Shanghai 200240, China; [email protected] The advances of biological science have fundamentally changed our world and our understanding of human beings. All the progress, from a simple biochemical reaction to systems biology, has come from interdisciplinary interactions in a variety of scientific fields. This is especially true and important for the current and future study of a broad range of biochem topics, the core of biological science. BioChem is an international and interdisciplinary open access journal encompassing the fields of molecular biology, cell biology, structural biology, nucleic acid biology, chemical biology, synthetic biology, disease biology, biophysics, and theoretical biochemistry. It publishes reviews, research articles, communications, and letters. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. In order to support scientists in different research fields, we have established a highly reputable Editorial Board with diverse research expertise. We expect that our team will grow to meet increasing demand to reach and serve scientists all over the world. Personally, I will also welcome submissions of carefully-verified negative results as well. With the aim of publishing interdisciplinary studies in great detail in mind, I fully understand the value of negative results from carefully designed experiments and theoretical/computational studies. I truly believe that new ideas, better and more novel methodologies will be inspired by sharing all aspects of scientific endeavors. On behalf of the Editorial Board and editorial office, I am pleased to announce that Citation: Ma, B. -
Artificial Immune Systems
Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques Edmund K. Burke (Editor), Graham Kendall (Editor) Chapter 13 ARTIFICIAL IMMUNE SYSTEMS U. Aickelin # and D. Dasgupta* #University of Nottingham, Nottingham NG8 1BB, UK *University of Memphis, Memphis, TN 38152, USA 1. INTRODUCTION The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical 2 messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. -
Editorial Advances in Computational Immunology
Hindawi Publishing Corporation Journal of Immunology Research Volume 2015, Article ID 170920, 3 pages http://dx.doi.org/10.1155/2015/170920 Editorial Advances in Computational Immunology Francesco Pappalardo,1 Vladimir Brusic,2 Marzio Pennisi,3 and Guanglan Zhang4 1 Department of Drug Sciences, University of Catania, 95100 Catania, Italy 2Nazarbayev University, Astana 010000, Kazakhstan 3Department of Mathematics and Computer Science, University of Catania, 95100 Catania, Italy 4Boston University, Boston, MA 02201-2021, USA Correspondence should be addressed to Francesco Pappalardo; [email protected] Received 13 December 2015; Accepted 13 December 2015 Copyright © 2015 Francesco Pappalardo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational immunology and immunological bioinfor- potential is to use a panel of multidisease antibodies that can matics are firm and quickly growing research fields. Whereas be instrumental to know what the infectious agents are in the former aims to develop mathematical and/or computa- circulationinagivenpopulationandtheirputativedynamics. tional methods to study the dynamics of cellular and molec- This idea has not been tested in practice, but definitely ular entities during the immune response [1–4], the latter will require the extension of classical mathematical models focuses on proposing methods to investigate -
Editorial Computational and Bioinformatics Techniques for Immunology
Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 263189, 2 pages http://dx.doi.org/10.1155/2014/263189 Editorial Computational and Bioinformatics Techniques for Immunology Francesco Pappalardo,1 Vladimir Brusic,2 Filippo Castiglione,3 and Christian Schönbach2 1 Department of Drug Sciences, University of Catania, 95125 Catania, Italy 2School of Science and Technology, Nazarbayev University, Astana 010000, Kazakhstan 3Istituto Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy Correspondence should be addressed to Francesco Pappalardo; [email protected] Received 22 July 2014; Accepted 22 July 2014; Published 31 December 2014 Copyright © 2014 Francesco Pappalardo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational immunology and immunological bioinfor- biology spanning different scales: an open challenge”by matics are well-established and rapidly evolving research F. Castiglione et al. fields. Whereas the former aims to develop mathematical Exploring the connections between classical mathemat- and/or computational methods to study the dynamics of ical modeling (at different scales) and bioinformatics pre- cellular and molecular entities during the immune response dictions of omic scope along with specific aspects of the [1–4], the latter targets proposing methods to analyze large immune system in combination with concepts and methods genomic and proteomic immunological-related datasets and like computer simulations, mathematics and statistics for the derive (i.e., predict) new knowledge mainly by statistical discovery, design, and optimization of drugs, vaccines, and inference and machine learning algorithms.