COMPUTATIONAL INTELLIGENCE – Vol
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Dynamics of Information As Natural Computation
Information 2011, 2, 460-477; doi:10.3390/info2030460 OPEN ACCESS information ISSN 2078-2489 www.mdpi.com/journal/information Article Dynamics of Information as Natural Computation Gordana Dodig Crnkovic Mälardalen University, School of Innovation, Design and Engineering, Sweden; E-Mail: [email protected] Received: 30 May 2011; in revised form: 13 July 2011 / Accepted: 19 July 2011 / Published: 4 August 2011 Abstract: Processes considered rendering information dynamics have been studied, among others in: questions and answers, observations, communication, learning, belief revision, logical inference, game-theoretic interactions and computation. This article will put the computational approaches into a broader context of natural computation, where information dynamics is not only found in human communication and computational machinery but also in the entire nature. Information is understood as representing the world (reality as an informational web) for a cognizing agent, while information dynamics (information processing, computation) realizes physical laws through which all the changes of informational structures unfold. Computation as it appears in the natural world is more general than the human process of calculation modeled by the Turing machine. Natural computing is epitomized through the interactions of concurrent, in general asynchronous computational processes which are adequately represented by what Abramsky names “the second generation models of computation” [1] which we argue to be the most general representation of information dynamics. Keywords: philosophy of information; information semantics; information dynamics; natural computationalism; unified theories of information; info-computationalism 1. Introduction This paper has several aims. Firstly, it offers a computational interpretation of information dynamics of the info-computational universe [2,3]. -
Natural Computing Conclusion
Introduction Natural Computing Conclusion Natural Computing Dr Giuditta Franco Department of Computer Science, University of Verona, Italy [email protected] For the logistics of the course, please see the syllabus Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Natural Computing Natural (complex) systems work as (biological) information elaboration systems, by sophisticated mechanisms of goal-oriented control, coordination, organization. Computational analysis (mat modeling) of life is important as observing birds for designing flying objects. "Informatics studies information and computation in natural and artificial systems” (School of Informatics, Univ. of Edinburgh) Computational processes observed in and inspired by nature. Ex. self-assembly, AIS, DNA computing Ex. Internet of things, logics, programming, artificial automata are not natural computing. Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Bioinformatics versus Infobiotics Applying statistics, performing tools, high technology, large databases, to analyze bio-logical/medical data, solve problems, formalize/frame natural phenomena. Ex. search algorithms to recover genomic/proteomic data, to process them, catalogue and make them publically accessible, to infer models to simulate their dynamics. Identifying informational mechanisms underlying living systems, how they assembled and work, how biological information may be defined, processed, decoded. New -
A Computational Model Inspired by Gene Regulatory Networks
Rui Miguel Lourenço Lopes A Computational Model Inspired by Gene Regulatory Networks Doctoral thesis submitted to the Doctoral Program in Information Science and Technology, supervised by Full Professor Doutor Ernesto Jorge Fernandes Costa, and presented to the Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra. January 2015 A Computational Model Inspired by Gene Regulatory Networks A thesis submitted to the University of Coimbra in partial fulfillment of the requirements for the Doctoral Program in Information Science and Technology by Rui Miguel Lourenço Lopes [email protected] Department of Informatics Engineering Faculty of Sciences and Technology University of Coimbra Coimbra, January 2015 Financial support by Fundação para a Ciência e a Tecnologia, through the PhD grant SFRH/BD/69106/2010. A Computational Model Inspired by Gene Regulatory Networks ©2015 Rui L. Lopes ISBN 978-989-20-5460-5 Cover image: Composition of a Santa Fe trail program evolved by ReNCoDe overlaid on the ancestors, with a 3D model of the DNA crystal structure (by Paul Hakimata). This dissertation was prepared under the supervision of Ernesto J. F. Costa Full Professor of the Department of Informatics Engineering of the Faculty of Sciences and Tecnology of the University of Coimbra In loving memory of my father. Acknowledgements Thank you very much to Prof. Ernesto Costa, for his vision on research and education, for all the support and contributions to my ideas, and for the many life stories that always lighten the mood. Thank you also to Nuno Lourenço for the many discussions, shared books, and his constant helpfulness. -
The Many Facets of Natural Computing
The Many Facets of Natural Computing Lila Kari0 Grzegorz Rozenberg Department of Computer Science Leiden Inst. of Advanced Computer Science University of Western Ontario Leiden University, Niels Bohrweg 1 London, ON, N6A 5B7, Canada 2333 CA Leiden, The Netherlands [email protected] Department of Computer Science University of Colorado at Boulder Boulder, CO 80309, USA [email protected] “Biology and computer science - life and computation - are various natural phenomena. Thus, rather than being over- related. I am confident that at their interface great discoveries whelmed by particulars, it is our hope that the reader will await those who seek them.” (L.Adleman, [3]) see this article as simply a window onto the profound rela- tionship that exists between nature and computation. There is information processing in nature, and the natu- 1. FOREWORD ral sciences are already adapting by incorporating tools and Natural computing is the field of research that investi- concepts from computer science at a rapid pace. Conversely, gates models and computational techniques inspired by na- a closer look at nature from the point of view of information ture and, dually, attempts to understand the world around processing can and will change what we mean by computa- us in terms of information processing. It is a highly in- tion. Our invitation to you, fellow computer scientists, is to terdisciplinary field that connects the natural sciences with take part in the uncovering of this wondrous connection.1 computing science, both at the level of information tech- nology and at the level of fundamental research, [98]. As a matter of fact, natural computing areas and topics come in many flavours, including pure theoretical research, algo- 2. -
A Plaidoyer for 'Systems Immunology'
Christophe Benoist A Plaidoyer for ‘Systems Immunology’ Ronald N. Germain Diane Mathis Authors’ address Summary: A complete understanding of the immune system will ulti- Christophe Benoist1, Ronald N. Germain2, Diane mately require an integrated perspective on how genetic and epigenetic Mathis1 entities work together to produce the range of physiologic and patho- 1Section on Immunology and Immunogenetics, logic behaviors characteristic of immune function. The immune network Joslin Diabetes Center, Department of encompasses all of the connections and regulatory associations between Medicine, Brigham and Women’s Hospital, individual cells and the sum of interactions between gene products Harvard Medical School, Boston, MA, USA within a cell. With 30 000+ protein-coding genes in a mammalian 2Lymphocyte Biology Section, Laboratory of genome, further compounded by microRNAs and yet unrecognized Immunology, National Institute of Allergy and layers of genetic controls, connecting the dots of this network is a Infectious Diseases, National Institutes of monumental task. Over the past few years, high-throughput techniques Health, Bethesda, MD, USA have allowed a genome-scale view on cell states and cell- or system-level responses to perturbations. Here, we observe that after an early burst of Correspondence to: enthusiasm, there has developed a distinct resistance to placing a high Christophe Benoist value on global genomic or proteomic analyses. Such reluctance has Department of Medicine affected both the practice and the publication of immunological science, Section on Immunology and Immunogenetics resulting in a substantial impediment to the advances in our understand- Joslin Diabetes Center ing that such large-scale studies could potentially provide. We propose Brigham and Women’s Hospital that distinct standards are needed for validation, evaluation, and visual- Harvard Medical School ization of global analyses, such that in-depth descriptions of cellular One Joslin Place responses may complement the gene/factor-centric approaches currently Boston, MA 02215 in favor. -
Systems Immunology Applied to the Integration of Non-Clinical Immunogenicity Data Integration of Non-Clinical Immunogenicity Da
Systems immunology applied to the integration of non-clinical immunogenicity data Integration of non-clinical immunogenicity data and its clinical relevance Non-clinical Immunogenicity Assessment of Generic Peptide Products: Development, Validation, and Sampling workshop, 26th January 2021 Tim Hickling, D. Phil., Immunosafety Leader A case for systems vs. linear thinking Observed effect Cause Solution A more complex problem… e.g. TNFi Complex problems need us to go beyond linear thinking to find solutions 2 Immune system components Tourdot & Hickling, 2019 Bioanalysis 3 Data integration has been limited by reductionism and variable biological models • Many technology platforms are developed for early immunogenicity risk assessment – In silico prediction tools – T-epitope-MHC binding assays – In vitro cell assays – Animal models • However, these platforms usually look at only one or two risk factors at a time – Lack of information integration – Difficult to intuitively interpret – Hard to directly correlate with end point (immunogenicity rate, ADA response, etc) 4 Features of systems • Systems are composed of lots of interconnected parts • Changing one part of the system affects other parts, sometimes with non-obvious connections. • Connections are as important as the parts themselves • System relationships are dynamic • Change of components over time obscures system behaviors • Delays and loops are common • Feedback and feedforward loops (+ve and –ve) complicate predictions • Lots of data is needed to describe and model these systems 5 -
Info-Computational Philosophy of Nature
Alan Turing’s Legacy: Info-Computational Philosophy of Nature Gordana Dodig-Crnkovic1 Abstract. Alan Turing’s pioneering work on computability, and Turing did not originally claim that the physical system his ideas on morphological computing support Andrew Hodges’ producing patterns actually performs computation through view of Turing as a natural philosopher. Turing’s natural morphogenesis. Nevertheless, from the perspective of info- philosophy differs importantly from Galileo’s view that the book computationalism, [3,4] argues that morphogenesis is a process of nature is written in the language of mathematics (The of morphological computing. Physical process, though not Assayer, 1623). Computing is more than a language of nature as computational in the traditional sense, presents natural computation produces real time physical behaviors. This article (unconventional), physical, morphological computation. presents the framework of Natural Info-computationalism as a contemporary natural philosophy that builds on the legacy of An essential element in this process is the interplay between the Turing’s computationalism. Info-computationalism is a synthesis informational structure and the computational process – of Informational Structural Realism (the view that nature is a information self-structuring. The process of computation web of informational structures) and Natural Computationalism implements physical laws which act on informational structures. (the view that nature physically computes its own time Through the process of computation, structures change their development). It presents a framework for the development of a forms, [5]. All computation on some level of abstraction is unified approach to nature, with common interpretation of morphological computation – a form-changing/form-generating inanimate nature as well as living organisms and their social process, [4]. -
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. -
How Do We Evaluate Artificial Immune Systems?
How Do We Evaluate Artificial Immune Systems? Simon M. Garrett [email protected] Computational Biology Group, Department of Computer Science, University of Wales, Aberystwyth, Wales, SY23 3DB. UK Abstract The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness’ and ‘effectiveness.’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Im- mune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory.’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty. Keywords Artificial immune systems, critical evaluation, negative selection, clonal selection, im- mune network models, danger theory. 1 Introduction What is an artificial immune system (AIS)? One answer is that an AIS is a model of the immune system that can be used by immunologists for explanation, experimentation and prediction activities that would be difficult or impossible in ‘wet-lab’ experiments. This is also known as ‘computational immunology.’ Another answer is that an AIS is an abstraction of one or more immunological processes. Since these processes protect us on a daily basis, from the ever-changing onslaught of biological and biochemical entities that seek to prosper at our expense, it is reasoned that they may be computa- tionally useful. It is this form of AIS—methods based on immune abstractions—that will be studied here. -
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]. -
Platforms EOI: Australian Systems Immunology Commons (Commune)
Platforms EOI: Australian Systems Immunology Commons (Commune) 20 September 2019 at 19:44 Project title Australian Systems Immunology Commons (Commune) Field of Research code(s) 01 MATHEMATICAL SCIENCES EOI Lead Name Christine Wells EOI lead Research Group Stemformatics.org EOI lead Organisation University of Melbourne EOI lead Email Collaborator details Research Name Organisation Group Flinders University and South Australian Health & Medical Research Prof David Lynn InnateDB Institute Prof Paul Hertzog Interferome.org Hudson Institute of Medical Research and Monash University Dr Sam Forster Interferome.org Hudson Institute of Medical Research and Monash University Steve Quenette eResearch Monash University Prof. Fiona Simon Fraser University, BC, Canada Brinkman Dr. Sandra EMBL European Bioinformatics Institute, UK Orchard Prof Bob Hancock University of British Columbia, Vancouver, Canad Project description The Systems Immunology Commons will bring together three existing, complementary, and internationally- recognised resources to create an integrated resource for biomedical researchers. These are Stemformatics.org; Interferome.org and http://innatedb.sahmri.com. Each platform incorporates a deep ecosystem of integrated software tools and services and has a track record of stakeholder engagement and long-term sustainability. Each platform is open source and open access; adopts and contributes to the development of international data standards; and adheres to the FAIR data principles. The Systems Immunology Commons will ensure the sustainability of the underlying platforms through shared development approaches for ease of automation, interoperability and maintenance. We will broaden accessibility and utility of the resources to a wider research community; enable analysis and integration of more diverse, higher- throughput, data to address emerging technology needs; and strengthen Australia’s position as a leader in this emerging field. -
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.