immunology without borders! - unict · paulo jose costa branco, universidade tecnica de lisboa,...

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~ Immunology without Borders! ~

July 17-18, 2015 Taormina – Sicily, Italy

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ICSI3 2015 International Congress on Systems Immunology,

Immunoinformatics & Immune-computation Hotel Villa Diodoro Congress Center

Taormina, Sicily, Italy July 17-18, 2015

Workshop Information

Website for the Workshop: http://www.dmi.unict.it/ais2015/ Email address for the organizers: [email protected] Workshop Venue: Hotel Villa Diodoro Congress Center Via Bagnoli Croci 75, 98039 Taormina, Messina, Italy +39 0942 23312 [email protected] http://www.hotelvilladiodoro.com/ http://www.hotelvilladiodoro.com/en/how-to-reach-us.html Registration Desk: it is located at the hallway outside the Main Conference Room (a.k.a. Ettore Majorana Room). It is open from 15:30 to 19:00 of July 16, and from 8:30 of July 17. During the congress you can find someone of the organization that can be help you on whatever you need. Wireless Internet Login: Networking ID: Taosciences Password: TBA Sponsorhip: TaoSciences Research Center (http://www.taosciences.it/) Technical Sponsorship: IEEE Compuational Intellligent Socisety- IEEE CIS – http://cis-ieee.org/ Patronage: IEEE CIS Task Force on Artificial Immune Systems – http://ieee-cis-ais.org/ List of Restaurants and Bar close to the Conference Venue: Ristorante Al Giardino, Via Bagnoli Croci 84, 98039 Taormina, Messina - +39 0942 23453 http://www.algiardino.net/ Ristorante La Bougainville, Via Bagnoli Croci 88, 98039 Taormina, Messina - +39 0942 625218 Al Settimo Cielo del Paradiso, Via Roma 2, 98039 Taormina, Messina - +39 0942 23922 Minimarket Venuto, Via Bagnoli Croci 68, 98039 Taormina, Messina - +39 0942 625556

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International Congress on Systems Immunology, Immunoinformatics & Immune-computation

Hotel Villa Diodoro Congress Center Taormina, Sicily, Italy

July 17-18, 2015

ICSI3 2015 Organizing Team

General Chairs Carlos A. Coello Coello, CINVESTAV-IPN, Mexico Vincenzo Cutello, University of Catania, Italy Doheon Lee, KAIST, Republic of Korea Mario Pavone, University of Catania, Italy Luca Zammataro, IIT – Italian Institute of Technology, Italy Publicity Chair Tao Gong, Donghua University, China & Purdue University, USA

Administrative & Onsite Staff Gianluca Arcidiacono, University of Catania, Italy Giovanni Carapezza, University of Catania, Italy Ivano Caruso, University of Catania, Italy Piero Conca, University of Catania, Italy Jole Costanza, Italian Institute of Technology, Milano Italy Giovanni Murabito, DiGi Apps Inc. Andrea Patanè, University of Catania, Italy Davide Romano, University of Catania, Italy Andrea Santoro, University of Catania, Italy

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July 17-18, 2015

ICSI3 2015 Program Committee

Uwe Aickelin, University of Nottingham, UK Colin C. Anderson, University of Alberta, Canada Bruno Apolloni, University of Milan, Italy Becca Asquith, Imperial College London, UK Soumya Banerjee, Harvard University, USA Sergio Baranzini, University of California San Francisco, USA Helio Barbosa, LNCC, Brazil Simone Bassis, University of Milan, Italy Nicole Baumgarth, School of Veternary Medicine, University of California, USA Catherine Beauchemin, Ryerson University, Canada Peter Bentley, University College London, UK Heder S. Bernardino, Federal University of Juiz de Fora, Brazil Hugues Bersini, Université Libre de Bruxelles, Belgium Hans Bitter, Roche Palo Alto, USA Joseph Blattman, Arizona State University, USA Jacek Blazewicz, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poland Catherine Blish, Division of Infectious Diseases & Geographic Medicine, Stanford Immunology, USA Christian Blum, University of the Basque Country, Spain Ulisses M. Braga-Neto, Texas A&M University, USA Gennady Bocharov, Russian Academy of Sciences, Russia Tadeusz Burczynski, Cracow University of Technology, Poland Zixing Cai, Central South University - Changsha, China Robin Callard, University College London, UK Salvador Eugenio Caoili, University of the Philippines Manila, Philippines Jonathan Carlson, Microsoft Research California, USA Gastone Castellani, University of Bologna, Italy Franco Celada, New York University, USA Cliburn Chan, Duke University, USA Zeineb Chelly, Institut Supérieur de Gestion de Tunis, University of Tunis, Tunisia Chang-Zheng Chen, Stanford University, USA Tong Joo Chuan, Institute for Infocomm Research, Singapore Dmitriy Chudakov, Russian Academy of Sciences, Russia Hilary Clark, Genentech - Roche Group, USA Guilherme P. Coelho, Unicamp, Brazil Carlos A. Coello Coello, CINVESTAV-IPN, Mexico George M. Coghill, University of Aberdeen, UK Francesco Colucci, University of Cambridge, UK Ernesto Costa, University of Coimbra, Portugal Paulo Jose Costa Branco, Universidade Tecnica de Lisboa, Portugal Jole Costanza, IIT - Italian Institute of Technology, Italy Nareli Cruz-Cortè, CIC-IPN, MEXICO Vincenzo Cutello, University of Catania, Italy Alberto D'Onofrio, International Prevention Research Institute, France Luca Daniel, MIT, USA Dipankar Dasgupta, University of Memphis, USA Leandro de Castro, Mackenzie University, Brazil David S. DeLuca, Broad Institute of MIT & Harvard, USA Alexander Diehl, Buffalo Clinical & Translational Research Center, USA

Yongsheng Ding, Donghua University - Shanghai, China Marco Dorigo, Université Libre de Bruxelles, Belgium Irini Doytchinova, Medical University Sofia, Bulgaria Ken Duffy, Hamilton Institute, National University of Ireland, Ireland Deborah Dunn-Walters, KCL School of Medicine, London, UK Sol Efroni, Bar Ilan University, Israel Fernando Esponda, University of New Mexico, USA Marco Ferrarini, University of Leeds, UK Marc Thilo Figge, Leibniz Institute for Natural Product Research & Infection Biology, Germany Grazziela Figueiredo, University of Nottingham, UK Darren Flower, Aston University, UK Stephanie Forrest, University of New Mexico, USA Nir Friedman, Weizmann Institute of Science, Israel Bruno Andre Gaeta, University of New South Wales, Australia Xiao Zhi Gao, Aalto University, Finland Masoud Ghaffari, GE Aviation, USA Elena E. Giorgi, Los Alamos National Laboratory, USA Maoguo Gong, Xidian University, China Tao Gong, Donghua University, China & Purdue University,USA Guido Grandi, Novartis, Italy Victor Greiff, ETH Zurich, Switzerland Irina Grigorova, University of Michigan, USA Feng Gu, Kingston University, London, UK Jin Kao Hao, University of Angers, France Alaa E. Abi-Haidar, Université Pierre et Marie Curie, France Andreas Handel, University of Georgia, USA Fernand Hayot, Mount Sinai School of Medicine, USA Yongqun He, University of Michigan, USA Jane Heffernan, York University, Canada Uri Hershberg, University of Drexel, USA Tomer Hertz, Vaccine & Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA Phil Hodgkin, Walter & Eliza Hall Institute of Medical Research, Australia Andy Hone, University of Kent, UK Peter Thomas Hraber, Los Alamos National Laboratory, USA John Iacomini, Tufts University, USA Mikhail Ivanchenko, University of Nizhniy Novgorod, Russia Christian Jacob, University of Calgary, Canada Licheng Jiao, Xidian University, China Colin Johnson, University of Kent, UK David Klatzmann, laboratoire I3, Université Pierre et Marie Curie, Groupe Hospitalier Pitié-Salpétriére, France Koichi S. Kobayashi, TAMHSC College of Medicine Harvard University, Texas, USA Oliver Kohlbacher, University of Tübingen, Germany Natalio Krasnogor, Newcastle University, UK Bruno Kyewski, German Cancer Research Center, Heidelberg Niels B. Larsen, DTU Nanotech, Technical University of Denmark, Denmark Henry Lau, University of Hong Kong, China Doheon Lee, KAIST, Korea Jay Lee, University of Cincinnati, USA

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July 17-18, 2015

ICSI3 2015 Program Committee

Klaus Ley, La Jolla Institute for Allergy & Immunology, USA Yiwen Liang, Wuhan University, China Jiming Liu, Hong Kong Baptist University, Hong Kong Wenjian Luo, University of Science & Technology, China Carolina Livi, The University of Texas Health Science Center, USA Fabio Luciani, University of New South Wales, Australia Paolo Lusso, National Institute of Allergy & Infectious Diseases, National Institutes of Health, USA Yoram Louzoun, Bar Ilan University, Israel Ernesto Marques, University of Pittsburgh, USA Andrew Martin, University College London, UK Vega Masignani, Novartis, Italy Piero Mastroeni, University of Cambridge, UK Polly Matzinger, National Institute of Allergy & Infectious Diseases, National Institutes of Health, USA Ramit Mehr, Bar-Ilan University, Israel Hongyu Miao, University of Rochester Medical Center, USA Sujatha Mohan, Research Center for Allergy & Immunology, The Institute of Physical & Chemical Research, Japan Lenny Moise, Institute for Immunology & Informatics, University of Rhode Island, USA Heiko Muller, IIT - Italian Institute of Technology, Italy Gioacchino Natoli, IFOM-IEO Campus, Italy Mark Neal, University of Wales, Aberystwyth, UK Giuseppe Nicosia, University of Catania, Italy German Nudelman, Mount Sinai School of Medicine, USA Yanay Ofran, Bar Ilan University, Israel Osamu Ohara, RIKEN Center for Integrative Medical Sciences, RIKEN Yokohama Institute, Japan Andrew Olaharski, Roche Palo Alto, USA Michal Or-Guil, Humboldt University Berlin, Germany Richard E. Overill, King's College London, UK Mathias Pacher, University of Frankfurt, Germany Mirko Paiardini, Emory University School of Medicine, USA Wei Pang, Jilin University, China & University of Aberdeen, UK Panos Pardalos, University of Florida, USA Mario Pavone, University of Catania, Italy Barbara Payne, Institute for Immunology & Informatics, University of Rhode Island, USA João P. Pereira, Department of Immunobiology, Yale University School of Medicine, USA Leila Perie, Utrecht University, Netherlands Dimitri Perrin, Queensland University of Technology, Australia Nikolai Petrovsky, Flinders University, Australia Philippe Pierre, Centre d'Immunologie, University of Marseille, France Camelia Pintea, Technical University Cluj-Napoca, Romania Stefano Pluchino, University of Cambridge, UK Guido Poli, San Raffaele Hospital, Italy G. P. S. Raghava, Institute of Microbial Technology, India

Srinivasan Ramachandran, Institute of Genomics & Integrative Biology, CSIR, India Shoba Ranganathan, Macquarie University, Australia Timothy Ravasi, KAUST, Kingdom of Saudi Arabia Pedro Reche, Department of Microbiology I – Immunology Universidad Complutense de Madrid, Spain Sai Reddy, Department of Biosystems Science & Engineering, ETH Zurich, Switzerland Roland R. Regoes, ETH Zurich, Switzerland Ruy Ribeiro , Los Alamos National Laboratory, USA Benedita Rocha, Universitè Paris-Descartes, France Luis M. Rocha, Indiana University, USA Andrea Roli, University of Bologna, Italy Peter Ross, Napier University, UK Sven Schaust, Gottfried Wilhelm Leibniz Universität Hannover, Germany Christian Schönbach, Kyushu Institute of Technology, Japan Anurag Sethi, Department of Molecular Biophysics & Biochemistry, Yale University, USA Alessandro Sette, La Jolla Institute for Allergy & Immunology, La Jolla, USA Johannes Sollner, Emergentec Biodevelopment GmbH, Austria Derek Smith, University of Cambridge, UK Stefan Stevanovic, University of Tuebingen, Germany Sean P. Stromberg, Emory University, USA El-Ghazali Talbi, Polytech'Lille, Univeristy of Lille 1, France Ying Tan, Peking University - Beijing, CHINA Stephen Taylor, University of Oxford, UK German Terrazas Angulo, University of Nottingham, UK Rodolphe Thiebaut, ISPED Bordeaux School of Public Health, France Paul G. Thomas, St. Jude Children's Research Hospital, USA Véronique Thomas-Vaslin, CNRS- UPMC, France Anna Tramontano, University "La Sapienza" in Rome, Italy Andy Tyrrell, University of York, UK Rajat Varma, National Institutes of Health , National Institute of Allergy & Infectious Diseases, USA Elena Vigorito, Babraham Institute & University of Cambridge, UK Mario Villalobos Arias, Universidad de Costa Rica, Costa Rica Fernando J. Von Zuben, State University of Campinas, Brazil Stefan Voss, University of Hamburg, Germany Aleksandra Walczak, Laboratoire de Physique Thèorique, Ecole Normale Supèrieure, France Ning Xiong, Mälardalen University, Sweden Gur Yaari, Yale University, USA Hong Yang, FDA, USA Andrew Yates, Albert Einstein College of Medicine, USA Alexey Zaikin, University College London, UK Luca Zammataro, IIT - Italian Institute of Technology, Italy Guanglan Zhang, Boston University, Metropolitan College, USA

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July 17-18, 2015

ICSI3 2015 Program Overview

Friday 17 July Saturday, 18 July

Alessandro Sette (9:00 – 10:00) Oral Presentations

Session IV (9:00 – 11:05) Oral Presentations

Session I (10:00 – 10:50)

coffee break (10:50 – 11:10)

coffee break (11:05 – 11:25)

Oral Presentations Session II

(11:10 – 13:15)

Oral Presentations Session V

(11:25 – 13:05)

on own for lunch (13:15 – 14:30)

on own for lunch (13:05 – 14:30)

Panos Pardalos (14:30 – 15:30) Oral Presentations

Session VI (14:30 – 15:45) Oral Presentations

Session III (15:30 – 16:20)

coffee break (16:20 – 16:40)

Concluding Remarks (15:45) Oral Presentations

Special Session (16:40 – 18:20)

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July 17-18, 2015

ICSI3 2015 Plenary Speakers

Alessandro Sette , La Jolla Institute for Allergy & Immunology, La Jolla, USA

Bioinformatic and Immunological Approaches to Map Immune Reactivity in Humans

Our group has been developing approaches to the unbiased, non-hypothesis driven, analysis of immune signatures associated with human disease. According to a first approach, we developed the Immune Epitope Database and Analysis resource "(IEDB). The IEDB is a freely accessible repository of all published epitope related information, derived from infectious agents, autoantigens, allergens and transplantation antigens. In addition, the IEDB analysis resource contains bioinformatics tools that allow predicting epitopes for any protein of interest, and analyzing epitope data generated from the user. In a second approach, we experimentally determine immune signatures of human responses to a particular infectious disease or allergen indication, by a combination of T cell mapping studies, coupled with phenotyping and RNA profiling. We have applied this strategy to the study of CD4 and CD8 memory T cells associated with Mycobacterium tuberculosis (MTB) and dengue virus (DENV) in the context of 1) natural immunity and/or control of infection, 2) active and severe disease and 3) administration of licensed or experimental vaccines.

Panos Pardalos , University of Florida, USA

Computational Models and Challenging Optimization Problems

Most of the conventional computer models are based on the von Neumann computer architecture and the Turing machine model. However, quantum computers (several versions!), analog computers, dna computers, and several other exotic models have been proposed in an attempt to deal with intractable problems. We are going to give a brief overview of different computing models and discuss several classes of optimization problems that remain very difficult to solve. Such problems include graph problems, nonlinear assignment problems, and global optimization problems. We will start with a historical development and then we will address several complexity and computational issues. Then we are going to discuss heuristics and techniques for their evaluation.

Friday, July 17 9:00 – 10:00

Friday, July 17 14:30 – 15:30

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July 17-18, 2015

ICSI3 2015 Daily Agenda

Friday, July 17

8:45 – 9:00 Open Ceremony Plenary Speaker – Chair: Mario Pavone 9:00 – 10:00 Alessandro Sette, La Jolla Institute for Allergy & Immunology, La Jolla, USA

Bioinformatic and Immunological Approaches to Map Immune Reactivity in Humans Oral Presentations Session I – Chair: Luca Zammataro 10:00 – 10:25 EBVdb: a data mining system for knowledge discovery in Epstein-Barr virus with applications in T

cell immunology and vaccinology GuangLan Zhang1, Derin Keskin2, Lou Chitkushev1, Ellis Reinherz2 and Vladimir Bruisic2 1 Boston University 2 Dana-Farber Cancer Institute, Harvard Medical School

10:25 – 10:50 A bioinformatic framework for immune repertoire diversity profiling enables detection of

immunological status Victor Greiff, Pooja Bhat, Skylar C. Cook, Ulrike Menzel, Wenjing Kang, and Sai T. Reddy ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland

10:50 – 11:10 Coffee Break Oral Presentations Session II – Chair: GuangLan Zhang 11:10 – 11: 35 Ebola: an analysis of immunity at the molecular level

Julia Ponomarenko1, Kerrie Vaughan1, Sinu Paul1, Maximilian Haeussler2, Sebastian Maurer-Stroh3, Bjoern Peters1 and Alessandro Sette1 1 La Jolla Institute for Allergy and Immunology, La Jolla, USA 2 University of California Santa Cruz, USA 3 Bioinformatics Institute (BII), Agency for Science Technology and Research (A*STAR), Singapore

11:35 – 12:00 Integrative analysis of the effect of in utero smoke exposure on the immune system points towards

alterations in memory T and B cell subsets Sandra Andorf a, John F. Ryan a, Michelle T. Graham a, Carlos O. Medina a, Unni C. Nygaard b, Kari C. Nadeau a a Sean N. Parker Center for Allergy Research at Stanford University, Stanford University School of Medicine, Lucile Packard Children's Hospital, Stanford Hospital and Clinics, United States b Department of Food, water and cosmetics Norwegian Institute of Public Health, Norway

12:00 – 12:25 A molecular model of STIM1-Orai1 movement and binding and their influence on calcium dynamics

in T cell receptor response Justin Melunis1, Bruce Freedman2 and Uri Hershberg1 1 Drexel University, USA 2 The University of Pennsylvania, USA

12:25 – 12:50 Lineage Tree Analysis of High Throughput Immunoglobulin Sequencing Clarifies B Cell

Maturation Pathways Helena Hazanov1, Yu-Chang Bryan Wu2, Deborah K. Dunn-Walters2 and Ramit Mehr1 1 Bar-Ilan University, Israel 2 King's College London, UK

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12:50 – 13:15 Discrimination between random and non-random processes in early bacterial colonization on biomaterial surfaces: application of point pattern analysis Daniel Siegismund a,b, Anja Schroeter b, Claudia Lüdecke a,c, Andreas Undisz a, Klaus D. Jandt a, Martin Roth c, Markus Rettenmayr a, Stefan Schuster b, Sebastian Germerodt b,* a Friedrich Schiller University Jena, Otto Schott Institute of Materials Research (OSIM), Löbdergraben 32, 07743 Jena, Germany b Friedrich Schiller University Jena, Department of Bioinformatics, Ernst-Abbe-Platz 2, 07743 Jena, Germany c Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Bio Pilot Plant, Jena, Beutenbergstrasse 11a, 07745 Jena, Germany

13:15 – 14:30 On own for lunch Plenary Speaker – Chair: Giuseppe Nicosia 14:30 – 15:30 Panos Pardalos, University of Florida, USA

Computational Models and Challenging Optimization Problems Oral Presentations Session III – Chair: Fabio Luciani 15:30 – 15:55 A Multi-Objective Clonal Selection Algorithm for Analog Circuit and Solar Cell Design

Giuseppe Nicosia, Andrea Patanè, Andrea Santoro and Giovanni Carapezza University of Catania, Italy

15:55 – 16:20 An Optimisation Framework of Dendritic Cells

Henry Lau and Nicole Lee The University of Hong Kong, Hong Kong

16:20 – 16:40 Coffee Break Special Session on “Artificial Immune Systems for Security and Privacy“ – Chairs: Wenjian Luo & Dongdong Zhao 16:40 – 17:05 A Fine-grained Algorithm for Generating Hard-to-reverse Negative Databases

Dongdong Zhao1, Wenjian Luo1, Ran Liu2 and Lihua Yue1 1 University of Science and Technology of China 2 China University of Geosciences, Wuhan

17:05 – 17:30 Multiple-Negative Survey Method for Enhancing the Accuracy of Negative Survey-based Cloud Data

Privacy Ran Liu and Shanyu Tang School of Computer Science, China University of Geosciences, China

17:30 – 17:55 GPU-Based Parallel Optimization and Embedded System Application of Immune Convolutional

Neural Network Tao Gong1, Tiantian Fan1, Jizheng Guo1 and Zixing Cai2 1 Donghua University, China 2 Central South University, China

17:55 – 18:25 SvdNPD: A Negative Data Publication Method Based on the Sensitive Value Distribution

Linli Wu, Wenjian Luo and Dongdong Zhao University of Science and Technology of China

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July 17-18, 2015

ICSI3 2015 Daily Agenda

Saturday, July 18

Oral Presentations Session IV – Chair: Ramit Mehr 9:00 – 9:25 Hybrid Agent-based Model of Aspergillus fumigatus Infection in Human Alveoli Predicts

Chemoattraction of Alveolar Macrophages and Parameter-Regimes of Chemokine Properties Johannes Pollmächer 1,2, Marc Thilo Figge 1,2 1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Jena, Germany 2 Friedrich Schiller University Jena, Jena Germany

9:25 – 9:50 A global analysis of antibody repertoires in chronic HIV-1 patients reveals signatures of neutralizing

antibodies Enkelejda Mihoa, Merle Schanzb, Michael Huberb, Victor Greiffa, Alexandra Trkolab and Sai T. Reddya a Department of Biosystems Science and Engineering, ETH Zürich, Switzerland b University of Zürich, Institute of Medical Virology, Switzerland

9:50 – 10:15 Modelling the FRC network of lymph node Gennady Bocharov1, Alexey Kislitsyn2, Rostislav Savinkov2, Mario Novkovic3 and Lucas Onder3 1 Institute of Numerical Mathematics of the RAS, Russian Federation 2 Lomonosov Moscow State University, Russian Federation 3 Institute of Immunobiology, Kantonsspital St.Gallen, Switzerland

10:15 – 10:40 Co-evolving mutations in hepatitis C virus in the context of immune escape against neutralising

antibody responses Preston Leung, Rowena Bull, Andrew Lloyd and Fabio Luciani UNSW Australia, Australia

10:40 – 11:05 Determining and tracking cells in grayscale timelapse images with NDICE

Stefan Lang, Christian Tokarski, Sebastian Germerodt and Stefan Schuster Department of Bioinformatics, Friedrich Schiller University Jena, Germany

11:05 – 11:25 Coffee Break Oral Presentations Session V – Chair: Sebastian Germerodt 11:25 – 11:50 Epistatic Analysis of NSAIDs Hypersensitivity using High Performance Computing

Alex Upton1, Oswaldo Trelles1, Lieh-Bang Liou2, Ming Ta Michael Lee3, Miguel Blanca4, Jose Antonio Cornejo-Garcia4 and James Perkins4 1 University of Malaga, Spain 2 Chang Gung Memorial Hospital at Lin-kou, Taiwan 3 RIKEN Center for Integrative Medical Sciences, Japan 4 IBIMA/Hospital Civil, Malaga, Spain

11:50 – 12:15 Modeling the host-pathogen interactions of the human immune system and Candida albicans using

game theory Sybille Dühring Department of Bioinformatics, Friedrich Schiller University Jena, Germany

12:15 – 12:40 Different Saccharomices Cerevisiae β-Glucans preparation's effect on Murine Dendritic Cells

Artur Javmen1, Saulius Grigiškis2, Aušra Nemeikaitė-Čėnienė1 and Mykolas Mauricas1 1 State Scientific Research Institute Centre for Innovative Medicine, Lithuania 2 JSC "Biocentras", Lithuania

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12:40 – 13:05 Visual NetLogo-Based Simulation of Anti-SARS Immune System and Low-to-High Resolution Reconstruction of Sequence Medical CT Images Tao Gong1, Lei Pei1, Shangce Gao1, Fang Han1, Shuguang Zhao1 and Zixing Cai2 1 Donghua University, China 2 Central South University, China

13:05 – 14:30 On own for lunch Oral Presentations Session VI – Chair: Luca Zammataro 14:30 – 14:55 Improved Immune Algorithm for Medical Image Enhancement

Tao Gong1, Tiantian Fan1, Lei Pei1 and Zixing Cai2 1 Donghua University, China 2 Central South University, China

14:55 – 15:20 Packing Equal Disks in a Unit Square: an Immunological Optimization Approach

Piero Conca1, Giovanni Stracquadanio2, Ornella Greco3, Vincenzo Cutello1, Mario Pavone1, Giuseppe Nicosia1 1 University of Catania, Italy 2 University of Oxford, UK 3 Royal Institute of Technology, Sweden

15:20 – 15:45 Industrial implementation of the immune network modeling of complex objects on the equipment Schneider Electric and Siemens Galina Samigulina1 and Zarina Samigulina2 1 Institute of Information and Computing Technologies, Kazakhstan 2 Kazakh National Technical University after K.I. Satpayev, Kazakhstan

15:45 Concluding Remarks

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July 17-18, 2015

ICSI3 2105 Abstracts

EBVdb: a data mining system for knowledge discovery in Epstein-Barr virus with

applications in T cell immunology and vaccinology

GuangLan Zhang1, Derin Keskin2, Lou Chitkushev1, Ellis Reinherz2 and Vladimir Bruisic2

1 Boston University 2 Dana-Farber Cancer Institute, Harvard Medical School

As the first cancer-causing human virus identified, Epstein-Barr virus (EBV) has been implicated in the development of a

wide range of B cell lymphoproliferative disorders, a subset of T/NK cell lymphomas, and post-transplant lymphoproliferative disorders. We made use of the immunological data on EBV available through publications, technical reports, and databases and constructed Epstein-Barr virus T cell Antigen Database (EBVdb). EBVdb contains 2622 curated antigen entries of EBV antigenic proteins, 610 verified T cell epitopes and 26 verified HLA ligands. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis, and sequence variability analysis, have been integrated within the EBVdb. Predicted Class I and Class II HLA-binding peptides for 15 common HLA alleles are included in this database as putative targets. EBVdb seamlessly integrates curated data and information with tailored analysis tools to facilitate data mining for EBV vaccinology and immunology. EBVdb is a unique data source providing a comprehensive list of EBV antigens and peptides and is publicly available at http://projects.met-hilab.org/ebv/.

A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status

Victor Greiff, Pooja Bhat, Skylar C. Cook, Ulrike Menzel, Wenjing Kang, and Sai T. Reddy

ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland

Lymphocyte receptor repertoires are continually shaped throughout the lifetime of an individual in response to its environmental and pathogenic exposure. Thus, they may serve as a fingerprint of an individual’s ongoing immunological status (e.g., healthy, infected, vaccinated) with implications for immunodiagnostics application. The advent of high-throughput immune repertoire sequencing now enables the interrogation of immune repertoire diversity in an unprecedented and quantitative manner. However, steadily increasing sequencing depth has revealed that immune repertoires vary greatly among individuals in their composition and correspondingly few shared sequences indicative of immunological status (“public clones”) have been reported. Disconcertingly, this means that the wealth of information gained from repertoire sequencing remains largely unused for determining the current status of immune responses thereby hampering the implementation of immune-repertoire-based diagnostics. Here, we introduce a bioinformatics repertoire-profiling framework, which possesses the advantage of capturing – as opposed to singular public clones – the diversity and distribution of entire immunological repertoires. The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires. We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients’ immunological statuses with their B- and T-cell repertoire data. We could predict with high accuracy (≥80%) a wide range of immunological statuses such as healthy, transplantation recipient, and lymphoid cancer suggesting as a proof of principle that diversity profiling can recover a large amount of immunodiagnostic fingerprints from immune repertoire data. Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; which exceeds the size of published immune repertoire datasets by 1 to 2 orders of magnitude. Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

Friday, July 17 Session I 10:00 – 10:50

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Ebola: an analysis of immunity at the molecular level

Julia Ponomarenko1, Kerrie Vaughan1, Sinu Paul1, Maximilian Haeussler2, Sebastian Maurer-Stroh3, Bjoern Peters1 and Alessandro Sette1

1 La Jolla Institute for Allergy and Immunology, La Jolla, USA 2 University of California Santa Cruz, USA

3 Bioinformatics Institute (BII), Agency for Science Technology and Research (A*STAR), Singapore

The 2014 Ebola outbreak in West Africa raised global concern requiring concerted effort from the worldwide scientific community to analyze immune responses, in natural infection and in the context of vaccination and therapy. Herein we review and analyze antibody and T cell epitope data reported in the scientific literature. More than 100 epitopes have been defined for viruses within the Filoviridae family, 73% of which are specific to Zaire ebolavirus (EBOV). While data are available for all EBOV proteins, the vast majority of epitopes relate to the surface glycoprotein, GP. Several neutralizing and/or protective sites are defined for monoclonal antibodies, including those used in the therapeutic cocktails, ZMAb, MB-003 and ZMapp. However, little human and non-human primate data (NHP) are currently available, highlighting a crucial gap, especially at the level of T cell responses. To fill this gap, we provide sets of predicted human and macaque CD4+ and CD8+ restricted T cell epitopes, covering most common MHC variants expressed in humans and NHPs and conserved within the EBOV species. This comprehensive analysis of molecular targets of the immune response to EBOV should assist the scientific community in the evaluation of the EBOV-specific immune response in infection, therapy and vaccination.

Integrative analysis of the effect of in utero smoke exposure on the immune system points

towards alterations in memory T and B cell subsets

Sandra Andorf a, John F. Ryan a, Michelle T. Graham a, Carlos O. Medina a, Unni C. Nygaard b, Kari C. Nadeau a

a Sean N. Parker Center for Allergy Research at Stanford University, Stanford University School of Medicine, Lucile Packard Children's Hospital, Stanford Hospital and Clinics, United States

b Department of Food, water and cosmetics Norwegian Institute of Public Health, Norway

Current CDC estimates reveal that roughly 1 out of 5 U.S. adults (>18 years of age), an estimated 42.1 million adults, smoke or have smoked compared to half of the population in 1964. Regardless of intense cessation campaigns, smoking is still prevalent throughout the U.S. population and contributes to increased risks in cancer, cardiovascular diseases, autoimmunity, and allergy and to altered immune responses to microbial pathogens. Furthermore, non-smokers exposed to tobacco toxins via secondary exposure are also at risk for these immune and non-immune pathologies. However, to date our understanding of how tobacco smoke impacts human immune function, especially in the developing immune system, is still limited; thus, revealing an unmet need to assess the impact of tobacco smoke on various immune parameters. To address this we designed a pilot study to assess the impact of in utero smoke exposure in monozygotic twin pairs compared to non exposed twin pairs. We assessed immune cell subsets and signal transduction mediators using Mass cytometry (CyTOF) and Phospho Flow assays, respectively. Data of all subjects was adjusted for age via regression models. For both assays, strongly correlated (absolute correlation > 0.8) features were identified. Of each correlated features, the one with the largest mean absolute correlation was removed from the further analysis. Subsequently, differently expressed cell populations were identified using a rank product method (R package RankProd). This non-parametric statistic identifies features that are consistently highly ranked in a number of lists, e.g. consistently up regulated in a number of replicate experiments, simulated by permutations of the given two classes data. Using a cutoff of 0.1 for the percentage of false prediction (pfp) and a significance level of 0.05 for the q values, the list of differently altered cell types are enriched for memory T and B cell subtypes. By using Citrus, a tool for data-driven identification of stratifying signatures in cellular subpopulations (R. Bruggner et al.), on the CyTOF fcs files, we could detect the same trend that memory, especially B and CD8+, cells seem to be altered to some extend by in utero smoke exposure (identified as two clusters with q < 0.01 based on the proportion of a sample's cells that belong to a cluster). Furthermore, we found that subjects with in utero tobacco smoke exposure seem to be less responsive to inflammatory mediators via theSTAT1 pathway. Among the significant features (pfp < 0.1, q < 0.05) are also for this assay memory cell subtypes (CD4+CD45RA- as well as CD8+CD45RA-) stimulated by IFNa or IL-6. Additionally, the impact of in utero smoke exposure on the responsiveness to the influenza vaccine was analyzed by hemagglutination inhibition (HAI) assays. While we saw a tendency of a lower seroconversion rate (proportion of subjects with a ? 4-fold increase in HAI antibody titer at post-vaccination versus pre-vaccination) in subjects exposed to tobacco smoke compared to the non exposed ones, it was not significant (p > 0.05). Our initial promising results could lead to further understanding of the mechanisms by which smoking impairs the immune system.

Friday, July 17 Session II 11:10 – 13:15

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A molecular model of STIM1-Orai1 movement and binding and their influence on calcium dynamics in T cell receptor response

Justin Melunis1, Bruce Freedman2 and Uri Hershberg1

1 Drexel University, USA 2 The University of Pennsylvania, USA

Calcium dynamics lie at the heart of many adaptive cellular signals and are thus tightly controlled. While the importance

of calcium in immune cell modulation has long been known, it is only in recent studies that immune cell differentiation, maturation and response to T cell receptor signaling, have been linked to the dynamics of calcium. Post-antigen binding calcium signaling is controlled by the release of internal stores from the endoplasmatic reticulum (ER) and the opening of channels in the plasma membrane (PM). The release of calcium from the ER allow the stromal interaction molecule 1s (STIM1s) to dimerize and localize at the ER-PM junction where they are then able to bind to calcium release-activated calcium channel protein 1s (Orai1s) at the PM. This causes the Orai1 channels to become permeable to calcium, causing a flux across the membrane. It is unclear if STIM1 and Orai1 are directed to bind by some signaling mechanism or simply co-localize because of normal diffusion constraints of molecular motion. We here present a stochastic model of STIM1 and Orai1 movement and interaction and relate them to observed patterns seen pre and post activation of the T cell receptor. With this simulation we have taken a sampling rate of molecule locations, similar to experimental observation, and determined observed rates of diffusion based on individual molecules that we simulated within our analysis. Thanks to our mode of simulation, we can model how the movement of individual molecules and the influx of calcium appear at the population level post T cell stimulation. Through our model, we find that diffusion trap mechanics are sufficient to describe the observed STIM accumulation at the ER-PM junction and that the boundary conditions of a diffusion trap and the decrease in diffusion due to STIM-Orai conjugate formations could account fully for the observed decrease in the rate of STIM motion after ER calcium release.

Lineage Tree Analysis of High Throughput Immunoglobulin Sequencing Clarifies B Cell Maturation Pathways

Helena Hazanov1, Yu-Chang Bryan Wu2, Deborah K. Dunn-Walters2 and Ramit Mehr1

1 Bar-Ilan University, Israel 2 King's College London, UK

Transitional (TR) B cells are immature B cells that have migrated from the bone marrow to peripheral lymphoid organs,

but can still undergo selection against autoreactivity. TR cells that survive selection eventually develop into mature naïve B cells (CD27-IgD+, NA). Upon exposure to antigen, NA cells may become IgM memory (CD27+IgD+, MM) or "classical", classswitched memory (CD27+IgD-, SM) B cells. Although MM immunoglobulin (Ig) genes do not undergo class switching, they do undergo somatic hypermutation, albeit with lower frequency than SM. It has been postulated that MM B cells originate from T-independent immune responses, while SM cells originate from T-dependent responses. Alternatively, MM cells may be early emigrants from T-dependent germinal centers. Double negative B cells (CD27-IgD-, DN) have been said to be exhausted memory cells, but their precise origin is unclear. Therefore, a definitive elucidation of lineage relationships between these different B cell subsets is needed.

In this study, we used lineage tree analysis of Ig heavy chain gene sequences from five human B cell subsets (TR, NA, MM, SM and DN) from three individuals, to study the relationships between these B cell populations and garner insights regarding their roles in immune responses. Our analyses confirmed that both MM and SM branches can include DN Ig sequences, sometimes identical to SM Ig sequences. MM trees were significantly shorter than SM trees. Even when they belonged to the same clone, MM branches were shorter, consistent with either an early exit of MM cells from germinal centers in a T-dependent response, before accumulating many mutations, or their generation by Tindependent responses. Our finding of combined trees that included both MM and SM sequences suggests that at least some MM cells originate from the same clones as SM, rather than develop separately.

Discrimination between random and non-random processes in early bacterial colonization on biomaterial surfaces: application of point pattern analysis

Daniel Siegismund a,b, Anja Schroeter b, Claudia Lüdecke a,c, Andreas Undisz a, Klaus D. Jandt a,

Martin Roth c, Markus Rettenmayr a, Stefan Schuster b, and Sebastian Germerodt b,*

a Friedrich Schiller University Jena, Otto Schott Institute of Materials Research (OSIM), Löbdergraben 32, 07743 Jena, Germany b Friedrich Schiller University Jena, Department of Bioinformatics, Ernst-Abbe-Platz 2, 07743 Jena, Germany

c Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Bio Pilot Plant, Jena, Beutenbergstrasse 11a, 07745 Jena, Germany

The dynamics of adhesion and growth of bacterial cells on materials’ surfaces plays an important role regarding the

formation of biofilms, e.g. on implant surfaces. The surface properties of biomaterials have major impact on cell adhesion processes, e.g. the random / non-cooperative adhesion of bacteria. In the present study, the spatial arrangement of Escherichia coli on four different biomaterials – titanium dioxide (TiO2), tissue culture poly(styrene) (TCPS), poly(tetrafluoroethylene) (PTFE) and silicate glass (glass) – is investigated in a time series during the first hours after exposing the biomaterial surfaces to a bacterial suspension. The respective confocal laser scanning micrographs are analyzed and processed via an image processing routine and the resulting point patterns are evaluated using second order statistics. Two main adhesion mechanisms can thus be separated – random adhesion

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and non-random processes consisting of cooperative adhesion and/or growth. The comparison with an appropriate null-model quantifies the transition between the predominance of both processes with statistical significance. On PTFE, the fastest transition to non-random processes was found to occur after 2-3 h of adhesion, followed by TCPS and TiO2 (6-9 h) and glass (30-38 h). In addition, the determination of bacterial cell size, bacterial cluster density and bacterial cluster size via image processing gives insight into further surface influenced differences of bacterial micro-colony formation.

A Multi-Objective Clonal Selection Algorithm for Analog Circuit and Solar Cell Design

Giuseppe Nicosia, Andrea Patanè, Andrea Santoro and Giovanni Carapezza

University of Catania, Italy

We present PareDA (ParetoDesignAutomation), a composite automated methodology for the simulation-based multi-scenario multi-objective optimization of analog circuits and thin-_lm cell devices, relying on randomized algorithms, both domain and constraints sensitivity analysis, epsilon-dominance and global robustness analysis. We test PareDA algorithm on the designing problem of a three stage operational amplifier, a yield-aware optimization of a folded-cascode operational amplifier (requiring multiple operating conditions) and an optical model for tandem thin-film silicon solar cells. In these scenarios, comparisons with state-of-the-art techniques (as NSGA-II and YdIRCO) undoubtedly demonstrate PareDA effectiveness, in terms of Pareto optimality of the design found and convergence time. The latter obtains, in fact, a signi_cant average performance improvement (from 35% to 49%), finding Pareto-optimal designs dominating the ones found by state-of-the-art algorithms. Moreover CPU time required by PareDA to converge is reduced of at least 75% compared to the other methodologies here analysed (e.g. optimal folded-cascode operational amplifier are found in just 320s). Finally, PareDA algorithm thanks to parallel computations gains a 5.62x speed-up with 70% efficiency, compared to the non-parallel version.

An Optimisation Framework of Dendritic Cells

Henry Lau and Nicole Lee

The University of Hong Kong, Hong Kong

The paradigm of a signal cascading network of Dendritic Cells (DCs) underpins T-cells priming in the human immune system, which is one of the critical cell-mediated components in the innate immunity. This study is motivated by the immunological behavior of DCs, intracellular signal processing, and the architecture of the aforementioned signaldriven network in the maturation and migration processes. In parallel with these immuno-features and problem-solving techniques, a distributed DC-inspired framework is developed for resolving optimization problems. In the context of the study, the capabilities of the proposed framework are experimented in a small-scale resources scheduling problem. As of the obtained results, sets of high-quality solutions are produced, which also provide significant insights for the further development of DCmediated framework as presented in this paper.

A Fine-grained Algorithm for Generating Hard-to-reverse Negative Databases

Dongdong Zhao1, Wenjian Luo1, Ran Liu2 and Lihua Yue1

1 University of Science and Technology of China 2 China University of Geosciences, Wuhan

The negative database (NDB) is a new technique for privacy preserving and information hiding. It hides information by

storing the complementary set instead of the original data. In order to protect the hidden information, NDBs should be hard-to-reverse. In this paper, we propose the K-hidden algorithm for generating hard-to-reverse NDBs (called K-hidden-NDBs). The K-hidden algorithm could be controlled in a more fine-grained manner than existing NDB generation algorithms. Moreover, in terms of the SAT solvers based on the local search strategy, we formally prove that the K-hidden-NDB could be more hard-to-reverse than

Friday, July 17 Session III 15:30 – 16:20

Friday, July 17 Special Session 16:40 – 18:20

Artificial Immune Systems for Security and Privacy

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the NDBs generated by the typical p-hidden algorithm. Furthermore, we show that the K-hidden-NDB could be more hard-to-reverse (against the local search strategy) than the q-hidden-NDB (NDBs generated by the q-hidden algorithm) when r is fixed (r is used for controlling the size of NDBs). Finally, as for the Unit Clause heuristic solvers, we prove that the K-hidden-NDB could be the same hard-to-reverse to the q-hidden-NDB.

Multiple-Negative Survey Method for Enhancing the Accuracy of Negative Survey-based Cloud Data Privacy

Ran Liu and Shanyu Tang

School of Computer Science, China University of Geosciences, China

Presently, the development of cloud computing brings convenience to our life because of the high efficiency, usability, accessibility and affordability. But the cloud data privacy faces severe challenges. Although Negative survey, which is spired by Artificial Immune System (AIS), can protect users’ privacy data with high efficiency and degree of privacy protection, the accuracy is influenced by the number of users. The number of client terminals is not large enough may cause much errors. This study focuses on multiple-negative survey method to remedying this defect. Comparing with traditional negative survey, the multiple-negative survey method collects each user’s multiple different negative privacy information categories rather than only one negative category. The accuracy is analysed and some simulation experiments verifies the analysis.

GPU-Based Parallel Optimization and Embedded System Application of Immune Convolutional Neural Network

Tao Gong1, Tiantian Fan1, Jizheng Guo1 and Zixing Cai2

1 Donghua University, China

2 Central South University, China

Nowadays the image recognition system is applied more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. As an image recognition technique, the traditional convolution neural network has some defects such as long training time, easy over-fitting and high misclassification rate. After our analysis on the network structure and parameters of the convolutional neural network, we used the immune mechanism to improve the convolutional neural network and put forward an algorithm of the new immune convolution neural network. Our algorithm not only integrated the network node location and the parameter adjustment, but also dynamically adjusted the base function smoothing factor. In addition, we utilized the NVIDIA GPU to accelerate the parallel computing of the new immune convolutional neural network and built a real-time embedded image recognition system of the new immune convolutional neural network. Experimental results show that our new immune convolutional neural network has higher recognition rate, more stable performance and faster computing speed than the traditional one.

SvdNPD: A Negative Data Publication Method Based on the Sensitive Value Distribution

Linli Wu, Wenjian Luo and Dongdong Zhao

University of Science and Technology of China

Existing data publication methods retain the relationship between the quasi-identifier attributes and sensitive attributes of published data. We call them “positive data publication”. However, it will lead to potential risk that attackers could deduce the privacy of the corresponding individuals from the published data. Recently, by combining the negative representation with k-anonymity and l-diversity, (k, m)-anonNPD and (l, m)-divNPD algorithms have been proposed, which improve the degree of privacy because the sensitive value of each individual is transformed into a negative value randomly. However, to reduce the average error of the reconstructed distribution, the data published by (k, m)-anonNPD and (l, m)-divNPD are much larger than the original data.

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Hybrid Agent-based Model of Aspergillus fumigatus Infection in Human Alveoli Predicts Chemoattraction of Alveolar Macrophages and Parameter-Regimes of Chemokine Properties

Johannes Pollmächer1,2, Marc Thilo Figge1,2

1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology,

Hans Knöll Institute (HKI), Jena, Germany 2 Friedrich Schiller University Jena, Jena Germany

Invasive aspergillosis due to A. fumigatus constitutes a significant threat to immunocompromised patients. Mortality rates

range from 25-90% depending on the site of infection and the underlying condition of the patient. The lung is the primary site of infection due to permanent inhalation of A. fumigatus conidia. In the lower respiratory tract, i.e. in lung alveoli, conidia quickly adapt to the humid and nutrient rich conditions, which in turn sets a tight timescale for removal by immune responses of the host before fungal germination starts. We developed a to-scale agent-based model of human alveoli in three-dimensional space in order to study the early immune response toward A. fumigatus under physiological conditions. Our first aim was to unravel the migration behaviour of alveolar macrophages (AM) to timely detect the fungus before the onset of invasive growth. Performing first-passage-time simulations in the experimentally relevant regime of parameters we found that AM require chemotactic cues in order to successfully complete the time-limited task of finding the needle in a haystack. Next, we refined our model to explicitly account for the AM chemoattractant by solving the reaction-diffusion equation on the curved alveolar surface. Again, we carried out virtual infection simulations, by that narrowing down the parameter space and predicting the relevant regime of characteristic chemokine parameters in terms of the diffusion coefficient, the molecule secretion rate of alveolar epithelial cells and the degradation rate.

Our spatio-temporal model is the first multi-scale implementation of a dynamical alveolus including respiration, alveolar epihelial cells (AEC) of type I and type II, the pores of Kohn, and migrating AM as well as reactions and diffusion of chemokines. This approach enables us to close important gaps in the understanding of the system as in vivo experiments in alveoli under physiological conditons, including live-cell imaging, are hard to realize. Reference: Pollmächer, J. and Figge, M.T. Agent-Based Model of Human Alveoli Predicts Chemotactic Signaling by Epithelial Cells during Early Aspergillus fumigatus Infection. PLOS ONE 9(10), e111630 (2014). A global analysis of antibody repertoires in chronic HIV-1 patients reveals signatures of neutralizing antibodies

Enkelejda Mihoa, Merle Schanzb, Michael Huberb, Victor Greiffa, Alexandra Trkolab and Sai T. Reddya

a Department of Biosystems Science and Engineering, ETH Zürich, Switzerland b University of Zürich, Institute of Medical Virology, Switzerland

There is renewed hope that the HIV global epidemic can be addressed by therapeutic interventions based on neutralizing

antibodies (NAbs) (Caskey et al., 2015). We apply new systems immunology-based methods, specifically next-generation sequencing (NGS) and bioinformatic analysis to interrogate the antibody repertoires present in the circulating B cell population of HIV-1 infected human patients (n = 3). Our comprehensive global antibody repertoire analysis revealed that HIV-1-infected individuals possessed antibody clones with a “signature of NAbs”; as properties that are common among all previoulsy discovered NAbs (over 90 broadly NAbs (bNAbs) discovered directly from B cells of human patients), such as a high number of somatic hypermutations (SHM) and long complementary determining region 3 (CDR3), were present at elevated levels in HIV-1 patient repertoires compared to that of healthy control individuals. In addition, we performed a longitudinal antibody repertoire analysis in HIV-1 patients and discovered that clones with very high SHMs increased over time (119, 138, and 225 weeks interval per patient). However, a longitudinal trend for CDR3 length was not concurrent across different HIV-1 infected individuals; yet, CDR3s were longer in these individuals compared to healthy individuals. We also performed clonotyping of CDR3s (clustering of clones based on CDR3 sequence simliarity) in order to filter out possible sequence variants resulting from PCR and sequencing errors, which have been reported to be abundant in repertoire NGS datasets (Shugay et al., 2014). Clonotype analysis revealed the same bNAb signature in HIV-1 patients, suggesting that errors in NGS datasets were not significantly biasing our results. Furthermore, since NGS data results often in a different number of sequence reads per sample, we performed bootstrapped analysis, which also confirmed that a bNAb signature was present in HIV-1 patient repertoires compared to healthy controls. We are currently working to develop and apply methods for network and phylogenetic analysis of antibody repertoires, which will reveal NAbs and bNAbs evolutionary pathways. In summary, our results show that a bNAb signature can be observed upon global analysis of the antibody repertoires of HIV-1 infected individuals, which may be indicative and predictive of their neutralizing activity in sera. We envision in the future that antibody reperoire analysis could be used a prediction tool for the discovery of Nabs and bNAbs directly from NGS data (Reddy et al., 2010). Reference: Caskey, M., Klein, F., Lorenzi, J. C. C., Seaman, M. S., West, A. P., Buckley, N., et al. (2015). Viraemia suppressed in HIV-1-infected humans by broadly neutralizing antibody 3BNC117. Nature. doi:10.1038/nature14411

Saturday, July 18 Session IV 9:00 – 11:05

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Shugay, M., Britanova, O. V., Merzlyak, E. M., Turchaninova, M. A., Mamedov, I. Z., Tuganbaev, T. R., et al. (2014). Towards error-free profiling of immune repertoires. Nature Methods , 11 (6), 653–655. doi:10.1038/nmeth.2960 Reddy, S. T., Ge, X., Miklos, A. E., Hughes, R. A., Kang, S. H., Hoi, K. H., et al. (2010). Nat Biotech 2010 Reddy. Nature Biotechnology , 28 (9), 957–961. doi:10.1038/nbt.1673

Modelling the FRC network of lymph node

Gennady Bocharov1, Alexey Kislitsyn2, Rostislav Savinkov2, Mario Novkovic3 and Lucas Onder3

1 Institute of Numerical Mathematics of the RAS, Russian Federation

2 Lomonosov Moscow State University, Russian Federation 3 Institute of Immunobiology, Kantonsspital St.Gallen, Switzerland

This work presents an idealized geometric model of the lymph node (LN) consisting of a set of ‘elementary’ macroscopic

structures such as subcapsular sinus, trabecular sinuses with a special focus on fibroblastic reticular cells (FRC) network. An experimental data driven computational algorithm has been developed to reconstruct the macroscopic structure of FRC network based on high-resolution confocal microscopy data of a murine LN. The FRC geometric model can be further discretized for use in computational studies of lymph and cytokines transport through the LN.

Co-evolving mutations in hepatitis C virus in the context of immune escape against neutralising antibody responses

Preston Leung, Rowena Bull, Andrew Lloyd and Fabio Luciani

UNSW Australia, Australia

Rapidly evolving pathogens, such as RNA viruses exploit the high mutation rate to avoid or escape host immune response.

Despite a random mutation process, the relatively small size of their genomes imposes evolutionary constraints. Viral evolution hence is characterized by highly connected mutations that arise within the same genome through a random sequential process. In this abstract we present a bioinformatics pipeline that aims at predicting coevolving mutations within ongoing hepatitis C viral infection which result in a significant advantage for the virus in terms of survival from the adaptive response exerted by neutralizing antibodies. This workflow utilizes the idea of Mutual information to identify pairs of potentially related genomic sites that confer a selective advantage.

Determining and tracking cells in grayscale timelapse images with NDICE

Stefan Lang, Christian Tokarski, Sebastian Germerodt and Stefan Schuster

Department of Bioinformatics, Friedrich Schiller University Jena, Germany

Live-cell imaging has become a powerful method to determine the number, motility and behavior of cells. Analysis of the pathogens' behaviour is important for drug and disease research. In order to automatically determine a cell's motility in live-cell images, several image-segmentation and tracking algorithms have been developed. While images of stained cells can be analyzed with high accuracy [1, 2], there is still a lack of automated tracking algorithms for grayscale images [3]. The proposed approach aims to provide a full environment for the tracking of ellipse-like shaped cells in any grayscale timelapse images created with a light microscope. The algorithm is embedded into a graphical user interface for easy use. It performs several image-segmentation steps and a final tracking step to estimate the motility of single and clustered cells. To obtain segmentation, the images are preprocessed to segregate the objects from the background, using intensity and variance information. Secondly, a rough estimation of the objects and their positions is performed by convolving the image with several circles. Using these estimations, single cells and clusters are detected. Inside the clusters, single cells are validated using an active contour algorithm which also provides a shape of the object. The final segmentation is achieved by eliminating false positive objects with a watershed-like process and a more detailed look at their shapes in a postprocessing step. In the tracking step, consecutive images are analyzed for nearest-neighbor associations to build up trajectories for the objects. Afterwards, short trajectories are built, lost and found objects are compared and trajectories are combined, if possible, to track the objects over the whole time. The parameters for the algorithm can be detected and optimized semi-automatically by labeling one example image. Altogether, NDICE provides a comfortable way to detect and track cells in grayscale timelapse images rapidly. References [1] Ettinger, A. and Wittmann, T. (2014). Fluorescence live cell imaging. Methods in cell biology, 123:p.77--94. [2] Mech, F., Thywissen, A., Guthke, R., Brakhage, A. A., and Figge, M. T. (2011). Automated image analysis of the host-pathogen interaction between phagocytes and aspergillus fumigatus. PLoS One, 6(5):e19591. [3] Ryoma, B., Kang, Liand Sungeun, E., and Takeo, K. (2009). Reliably tracking partially overlapping neural stem cells in dic microscopy image sequences. In MICCAI Workshop on Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy th, Imperial College London.

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Epistatic Analysis of NSAIDs Hypersensitivity using High Performance Computing

Alex Upton1, Oswaldo Trelles1, Lieh-Bang Liou2, Ming Ta Michael Lee3, Miguel Blanca4, Jose Antonio Cornejo-Garcia4 and James Perkins4

1 University of Malaga, Spain

2 Chang Gung Memorial Hospital at Lin-kou, Taiwan 3 RIKEN Center for Integrative Medical Sciences, Japan

4 IBIMA/Hospital Civil, Malaga, Spain

Genome wide association studies (GWAS) have tended to focus on the association between single nucleotide polymorphisms (SNPs) and human diseases by looking at individual variants. However, complex diseases are more likely due to certain combinations of genetic variants rather than single variants acting independently in an additive fashion. As such, the interactions between multiple SNPs, epistatic interactions, have the potential to provide insights about their underlying causes and mechanisms. Using epistatic analysis methods, it is possible to identify SNP pairs associated with disease; these can then be mapped to genes allowing the inference of a gene interaction network. This network can be analyzed using graph theory metrics to identify important hub genes, which although often insignificant on their own can be important in combination with other variants. Here, we analyze SNP and gene interaction networks obtained from a nonsteroidal anti-inflammatory drugs (NSAIDs) hypersensitivity GWAS dataset, obtained through the application of epistatic analysis methods. We identify several combinations of SNPs and genes potentially involved in and predictive of NSAIDs hypersensitivity, that may not be identified through a single SNP association approach.

Modeling the host-pathogen interactions of the human immune system and Candida albicans using game theory

Sybille Dühring

Department of Bioinformatics, Friedrich Schiller University Jena, Germany

To understand the complex host-pathogen interactions of the human immune system with Candida albicans using computational systems biology approaches are very useful. C. albicans is one of the most important human pathogenic fungi. This opportunistic, polymorphic yeast usually resides as a commensal on the skin and mucosal surfaces of 30 to 70 % of the human population without causing any symptoms of disease. Alterations in the host environment, however, can render commensal factors into virulence attributes once the conditions favor pathogenicity. C. albicans then causes infections ranging from superficial mucosal diseases and thrush in immunocompetent hosts to severe, life-threatening systemic infections in immunocompromised individuals. Those systemic infections are associated with a severe morbidity, an unacceptably high mortality and high healthcare costs. With the innate immune system as the primary line of defense against systemic fungal infections the host defense relies mainly on phagocytes, especially neutrophils and macrophages. Using mathematical modeling, particularly game theory, differential equation systems and dynamic optimization we gain insights into the interactions of C. albicans and macrophages. Pure and mixed Nash equilibria as well as an optimal control are determined. The theoretical results can explain why macrophages sometimes release Candida cells from the phagolysosome without killing them.

Different Saccharomices Cerevisiae β-Glucans preparation's effect on Murine Dendritic Cells

Artur Javmen1, Saulius Grigiškis2, Aušra Nemeikaitė-Čėnienė1 and Mykolas Mauricas1

1 State Scientific Research Institute Centre for Innovative Medicine, Lithuania 2 JSC "Biocentras", Lithuania

Nowadays non-cellulosic, β-glucans from the different source are intensively investigated due to the fact that these

biological polymers are recognized as potent immunological stimulators for the mammal's immune system. Dendritic cells are the most important antigen presenting cells for activating naive T cells. The research goals of the recent investigation were to determine how the different molecular weight Saccharomyces cerevisiae β-glucan preparations affect such properties of the murine DC as phagocytosis, proliferation and cytokine synthesis in vitro.

Saturday, July 18 Session V 11:25 – 13:05

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Visual NetLogo-Based Simulation of Anti-SARS Immune System and Low-to-High Resolution Reconstruction of Sequence Medical CT Images

Tao Gong1, Lei Pei1, Shangce Gao1, Fang Han1, Shuguang Zhao1 and Zixing Cai2

1 Donghua University, China 2 Central South University, China

In the immune responses against the SARS (Severe Acute Respiratory Syndromes), human immune systems are complex

intelligent systems, which show good properties such as the self-organizing and adaptivity. Modeling the immune systems has important significance in both immunology and artificial immune system. In order to improve the visualization and readability of the anti-SARS immune system model, the visual tri-tier computational model of the anti-SARS immune system was simulated with NetLogo, which is a multi-agent-based tool. On the other hand, to fight against the SARS disease, the low-resolution medical CT (Computed Tomography) images should be transformed into the high-resolution ones for better SARS analysis. In order to obtain the high-resolution image from some low-resolution chest CT sequence images of a SARS patient, the low-to-high resolution reconstruction was designed and tested in this paper. First, the low-resolution medical images were preprocessed. Then the pretreated low-resolution medical images were registered with the sub-pixel-level image registration techniques. Finally, the POCS (Projections onto Convex Sets) image reconstruction algorithm was designed and tested. We obtained higher entropy and more detail information of the medical images with our approach than the Marcel method, especially for the rotated medical images in our experiments. Multiple-user browser-based experimental results show that the visual NetLogo-based simulation of the immune system is better to understand than the traditional mathematic equation model of the immune system.

Improved Immune Algorithm for Medical Image Enhancement

Tao Gong1, Tiantian Fan1, Lei Pei1 and Zixing Cai2

1 Donghua University, China 2 Central South University, China

Traditional clonal selection algorithm gives us some inspiration for many applications, but in the medical image

enhancement application we found some defects of this algorithm. So we improved the clonal selection algorithm in three ways. First, we designed the real coding of the MRI brain image, instead of binary coding. Second, we added the mutation distance to control the mutation progress and avoid only the local optimization. In addition, we adjust the clone selection and the mutation together in the Gauss distribution, the uniform distribution, and the chaotic distribution, rather than in only the Gauss distribution. Then we use the real MRI brain images to test the image enhancement of our improved clonal selection algorithm. The experimental results show that our approach outperform the median filtering (MF) and the adaptive template filtering (ATF) in enhancing the MRI brain images.

Packing Equal Disks in a Unit Square: an Immunological Optimization Approach

Piero Conca1, Giovanni Stracquadanio2, Ornella Greco3, Vincenzo Cutello1, Mario Pavone1, Giuseppe Nicosia1

1 University of Catania, Italy 2 University of Oxford, UK

3 Royal Institute of Technology, Sweden

Packing equal disks in a unit square is a classical geometrical problem, which arises in many industrial and scientific fields. Finding optimal solutions has been proved to be NP-hard, and hence only local optimal solutions can be identified. To tackle this problem, we used the optimization Immune Algorithm (optIA), which has been proved to be among the best derivative-free optimization algorithms. We used optIA to pack up to 150 disks in a unit square: experimental results showed that the immune algorithm is able to locate the putative global optimum for all the instances. Moreover, the comparison with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) showed that optIA is more robust.

Saturday, July 18 Session VI 14:30 – 15:45

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Industrial implementation of the immune network modeling of complex objects on the equipment Schneider Electric and Siemens

Galina Samigulina1 and Zarina Samigulina2

1 Institute of Information and Computing Technologies, Kazakhstan

2 Kazakh National Technical University after K.I. Satpayev, Kazakhstan

Organization of cross-brand compatibility of equipment from various manufacturers, and integration with the modern

intellectual ones is a promising direction for the development of automatic control and forecasting systems. This article discusses an approach of data collection organization from the industrial equipment of the companies Schneider Electric and Siemens with the help of OPC server and immune network modeling in the MATLAB environment.

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July 17-18, 2015

ICSI3 2105 – Instructions for Session Chairs & Presenters

Thank you very much for agreeing to chair a session. Session chairs are essential to keep sessions on schedule and moderate the question period. Your main duties are: • arrive few minutes early to check the equipment set-up. Please, let the organizers team at the registration desk know

if problems arise or adjustments are needed; • please follow the scheduled order of talks, as well as presentation times; • each Speaker is allocated 25 minutes for his/her presentation including questions; • moderate questions. If a session is without a chair, please inform quickly the organizer team at the registration desk.

Projectors and screens will be available for all presentations. Presenters are requested to bring their own laptops. • For each speaker is allocated 25 minutes for any presentation including set-up and questions; • please, check that your computer works fine with the video projector before the beginning of your session; • please, adhere to the scheduled slot of your presentation.

Instructions for Session Chairs

Instructions for Presenters

23



July 17-18, 2015

ICSI3 2105 – Tourist Information

Taormina As soon as you arrive in Taormina, you will feel the magical, mythical atmosphere spread all around which has enchanted visitors from all over the world for yeas. Settled on a hill of the Monte Tauro, Taormina dominates two grand, sweeping bays below and on the southern side, the top of Mount Etna, the European highest active volcano, often capped with snow, offering to the visitors a breath taking, dramatic and memorable view over almost one hundred miles of Mediterranean sea. Taormina seems to be born as a tourist resort since past times, when ancient people like the Sicels, Greeks, Romans Byzantines, Saracens, Arabs, Norman and Spaniards chose it as their residential site thanks to its favourable position, mild climate and magic atmosphere. Nowadays, visitors can still find fine examples of Taormina’s golden times: the splendid Greek Theatre, the Roman “Naumachiae”, the 13th century Cathedral of Saint Nicolò, the 14th century Palazzo Corvaja, the 16th century Palace of the Dukes of Saint Stefano, the public gardens, the “Ancient Abbey“ (Badia Vecchia) and many others. The Greek Theatre The most significant monument in Taormina is the ancient theatre, not just because of its artistic and historical values but also because of its unique position. The view from here is called panorama par excellence and is definitely worth experiencing when you come to Sicily. Second in size only to the Theatre in Syracuse, the Greek Theatre in Taormina although was built during Hellenic times in the 3rd to 2nd centuries BC, it was completely reconstructed and extended 300 years later by the Romans and used for gladiatorial shows. The theatre is situated at the very top of a hill, levelled for the purpose, using the natural incline of the valley for the "cavea": the auditorium seating. The backdrop view would doubtless have added splendid dramatic impact to past productions. The remains of a small temple stand on the side of the theatre. Remnants of an arcade, once leading to the theatre, stand at the top of the auditorium. Scenery consisted of nine columns, raised and placed in their original positions during the theatre’s restoration in the Eighteen Hundreds. The majestic panorama, combined with a spectacular view of Etna and the Calabrian mountains, renders this hollowed out hill a natural stage, as well as a stage for natural beauty. Getting Around Travelling in Sicily by comfortable buses is a very smart idea: they are fast, reliable and cheap and take the traveller almost everywhere. Taormina has its own bus terminal right in the town centre. Frequent buses (“Interbus”) run from Palermo, Catania (airport included) and Messina. Moreover, there are connections from Rome and Amalfi (Interbuisness), Bari, Brindisi (ferries from Greece) and Taranto (Etna). Cheaper transportation might be possible by sharing car service. The SAT group (http://www.satgroup.it/) arranges transfer service between Catania Airport and Taormina.

immunology without borders! - unict · paulo jose costa branco, universidade tecnica de lisboa,...

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