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Raffaele Giancarlo
Alexander Kel
Chiara Marchiori
Emanuela Merelli
  Francis Ouellette
Allegra Via

Anita Grigoriadis,
Department of Research Oncology, King’s College, London, UK.

Anita Grigoriadis is lecturer in Cancer Bioinformatics at King’s College of London.
She became a lecturer in Cancer Bioinformatics at King’s College London (KCL) in 2013. She received her Master’s degree at the Institute of Molecular Pathology, University of Vienna (Austria) before moving to London (UK) to do her PhD at the Ludwig Institute for Cancer Research (LICR). At the time when omics data was slowly beginning to establish itself in biomedical research, Anita started to work as a postdoctoral bioinformatician on breast cancer genomics at the LICR and at the Breakthrough Breast Cancer Centre(London) under Professor Alan Ashworth. In 2008, she joined the Breakthrough Breast Cancer Research Unit at King’s College London under the leadership of Professor Andrew Tutt, where her bioinformatics interest in researching genomic instability and immune-related features in triple-negative breast cancer started.

Interoperability of clinical, pathological and omics data to execute personalised medicine
Translational research has seen an increasing trend towards omics techniques imaging approaches, in combination with clinical and pathological data. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective, secure manner so that it can be utilised and searched by researchers and clinicians. Small- to moderate-sized research groups need to find solutions to handle and administer enormous data volumes whilst researching for new discoveries.
Here, I represent solutions to support data management, the integration of digital microscopy and pathology, and illustrate the utility of R-shiny to make high-throughput data searchable.

Winston Hide,
Sheffield Institute for Translational Neuroscience, University of Sheffield, UK, and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, USA.

Winston Hide is Professor of Computational Biology at University of Sheffield, UK.
Professor Hide graduated in Zoology from the University of Wales (Cardiff) in 1981. He attended Temple University, Philadelphia and graduated with a PhD in molecular genetics in 1991.
In 1992 he performed post doctoral training with Wen Hsuing-Li at the University of Texas, Houston, where he published his first Nature paper and in 1993 went on to train with David Pawson at the Smithsonian Institution National Natural History Museum, in 1994 with Richard Gibbs at the Baylor Human Genome Centre, Houston, and with Dan Davison at the University of Houston. In 1995 he gained industrial experience in Silicon Valley at MasPar Computer corporation as Director of Genomics.
In 1996 Professor Hide founded the South African National Bioinformatics Institute at the University of Western Cape, South Africa and was appointed Professor in 1999.
In 2007, he became visiting Professor of Bioinformatics at Harvard School of Public Health.
In 2008, Hide was the subject of a directed search and became an associate professor at the Department of Biostatistics at Harvard School of Public Health. Also, in 2008 Hide founded the Harvard School of Public Health bioinformatics Core and became Director of the Harvard Stem Cell Institute Center for Stem Cell Bioinformatics.
In 2014, Hide accepted a Chair, and became Professor of Computational Biology, at the Sheffield Institute for Translational Neurosciences within the Department of Neuroscience at the University of Sheffield.
Hide has been awarded the National President’s Award for research in 1998, was elected to membership of the Academy of Science of South Africa in 2007 and also in 2007, won the Oppenheimer Foundation Distinguished Sabbatical Research Fellowship. In 2011, he was the first recipient of the International Society for Computational Biology award for Outstanding Achievement – in recognition of his work for the development of computational biology and bioinformatics in Africa. Hide has now established the Centre for Genome Translation at the University of Sheffield and leads on bioinformatics for the Cure Alzheimer’s foundation Genome Project. His group specialises in target prioritisation, drug repurposing and biomarker discovery in neurodegenerative diseases.

Making genomics Come true: How can we achieve real acceleration of genomics into medicine?
We are now rapidly moving from single human genomes to deca-, centi- and even milligenome projects. With more ways to compare gene variation against a background, comes new methods to select variants and genes for their potential in prediction and impact for a disease. Gene hunting is still very much a fashion and genes represent tempting targets for drug development.
But like David Bowie we need to push the boundaries to embrace the growing realisation that genes work in cohorts and it is the interaction of these cohorts that drive the disease phenotype. Identifying and targeting pathways and processes that drive disease is the new black. To action discovery, we need to address ways in which to benchmark selection of disease genes, pathways and processes. In turn we need to develop more efficient (read less ineffective) ways to select therapeutics that are likely to be acceptable for real health interventions.
The talk will present how we address these challenges through commoning for data sharing, provenance, reproducibility and workflows, benchmarks for assessment of approaches, standardisation for pathway activity, and integrative approaches to discovering the relationships between therapeutic target prioritisation, network topology, pathway interaction, genome variation, disease modelling and drug repurposing.

Inna Kuperstein,
Institut Pasteur, Paris, France.

Inna (Faina) Kuperstein is a scientist at Institut Curie in Paris, working on systems biology of cancer and other human diseases (https://sysbio.curie.fr/). She obtained her PhD in neurochemistry at the Weizmann institute of science, Israel, where she studied molecular mechanisms of stroke and oxidative stress. Then she did a postdoctoral research in the Center for the Biology of Disease at the Flanders institute of biotechnology (VIB) in Leuven, Belgium in the field of molecular mechanisms of synaptic and neuronal toxicity in Alzheimer‘s disease.
Molecular and cell biologist with wide experience in signal transduction research, since 2009 she is applying her biological knowledge in the field of computational systems biology in the group of Computational Systems Biology of Cancer, Institut Curie. She participates in multidisciplinary projects with pharmaceutical companies, biologists, mathematicians, systems biologists and clinicians.
In particular, she specialises in construction of comprehensive cell signaling maps, their applications for analysis with high-throughput data and results interpretation.
Among others, she has initiated and is currently involved in development of web-based tool NaviCell (https://navicell.curie.fr/) dedicated for manipulations with big signalling maps. This Google Maps engine-based tool allows user-friendly navigation through big molecular map as well as integration and visualisation of high-throughput data in the context of those maps.
In addition, she has initiated and is now leading the Atlas of Cancer Signalling Network (ACSN) project (https://acsn.curie.fr/). This project is dedicated to systematic and detailed representation of molecular mechanisms involved in cancer. ACSN maps are applied for network analysis and modelling new synthetic interactions between genes in cancer, for predicting drug response and resistance mechanisms in patients.

Atlas of Cancer Signaling Network and NaviCell: Systems Biology resources for studying cancer biology
Studying reciprocal regulations between cancer-related pathways is essential for understanding signaling rewiring during cancer evolution and in response to treatments. With this aim we have constructed the Atlas of Cancer Signaling Network (ACSN), a resource of cancer signaling maps and tools with interactive web-based environment for navigation, curation and data visualization. The content of ACSN is represented as a seamless ‘geographic-like’ map browsable using the Google Maps engine and semantic zooming. The associated blog provides a forum for commenting and curating the ACSN maps content. The integrated NaviCell web-based tool box allows to import and visualize heterogeneous omics data on top of the ACSN maps and to perform functional analysis of the maps. The tool contains standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values projected onto a pathway map.
To demonstrate applications of ACSN and NaviCell we show a study on drug sensitivity prediction using the networks. We performed a structural analysis of Cell Cycle and DNA repair signaling network together with omics data from ovary cancer patients resistant to genotoxic treatment. Following this study we retrieved synthetic lethal gene sets and suggested intervention gene combinations to restore sensitivity to the treatment. In another example, analysis of cell lines multi-level omics data, interpreted in the context of signaling network maps, highlighted different DNA repair molecular profiles associated with sensitivity to each one of the drugs, rationalizing combined treatment in some cases.
Analysis of multi-omics data together with cell signaling information helps finding personalized treatments. In additional study we show how epithelial to mesenchymal transition (EMT) signaling network from the ACSN collection has been used for finding metastasis inducers in colon cancer through network analysis. We performed structural analysis of EMT signaling network that allowed highlighting the network organization principles and complexity reduction up to core regulatory routs. Using the reduced network we modeled single and double mutants for achieving the metastasis phenotype. We predicted that a combination of p53 knock-out and overexpression of Notch would induce metastasis and suggested the molecular mechanism. This prediction lead to generation of colon cancer mice model with metastases in distant organs. We confirmed in invasive human colon cancer samples the modulation of Notch and p53 gene expression in similar manner as in the mice model, supporting a synergy between these genes to permit metastasis induction in colon.

Alfonso Valencia,
Barcelona Supercomputing Center (CBS), Spain.

Disease comorbidities and network approaches

Raffaele Giancarlo,
Department of Mathematics and Computer Science, University of Palermo, Italy.

Giancarlo Raffaele is Full Professor of Computer Science, Faculty of Science, Università di Palermo. He obtained a Ph.D. in Computer Science from Columbia University in 1990 defending one of the first Ph.D. thesis about Algorithms and Computational Biology. He was awarded several fellowships, among which the AT&T Bell Labs Post-Doctoral Fellowship in Mathematical and Information Sciences and a CNRS Visiting Scientist Grant. He has also been Invited Keynote Speaker to several conferences and summer schools, including SIAM International Conference in Applied Mathematics and has held several visiting scientist positions with many research labs and Universities both in USA and Europe such as AT&T Shannon Laboratories and Max Plank Institute for Molecular Genetics- Bioinformatics Section, Berlin, Computer Science Haifa University. Moreover, he has served either as Chairman or as Member of Scientific Committees in several conferences relating to Theoretical Computer Science and Bioinformatics, such as Workshop on Algorithms in Bioinformatics, Combinatorial Pattern Matching and String Processing and Information Retrieval, ICALP, COOCON, Recomb. He is currently on the Editorial Board of Theoretical Computer Science, Algorithms for Molecular Biology, BMC Bioinformatics an BMC Research Notes. He has also been the principal investigator of several Italian Ministry of Education research projects in Bioinformatics and one CNRS Grant. Moreover, he has been a reviewer for most of the best established Journals and Conferences in Computational Biology and Theoretical Computer Science. In addition, he has also been reviewer for several National Granting Agencies, including US NSF and the Royal Society. He is also regularly consulted by Universities nationally and internationally in order to assess Faculty promotions. As for involvement in National and Local Higher Education Structures, he has been President of the Computer Science Curricula, Università di Palermo and Member of the Italian Computer Science Curricula Commission of the Italian Association of Computer Science Researchers (GRIN). He is currently on the Board of Directors of the CINI Consortium, that represents all of the academic competences in Computer Science and Engineering present in Italy. Finally, he is on the Scientific Advisory Board for Research at Università di Palermo.
His main scientific interests include design and analysis of combinatorial algorithms, with particular emphasis on string algorithms, ranging from bioinformatics to data compression and data structures and automata theory, with applications to Bioinformatics. His scientific production consists of more than 90 papers appeared in established journals and conferences. Moreover, he is coauthor of many patents, granted by the US Patent Office, in information retrieval and software maintenance.

Getting Beyond Proof of Principle for Big Data Technologies in Bioinformatics: MapReduce Algorithmic, Programming and Architectural issues
High Performance Computing (HPC) in Bioinformatics has a classic architectural paradigm: shared-memory multi-processor. With the advent of Cloud Computing, such a new way of managing Big Data has been considered for Bioinformatics. Initially, with Proof of Concept results investigating the advantages of the new computational paradigm. They have been followed by an increasing number of specific Bioinformatics tasks, developed mainly with the use of the MapReduce programming paradigm, which is in turn supported by Hadoop and Spark Middleware.
A careful analysis of the State of the Art indicates that the main advantage of those Big Data Technologies is the perception of boundless scalability, at least in terms of time. However, how effectively the computing resources are used in the Cloud… is rather cloudy, as most of the software available almost entirely delegates the management of the distributed workload to the powerful primitives of Hadoop and Spark. On a private cloud, i.e., a physical computing cluster that can be configured at will by the user, one can show that carefully designed MapReduce algorithms largely outperform the ones that naively “delegate” to Hadoop and Spark. In the public cloud, e.g., virtual clusters (for instance created via OpenStack) with a dynamic and instance-dependent allocation of physical resources, issues of an architectural nature or related to configuration of the virtual cluster, largely oblivious to the end-user, may translate in a lack of data locality that results in a poor MapReduce performance with respect to the resources used.
In order to obtain resource-effective, portable, Cloud-based software for Bioinformatics pipelines, the issues mentioned earlier must be carefully studies and accounted for, in particular to have an impact for Personalized Medicine. As a matter of fact, the need is so pressing and apparently the expected demand so high that Edico genome and Amazon have started a collaboration that makes available Bioinformatics pipelines that are highly engineered to take advantage of FPGA programmability and the Cloud. The objective is to take the already highly performing shared-memory multi-processor based solutions offered by Edico genome in order to make them “real time”. Fortunately, this is only the “high end” of the spectrum where a transition from the old HPC paradigm to the new of Cloud Computing one has gone beyond Proof of Concept.

Alexander Kel,
GeneXplain, Germany.

Alexander Kel received his Ph.D. in Bioinformatics, Molecular Biology and Genetics in 1990. He studied biology and mathematics at Novosibirsk State University and obtained his M.S. in biology in 1985. He worked for 15 years at the Institute of Cytology and Genetics, Russia (ICG) holding positions as a programmer, scientist, senior scientist and Vice-Head of the Laboratory of Theoretical Molecular Genetics. In 1995, he won the Academician Belaev Award. In 1999 he received an independent funding from the Volkswagen foundation and organized a Bioinformatics group at ICG. From 2000 to 2010, he has been the Senior Vice President Research & Development of BIOBASE GmbH, Wolfenbüttel, Germany.
During his career, he has worked in almost all branches of current bioinformatics including: theoretical models of molecular genetic information systems, sequence analysis, gene recognition, promoter analysis and prediction, analysis of protein secondary structure, prediction of RNA secondary structure, theory of mutation and recombination process, molecular evolution, databases and gene expression studies.
He is the author of more than 90 scientific publications and of several chapters in books on bioinformatics, tutorials and education materials.

Title to be defined

Chiara Marchiori,
IBM Research – Zurich, Switzerland.

Emanuela Merelli,
University of Camerino, Italy.

Emanuela Merelli is Full Professor of Computer Science at the University of Camerino where she coordinates the PhD Program in Computer Science, International School of Advanced Studies.
She got a Doctoral Degree in Computer Science at the University of Pisa in 1985 and a PhD in Artificial Intelligent Systems at the "Università Politecnica delle Marche" in Ancona in 2000. Among her appointments she has been a Fulbright Scholar at University of Oregon, Computer Science Department and a Visiting Researcher at University of East Anglia, School of Information System, Norwich.
Her main research interests are in the following fields: Bio-inspired formal methods, concurrency theory, agent-oriented modelling & multi-level complex systems, topological field theory of data and new models of computation. Computational biology of RNA. Folding and Immune System Memory Evolution.
Among her achievements are bio-inspired formal languages, such as BioAgent, SHAPE Calculus, BIOSHAPE and BOSL, for modelling, simulating and analysing autonomous agents, the construction of Hermes, an agent-based middleware for mobile computing; a research program towards a new strategy for mining data through data language that turns out to be the shape language: topological field theory of data; the design and implementation of jHoles algorithm to study the connectivity features of complex networks, with application on epidermal tumor diagnosis; a new data model, Resourceome, that allows to manage declarative and procedural knowledge with a unique model whose actions connect the use of a resource to its domain.

Topological Field Theory of Data: a new venue for Biomedical Big Data Analysis
In her talk, she will challenge the current thinking in IT for the Big Data question, proposing a program aiming to construct an innovative methodology to perform data analytics that goes beyond the usual paradigms of data mining rooted in the notions of Complex Networks and Machine Learning. The method presented – at least as scheme – that returns an automaton as a recognizer of the data language, is, to all effects, a Field Theory of Data.
She will discuss, by using biomedical case studies, how to build, directly out of probing the data space, a theoretical framework enabling to extract the manifold hidden relations (patterns) that exist among data as correlations depending on the semantics generated by the mining context.
The program, that is grounded in the recent innovative ways of integrating data into a topological setting, proposes the realization of a Topological field theory of data, transferring and generalizing to the space of data notions inspired by physical (topological) field theories and harnesses the theory of formal languages to define the potential semantics necessary to understand the emerging patterns.

Francis Ouellette,
Génome Québec, Canada.

Title to be defined

Allegra Via,
Institute of Biomembranes and Bioenergetics (IBBE) / CNR, Bari, and ELIXIR-IIB, Italy.

Allegra Via is a physicist and scientific researcher at the Institute of Biomembranes and Bioenergetics (IBBE) of the National Research Council (CNR, Bari, IT). In 2003, she got her PhD at the University of Rome “Tor Vergata”, where she also worked six years as postdoc. In 2009 she moved to the La Sapienza University as researcher, and, since January 2016, she is working full time as the ELIXIR Italy Training Coordinator.
She is involved in the design, organisation and delivery of bioinformatics training courses, in Train the Trainer activities, and collaborates with other ELIXIR’s nodes on many training-related initiatives. She has a long track record of academic teaching (Macromolecular Structures, Python programming, Bioinformatics, Biochemistry, Protein interactions).
Her main research interests include protein structural bioinformatics, protein function prediction and analysis, and protein interactions. She is a member of the Global Organisation of Bioinformatics Learning, Education and Training (GOBLET) and a Software/Data Carpentry Instructor.

ELIXIR-IIB (provisional title)