NETTAB 2008 workshop aims at:
- introducing the basic knowledge of standards and technologies related to focus theme,
in a non trivial way, through invited lectures
- outlining the promising features of the involved technologies
through invited lectures and open discussion
- showing some valuable examples in bioinformatics through invited lectures,
oral communications and posters
- allowing for as much discussion as possible
- demonstrating "how it works" practically through tutorials
The workshop will therefore includes:
- invited lectures
- oral communications
- a poster session
- Text-mining, pathways, and human disease
Andrey Rzhetsky, University of Chicago, USA
Andrey Rzhetsky is Associate Professor at the Department of Biomedical Informatics, Judith P. Suzberger MD Columbia Genome Center, Center for Computational Biology and Bioinformatics, Columbia University in the City of New York.
Research interests include: Bioinformatics and phylogenetics applied to analysis of genes, proteins, molecular pathways, and human disease. Application of statistics to sequence analysis and analysis of molecular networks. Development of algorithms and programs for pathway/sequence analysis, pathway/sequence comparison and phylogeny reconstruction
The information overload in molecular biology is a mere example of the status common to all fields of the current science and culture: An ever-strengthening avalanche of novel data and ideas overwhelms specialists and non-specialists alike, unavoidably fragments knowledge, and makes enormous chunks of knowledge invisible/inaccessible to those who desperately need it.
The help of relieving the information overload may come from the text-miners who can automatically extract and catalogue facts described in books and journals.
The talk will touch the following six questions: What is text-mining? In what ways is text-mining useful? What can large-scale analyses of scientific literature tell us about both active and forgotten knowledge? What can such analyses tells us about the scientific community itself? How do mathematical models help us to differentiate true and false statements in literature? How will text-mining help us to find cures for human and non-human maladies?
- Seeds for sequence comparison
Mikhail A. Roytberg, Russia
Mikhail Roytberg is Head of Applied mathematics Lab in the Institute of the Mathematical Problems in Biology, Russian Academy of Sciences. The recent results (obtained in collaboration with other labs) include the OWEN program for genome comparison, series works on seed design, implemented in the YASS genome comparison program, the efficient algorithm predicting internal loops in RNA secondary structures implemented in the AFold program and the study of the quality of algorithmic alignments. The main areas of his research interests are algorithmic bioinformatics ("bioalgorithmics"), programming (design of educational computer systems) and education (including high school and elementary school education in science and math).
Pair-wise sequence alignment is the basic method of comparative analysis of proteins and nucleic acids. Studying the results of the alignment one have to consider two questions: (1) did the program find all the interesting similarities ("sensitivity") and (2) are all the found similarities interesting ("selectivity"). Definitely, one has to specify, what is interesting. Analogous questions can be addressed to each of the obtained alignments: (3) which part of the aligned positions is aligned correctly ("confidence") and (4) does alignment contain all pairs of the corresponding positions of compared sequences ("accuracy"). Naturally, the answer on the questions depends on the definition of the correct alignment. The presentation addresses the above two pairs of questions that are extremely important in interpreting of the results of sequence comparison
- Gene regulatory code and evolution of complex systems
Alexander Kel, Biobase, Germany
Dr. Alexander E. Kel has received both Master of Science and Ph.D. degrees both in genetics and mathematical biology from the Institute of Cytology and Genetics, Novosibirsk, Russia in 1985 and 1991 respectively. His scientific experience totalling in more then 20 years of research in the field of mathematical/computational biology and bioinformatics. Dr. Kel was involved in research in many fields of bioinformatics, from molecular evolution, and structural biology to database development, application of machine learning techniques and sequence analysis. In the recent years his interests shifted towards system biology including structural analysis of signal transduction and gene regulatory pathways and dynamic modelling of regulatory circuits of such complex cellular processes as cell cycle, differentiation and appoptosis.
Currently, Dr. Alexander Kel holds the position of Senior Vice President Research and Development. He joined BIOBASE in 2000. Before joining the Biobase GMBH, Dr. Kel was a Group Leader and Principal Investigator at the Institute of Cytology and Genetics, Novosibirsk, Russia. His group has developed one of the first databases on gene regulation and the first program for promoter recognition.
Regulation of gene expression is accomplished through binding of transcription factors (TFs) to distinct regions of DNA (TF binding sites, TFBS), and, after anchoring at these sites, transmission of the regulatory signal to the basal transcription complex. Gene regulation code which would enable a .translation. of DNA regulatory sequences into regulatory function they possess are still unclear. Some of TFs are specific for a particular tissue, a definite stage of development, or a given extracellular signal, but most transcription factors are involved in gene regulation under a rather wide spectrum of cellular conditions. It is clear by now that combinations of transcription factors rather than single factors drive gene transcription and define its specificity. Dynamic function-specific complexes of many different transcription factors, so called enhanceosomes are formed at gene promoters and enhancers controlling gene expression in a specific manner. At the level of DNA, the blueprints for assembling such variable TF complexes on promoter regions may be observed as specific combinations of TFBS located in close proximity to each other. We call such structures .Composite Modules (CMs)..
The multiplicity of cellular conditions in which eukaryotic genes should be expressed is the cause of polyfunctionality of the structure of their transcription regulatory regions. We believe that this polyfunctionality is governed through alternative CMs. In the lecture I will present a .fuzzy puzzle. model of the gene regulatory code, which based on the principle of encoding multiple regulatory messages in the same DNA sequence. The structure of regulatory sequences on one hand and the specific features of transcription factors on the other provide a possibilities to encode several regulatory programs within one regulatory region. Such structure allows receiving and integration of multiple regulatory signals through reuse of the same DNA sequence of gene promoters and enhancers.
We developed a novel tool, Composite Module Analyst (CMA) (Kel et al., 2006), that applies a novel approach for defining promoter models based on composition of single transcription factor binding sites as well as their pairs located inside local regulatory domains (corresponding to enchancer/silencer subregions). We use the genetic algorithm technique and utilize a multicomponent fitness function for defining the function specific composite modules in promoters of genes co-regulated in specific conditions (tissue, organ, stage of development, cell cycle, response to particular extracellular signal).
We applied the CMA tool to analyze promoters of tissue/organ specific genes (based on the organ ontology Cytomer® and define Composite Modules characteristic for promoters of genes specifically (or abundantly) expressed in those tissues, which might be considered as the components of the gene regulation code.
We think that .fuzzy puzzle. principle of the gene regulatory code is the result of genome evolution of multicellular organisms that shall overcome evolutionary bottlenecks caused by the requirement of multiple ontogenetic programs to be encoded in a single genome.
- Data Integration in Clinical Bioinformatics
Pietro Liò, University of Cambridge, Cambridge, UK
Pietro Liò is Senior Lecturer at the University of Cambridge, Computer
Laboratory in England, a member of the AI group (Artificial Intelligence
Group), Fellow and Director of Studies at Fitzwilliam College. He undertakes
research and teaching in the general area of Computational Biology, Systems
Biology, and Bio-inspired computer science. Previous positions with Nick
Goldman (European Bioinformatics Institute) and Newton Morton (University of Southampton).
Complex biochemical data originates from several disparate sources.
Medical observations and clinical data are commonly interpreted by medical practitioners for diagnostic purposes.
On the other hand, genetic/biochemical high throughput screening technologies are increasingly applied to complex
disorders with the hope of identifying disease related molecular abnormalities.
The two procedures and the resulting data are often kept separated resulting in a loss of valuable information
about the disease state.
Here, we show how statistical methodology can be devised to combine molecular and clinical data.
We present a methodology based on False Discovery Rate estimation that bridges the gap between exploratory data
analysis and a network medicine approach that investigates the dependencies between the variables.
We present a case study comprising a large cohort of patients with psychiatric illnesses and describe the
combination of multiple sets of high throughput data with clinical information.
- Goals, organization and recent achievements of NCRI Informatics Initiative
Max Wilkinson, National Cancer Research Instutute, UK
Max Wilkinson has been a researcher in biomedical research for the past 18 years.
Principally a molecular biologist he has applied cutting edge technology in the fields of cyanobacteria,
mycology, vertebrate virology, transplantation, diabetes.
Presently, he is a member of the Informatics Coordination Unit at the National Cancer Research Institute.
Dr Wilkinson took his PhD from University College London's Medical School in applying bioinformatics to
transcriptomic analyses in disease models.
As a member of the NCRI Informatics Initiative, Dr Wilkinson wrote a training review to compliment the
strategy to build a fully integrated cancer informatics platform in the UK.
Presently, he is focussing on bridging the divide between individuals involved in building informatics
technology solutions with those that 'use' such technology in the research and clinical environments.
He regularly speaks at conferences and meetings to communicate developments in the Initiative and encourages
participation from all areas of cancer research.
Research into cancer is generating massive amounts of diverse information, which increasingly reveals
the multifaceted complexity of the disease. The inherent opportunity for understanding and improving
prevention, diagnosis and treatment is substantial, but requires a revolution in the way information is managed.
The goal of the National Cancer Research Institute (NCRI) Informatics Initiative is to maximise data sharing
by effective use of informatics. The scope of the Initiative is full spectrum; from molecular level science
to clinical cancer research and the bridge into health service delivery through collaboration with government,
industry, research and regulatory authorities. In addition the NCRI has been forging relationships with
international programmes aiming for similar goals.
Currently there are many informatics resources, standards and services, but they have evolved separately and so
present an incoherent, fragmented landscape.
The concept of an 'Informatics Platform' is an organised set of resources that provide benefit to cancer
research, the clinical community, funders and ultimately patients.
These include access to tools for data acquisition, storage, sharing and integration, increased cost-effectiveness
by re-use of data and improved patient safety through better use of available information.
The process of developing the NCRI Informatics Platform concept involved consulting leaders in diverse fields
(through an Informatics Task Force) then constructing a implementation plan that enables sharing and integration
within and between research domains.
This approach has generated demonstrator projects exemplifying an integrated and multidisciplinary solution to
complex problems and construction of a dedicated infrastructure component that facilitates the sharing of data and information.
The NCRI Informatics Initiative is now in the initial implementation phases, which is characterised by a collaborative
and multidisciplinary method of working between scientific, informational and medical communities, ultimately for the
prevention and treatment of cancer.
Two tutorials was held in the morning of Monday, May 19, 2008.
Participation in tutorials was free for all workshop's registrants.
- Systems Biology Tools
Roberta Alfieri and Ettore Mosca, Biomedical technologies Institute, ITB/CNR, Milano, Italy
- Systems Biology Standards
Fedor Kolpakov, Institute of Cytology and Genetics, Novosibirsk, Russia
|Monday, May 19, 2008
|Systems Biology Tools
|Roberta Alfieri and Ettore Mosca,
|Coffee break 1
|Systems Biology Standards
Institute of Cytology and Genetics, Russia
|Introduction NETTAB 2008
|Text-mining, pathways, and human disease
University of Chicago, USA
|Protopia: a Protein-Protein Interaction Tool
|Alejandro Real-Chicharro, Iván Ruiz-Mostazo, Ismael Navas-Delgado,
Francisca Sánchez-Jiménez, Miguel Ángel Medina, José F. Aldana-Montes
|A machine learning approach to predict cancer-related mutations
|Remo Calabrese, Emidio Capriotti, Rita Casadio
|Coffee break 2
|Bioinformatics methods for complex systems
|Gene regulatory code and evolution of complex systems
|Guidelines for parallel simulation of biological reactive systems
|Tommaso Mazza, Rosita Guido
|KOMF: The Khaos Ontology-based Mediation Framework
|Othmane Chniber, Amine Kerzazi, Ismael Navas-Delgado, José F. Aldana-Montes
|Automating Functional Genomics Data Analysis: a Use Case With an Interdisciplinary Approach
|Anahi Balbi, Luca Corradi, Marco Fato, Valentina Mirisola, Ulrich Pfeffer, Ivan Porro, Paolo Romano, Armando Tacchella
|HTC for Aspic: a distributed web resource for alternative splicing prediction and transcript isoform characterization
|D'Antonio M, Paoletti D, Carrabino D, D'Onorio De Meo P, Sanna N, Castrignanò T, Anselmo A, D'Erchia A, Licciulli F, Mangiulli M, Mignone F, Pavesi G, Picardi E, Riva A, Rizzi R, Bonizzoni P and Pesole G.
|Towards a computational framework for qualitative simulation of gene regulatory networks
|Liliana Ironi, Luigi Panzeri
|Fuzzy Central Approaches to Biclustering
|Maurizio Filippone, Francesco Masulli, Stefano Rovetta, Luca Zini
|An information management system for distributed proteomic images: a computational GRID technologies or a scale-free network of biobanks?
|G. Mercurio, S. Maglio, A. Agrusti, G. De Nunzio, R. Cataldo, I. De Mitri, M. Favetta, A. Massafra, G. Marsella, D. Vergara, M. Maffia
|Phenotype Forecasting with SNPs Data through Gene-Based Bayesian Networks
|Alberto Malovini, Fulvia Ferrazzi, Angelo Nuzzo, Annibale A. Puca, Riccardo Bellazzi
|Analysis and modelling of motility of cell populations
|Concita Cantarella Leandra Sepe, Francesca Fioretti, Giovanni Paolella
|Tuesday, May 20, 2008
|Biomedical methodology for drug discovery
|Seeds for sequence comparison
|Mikhail A. Roytberg
|Extraction, Integration and Analysis of Alternative Splicing and Protein Structure Distributed Information
|Matteo D'Antonio, Marco Masseroli
|Protein Complexes Prediction as a Data Integration Application
|Mario Cannataro, Pietro H. Guzzi, Pierangelo Veltri
|TDHand: a highthroughput Transcriptomic Data Handling system
|Danilo Carrabino, Tiziana Castrignanò, Francesco Dondero, Alessandro Negri, Caterina Oliveri, Graziano Pesole, Aldo Viarengo, Flavio Mignone
|A pipeline for saturation mutagenesis and docking, applied to the endothelial protein C receptor
|Federica Chiappori, Pasqualina D'Ursi, Ivan Merelli, Marco Salina, Ermanna Rovida, Luciano Milanesi
|Coffee break 3
|Mathematical methods for System Biology
|Combining Molecular and Physiological Data of Complex Disorders
University of Cambrdige, United Kingdom
|Bayesian methods for time course microarray
|Claudia Angelini, Daniela De Canditiis, Marianna Pensky
|A spatial model and simulator for metabolic pathways
|Nicola Cannata, Flavio Corradini, Emanuela Merelli, Luca Tesei
|Multiscale approach to Genome-Wide Association studies: network structure, linkage disequilibrium blocks, genes and pathways
|Francesco Lescai, Daniel Remondini, Mirko Francesconi, Luciano Milanesi , Claudio Franceschi, Gastone Castellani
|A novel approach to gene-set enrichment for two-class time-course expression data
|Daniele Merico, Giancarlo Mauri, Andrew Emili, Gary Bader
|Announcement and presentation of NETTAB 2009
|Rosalba Giugno, Paolo Romano
|Coffee break 4
|Parallel projects' meetings
|Wednesday, May 21, 2008
|Oncology Bioinformatics 1
National Cancer Research Institute, Italy
|Goals, organization and recent achievements of NCRI Informatics Initiative
National Cancer Research Institute, United Kingdom
|RNBIO: the Italian Network for Oncology Bioinformatics
|Paolo Romano, Marco Crescenzi and RNBIO partners
|Coffee break 5
|Oncology Bioinformatics 2
|Partial Logistic Artificial Neural Networks (PLANNs) for the Flexible Modelling of Censored Survival Data
|Elia M. Biganzoli, Federico Ambrogi, Patrizia Boracchi
|Genetic Algorithms and selection of Artificial Neural Networks for Survival Data
|Federico Ambrogi, Patrizia Boracchi, Elia Biganzoli
|Neural Networks for Mass Spectra Classification: Preliminary Results
|M. Ceccarelli, A. D'Acierno, A. Facchiano, A. Maratea
|Conditional independence under heterogeneity: a case study on cancer biomarkers
|Federico M. Stefanini, Elia M. Biganzoli
|Low Duplicability and Network Fragility of Cancer Genes
|Davide Rambaldi, Federico Giorgi, Fabrizio Capuani, Andrea Ciliberto, Francesca D. Ciccarelli
|Penalized survival analysis in genome expression experiments
|Michela Baccini, Giulia Tonini, Annibale Biggeri
|Free leisure time
Suggested leisure activities
Visit lakeside villas:
Visit the picturesque towns:
- Villa Carlotta, Tremezzo,
- Villa d'Este, Cernobbio,
- Villa del Balbianello, Lenno
- Villa Melzi, Bellagio
- Villa Serbelloni, Bellagio
- Using SKOS to formalize parameter estimation in systems biology
Roberta Alfieri, Luciano Milanesi, Emanuela Merelli, Luca Tesei, Nicola Cannata
- Data recovery and integration from public databases for a Systems Biology analysis of cAMP-PKA and oncogenic Ras pathways
Chiara Balestrieri, Daniela Gaglio, Lilia Alberghina, Marco Vanoni And Ferdinando Chiaradonna
- A Framework for the Analysis of MALDI Mass Spectrometry Imaging Data
Nicola Barbarini, Christian Fuchsberger, Wolgang Wieder, Georg Bartsch, Gü Bonn, Helmut Klocker, Riccardo Bellazzi
- High Performance Computing Infrastructures for Genetic Linkage Analysis
Davide Di Pasquale, Andrea Calabria, Gabriele Trombetti, Alessandro Orro, Luciano Milanesi
- The Human EST Ontology Explorer: a tissue-oriented visualisation system for ontologies distribution in human EST collections
Ivan Merelli, Andrea Caprera, Alessandra Stella, Luciano Milanesi, Barbara Lazzari
- XML-based approaches for the integration of heterogeneous bio-molecular data
Marco Mesiti, Ernesto Jimnez Ruiz, Ismael Sanz, Rafael Berlanga, Giorgio Valentini, Paolo Perlasca, David Manset
- An innovative way to validate the HME classification
Marina Mordenti, Enrico Ferrari
- The BreastCancerDB: a data integration approach for breast cancer research oriented to systems biology
Ettore Mosca, Roberta Alfieri, Luciano Milanesi
- Warehousing of Genomic and Proteomic Information: Integration, Management and Interrogation
Marco Nardelli, Marco Masseroli
- Phenotypic and Genotypic Data Integration and Exploration through a Web-Service Architecture
Angelo Nuzzo, Alberto Riva, Riccardo Bellazzi
- Data management for SNP genotyping technologies
Alessandro Orro, Luciano Milanesi
- Adding Semantics to Scientific Workflows
- Ancestry correction in genome-wide association studies using stratification methodologies
Erika Salvi, Alessandro Orro, Guia Guffanti, Fabio Macciardi, Luciano Milanesi
- CNET: an algorithm for Reverse Engineering of Causal Gene Networks
Francesco Sambo, Barbara Di Camillo, Gianna Toffolo
- A Perl procedure for protein identification by Peptide Mass Fingerprinting
Alessandra Tiengo, Nicola Barbarini, Sonia Troiani, Luisa Rusconi, Paolo Magni
Presentations of invited lectures, oral communications, selected posters and tutorials will be available in a separate file
after the conclusion of the workshop.
Call for Papers
Type of contributions
Subscribe mailing list
How to reach
Institutes & Societies
Interdepartmental CNR-BIOINFORMATCS project
National Research Council