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Immediately following CHI's Lab-on-a-Chip and Microarrays, January 14-16, 2002
Corporate Sponsors: Corporate Support:
Official Publication:
Sponsoring Publications:
BioArray News
Bioinform
Genome Research
Lab on a ChipWeb Partners:
Genome Biology
Lab-on-a-chip.comSponsoring Society:
International Society for Computational BiologySCIENTIFIC ADVISORY COMMITTEE AND SESSION CHAIRS
Dr. Roland Eils, German Cancer Research Center (DKFZ)
Prof. Peter Ghazal, University of Edinburgh
Dr. Heiko Mueller, Pharmacia Corporation
Dr. Christos Ouzounis, European Bioinformatics Institute
Dr. Thomas Werner, GSF-National Research Center for Environment and Health and Genomatix Software GmbHSTATISTICAL ANALYSIS OF MICROARRAY DATA
Advanced Methods for the Analysis of Microarray Data
Computational Amplification Platform for Expression Microarrays
Key Considerations in Microarray Data Analysis
Automated Image Analysis for Array Hybridization Experiments
Map to Pitfalls on the Road to DiscoveryCOMPUTATIONAL CHALLENGES IN GENOMIC AND PROTEOMIC DATA
Specific Protein Family Databases for Improved Annotation
Generating a Covering Set of Protein Family Profiles
Clustering Large Protein Databases
Bioinformatics and Data Integration
Functional Characterization of Post-genomic DataENGINEERING PROTEIN NETWORKS
Bioinformatics of Protein Interaction Maps
Predicting Protein-Protein Interactions from Primary Structure
Bridging the Gap between Sequence and Function
Elucidating the Functional Context of Genes
Mining Literature for Protein-Protein Interactions
Unraveling Genomewide Networks of Transcriptional Repression
Integrated Resource for Protein Families, Domains, and Functional SitesPATHWAY INFORMATICS
Evolution of an Integrative Approach to Post-genomic Research
Customized Chip Design for Prediction of Pathway Interaction
Analysis and Understanding of Biological Pathways
Expression Analysis of the pRB Tumor Suppressor PathwayLINKING GENE EXPRESSION WITH CLINICAL OUTCOMES
Getting the Most Out of a Gene Expression Experiment
New Insights in the Clinical Impact of Molecular Genetic Data
Molecular Technologies Used for the Diagnosis and Prediction of Clinical Outcome
Analysis of the Genome in Normal and in Cancer Mammary Tissue Using Proteomics
Quantitative Reverse Engineering of Gene Networks
Genetic Regulatory Networks
Haplotype Variability of the Human GenomeWednesday, January 16
12.00m Registration
Statistical Analysis of Microarray Data
13.30 Chairperson's Remarks
Prof. Peter Ghazal, Director, Genomic Technology & Informatics Centre, University of Edinburgh13.40 Advanced Methods for the Analysis of Microarray Data
Dr. Sorin Draghici, Project Manager, BioDiscovery Inc.
A common purpose of microarray experiments is to find genes that are differentially regulated. Commonly, this is done by looking for genes that have expression levels at least two standard deviations away from the mean or having an unusual ratio between experiment and control. Is this justified? Is this correct? Can the results of such an analysis be trusted? This presentation will address such questions and present alternate methods for defining differential regulation.14.10 Quantum Resonance Interferometry-Based Computational Amplification Platform for Expression Microarrays
Dr. Doug Lane, President and Chief Executive Officer, ViaLogy Corporation
Expression amplification middleware for enhancing the detection and quantitation of hybridized microarray signals by orders of magnitude over current techniques is presented. The technological principle underlying quantum resonance interferometry is analogous to speckle interferometry and active radar imaging, which have enabled dramatic improvements in ultraweak signal imaging and spectral informatics over the past decade. Specifically, gene expression amplification is achieved by inducing "interference" between a mathematically transformed, hybridized, digitized microarray output and an external designer stimulus. In a molecular diagnostic context, the external synthetic stimulus is designed based on a mutation or a target sequence of interest. Alternately, in a gene expression application for drug target discovery, the microarray "inherent noise response" statistics are used to construct the external synthetic stimulus. This presents an attempt to get an over 100- to 10,000-fold quantitative improvement in detection sensitivity over classic state-of-the-art bioinformatics. The resulting enhancement is enabling to microarray-based drug target discovery and diagnostic applications. Expression detection performance results comparing classic bioinformatics techniques and quantum interferometry will be presented for several classes of standard and spotted arrays.14.40 Poster and Exhibit Viewing, Refreshment Break
15.30 Key Considerations in the Microarray Data Analysis Mill Wheel
Dr. Jason Goncalves, Chief Scientific Officer, Iobion Informatics
Microarray data analysis is an iterative process that involves data validation and quality control at each computational iteration-it is a classical data analysis mill wheel. The main steps include (1) defining the experiment's aim, scope, and field; (2) describing the biological sources and experiment protocols; (3) loading, filtering, and normalizing microarray data; (4) verifying data quality at the level of each hybridization and spot; (5) generating qualified aggregate data sets and mining them using multivariate data analysis techniques; and (6) exchanging experiment results with peers. Software that supports the data analysis mill wheel presents the scientist with analytical tools of unprecedented flexibility and power. In this presentation I will discuss the key aspects of iterative analysis of microarray data using Iobion GeneTraffic Microarray Data Analysis software.16.00 Title to be Announced
Speaker to be Determined16.30 Panel Discussion
17.00 Reception (sponsored by Cambridge Healthtech Institute)
18.15 Close of Day One
Thursday, January 17
7.30am Poster and Exhibit Viewing and Light Continental Breakfast
Computational Challenges in Genomic and Proteomic Data
8.30 Chairperson's Remarks
Dr. Christos Ouzounis, European Bioinformatics Institute8.35 Generating a Covering Set of Protein Family Profiles
Dr. Andreas Heger, Research Fellow, Structural Genomics Group, European Bioinformatics Institute
Picasso is a global classification of protein sequences. The map of protein space resulting from all-against-all sequence comparison is partitioned into clusters representing protein domain families. Each domain family is defined by a unifying motif in explicit multiple alignments. Each protein sequence is completely covered by a set of such domains. I will present the concepts and validation of Picasso and illustrate its uses, e.g., for target selection in structural genomics efforts and navigating the network of domain co-occurrences.9.05 Clustering Large Protein Databases
Dr. Weizhong Li, The Burnham Institute
Sequence clustering replaces groups of similar sequences in a database with single representatives. A method that can efficiently cluster sequences with medium or high homology in large protein databases like the Non-Redundant (NR) database was developed. With the clustered databases, significant gains were obtained in both search speed and homology detection.9.35 Poster and Exhibit Viewing, Refreshment Break
10.15 Automated Protein Structure Clustering : Comparison to Manual Classification, and Application to Remote Homology Detection
Dr. Wolfram Altenhofen, Director of Scientific Services, Chemical Computing Group AG10.45 Extending Traditional Query-Based Integration Approaches for Functional Characterization of Post-genomic Data
Leo Laroco, IBM Corporation
Integration of a diverse set of databases and applications, including in-house datastores, internet web sites, and the results of runtime analysis, is needed to identify and characterize regions of functional interest in genomic sequence. Many data integration systems, while engineering tours-de-force, fall short of their potential in adding value to scientific investigations. This talk will describe our efforts in applying data integration technology to questions of real pharmaceutical interest, highlighting sample queries such as: "Find homologues in genomic sequence to all novel genes cloned and reported in the scientific literature within the past three months that are linked to the MeSH term 'neoplasms'".11.15 High Throughput, Enterprise Level, Scalable Informatics Products and Services for the Post-Genomics Era
Dr. Deepak Thakkar, Senior Director, Marketing, Silicon Genetics
A case study outlining the use of this technology for genetic marker identification will be presented. In this particular study GeneSpring™ was used to identify predictive genes or "marker genes" responsible for epithelial mesenchymal transition (EMT), a change associated with cancer metastasis. The study started with almost 16,000 genes on a microarray and using the Silicon Genetics informatics solution, a well defined set of marker genes for further studies, was identified. Silicon Genetics' GeneSpring™, GeNet™ and MetaMine™ solutions are being used by over 4000 scientists globally and several such examples, demonstrating the use of our proprietary technology portfolio for gene identification, pathway prediction, large data set comparison, data sharing, etc will be presented.11.45 Panel Discussion
12.15 Lunch (on your own)
Engineering Protein Networks
13.45 Chairperson's Remarks
Dr. Thomas Werner, Chief Executive Officer, Genomatix Software GmbH; GSF-National Research Center for Environment and Health13.50 The Bioinformatics of Protein Interaction Maps
Dr. Vincent Schächter, Director of Bioinformatics Research, Hybrigenics
In the first part of the presentation, we will describe the algorithms and the software platform designed by Hybrigenics to transform raw experimental results from a high-throughput, two-hybrid platform into large-scale, reliable Protein Interaction Maps (PIMs) with interaction domain information. The second part will describe strategies to integrate PIMs with other categories of functional information and the use of the PIM of a reference organism to predict a PIM in a target organism.14.20 Predicting Protein-Protein Interactions from Primary Structure
Dr. David A. Gough, Department of Bioengineering, University of California, San Diego
An ambitious goal of proteomics is to elucidate the structure, interactions, and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. Our research has been focused on the application of machine learning methods to recognize and predict protein-protein interactions based solely on primary structure and associated physiochemical properties. This talk will summarize the predictive methodology and present bioinformatic results obtained on both heterogenous and organism-specific protein interaction data sets. A systematic approach to integrate computational and experimental interaction data will be presented. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.14.50 Using Protein Structure to Bridge the Gap between Sequence and Function
Dr. Ben Hitz, Scientific Applications Manager, ProCeryon Biosciences Inc.
Interpretation of genomic data in terms of structure and function, or "annotation," is an absolute requirement for understanding the biology behind both healthy and disease states of organisms. Fold Recognition exploits the fact that structure is more conserved than sequence in evolution. Sequence-structure alignments, coupled with functional data, allow connections to be made between known structures and sequences of unknown function, even if there is no significant sequence similarity. Such connections are a much richer source of information than pure sequence alignments and provide the highest quality in silico annotations.15.20 Using Coregulation to Elucidate the Functional Context of Genes
Dr. Thomas Werner, Chief Executive Officer, Genomatix Software GmbH; GSF-National Research Center for Environment and Health
Genes in the human genome act in concert with other genes, and the biological function of a gene usually depends on those other genes. There are two types of interacting genes that constitute the functional context of a gene. Either proteins come into direct physical contact or genes resp. gene products interact indirectly like enzymes in a metabolic pathway or compounds of signaling cascades. The latter type of functional context cannot be elucidated from the amino acid sequence as it is not an intrinsic feature of the proteins. However, genes coordinated functionally are often coexpressed at least in certain cells/tissues. This coexpression is reflected by promoter and/or enhancer substructures shared by such genes. This allows detection of potential candidates for the functional context of a given gene by promoter analysis, as will be shown in examples.15.50 Poster and Exhibit Viewing, Refreshment Break
16.30 Mining Genomes and Literature for Protein-Protein Interactions
Dr. Edward M. Marcotte, Assistant Professor, Department of Chemistry and Biochemistry, Institute of Cell and Molecular Biology, University of Texas, Austin
New algorithms, such as the calculation of phylogenetic profiles, Rosetta Stone proteins, and gene neighbors, analyze information embedded in genomes to reveal the relationships between the genes of an organism. Such algorithms analyze not the sequences of the genes but the contexts in which they occur, thereby revealing functions and interactions for many genes of previously unknown function. Combining data from such algorithms with knowledge of known protein interactions extracted from the biological literature allows us to automatically reconstruct large-scale protein and gene interaction networks encompassing many of the genes in a genome.17.00 Unraveling Genomewide Networks of Transcriptional Repression
Dr. Frank Pugh, Associate Professor of Biochemistry and Molecular Biology, Department of Biochemistry and Molecular Biology, Pennsylvania State University
A network of transcriptional repressors keeps the genome in a quiescent state, unless directed otherwise by transcriptional activators. We have employed DNA microarray analysis in the yeast Saccharomyces cerevisiae to peel back layers of repression and reveal the mechanisms by which each repressor exerts control over gene expression. Surprisingly, our findings indicate that chromatin is highly accessible and that a significant amount of global repression is targeted directly at the general transcription machinery by preventing it from assembling at permissive chromatin.17.30 The InterPro Database, an Integrated Documentation Resource for Protein Families, Domains, and Functional Sites
Dr. Margaret Biswas, Scientific Curator, European Bioinformatics Institute
InterPro is an integrated knowledge base for protein families, domains, and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam, ProDom, and SMART database projects. Each InterPro entry includes a functional description, annotation, and literature references and links back to the relevant member database(s). Each InterPro entry lists all the matches against SWISS-PROT + TrEMBL. InterPro is accessible for sequence- and text-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to Interhelp@ebi.ac.uk.18.00 Panel Discussion
18.30 Close of Day Two
Friday, January 18
8:00am Poster and Exhibit Viewing and Light Continental Breakfast
Pathway Informatics
8:30 Chairperson's Remarkss
Dr. Heiko Mueller, Department of Pharmacology, Pharmacia Corporation8:35 Evolution of an Integrative Approach to Post-Genomic Research
Prof. Peter Ghazal, Director, Genomic Technology & Informatics Centre, University of Edinburgh
New genomics technologies enable high-throughput analysis of a huge spectrum of genetic content, gene expression, and protein activity. Accordingly, biology has become a rapidly expanding domain of information science. In this context, the integration of the qualitative and quantitative measuring technologies with appropriate statistical and computational systems is a fundamental challenge. I will discuss our collaborative efforts to evolve new biologic and informatic structures for accelerating a systems-level understanding of biology.9:05 Pathways as a Conceptual Hub in Computational Biology
Dr. Reinhard Schneider, Chief Information Officer, LION bioscience AG
Biological systems are defined by the genes and gene products encoded in the genome. These genes cooperate in an organized manner, and form regulatory circuits and interaction pathways. Methods to identify, navigate and query those systems and the various facts and sources will become a critical component of research planning and information systems.9:35 Poster and Exhibit Viewing, Refreshment Break
10:00 Expression Analysis of the pRB Tumor Suppressor Pathway
Dr. Heiko Mueller, Department of Pharmacology, Pharmacia Corporation
We have used Affymetrix oligonucleotide microarrays to identify genes that are regulated by the pRB tumor suppressor pathway. Gene expression patterns were determined in cell lines with inducible E2F1, E2F2, E2F3, p16, and pRB. Part of this work has revealed a direct link between cell cycle regulation and apoptosis via Apaf1 as well as a first glimpse at the range of genes that are subject to regulation by E2F. The talk will address ways to read complex gene expression patterns in order to derive hypotheses about the biology of the pathway under study and will also address the question of whether genes that are regulated by the pRB tumor suppressor pathway are deregulated in cancer.10:30 Panel Discussion
Linking Gene Expression with Clinical Outcomes
11.00 Chairperson's Remarks
Dr. Roland Eils, Intelligent Bioinformatics Systems Division, German Cancer Research Center (DKFZ)11.05 A Database Approach for Getting the Most Out of a Gene Expression Experiment
Dr. Gregory Grant, Postdoc, Computational Biology and Informatics Laboratory (CBIL), University of Pennsylvania Center for Bioinformatics
RNA Abundance Database (RAD) contains gene expression experiments from array-based and SAGE gene expression experiments. RAD follows MGED guidelines in using controlled vocabularies/ontologies, storing raw and processed data, and storing full experimental details. RAD is linked to a sister database, GUS (Genomics Unified Schema), that provides annotation and sequence for the genes under study. The PlasmoDB (http://plasmodb.org) and EPConDB (http://www.cbil.upenn.edu/EPConDB) web sites allow queries taking advantage of this linkage.11.35 New Insights in Pathomechanism and Clinical Impact of Molecular Genetic Data by Intelligent Data Mining
Dr. Roland Eils, Phase-It Intelligent Solutions AG, and Division „Intelligent Bioinformatics Systems", German Cancer Research Center (DKFZ)
Traditionally, classification of complex genetic diseases such as cancer has been performed on the basis of nonmolecular considerations. It has been generally accepted that some patients grouped into a given category will have a certain survival prognosis and response to a particular therapy. To link complex and heterogeneous molecular genetic data with clinical parameters, we have developed a generic data mining framework, which was applied for the classification of B-Cell chronic lymphocytic leukemia (B-CLL) patients into genetic risk groups. This knowledge-driven framework is an important step forward for our capabilities to study complex functional relationships between molecular genetic and clinical data.12.05 Lunch (on your own)
13.15 Cytogenetic and Molecular Technologies Used for the Diagnosis and Prediction of Clinical Outcome
Prof. Peter Lichter, German Cancer Research Center (DKFZ)
Summary unavailable at time of printing.13.45 Poster and Exhibit Viewing, Refreshment Break
14.00 Analysis of the Genome in Normal and in Cancer Mammary Tissue Using Proteomics Technologies
Dr. Walter B. Battistutti, Laboratory Scientist, Department of Gyneco-Pathology, University Hospital, Vienna
Now that we have completed mapping the human genome, the logical next step is to determine the products of the individual genes. The expanding knowledge of the genome (DNA) and its macromolecular products (RNA, protein) is beginning to reveal the cancers that can be linked to specific alterations in the molecular process of affected cells and tissues. Knowledge of the proteome has been greatly enhanced thanks to microdissection in association with the following techniques-Microarray, 2-D, and MALDI. A major advantage of proteomics over genomics is the ability to analyze post-translational modification. Areas where proteomics can make an impact in functional genomics are now becoming clear.14.30 A Quantitative Method to Reverse Engineer Gene Networks from Microarray Data
Dr. Alberto de la Fuente, Research Associate, Virginia Bioinformatics Institute (0477), Virginia Tech
A quantitative method is proposed to reverse engineer gene networks from large-scale gene expression measurements. The method relies on a specific experimental design by which genes are perturbed one at a time and the effect measured on all other genes. The data obtained from those experiments are then subject to an analysis that is based on the concepts of co-control coefficients and regulatory strengths. The method will be illustrated with in silico examples.15.00 Reverse Engineering of Genetic Regulatory Networks
Dr. Mattias Wahde, Chalmers University of Technology
In this talk I will present a method for reverse engineering of genetic regulatory networks. In addition to describing the method in detail, I will also present some theoretical limitations concerning the applicability of the method to various types of data sets.15.30 Haplotype Variability of the Human Genome
Dr. Andreas Windemuth, Director, Algorithm Development, Genaissance Pharmaceuticals, Inc.
We have investigated the level of DNA-based variation (both SNPS and haplotypes) for over 4,000 human genes. In addition, we have characterized how this variation is distributed in a number of biologically and clinically important ways. First, we have determined how SNPs are distributed in human genes: where they occur relative to various functional regions, levels of variability of human SNPs, pattern of the molecular sequence of SNPs, and how these compare to the corresponding sequence of a chimpanzee. Second, we have determined how these aspects of SNP distribution vary among four human population samples. Third, we have determined the patterns of linkage disequilibrium among SNPs, which, of course, determines the haplotype variability of each gene. In order to connect important clinical variability (e.g., genetic disease or susceptibility, variable drug response) to the DNA variability of human genes, an understanding of these patterns of variability within and among human genes is a fundamental prerequisite.16.00 Panel Discussion
16.30 Close of Conference
CORPORATE SPONSOR BIOGRAPHIES
Agilent Technologies Inc. is a diversified technology company, resulting from Hewlett-Packard Company's strategic realignment into two fully independent companies. Agilent Technologies is a global leader in designing and manufacturing of test, measurement and monitoring instruments, systems and solutions, and semiconductor and optical components. The company serves markets that include life sciences, healthcare, communications and electronics.Agilent's Chemical Analysis Group is a leading provider of Life Science and Chemical analytical measurement and information solutions to scientific laboratories in industry, government and academia. As a high growth technology company, Agilent Technologies is committed to accelerating the pace of disease and drug discovery.
Agilent develops and commercializes established or new technologies that greatly enhance the productivity of life science research. Continually we are improving or adding to those solutions already in place such as lab-on-a-chip application products, liquid chromatograph separations, mass spectrometry characterizations, and networked data systems.
Agilent now proudly introduces the development and commercialization of our latest technologies - DNA microarrays and related bioinformatic software which are beginning to revolutionize the disease and drug discovery process.
Information about Agilent chemical analysis products and services can be found on the Web at www.chem.agilent.com/cag/products/products.html.
Amersham Pharmacia Biotech is a leading global provider of biotechnology systems, products and services used in gene and protein research, drug discovery and development, and biopharmaceutical manufacture.
Our vision is to enable the new era of molecular medicine in which the genetic basis for disease will be better understood, leading to earlier and more accurate diagnosis, and more effective and personalized treatment.
Hotel Information
Swissôtel Zurich
Am Marktplatz Oerlikon
CH-8050 Zurich, Switzerland
T: 41-1-317-3111 o F: 41-1-312-4468
Room Rate: 245 CHF S/D
Cut-off Date: December 14, 2001
Please call the hotel directly to make your room reservation. Identify yourself as a Cambridge Healthtech Institute conference attendee to receive the reduced room rate. Reservations made after the cut-off date or after the group room block has been filled (whichever comes first) will be accepted on a space-and-rate-availability basis. Rooms are limited, so please book early.Airline Information
Great International Travel is offering special rates to Zurich from the
United States. To make your reservations, please email jdunn@greatintltravel.comCall for Posters
Cambridge Healthtech Institute encourages attendees to gain further exposure by presenting their work in the poster sessions. Please fill out the registration form, with the poster title and primary author. To ensure inclusion in the conference CD, a one-page summary must be submitted and registration must be paid in full by December 7, 2001.
Click here for poster instructionsCall for Exhibitors
This meeting will focus on the newest techniques and technologies available for integrating genomic data into a variety of biological pathways. This year, we expect at least 300 attendees consisting of scientific researchers, executives, managers and lab directors from pharmaceutical and biotechnology industries, as well as academic and research institutes to visit the exhibit hall.We strongly encourage any company with services or products related to labchips, microarrays, microarray analysis, protein networks, pathway informatics, clinical trials, and rational drug design to consider sponsoring or exhibiting at this event. Please contact Mike Handy at 781-972-5492 for more information or to reserve a booth. Registrations received by September 28, 2001 can save your company up to $750!
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