Morning Session |
AM1 |
Ontology Development for the Semantic Web: Protégé's Web Ontology Language (OWL) interface |
AM2 |
Integration of Genomic, Biomedical and Clinical Databases and Tools to Enable Genomic Medicine |
AM3 |
Computational analyses across the BioCyc collection of Pathway/Genome Databases |
AM4 |
Introduction to Statistics for Bioinformatics |
AM5 |
Introduction to Molecular Visualization |
Afternoon Session |
PM6 |
Ontologies for Biomedicine – How to make and use them |
PM7 |
Computational Analysis of Tiling Arrays for ChIP-chip on Mammalian Genomes |
PM8 |
Installing, Configuring and Using GMOD Web-based Genome Visualization Tool (GBrowse) |
PM9 |
Improvements
in automated identification of protein sequences and post-translational
modifications from tandem mass spectroscopy data |
PM10 |
Systems Biology of Host-Pathogen Interactions and Microbial Communities |
AM1
Ontology Development for the Semantic Web: Protégé's Web Ontology Language (OWL) interface
Daniel Rubin, MD, MS, Kaustubh Supekar, Stanford Medical Informatics
The Semantic Web is a potentially promising technology that makes
ontologies accessible and connectable to data and computer processing
in a decentralized manner. The Semantic Web may have particular
applicability to the life sciences, a field that is rich in the
diversity of existing bio-ontologies and the vast amounts of data
becoming available in cyberspace. Protégé OWL is an open source tool
created to support ontology development for the Semantic Web. It is a
plug-in extension to the Protégé ontology development platform. Protégé
OWL allows users to edit ontologies in the Web Ontology Language (OWL)
and to use description logic classifiers to maintain consistency of
their ontologies. Protégé OWL can also assist developers of intelligent
applications, because many of the problem-solving tasks they seek to
automate can be construed as classification tasks, and thus they can
use Protégé OWL to enable these applications. Being integrated with
Protégé, Protégé OWL allows users to exploit Protégé’s core features
and services such as graphical user interfaces, a variety of storage
formats, and data acquisition and visualization tools. In this
introductory tutorial, we will demonstrate the fundamental features of
Protege OWL to help users develop and manage OWL ontologies. We will
also show how to use automatic classification to help content authors
create robust ontologies. We will motivate our tutorial by
demonstrating the exciting possibilities of these technologies with a
real-world example Semantic Web application in the biomedical domain
that was engineered using OWL, automatic classification, and the
Protégé OWL platform.
Expected goals and objectives:
1. Introduce bio-ontologies and Semantic Web technologies, particularly focused on the biomedical domain.
2. Provide an overview of open source tools for creating ontologies and Semantic Web applications (Protégé and Protégé-OWL)
3. Demonstrate how to use Protégé-OWL in concreting a simple Semantic Web application ontology step-by-step
4. Familiarize participants with active efforts in the community to
apply Semantic Web technologies to biomedical problems (such as the
National Center of Biomedical Ontology and the W3C Semantic Web for
Life Sciences group).
Intended audience:This is an introductory tutorial. Some conceptual understanding of biomedicine would be helpful.
Daniel Rubin:
My research focus since completing the biomedical informatics program
at Stanford has been in developing and exploiting knowledge
representation approaches in biomedicine. I have previously been a
participant in the PharmGKB project, during which time I developed
ontologies for representing genetic and pharmacokinetic knowledge to
enable browsing, searching, and analyzing pharmacogenetics data.
Subsequently, I coordinated a project at Stanford to create a virtual
human by linking ontologies to segmented images of the visible human
and mathematical simulation models to predict the physiological
consequences of penetrating injury. A key aspect of this work was using
OWL as a representation formalism to support automated computer
reasoning in this task. Currently, I am the Executive Director of
the National Center of Biomedical Ontology (http://bioontology.org), a
National Center of Biomedical Computation funded under the NIH
roadmap. We are collaborating with biomedical researchers to
develop and use biomedical ontologies to streamline discovery in
contemporary large-scale science projects.
Kaustubh Supekar
has extensive background in creating Semantic Web applications.
He is currently working on a project with the Biomedical Informatics
Research Network (BIRN) to use ontologies to support data integration
and analysis in electron microscopic images of neural tissue.
Experience: We
have individually taught courses and performed demonstrations at
Protégé workshops that have been given over the past year as well as at
scientific meetings such as the Semantic Web conference and ISMB.
We will be doing a tutorial on the Semantic Web at the Semantic
Technology Conference in March 2007. Daniel Rubin has given guest
lectures in many Stanford courses and seminars, including BMI 210, 211,
and 212, in Doug Brutlag’s bioinformatics class, in the SMI and
bioinformatics short courses, and in the database seminar series.
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AM2
Integration of Genomic, Biomedical and Clinical
Databases and Tools to Enable Genomic Medicine
Atul Butte, MD, PhD, Stanford Medical Informatics
The next step for genetics and genomics is associating such data with human phenotypic data,
and the largest source of phenotypes is in the clinical record. We will cover the various types
of clinical data, the ontologies currently used in medicine, and how these can interface with
genetics, genomics, and proteomics.
Expected Goals and Objectives: Over the past 10 years, high-dimensional investigations related to human disease have
expanded considerably in breadth and depth. The breadth of such investigations spans at least
30 types of high-dimensional measurement and experimental modalities, including RNA
expression microarrays, DNA sequencing, protein identification, mutagenesis, RNA
interference, and many others. The depth of such investigations has grown to include
measurements of entire sets of transcripts, proteins, and genomes. Most recently, these
technologies have started to be applied to the study of many diseases. However, application
of these measurement modalities and related algorithms to clinical data presents its own set of
challenges, including the paucity of cases, the difficulty in representing and measuring the
effects of the environment on people, the distinction between diseases and phenotypes, and
even the legal restrictions against additional data acquisition. These are all actively studied
challenges, and proposed solutions to these are commonly viewed as cutting-edge.
In the US, the NIH Roadmap for Medical Research has led to multiple funding opportunities
for bioinformaticians to collaborate with clinical researchers to promote and facilitate
translational research. For example, the Clinical and Translational Science Awards, the
replacement for the General Clinical Research Centres, require a strong biomedical
informatics collaborative component. This tutorial is a timely one, as bioinformatics
professionals are being increasingly asked toparticipate in, and even organize, these groups.
Topic Area:
• Medical Bioinformatics: 40%
• Transcriptomics: 10%
• Proteomics: 10%
• Sequence Analysis: 10%
• Database and Data Integration: 20%
• Ontologies: 10%
Tutorial Outline:
1. Review (45 minutes)
The first part of the tutorial will be the most didactic. It will include a review of:
- The biology behind the measurement modalities: teaching just enough biology to
understand the various measurement modalities: polymorphisms, haplotypes,
proteomics, gene expression, metabolomics, protein-protein interactions, and RNAi.
- Nature and format of expression, polymorphism, and proteomic data. Emphasis on the
different characteristics of these measurement systems, including noise profiles, and
how normalization of the data sets can be approached (and common mistakes).
- Description of the most frequently used analysis techniques for each measurement
type. Strengths and weakness will only be summarized. The questions for which each
might be better suited will be addressed, as well as reasonable approaches to the
interpretation of results generated by these techniques.
- An overview of the most commonly used structured vocabularies, taxonomies, and
ontologies used in clinical medicine and research.
2. Clinical reasons to interface genomic and clinical data (45 minutes)
The second part of the tutorial will focus on motivating why genomic and genetic data should
be interfaced with the clinical record, for the direct benefit of patients. What kind of clinical
tools would such an interface enable?
- What is disease and genetic predisposition to disease?
- How is clinical genetic and genomic data collected and used today? Examples given
of specific institutions and their practices.
- How is genetic information currently used in all medical specialties?
- How genetic data is used to guide therapy, and how clinical genetic tests are found
- Differences between research and clinical genomic and genetic data; CLIA approval
in the United States.
3. Research goals in interface genomic and clinical data (45 minutes)
The third part of the tutorial will focus on specific hypotheses and questions that can be asked
if genetic and genomic data were better integrated.
- How do we interface genomic and clinical data to study patient disease-free status and
survival? How do we interface genomic and clinical data to study a disease and
potentially find clinically relevant subtypes of a disease?
- Where do animals and cell lines fit in? How can the study of these directly enable
clinical diagnosis?
- How is genomics being used to identify potential drug targets?
- What are the categories of biomarkers and why are they useful? What are the unique
challenges in applying supervised machine learning techniques to clinical questions,
in terms of prior probabilities of disease?
- Can we relate genomic and clinical data through the diagnosed and studied diseases in
both domains?
2. Participants will be encouraged to explore how they might use these techniques in domains
that are of interest to them, through questions and answers throughout the tutorial. We will
leave 15 minutes for discussion at the end of the tutorial as well.
Tutorial level:
Biomedicine: Advanced
Computer Science: Basic
Statistics: Basic
Programming: Basic
Prior knowledge required:
Basic statistics (such as t-tests), basic biology (such as DNA, RNA, synthesis and function of
proteins), some awareness of high-dimensional measurement systems in molecular biology
(such as genetics, microarrays, mass spectrometry, or sequencing), and an interest in medical
or clinical problems.
Intended audience:
The intended audience includes academic faculty or professionals
setting up bioinformatics facilities and/or relating these to clinical
data; health information professionals responsible for clinical
databases or data warehouses and tying these to researchers;
informaticians, clinicians, and scientists interested in genetics,
functional genomics, and microarray analysis; and students.
Atul Butte, MD, PhD
is Assistant Professor in Medicine (Medical Informatics) and Pediatrics
at the Stanford University School of Medicine, and a board-certified
pediatric endocrinologist. He obtained his B.A. Computer Science
(Honors) from Brown University: worked several stints as a software
engineer at Apple Computer (on the System 7 team) and Microsoft
Corporation (on the Excel team). Dr. Butte obtained his M.D. from Brown
University School of Medicine: worked as a research fellow at NIDDK
through the Howard Hughes/NIH Research Scholars Program, studying
insulin receptor signal transduction. His Ph.D. is in Health Sciences
and Technology from the Medical Engineering / Medical Physics Program
in the Division of Health Sciences and Technology, at Harvard Medical
School and Massachusetts Institute of Technology.
Dr. Butte’s laboratory focuses on solving problems relevant to genomic medicine by
developing new biomedical-informatics methodologies in integrative biology.
He has authored more than 25 publications in bioinformatics, medical informatics, and
molecular diabetes. He is co-author of one of the first books on microarray analysis titled
Microarrays for an Integrative Genomics.
His recent awards include the 2006 PhRMA Foundation Research Starter Grant, the 2001
American Association for Cancer Research Scholar-In-Training Award and the 2001
Lawson Wilkins Pediatric Endocrine Society Clinical Scholar Award.
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AM3
Computational analyses across the BioCyc collection of Pathway/Genome Databases
Peter Karp, PhD, Bioinformatics Research Group
Institution at SRI International
BioCyc is a collection of 205 pathway/genome databases for most
organisms whose genomes have been completely sequenced. It is a large
and comprehensive resource for systems biology research. We expect that
many bioinformatics and computational biology researchers will be
interested in computing with BioCyc to address global biological
questions, such as studying the phylogenetic distribution and evolution
of metabolic pathways. The goal of this tutorial will be to provide
researchers with the information they need to perform global analyses
of BioCyc. The tutorial will cover the methodologies used to create
BioCyc, a description of the complex database schema and ontologies
that underly BioCyc, and descriptions of the APIs that are available to
query BioCyc. The tutorial will also present the Pathway Tools semantic
inference layer, which is a library of commonly used queries that we
have encoded to save researchers time. We will also consider common
stumbling blocks and misconceptions that can lead to misinterpretations
of the data.
Expected outcomes and goals
Students will learn how to perform computational analyses across
the large BioCyc collection of Pathway/Genome Databases.
Prequisites: Basic familiarity with programming and databases and basic familiarity required with concepts
in biology and metabolic pathways,
genetics,
structural biology algorithms.
Teaching experience and background:
Dr. Karp has given several tutorials at past ISMB meetings, and many
lectures at conferences and in classrooms.
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AM4
Introduction to Statistics for Bioinformatics
Michael G. Walker, Ph.D., President, Walker Bioscience
This tutorial will introduce the most widely used statistical methods
for bioinformatics, including descriptive statistics, probability,
analysis of variance, discriminant analysis and cluster analysis.
Examples will be drawn from biomedical cases, including gene expression
microarray data, and illustrated using Excel and other analysis
packages.
Background and Experience: [Added
by CSB Tutorial Chair] Dr. Walker has extensive teaching experience; he
has taught introductory statistics in both academia and industry. He
has won world-wide praise for excellence in teaching and developing
lecture content which focuses on essential topics in statistics for
bioinformatics.
Prework 1 and 2 for AM4 Tutorial Attendees.
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AM5
An Introduction to Molecular Visualization
Scooter Morris, Conrad Huang, Pharmaceutical Chemistry, University of California, San Francisco
Projects such as Structural Genomics are providing increasing numbers
of experimental protein and protein-complex structures. Furthermore,
increasing numbers of theoretical models are being predicted from
primary sequence. Biologists have an increasing need to understand and
communicate the structures, functions and relationships between these
protein and protein-complex structures. As a result, molecular
visualization is becoming an important tool for the presentation and
communication of the results of biological experiments and research.
This tutorial will provide a basic foundation for the understanding of
molecular structures through use of visualization tools. Attendees will
learn the basics of molecular visualization and will be provided an
overview of available tools and techniques for visualization, analysis
and modeling of protein structure. To make these concepts more
concrete, attendees will be shown the academic program UCSF Chimera in
more detail, and receive instruction in its features and use. The field
of structural biology is still changing, and new techniques are
continually being developed. Attendees will be shown how they can add
new analysis techniques and their own data to the visualization.
Expected outcomes and goals:
Attendees will learn tools and techniques for molecular visualization
of macromolecular systems. Specific instructions will be given for UCSF
Chimera to provide a basic working knowledge of how to load,
manipulate, analyze and visualize macromolecules.
Prerequisites: Conceptual understanding of programming languages, structural biology
Outline:
- Introduction to Molecular Visualization
- a. Data sources
- b. Representations
- c. Manipulations
- d. Analysis
- e. Modeling
- Available Tools
- a. Visualization
- b. Analytical tools
- c. Modeling tools
- Using UCSF Chimera
- a. Basic features
- b. Comparison with other packages
- c. Concepts
- Scenarios of use
- a. Structure analysis
- b. Sequence-structure relationships
- c. Docking
- d. Publication
- e. Animation
- Extending Chimera
- a. Incorporating user data
-
b. Scripting
- c. Python extensions
Teaching experience and background:
Both instructors have taught in numerous workshops and presented in a
variety of conferences, seminars and tutorials. Both instructors are
also lecturers at UCSF.
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PM6
Ontologies for Biomedicine – How to make and use them
Amar Das, MD, PhD, Nigam Shah, MBBS, PhD, Stanford Medical Informatics, Stanford School of Medicine
Ontologies are becoming essential as the amount and
types of data we handle in the biology domain rises. Simultaneously,
the need to organise, co-ordinate and disseminate ontologies as well as
ontology development methods is now accepted and is evidenced by the
funding of the National Center for Biomedical Ontology (NCBO). Part of
the mission of NCBO is to conduct education and dissemination
activities in the field of biomedical ontologies. Though the need of
using ontologies is widely appreciated, the right manner in which they
should be used and developed is not. Researchers still resort to ad-hoc
methods while using and/or developing ontologies. This tutorial will
provide an overview of the various ways in which ontologies are used in
bioinformatics and biomedicine along with pointers to lesser known but
potentially more rewarding applications of ontologies. This tutorial
will educate the participants on what ontologies are and how they are
currently used as well as outline best practices for their development.
Goals and Objectives: The tutorial will provide an
overview of the current uses of ontologies in bioinformatics and
instruction on ontology design and use. The instruction will be via an
interactive session emphasizing the best practises for ontology design
and use.
Intended Audience: This tutorial will
be aimed at advanced graduate students and active researchers who will
need to use ontologies as a part of their routine research work; either
for interpreting their own data or developing applications that assist
in data integration.
Prerequisites: Familiarity with
concepts in molecular biology such as genes, proteins, promoter, intron
and exon is expected. Basic understanding (about one semester) of
discreet mathematics and programming concepts would be helpful.
Attendance at the "Ontology Development for the Semantic Web" tutorial
(AM 1 session) is HIGHLY RECOMMENDED.
Tutorial Outline:
- Overview of current uses of ontologies in Bioinformatics [40 min]
- As a controlled vocabulary to describe genes and gene products
- The Gene Ontology
- As a data exchange format and for data integration
- MGED, SBML and BioPax as examples
- To define a knowledgebase schema
- BioCyc and Reactome as examples
- For driving natural language processing
- Textpresso as example
- For semantically rich querying of federated databases
- TAMBIS as example
- Creating formal representations of biological processes
- Ontologies – What they are and What they are not [20 min]
- The various meanings of “ontology” from philosophy, computer science and information science will be discussed.
- This
module will clarify the various interpretations of ontology such as
terminologies, taxonomies, application ontologies, depicting
ontologies, upper ontologies as well as explain how the
computer/information science meanings are different - but related to -
the philosophical meaning of the word ontology.
- What ontologies are not
- Ontologies
and ontology representation languages are not adequate to perform
“simulations”. We need to support these activities in biomedicine. How
do we allow for that?
- Basics of Developing ontologies (learn the most common mistakes and the kind of design decisions to make) [75 min]
- Ontology design 101 (The computer science perspective – 30 min)
- When to make a class? When to subclass?
- Choice of the representation formalism.
- What is the level of domain expertise required?
- Ontology design 201 (The philosophy perspective – 30 min)
- Logic and model theory in ontologies
- How
the reasoning used by philosophical ontologists can be helpful in
recognizing and avoiding potential logical mistakes such as use-mention
confusion and circular definitions.
- What are the advantages of being this rigorous?
- Ontology design in practice (The GO perspective – 15 min)
- What mistakes to avoid if starting today?
- Community awareness: Ontology development is a community effort, what are the essentials that everyone should know?
- Wrap up question and answers with discussion [15 min]
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PM7
Computational analysis of Tiling arrays for ChIP-chip on Mammalian Genomes
W. Evan Johnson, Dept. of Biostatistics, Harvard School of Arts and Sciences
Chromatin immunoprecipitation coupled with DNA microarray analysis
(ChIP-chip) has quickly evolved as a popular technique to study the in
vivo targets of DNA-binding proteins at the genome level. Generally,
DNA is crosslinked to proteins at sites of protein-DNA interaction,
sheared into small fragments, and then precipitated by antibodies
specific to the protein of interest. The precipitated protein-bound DNA
fragments are purified, amplified, labeled, and hybridized to tiling
microarrays.
Many
tiling array platforms have now been developed for mammalian genomes,
allowing for the unbiased mapping of transcription factor binding sites
across these genomes. These platforms come in many varieties including
short and long oligos, and one and two channel arrays. Because of the
complex nature of mammalian genomes and the massive amounts of data
produced by the arrays, there are many computational challenges to
dealing with the data produced by tiling arrays. The data are often
quite noisy, so low-level analysis methods must be applied for chip
normalization and probe background adjustment. Additionally, the nature
and amount of data produced by tiling arrays requires innovative
methods to accurately detect binding sites across the genome.
Once
the binding sites have been identified, one can conduct de novo motif
finding using available computational analysis methods to find new
binding motifs or use available tools to search for enrichment of
previously known transcription factor binding motifs, locate areas of
conservation across genomes, and find protein cofactors, target genes
(and their functions), and other elements of the regulatory network of
interest inferred by the binding regions.
Many
well-known analysis procedures will be introduced in this tutorial. We
will also present an example from our research to illustrate
normalization, background adjustment, and identification of
transcription factor binding sites. Finally, we will apply available
web tools to find biologically relevant information from our binding
sites.
Goals, objectives:
This tutorial will briefly introduce a Chromatin ImmunoPrecipitation
(ChIP) on tiling arrays (chip) experiment on mammalian genomes, discuss
the purpose ChIP-chip experiments, and give a detailed description and
demonstration of some of the tools and methods available to analyze of
ChIP-chip data.
Intended audience:
Biologists interested in utilizing ChIP-chip technology for biological
discovery in their labs; Computational Biologists/Bioinformaticians
with collaborators in conducting ChIP-chip experiments or with interest
in analyzing ChIP-chip data.
Prerequisites: Basic understanding of Biology and microarrays; Basic statistics
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PM8
Installing, Configuring and Using the GMOD Web-based Genome Visualization Tool (GBrowse)
Scott Cain, Ph. D. GMOD Project Coordinator, Cold Spring Harbor Laboratory The
Generic Genome Browser (GBrowse) is a web-based, graphical browser for
genomic information that has be adopted for use by over 100
organizations. This tutorial will cover installing and configuring
GBrowse, starting with the basics of how data needs to be formatted and
simple configuration for viewing that data, and moving on to more
complex topics like showing multi-segmented features, protein reading
frames, and genome-wide graphs. This tutorial will also cover the use
of small snippets of perl in the configuration to demonstrate the
considerable versatility of display that GBrowse gives it users.
Intended Audience:
This is an introductory tutorial; attendees should be comfortable
performing simple system administration tasks like stopping and
starting services. If attendees want to follow along "live", they
should have a laptop with GBrowse and prerequisites installed. Please
see http://www.gmod.org/ggb for instructions and downloads.
Scott Cain
is a member of the professional research staff at Cold Spring Harbor
Laboratory and is the GMOD (Generic Model Organism Database) project
coordinator. As coordinator, he has participated in the development of
several GMOD components, including the schema (known as 'Chado') and
related tools and the Generic Genome Browser (GBrowse). Previously he
has taught several tutorials for GBrowse. Scott has taught computer
science and programming courses and is currently on the faculty of the
University of Phoenix. Scott was formerly the lead bioinformatics
developer for the biotechnology company Athersys, Inc.
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PM9
Computational mass-spectrometry advances in the identification of proteins and posttranslational modifications
Nuno Bandeira, Computer Science and Engineering, University of California, San Diego
Tandem
mass spectrometry (MS/MS) is nowadays a fundamental and far reaching
instrumentation technique that enables many different types of
proteomic studies. One of its most important areas of application is
that of peptide and protein identification and several mainstream
identification tools have been based on the concept of matching MS/MS
spectra against databases of protein sequences (e.g SEQUEST and
Mascot). However, these tools face a severe bottleneck when attempting
identification of unexpected post-translational modifications and
provide no solutions when the putative protein sequences are not known
in advance. This tutorial will focus on recent mass spectrometry-based
identification approaches based on the combination of unidentified
MS/MS spectra. These approaches have been shown to be widely applicable
to everyday MS/MS samples and to substantially improve the quality of
de-novo sequence reconstructions and the number of identified
post-translational modifications. The simplest example of this type of
approach is clustering – combining different MS/MS spectra from the
same peptide. This tutorial will additionally cover the combination of
spectra from other types of related peptides like partially overlapping
peptides or different variants of the same peptide (e.g. a peptide P
and its modified variants P*, P#, P*#, etc.). The latter have also
provided the foundations of a recently proposed database search
approach that never compares a spectrum against a database (Bandeira et
al., RECOMB 2006). Participants in this tutorial can expect to gain
both a conceptual understanding of the algorithms and techniques
developed in this field and a practical introduction to the usage of
related novel tools.
Expected outcomes and goals:
This tutorial will focus on recent tandem mass spectrometry
(MS/MS)-based approaches to the identification of proteins and
post-translational modifications. We will cover several new promising
techniques that have been proposed based on the combination of
unidentified MS/MS spectra followed by additional interpretation steps
and show how these can be used to overcome the difficulties faced by
currently available mainstream tools. Participants in this tutorial can
expect to gain both a conceptual understanding of the recent algorithms
and techniques developed in computational mass spectrometry and a
practical introduction to the usage of related novel tools.
Prerequisites: Conceptual
understanding of programming languages, some experience with statistics
and algorithms, conceptual understanding of molecular biology.
Nuno Bandeira is
a 4-year Ph.D. student at the department of Computer Science and
Engineering (CSE) of the University of California, San Diego (UCSD).
Over the last 3.5 years he has worked with Prof. Pavel Pevzner on the
computational analysis of tandem mass spectrometry data, resulting in
the development of published novel approaches to the identification of
proteins and post-translational modifications. Before coming to UCSD,
he focused on computer science techniques and studied their application
to the biomedical sciences since 1999.
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PM10
Systems Biology of Host-Pathogen Interactions and Microbial Communities
Christian V. Forst, Bioscience Division, Los Alamos National Laboratory
Unlike
traditional biological research that focuses on a small set of
components, systems biology studies the complex interactions among a
large number of genes, proteins and other elements of biological
networks. Host-Pathogen Systems Biology examines the interactions among
the components of two distinct organisms, either a microbial or viral
pathogen and its animal host or two different microbial species in a
community. With the availability of complete genomic sequences of both
host and pathogens, together with breakthroughs in proteomics,
metabolomics and other experimental areas, the investigation of
host-pathogen systems on a multitude of levels of detail comes within
reach. Mathematical models of the immune system describing
host-pathogen interactions have a long history in mathematical biology.
Nevertheless, the continuing and accelerating emergence of new
biological threats requires the development of new and innovative
approaches to combat them.
Intended audience:
The tutorial is aimed at an audience with bioinformatics/computational
biology background and with interest in systems biology. A significant
part of host-pathogen interactions involves some aspect of the immune
system, thus a background in immunology is useful.
Tutorial Level: Intermediate; basic knowledge on immunology is useful; bioinformatics, computational/theoretical biology background required
Goals and Objectives:
The primary goal of this tutorial is to provide the audience with a
hands-on guide to network biology and its application in systems
biology of one and two-component systems. Systems biology is a still
rather young research area with fast development.
A section of the tutorial hand-outs will serve as a reference to web-sites and will provide a glossary, respectively.
Outline of the Tutorial
1. Introduction
(a) A brief overview of the immune system with emphasis on innate immunity
(b) Host-pathogen systems and “hijacking” of the host by pathogens
(c) Quorum sensing, quorum jamming and microbial communities
2. A word on models and scales
3. Bottom-up approaches
(a) Network biology and response networks
(b) A “true” metabolic host-pathogen network
(c) A two-microbe system
(d) The -phage
(e) Combinatorics of immune receptor signaling networks
(f) Immune system models
4. Top-down approaches
(a) Hierarchical models, The Physiome Project and PhysioLab
(b) Cardiac biosimulation and Asthma PhysioLab
5. Conclusion and Outlook
(a) Hybrid approaches
(b) Full scale in silico system models
Christian Forst is
a staff member in the Bioscience Division at Los Alamos National Lab
involved in a research effort on Computational Biosystems. His group is
committed to Network Biology and Network Genomics, the analysis of
genomes in the context of biological networks, their construction,
inference and regulation. In this context he is interested in the
genomic foundation of networks within a single organisms as well
between organisms as, for example, in host-pathogen interactions. He is
also dedicated to network analysis and the identification of Response
Networks in large biological networks that represent responses to
generic stress and specific drug treatment, differential network
expression analysis during different drug treatments as well as network
analysis and proteomics studies. A review paper on Host-Pathogen
Systems Biology is in press with the journal Drug Discovery Today. His
previous research areas include comparative network genomics,
theoretical/computational analysis of molecular evolution, the specific
properties of genotype-phenotype maps necessary for the success of
molecular evolution and the evolutionary dynamics of entities within an
evolving auto-catalytic reaction network of species with distinct
genotype-phenotype relationships. Dr. Forst was trained as a chemist
and have a background in dynamical systems, complex dynamics,
optimization in combinatorial landscapes, graph-theory,
sequence/context analysis, whole genome annotation, network
construction, gene-expression analysis and phylogeny. He teachs two
summer courses, one on bioinformatics and one on systems biology at the
Los Alamos Summer School. He taught a tutorial on Network Genomics and
Systems Biology at the ISMB 2001. An early version of this tutorial has
been presented at the PSB 2004. An improved version has been taught at
the ISMB 2004 .
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