Algorithmic Biology: Algorithmic Techniques
in Computational Biology
(1 Jun - 31 Jul 2006)
~ Abstracts ~
An introduction to systems biology
Mehmet M. Dalkilic, Indiana University, USA
Recent availability of not only scores of genomes, but
readily accessible generation and collection of genome-scale
data has allowed scientists to begin asking questions more
broadly on what is often called a "systems level". By
assembling many disparate kinds of local gene-gene
interactions, we can finally start to observe globally how
these interactions work. We hope a more complete picture is
formed about, say, disease development, and even evolution.
And from this holistic perspective, perhaps we can better
understand the local interactions as well. There are
numerous challenges to creating these so-called integrated
networks--assembly, validation, interpretation,
visualization, etc. In this tutorial we will introduce the
participants to applied systems biology by presenting its
constituent parts then delve into an ongoing project using
Drosophila. The tutorial will conclude with identifying
exciting open problems in this area.
I. Introduction and Motivation to Systems Biology
II. Systems Biology Discovery Process and Framework
III. Genome-scale Data
IV. Gene Circuits
V. Organisms
VI. Literature Review
VII. Working Example on Complex Metazoan, Indigene:
Integrated
Discovery in Gene Networks (Drosophila)
VIII. Future Directions of Systems Biology
IX. Summary & Conclusions
An introduction to computational
proteomics and glycomics
Haixu Tang, Indiana University, USA
Hon Wai Leong, National University of Singapore
The success of human genome project has demonstrated the
power of automated analytical technologies
in advancing life sciences. In recent years, because of its
high sensitivity, mass spectroscopy (MS) has become an
essential analytical technology in analyzing proteins,
glycans, lipids and metabolites, and resulted in several new
multidisciplinary fields, proteomics, glycomics and
metabolomics. Proteomics aims to identify the whole set of
proteins inside a cell ('proteome') and to study their
dynamic changes across different physiological conditions.
Similarly, glycomics aims to identify the functional glycans,
sometimes attached to proteins or cell membrane, inside a
cell ('glycome'). In this tutorial, we will introduce the
fundamental concepts of proteomics and glycomics, with a
focus on the bioinformatics approaches to analyzing the data
from these projects, and the open problems in the field.
Lecture 1: Instrumentation and experiment design in
proteomics
Mass spectrumetry instruments
Ionization
ESI
MALDI
Mass spectrometers
Ion trap (IT)
Time of flight (TOF)
Tandem mass spectrometry
LC/MS/MS
2DLC/MS/MS
Peptide fragmentation
Experimental design in proteomics
2D Gel electrophoresis
Shotgun proteomics
Lecture 2: Protein identifition and quantification
Peptide identification
Sequest
Mascot
Prediction of peptide fragmentary spectra
Protein inference problem
Protein quantification
Isotopic labeling
Label-free methods
Lecture 3: Peptide de novo sequencing
Spectrum graph
Learning spectrum graph
PepNovo
NovoHMM
Post translational modifications (PTMs)
Spectrum alignment
Spectrum assembly
Lecture 4: Glycomics and glycoproteomics
Structure of oligosaccharides
Glycan sequencing
Fragmentation of glycans
Glycan sequencing algorithm
Linkage elucidation
Glycoproteomics
Tandem mass spectra of glycopeptides
In-source fragmentation
The role of mathematics and computer
science in molecular biology research
Martin Tompa, University of Washington, USA
What role do mathematicians and computer scientists have
to play in the genome projects that have revolutionized
biology over the past decade? I will try to give some
indication by looking in some depth at two particular
problems in the analysis of biological sequences. One is an
overview of how the human genome was sequenced. The other is
called "phylogenetic footprinting", and is a method for
discovering functional regions of DNA by comparing the DNA
sequences of multiple species.
No prior knowledge of mathematics, computer science, or
molecular biology will be assumed.
