Introduction to Molecular
Systems Biology
Mark S. Lechner, Ph.D.
Drexel University
Aleistar Saunders, Ph.D.
Drexel University

Course will focus on fundamentals of cell biology: information flow from genes to RNA to proteins, genome structure and organization, principles of regulation of genes and proteins, cells as the basic units of life, the cell division cycle, etc.

This course adopts a systems view to discuss the communication of cells with other cells and with the external environment. Particular emphasis will be given to cell division cycle because of its crucial role in development and in diseases such as cancer. Course includes a daily wet laboratory session. The wet lab sessions include aseptic/sterile cell culture techniques, cell proliferation, cell migration, NGF-induced cell differentiation and angiogenesis experiments.

Modeling in Systems Biology
Bahrad Sokhansanj, Ph.D.
Drexel University

With the advancement of quantitative measurement techniques, single molecule tracking in live cells, and large data sets available through genomics and proteomics, there is a rapidly growing need to use computers to integrate information, interpret experimental results and design new experiments, and predict the impact of gene defects and therapeutics.

This course will introduce the concept of system modeling and present a wide range of methods for simulating biological systems, discussing their potential power and pitfalls in terms of the practical needs of biological scientists. Through interactive electronic laboratories, students will work on real biological problems using a variety of computer simulation tools. Guest speakers will discuss examples of computer modeling in biological research projects and development of next-generation computational methods and tools.

Microarrays and Differential
Gene Expression
Don Baldwin, Ph.D.
University of Pennsylvania

This course will present the fundamentals of microarray experiments for high throughput measurement of RNA abundance profiles, focusing on the labwork and data analysis required to determine the probability that the genes represented on a microarray show differential expression between two or more conditions.

The course covers the initial steps necessary for analyzing the data, including experimental design, image analysis, normalization, background correction, basic statistical analysis, filtering techniques, and visualization. Tools used will include Affymetrix GeneChips, Affymetrix analysis software, and the Multi-Experiment Viewer open source application from the Institute of Genomics Research to provide hands-on experience with techniques. The course includes wet laboratory sessions conducted at the Penn Microarray Facility.

Pharmacogenomics
Kristin Ardlie, Ph.D.
Vice President of Genetics,
Genomics Collaborative, Inc.
Andres Kriete, Ph.D.
Drexel University

Pharmacogenomics is an emerging discipline focusing on the use of genome information for informed decision making along the pharmaceutical pipeline. Drug discovery has the challenging task to identify genes and gene products which are most attractive as therapeutic targets. This course reviews the bioinformatics and high-throughput methods for identification of targets, candidate drugs and their potential adverse effects, which will be exemplified further by the findings of recently conducted studies. The course presents hands on experience on computational methods used in pharmacogenomics.

Biostatistics, Clustering
and Classification
Michael Ochs, Ph.D.
Fox Chase Cancer Center

This course will introduce and define biostatistics measures used in the analysis of high throughput biological data. The microarray data analysis comprises two main parts: 1) the use of statistical methods to reliably estimate the presence of changes in mRNA levels between different conditions measured using microarrays or GeneChips and 2) the application of pattern recognition in microarray and proteomic analysis, including clustering methods and more advanced methods making use of the large body of biological knowledge for interpretation of microarray and proteomic results.

The course will include hands-on data analysis using TMEV open source tool from The Institute for Genomics Research (TIGR), Oak Ridge National Laboratory’s GO Tree Machine (GOTM) gene ontology tool, and ClutrFree open source visualization tool from Fox Chase Cancer Center.

Bioinformatics Databases
and Biomolecular Networks
Greg Gonye, Ph.D.
Thomas Jefferson University
Michael Ochs, Ph.D.
Fox Chase Cancer Center

This short course will focus on computational data resources and approaches which allow expanding from single gene analyses to system-level analyses. In particular integrative databases which combine diverse data classes, from genome sequence to gene expression to functional ontology classification will be showcased. Tools which build on these resources to predict regulatory, functional and/or “knowledge” network interactions will be used to analyze the data produced in the previous weeks.

A focus on hands-on tutorials using real data should provide participants with a good working knowledge of these valuable systems biology resources.

Protein Modeling and Bio-Nanotechnology

Both days of the colloquia will form around the on-going advances in protein modeling and bio-nanotechnology. Participants are welcomed to attend, free of charge, to listen, contribute their ideas, and discuss collaborative opportunities in these fields.

Click our link for a detailed schedule that includes speakers.

more information

* This week will be open and free of charge to all participants

Proteomics
David Speicher, Ph.D.
The Wistar Institute
Hsin-Yao Tang, Ph.D.
The Wistar Institute
Nadeem Ali-Khan, Ph.D.
The Wistar Institute
Peter I. Lelkes, Ph.D.
Drexel University

Proteomics, the systematic analysis of proteins in cellular machines, entire cells, tissues or entire organisms, represents an exciting new way to pursue biological and biomedical science in taking a comprehensive, systematic approach to understanding biology.

This course will provide an in-depth examination of the types of problems that proteomics can address, the alternative tools that are available, and how to match biological problems to available analytical tools. A major focus will be the central role of mass spectrometry in proteomics and the computational challenges associated with analysis and interpretation of diverse types of proteomic data. Lectures will be interspersed with hands-on data analysis tutorials and lab demonstrations.