EECS 600: Systems Biology & Bioinformatics, Fall 2007
Department of Electrical Engineering & Computer Science
Case Western Reserve University
 

Syllabus in pdf

Calendar

  1. Introduction to Molecular Biology
    Life, cell, genes, DNA, RNA, proteins, structure, function, evolution, central dogma, domains of life.

  2. Introduction to Systems Biology
    Concepts relating to systems biology, properties of biological systems, level of detail in modeling and studying systems, organization vs dynamics, modularity, promises, challenges.

  3. DNA Microarrays
    DNA microarray technology, oligonucleotide arrays, cDNA arrays, normalization and transformation of micorarray data.
    3.a. Discussion paper: I. Shmulevich and W. Zhang, Binary analysis and optimization-based normalization of gene expression data, Bioinformatics, 2002.

  4. Knowledge Discovery on Gene Expression Data
    Pattern discovery, clustering, biclustering, SVD. 4.a. Discussion papers:

  5. Supervised Analysis of Gene Expression Data
    Differential gene expression, classification, functional annotation, gene selection.
  6. Gene Regulatory Networks
    Graph models for genetic regulation, Boolean network model, Bayesian network model, network dynamics, inference of genetic networks.
  7. Protein Interaction Networks
    Protein-protein interactions, high-throughput screening of protein interactions (Y2H, TAP), prediction of protein interactions (co-expression, co-evolution, gene fusion, in silico two-hybrid, structural prediction). 7.a. Discussion paper: Y. Kim, M. Koyuturk, U. Topkara, A. Grama, and S. Subramaniam, Inferring functional information from domain co-evolution, Bioinformatics, 2006.

  8. Topology of Biological Networks
    Degree distribution, clustering coefficient, scale-free networks, hierarchy, modularity, topological motifs, robustness. 8.a. Discussion paper: M. Middendorf, E. Ziv, and C. Wiggins, Inferring network mechanisms: The Drosophila melanogaster protein interaction network, PNAS, 2005.

  9. Pattern Discovery in Molecular Interaction Networks
    Reconstructing signaling networks, identification of functional modules, network clustering, modular decomposition of networks, identification of signaling pathways.

  10. Comparative Interactomics
    Alignment of molecular interaction networks, multiple network alignment, global network alignment, graph mining.

  11. Network Based Functional Annotation
    Projection of function based on network neighborhood, modularity based annotation, network based ortholog identification. 11.a. Discussion Paper: M. Kirac, G. Ozsoyoglu, and J. Yang, Annotating proteins by mining protein interaction networks, ISMB, 2006.

  12. Integration of Interaction Data
    12.a. Discussion paper: R. Kelley and T. Ideker, Systematic interpretation of genetic interactions using protein networks, Nature Biotechnology, 2005.

  13. Domain Interaction Networks
    Inferring domain-domain interactions from protein-protein interactions, domain interaction databases.

Guest Lectures

  1. Gurkan Bebek, Center for Proteomics and Mass Spectrometry, November 21, 2007.

  2. Sree Sreenath, Department of Electrical Enginering & Computer Science,Complex Systems Biology Center, December 3, 2007.

Class Presentations

  1. Vishal Patel, Identification of active pathways based on genomic and proteomic data, November 5, 2007.

  2. Nathan Johnson, Use of ontologies in understanding biological networks, November 7, 2007.

  3. Van Anh Tran, PPI network alignment, November 12, 2007.

  4. Sinan Erten, Evolution of PPI networks, November 19, 2007.

  5. Michael Weis, Using protein-level data to predict cell-level response, November 26, 2007.

  6. Xin Li, Clustering methods in biological networks, November 28, 2007.

Assignments

  1. Clustering gene expression data, Due:October 3, 2007

  2. Network based functional annotation, Due:December 5, 2007

Class Meeting

MW 2:00pm-3:15pm, WHTE 322

Office Hours

MW 1:00pm-2:00pm, OLIN 512

Instructor

Mehmet Koyuturk

Description

Bioinformatics is the science of making sense of biological information. In the genomic era, efforts on developing algorithmic and computational methods for organizing, integrating, analyzing, and querying biological data proved invaluable. Today, availability of high-throughput data relating to the interactions between biomolecules, coupled with past accomplishments in molecular biology, make it possible to study the cell at the systems level. The organization of the cell is abstracted using various models, including protein interaction networks, gene regulatory networks, signaling pathways, and metabolic pathways, each representing a different aspect of the same system. The data relating to these models is obtained via various high-throughput techniques, including DNA microarrays, yeast two-hybrid, affinity chromatography, mass spectrometry, and nuclear magnetic resonance. Integration of sample-specific molecular data with species specific interaction data opens new doors for a range of medical applications, through analysis of genetic and phenotypical association from a systems perspective. In this course, we cover algorithmic, analytical, and statistical techniques used to effectively organize, integrate, and analyze these novel sources of biological data. This course targets graduate students with research interests in diverse areas, including bioinformatics, computational biology, algorithms, data mining, database systems, and machine learning.