Module 3. Bioinformatics and Systems Biology

Computational analysis of OMICs data (genomics, transcriptomics, proteomics). Analysis of protein-protein interactions and gene expression data. Pathways and networks, machine learning. Example applications from translational medicine and cell biology.

N.B. Please complete this pre-course questionnaire if you have not already done so.

     
3.1 Lecture Introduction to systems biology
  Lab Network analysis for systems biology
3.2 Lecture Epigenome data mining to understand disease predisposition
  Lab Epigenome profiling and disease links
3.3 Lecture Computational clinical decision support systems
  Lab WEKA for machine learning and feature analysis
3.4 Lecture Application of systems biology to translational medicine
  Lab Systems biology resources for translational medicine



Lecture (3-1): Introduction to systems biology


Lab (3-1): Network analysis for systems biology



Lecture (3-2): Epigenome data mining to understand disease predisposition

  • Instructor: Dr. Stephen Parker
  • Time: Mar 29 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    For decades, substantial research efforts have focused on the <2% of the human genome that encodes proteins. Recent epigenome-based functional genomic analyses and genome-wide association studies (GWAS) together implicate non-coding DNA regulatory elements as critical regions influencing gene expression, risk for common diseases, variation in physiological traits, and evolution across species. Because they represent the convergent point of evolutionary, genetic, developmental, and environmental inputs, basal epigenomic signatures and their dynamic changes are central to understanding biological function. This lecture will explore epigenomic assays and bioinformatic analyses and how these approaches can help untangle disease mechanisms.
  • Material:
    Lecture Slides
    Readings
    Integrative analysis of 111 reference human epigenomes


Lab (3-2): Epigenome profiling and disease links

  • Instructor: Dr. Stephen Parker
  • Time: 2:30 – 4:00 PM, Mar 31 (Thursday) or Apr 1, 10:30 - 12:00 PM, (Friday)
  • Topics:
    Students will learn how to computationally process epigenomic data, create interactive displays of these profiles, and then use the profiles to interpret disease associated genetic variations.
  • Material:
    Lab Link
    Muddy point assessment



Lecture (3-3): Computational clinical decision support systems

  • Instructor: Dr. Kayvan Najarian
  • Time: Apr 5 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    Introduction to computational clinical decision support systems. Machine learning and its application to biomedical informatics.
  • Material:
    Lecture Slides: PDF, PPTX


Lab (3-3): WEKA for machine learning and feature analysis

  • Instructor: Dr. Kayvan Najarian
  • Time: 2:30 – 4:00 PM, Apr 7 (Thursday) or Apr 8, 10:30 - 12:00 PM, (Friday)
  • Topics:
    Introduction to WEKA, using machine learning methods such as SVM, Random Forest, Neural Networks for simple examples in systems biology, using WEKA for feature extraction and analysis.
  • Material:
    [Lab worksheet]
    Supporting Files:
    TestData525 info gain filtered.csv,
    TestData525 v2.csv



Lecture (3-4): Application of systems biology to translational medicine

  • Instructor: Dr. Matthias Kretzler
  • Time: Apr 12 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    Integrating genome wide data sets with high-resolution clinical phenotypes, molecular marker definition, regulatory network generation in patient samples.
  • Material:
    Lecture Slides


Lab (3-4): Systems biology resources for translational medicine

  • Instructor: Felix Eichinger
  • Time: 2:30 – 4:00 PM, Apr 14 (Thursday) or Apr 15, 10:30 - 12:00 PM, (Friday)
  • Topics: Introduction to web based systems biology resources including Oncomine and Nephromine.
  • Material:
    Lab Slides:
    PPTX
    PDF