Module 3. Bioinformatics and Systems Biology

Computational analysis of biological networks, OMICs data (genomics, transcriptomics, metabolomics, proteomics). Application of advanced analysis and modeling approaches to study pathways and networks. Emphasis on using existing high throughput data sets alongside newly generated data to analyze and interpret research findings.

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 High throughput sequencing methods in systems biology
  Lab Mapping genetic regulatory networks
3.3 Lecture Network modeling for hypothesis testing and generation
  Lab Modeling and inference of metabolic networks
3.4 Lecture Machine learning approaches in systems biology
  Lab Application of machine learning to biological network analysis



Lecture (3-1): Introduction to systems biology

  • Instructor: Dr. Peter Freddolino
  • Time: Mar 21 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    What are bioinformatics and systems biology? Major areas of research and application.
  • Material:
    Lecture slides (PDF)


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



Lecture (3-2): High throughput sequencing methods in systems biology

  • Instructor: Dr. Peter Freddolino
  • Time: Mar 28 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    Overview of high throughput sequencing-based methods used to investigate biological networks, along with an introduction to databases and analysis considerations for each.
  • Material:
    Lecture slides (PDF)


Lab (3-2): Mapping genetic regulatory networks using high-throughput sequencing

  • Instructor: Dr. Peter Freddolino
  • Time: 2:30 – 4:00 PM, Mar 30 (Thursday)
  • Topics:
    Finding and interpreting RNA-seq and ChIP-seq data sets to study regulatory networks, including identification of differentially expressed genes, location of transcription factor binding sites, and inference of regulatory motifs.
  • Material:
    Lab worksheet
    Lab worksheet with key
    Supporting Files: lab2_files
    Muddy point assessment
  • Homework (Due 4/6 @ 5:00pm):
    Homework Assignment 2


Lecture (3-3): Network modeling for hypothesis testing and generation

  • Instructor: Dr. Peter Freddolino
  • Time: Apr 4 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    Conceptual understanding of kinetic models and constraint-based models of metabolic networks; approaches for translating biochemical schematics into quantitative frameworks.
  • Material:
    Lecture slides (PDF)


Lab (3-3): Modeling and inference of metabolic networks



Lecture (3-4): Machine learning approaches in systems biology

  • Instructor: Dr. Peter Freddolino
  • Time: Apr 11 (Tuesday), 2:30 - 4:00 PM
  • Topics:
    Introduction to machine learning and overview of key algorithms (linear classifier, SVM, decision trees and random forests). Existing and potential areas for application of machine learning in the analysis of biological networks.
  • Material:
    Lecture slides (PDF)


Lab (3-4): Application of machine learning to biological network analysis


Reference material