Course Syllabus

Jump to Today

Course Description

Algorithmic and statistical approaches in computational functional genomics and systems biology; Biological Information Integration – Knowledge (ontology) driven and statistical approaches; Qualitative, probabilistic, and dynamic network models; Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks; metabolic networks; cells and systems.

Location and Time:

  • Sci 2 0115
  • T,TH 12:40-1:55

Syllabus

Introduction:

    • What is systems biology? From parts to interactions to wholes; Data integration, predictive model construction, simulation and model-based prediction, model-driven experimentation, bridging levels of abstraction.
    • What is a (mathematical or computational) model? What are models good for? How can we construct models? How can we evaluate models?

Networks and Graph Models

    • Introduction to networks and network types
    • Basic data structures for biological network applications
    • Topological Structure; Finding structure in networks

Network Construction and Analysis 

    • Inferring or building networks; correlation and association
    • Modules (Clustering) in networks
    • Analysis – module identification (spectral clustering), comparative analysis
    • Network comparison
    • Network Visualization

Predicting Function

    • Ontologies
    • functional annotation
    • Homology and orthology
    • Evolution and function
    • Phylogenomics
    • Biocuration
    • Critical Assessment of protein Function Annotation (CAFA)

Metabolic Networks and Pathways 

    • Modeling metabolic networks
    • Hypergraphs
    • Metabolic flux and steady-state models
    • Whole genome modeling
    • Pathway Databases and Pathway Models
    • differential equation models and basic enzyme kinetics

 Data Integration

    • Sources
    • Model (ontology)-driven integration – ontologies, mappings, database federation
    • Graph-theoretic methods
    • Probabilistic methods, canonical correlation
    • Multi-scale modeling

Grading

Grades will be based on a combination of homework assignments (52%, 4% per week 1-13), class participation (10%) and your final project report (24%) and presentation (12%).

Group-work policy

In order to facilitate learning, students are encouraged to discuss homework problems amongst themselves. Copying a solution is not, however, the same as ``discussing.'' A good rule of thumb is the ``cup of coffee'' rule. After discussing a problem, you should not take away any written record or notes of the discussion. Go have a cup of coffee, and read the front page of the newspaper (or reddit, or X). If you can still re-create the problem solution afterward from memory, then you have learned something, and are not simply copying.

BCB 570 Course Objectives

Objectives

Algorithmic and statistical approaches in computational functional genomics and systems biology; Biological Information Integration – Knowledge (ontology) driven and statistical approaches; Qualitative, probabilistic, and dynamic network models; Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks; metabolic networks; cells and systems.

 Learning Outcomes

1. Learn to create, verify and analyze large-scale network models in the context of biology.

2.  Learn basic data types used in systems biology, define an ontology, and interpret supporting evidence in biological databases to be able to draw supported inferences about biological function.

3.  Apply basic machine learning methods to biological data sets for grouping data (clustering) and distinguishing between different situations (supervised).

4. Integrate two (or more) different data types to learn more about system behavior.

Class Schedule

BCB 570 (Systems Biology) will be co-taught by Dr Julie Dickerson of Electrical and Computer Engineering, Dr. Claus Kadelka of Math and Dr. Iddo Friedberg of Vet Med. 

Weeks 1-2 will be taught by Dr. Dickerson,
Weeks 3-7 will be taught by Dr. Kadelka,
Weeks 8-10 will be taught by Dr. Friedberg,
Weeks 11-13 will be taught by Dr. Dickerson,
Weeks 14-15 will be taught by Dr. Friedberg.

Week Dates (Tues) Dates (Thu)
1 8/22 8/24 Dr. Dickerson
Graph theory intro
2 8/29 8/31 Dr. Dickerson
Graph theory intro
3 9/5 9/7 Dr. Kadelka
Correlation and association
4 9/12 9/14 Dr. Kadelka
Regulatory networks
5 9/19 9/21 Dr. Kadelka
Regulatory networks
6 9/26 9/28 Dr. Kadelka
Dynamic network modeling
7 10/3 10/5 Dr. Kadelka
Dynamic network modeling
8 10/10 10/12 Dr. Friedberg Function
9 10/17 10/19 Dr. Friedberg Function
10 10/24 10/26 Dr. Friedberg Function
11 10/31 11/2 Dr. Dickerson PCA and t-SNE
12 11/7 11/9 Dr. Dickerson
UMAP
13 11/14 11/16 Dr. Dickerson
PLS, Canonical correlation
Thanksgiving Break
14 11/28 11/30 Dr. Friedberg
Metagenomics / sequence assembly
15 12/5 12/7 Dr. Friedberg
Metagenomics / sequence assembly
16
Project hand-in (due 12/14)

 

 

Course Summary:

Date Details Due