Course Syllabus

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:

• Sweeney 1120
• T,TH 12:40-1:55

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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

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Grading

Grades will be based on a combination of homework assignments, class participation and your final project report and presentation.

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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.

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 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.

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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. The current schedule for teaching is:

Weeks 1-2 Dr. Dickerson

Weeks 3-7 Dr. Kadelka

Weeks 8-10 Dr Friedberg

Weeks 11-13 Dr. Dickerson

Weeks 14-15 Dr Friedberg

Week	Dates (Tues)	(Thu)
1	18-Jan-2022	20-Jan-2022	Julie	Graph theory intro
2	25-Jan-2022	27-Jan-2022	Julie	Graph theory intro
3	1-Feb-2022	3-Feb-2022	Claus	Correlation and association
4	8-Feb-2022	10-Feb-2022	Claus	Regulatory networks
5	15-Feb-2022	17-Feb-2022	Claus	Regulatory networks
6	22-Feb-2022	24-Feb-2022	Claus	Dynamic network modeling
7	1-Mar-2022	3-Mar-2022	Claus	Dynamic network modeling
8	8-Mar-2022	10-Mar-2022	Iddo	Function
Spring Break	15-Mar-2022	17-Mar-2022	No Class
9	22-Mar-2022	24-Mar-2022	Iddo	Function
10	29-Mar-2022	31-Mar-2022	Iddo	Function
11	5-Apr-2022	7-Apr-2022	Julie	PCA and t-SNE
12	12-Apr-2022	14-Apr-2022	Julie	UMAP
13	19-Apr-2022	21-Apr-2022	Julie	PLS, Canonical correlation
14	26-Apr-2022	28-Apr-2022	Iddo	Metagenomics / sequence assembly
15	03-May-2022	05-May-2022	Iddo	Metagenomics / sequence assembly