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.

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)

Data Mining and Inference in Large Data Sets

• • Data and Standards
  • Basic machine learning
  • Cluster analysis – hierarchical clustering, SOM, k-means, PCA, how many clusters?

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

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.