Overview

Description

CALENDAR DESCRIPTION:

Consideration of recent literature concerning selected contemporary research topics. Can be taken more than once with permission of the instructor.

COURSE DETAILS:

The process of extracting biochemical content from genome annotations and literature sources to computationally catalog and interconnect the metabolic pathways available to the cell (i.e., metabolic reconstruction) is well established and has been carried out for a growing number of organisms on the genome scale. Such network reconstruction has led to the development of modeling approaches that gain a better understanding of the observable phenotypes and coordinated functions of the cell. As a result, these approaches are being used to apply and develop in silico models for biological discovery and engineering applications.

In this course we will cover conceptually some methods that enable the integration of Biochemical, Genetic and Genomic knowledge (BiGG) to reconstruct a genomic scale network that defines the metabolic physiology of an organism. We will also describe through examples computational models that integrate high-throughput data sets for prospective experimentation and validation. Finally, we will show how valuable and relevant these approaches are at making important biological predictions that can be validated experimentally. Applications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery will be discussed through student presentations      

LECTURE TOPICS:

- What is System Biology?
- Network reconstruction from biological data
- Genomic scale reconstruction of prokaryotes
- Genomic scale reconstruction of eukaryotes
- Biochemical networks
- Genome metastructures
- Biochemically, Genomically and Genetically structured database (BiGG)
- Properties of reconstituted networks
- Phenotype potential of reconstituted networks
- Applications in microbial evolution, genetic engineering, drug discovery, environmental science, synthetic biology and biomedicine

Pre-requisite: An undergraduate bioinformatics course or equivalent

Grading

Materials

REQUIRED READING: