| Advanced Electives - 2 Required |
Course Descriptions:
|
Hours: |
|
| Computational Methods in the Biological Sciences |
Credit: 1.5 hours |
|
Biocomputing and computational biology are synonyms that describe the use of computers and computational techniques to analyze biological systems, from individual molecules to organisms to higher-order systems. This course will cover the computational techniques used to access, analyze, and interpret the biological information in common types of biological databases and the biological questions that can be addressed by such methods, applicable to the study of the context of genes within the same genome and across different genomes, the study of molecular sequence data for the purpose of inferring the function, interactions, evolution, and structure of biological molecules, and the study of annotation and ontology.
|
|
Bioinformatics and DNA Microarray Data Analysis
|
Credit: 1.5 hours |
| High-throughput methodologies are generating complex experimental data at an incredible rate. As a result, these developments are forcing a paradigm shift in how the result from biological experimentation are interpreted, in which computers are playing an increasingly important role. The increasing use of computers for data storage, data retrieval, and data analysis is leading to the evolution of two biological disciplines - bioinformatics and computational biology. In this course, we will use the gene expression microarray experimental platform as a model high-throughput methodology to examine how bioinformatics, statistics and computation are being used to support the discovery of new biomedical knowledge. In addition to didactic lectures and discussion, this class will include a series of hands-on workshops focused on the basic steps for microarray data processing. |
|
|
Quantitative Analysis of Genes and Genomes
|
Credit: 1.5 hours |
|
Advances in biotechnology are making it possible to obtain massive amounts of data about the genetic information contained in living cells, the transmission of this information from parent to child, the changes in the information over evolutionary time, and the manner in which this information influences the chemical activity of cells. This course is an introduction to algorithmic techniques for the acquisition, analysis and interpretation of such data.
|
|
| Computational Approaches in Protein Science |
Credit: 1.5 hours |
|
The basics of computational methods used to analyze protein sequences and structures. Topics include sequence similarity searches using profile-based tools, functional prediction, structure prediction and threading, homology modeling, energy-based simulations, protein classification, and evolutionary concepts: homology inference and tree reconstruction.
|
|
| Computational Modeling of Signaling Systems |
Credit: 1.5 hours |
|
Biological signal transduction networks are characterized by complexity: combinatoric incoming and intracellular signals, combined slow and fast responses, interlocking pathways, adaptive responses, feedback controls, etc. Computational Modeling provides an introduction to computational analytical approaches, modeling strategies and other quantitative techniques for understanding cellular signaling networks beginning with simple kinetic, equilibrium and probabilistic approaches to studying individual regulatory pathways. These strategies are then extended to describe complex systems using both deterministic and stochastic methods and includes examples chosen to stress analysis, evaluation and interpretation of experimental data in real signaling systems.
|
|
|
| Home > Education > Graduate School >
