At the graduate level, the University of Ottawa and Carleton University offer joint courses and joint programs. Below is a brief outline of current graduate courses and programs relating to quantitative biology. As a reference, a list of undergraduate courses for biology students interested in acquiring a quantitative background has been compiled. Some of these undergraduate courses are core level courses while others maybe taken as elective in later years of the undergarduate program. The list is not exhaustive, please also refer to course and program description websites (see links page).
Graduate Level courses and programs - Ottawa-Carleton
Undergraduate level - University of Ottawa
Undergraduate level - Carleton University
Graduate level - Ottawa-Carleton
Courses
BIO 5106 (BIOL 5506) BIOINFORMATICS
Major concepts and methods of bioinformatics. Topics may include, but are not limited to: genetics, statistics and probability theory, alignments, phylogenetics, genomics, data mining, protein structure, cell simulation and computing.
BIO 5305 (BIOL 5407) QUANTITATIVE ECOLOGY
A course on analysis of the distribution and abundance of plants and animals, and of related environmental phenomena. Computer assignments and a major data analysis project will be required. Prerequisites: Graduate standing, courses in elementary ecology and statistics and permission of the department.
BIO 5306 (BIOL 5409) MATHEMATICAL MODELLING FOR BIOLOGISTS
This course is designed to develop mathematical tools for the modelling of biological processes. The student is taught the necessary mathematics, a computer language, and guidance is given in the choice of simulation of a biological process.
BCH 8110 ADVANCED TOPICS IN SYSTEMS BIOLOGY
see http://intermed.med.uottawa.ca/Associations/OISB/courses/BCH8110/index.html
NSC 8104 COMPUTATIONAL NEUROSCIENCE SUMMER SCHOOL
see http://www.neurodynamic.uottawa.ca/summer.html
CSI 5126 (COMP 5108) ALGORITHMS IN BIOINFORMATICS
(last given in Fall 2006)
SYSC 5104 - METHODOLOGIES FOR DISCRETE-EVENT MODELLING AND SIMULATION
The main goal of this course is to let the students understand recent advances in modelling and siulation methodologies. The idea is to build complex multicomponent systems, attacking the complexity in a methodic fashion. We are interested in the Software Engineering aspects when developing simulation models, such us how to achieve good development performance, reduce testing time and improve the development process. We are also interested in showing recent techniques to achieve improved execution performance through parallel execution of the simulation models.
Programs
Students may be enrolled in general graduate programs of their main background discipline (e.g. biology, physics, computer science, mathematics). Here are listed graduate programs/specializations that are more specifically related to quantitative biology.
Joint Collaborative Program in Bioinformatics at the Master's level
Bioinformatics is an emerging and increasingly important scientific discipline dedicated to the pursuit of fundamental questions about the structure, function and evolution of biological entities through the design and application of computational approaches. Fundamental research in these areas is expected to create new discoveries towards increasing our understanding of human health and disease which will translate to innovation in industry (i.e. drug discovery). As a field of research, it crosses traditional disciplinary boundaries such as computer science, chemistry, biology, biochemistry, engineering and the medical sciences. While individual researchers usually specialize in a particular area, bioinformaticians today must be able to appreciate significant research in other fields and therefore require an understanding of the basic principles of other disciplines. To meet this challenge Carleton University and the University of Ottawa offer a Collaborative Program leading to a Master of
Science degree with Specialization in Bioinformatics or Master of Computer Science with Specialization in Bioinformatics.
Undergraduate level - University of Ottawa
Undergraduate level - Carleton University
Courses
BIOC 3008/COMP 3308 - INTRODUCTION TO BIOINFORMATICS
This practical course explores the broad scope of bioinformatics and provides insight into the theory, implementation, applications and limitations of computational approaches. Topics may include introductory programming, data modeling, biological databases, sequence alignment, phylogeny, pathways and biological networks.
BIOC4008/COMP 4308 - ADVANCED BIOINFORMATICS
A computational course that explores the dynamic nature of proteins and cellular networks. Topics may include object oriented programming, integrated databases, protein structure prediction, drug discovery and cell simulation.
BIOL 3604 - ANALYSIS OF ECOLOGICAL RELATIONSHIPS
The purpose of this undergraduate course is to provide students (biologists and engineers) with an introduction to statistical analysis of ecological data. The primary unifying framework is the General Linear Model (GLM), which provides a single way to view techniques ranging from analysis of variance, analysis of covariance and multiple regression. My approach is hands-on, in that students are not only introduced to the underlying statistical theory, but they are also given experience in putting these ideas into practice using SPSS statistical software. On completing the course students have a working knowledge of many of the most useful statistical methods in a range of scientific disciplines, and have the grounding and confidence necessary to learn about methods not covered in the course.
BiO3612 - COMPUTATIONAL METHODS IN ECOLOGY AND EVOLUTION
This course introduces undergraduate students to the development and use of computer programs to address biological problems. Topics include the development of programs to analyse ecological data, models of population dynamics, deterministic chaos, cellular automata, simulations of foraging behaviour, game theoretical models and evolutionary computation. The programming language introduced is Visual Basic 6 (widely used in academia and industry), but the emphasis is on introducing applications rather than on enhancing technical programming skills. Students successfully finishing this course are able to write their own computer programs to analyse data in a tailor-made way, and they have a far better understanding of how a particular biological process might be simulated using a computer.
BIOC4008/COMP 4308 - ADVANCED BIOINFORMATICS
A computational course that explores the dynamic nature of proteins and cellular networks. Topics may include object oriented programming, integrated databases, protein structure prediction, drug discovery and cell simulation.
|
