BIOENG241
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BIOENG 241 - Probabilistic Modeling in Computational Biology
Course Title
Probabilistic Modeling in Computational Biology
Course Description
This course reviews the statistical and algorithmic foundations of bioinformatics viewed through the lens of paleogenetics, the science of "Jurassic Park", i.e., the reconstruction of ancient genes and genomes by reverse Bayesian inference under various stochastic models of molecular evolution. Such methods, first proposed in the 1960s by Linus Pauling (and others), are now in reach of practical experimentation due to the falling cost of DNA synthesis technology. Applications of these methods are granting insight into the origin of life and of the human species, and may be powerful tools of synthetic biology. Lectures will review the theoretical content; homework and laboratory exercises will involve writing and applying programs for computational reconstruction of ancient protein and DNA sequences and other measurably evolving entities, both biological (e.g., gene families) and otherwise (e.g., natural language).
Minimum Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; S/U Option
Instructors
Holmes
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisites
Recommended preparation:
Math 53: multivariable calculus (or equivalent)
Math 54: linear algebra (or equivalent),
Math 126: partial differential equations (or equivalent)
or consent of instructor.
Math 53: multivariable calculus (or equivalent)
Math 54: linear algebra (or equivalent),
Math 126: partial differential equations (or equivalent)
or consent of instructor.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses
-
Course Objectives
Student Learning Outcomes
Formats
Lecture, Laboratory
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Laboratory Hours
3
Outside Work Hours
6