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BIOENG241

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BIOENG 241 - Probabilistic Modeling in Computational Biology

Bioengineering Graduate COE - College of Engineering

Subject

BIOENG

Course Number

241

Course Level

Graduate

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.

Repeat Rules

Course is not repeatable for credit.

Credit Restriction Courses

-

Course Objectives

To introduce the most commonly used statistical models and associated inference techniques for the analysis and organization of biological sequences, with a focus on models based on evolutionary theory.

Student Learning Outcomes

Students will be familiar with the bioinformatics literature and underyling theory for discrete Markov processes, Bayesian networks, stochastic grammars, birth-death processes, Chinese restaurant processes, data compression algorithms, and related methods such as dynamic programming and MCMC.

Formats

Lecture, Laboratory

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours

3

Laboratory Hours

3

Outside Work Hours

6