Recent Advances in Computational Biology/Computational Biology
BIO 384K/BIO 337J - Professor Robin Gutell

Spring semester, same class for undergraduate and graduate students

An introductory projects-oriented class in computational biology and bioinformatics. Lectures once a week discuss different aspects of nucleic acid and protein sequence analysis. Students work on one or two team and/or individual projects that utilize computational tools on the web and programs, or development of computational biology/bioinformatics computer programs. Primary focus is sequence analysis and not about algorithms. Thus this class is complementary to Professor Marcotte's CH391L Bioinformatics class

Class URL is:
http://www.rna.icmb.utexas.edu/CLASS/ direct access from campus IP addresses, otherwise account name and password is required

Prerequisites for Biology students:
* BIO 211: Intro. Biology: Cell Biology
* BIO 212: Intro. Biology: Genetics and Evolution
* BIO 213: Intro. Biology: Diversity and Ecology
* BIO 214: Intro. Biology: Structure and Function of Organisms
* BIO 325: Genetics (concurrent enrollment is okay)
* BIO 344: Molecular Biology (concurrent enrollment is okay)

CS students are encouraged to enroll. It is not mandatory that they have all of the classes listed above. However they should have some knowledge of biology, and in particular genetics and molecular biology.

BME 346/383J: Computational Structural Biology - Professor Pengyu Ren

Course description: The BME 346/383J is an introduction to computational structural biology course for senior undergraduate and graduate students. In this course we first review the fundamentals of structural biology from genetic sequence to protein structure and function. After the discussion of thermodynamic driving forces in biomolecular structure and interactions, the students will be introduced to the principles and numerical algorithms of molecular modeling techniques. Towards the end, applications of molecular modeling to structural biology are examined in the context of drug design and protein structure modeling. Throughout the course, the students are given hands-on exercises in structural biology using a variety of software tools in the computer lab.

BME385J: Genomic Signal Processing and Bioinformatics - Professor Orly Alter

Last taught Fall 2004.

This graduate and advanced undergraduate course discusses: (a) Technologies such as sequencing, DNA microarrays and protein mass spectrometry, for high throughput acquisition of molecular biological data, such as DNA copy number, RNA expression and proteins' DNA-binding, on genomic and proteomic scales. (b) Databases and large-scale datasets generated by national and international consortia as well as individual research groups using these technologies. (c) Mathematical Analysis & Modeling of these data using ideas from signal processing, numerical computation and information systems. (d) Biological & Medical Predictions made by these analyses and models, their experimental tests and their as medical diagnosis, treatment and drug design.

BME 341: Computational Genomics Laboratory - Professor Orly Alter
(previously "Engineering Tools for Computational Biology")

To be taught Spring 2007. Last taught Spring 2006.

This Engineering and Natural Sciences advanced undergraduates and beginning graduates lab course covers: (a) Technologies such as sequencing, DNA and protein arrays, for high throughput acquisition of molecular biological data on genomic and proteomic scales; (b) Databases and large-scale datasets generated by national and international consortia as well as individual research groups using these technologies; (c) Mathematical analysis and modeling of these data using ideas from signal processing, numerical computation and information systems; and (d) Biological and medical predictions made by these analyses and models, their experimental tests and their applications toward better fundamental understanding of biology as well as medical diagnosis, treatment and drug design.

CH391L: Bioinformatics - Professor Edward Marcotte

An introduction to computational biology and bioinformatics. The course covers typical data, data analysis, and algorithms encountered in computational biology. Topics will include introductory probability and statistics, basics of programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, protein structure prediction, analysis of DNA microarray data, data clustering, biological pattern recognition, and biological networks. This is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on the underlying algorithms and analyses.

Open to graduate students and upper division undergraduates in natural sciences and engineering. For more information, see: http://polaris.icmb.utexas.edu/CH391L/

CH391L: Genomes: From Molecules to Ecosystems - Professor David Graham

Offered in alternate years. Last offered in Fall 2005.

Course description: This course asks "what can you do with a genome sequence?" In particular, we will examine how biological problems can be posed in a genomic context. We will consider how thinking at a genomic level allows us to extrapolate new findings to other organisms and to study organisms at the systems level. Through short lectures and student-led discussions we explore genome organization, rearrangements, genome annotation, proteomics and metabolomics, horizontal gene transfer, evolutionary genomics, reductive evolution and biochemical adaptation. Students complete final written projects using genomics to study a current problem of interest. This graduate course is designed for students who are familiar with molecular biology techniques and are interested in Microbiology, Evolution and ecology, Biochemistry and Plant biology.

CS329E Elements of Algorithms in Bio-Informatics - Professor David Kincaid

http://www.bioalgorithms.info
http://www.cs.utexas.edu/users/kincaid/cs329e-info.html

• An introduction to algorithms in bioinformatics and the computational ideas behind them. Acquire a knowledge of algorithms employed in popular bioinformatics software systems and gain the ability to understand and evaluate them.
• Exposure to a broad range of algorithms and performance issues in bioinformatics and scientific computing. What a bio-student ought to know about computer science.
• Focus on computational aspects of new algorithms in bioinformatics and Web searching as well as traditional ones such as dynamical systems, demographics, and economic models.
• An intuitive demonstration that a few design techniques can be used to solve a large number of practical problems in bio-science. A theoretical and practical study of computational genomics.
• Course strikes a balance between mathematics and practical techniques by emphasizing the ideas underlying the algorithms.
• A tour of some bioinformatics algorithms: complexity, exact and approximate pattern matching, sequence alignment, multiple alignment, statistical and machine learning approaches.

CS 354 - Introduction to Computer Graphics - Professor Chandrajit Bajaj

This course provides a top down and stepwise refinement introduction to computer graphics as well as lays the fundation for a graduate course in computer graphics and data visualization. There is a choice of both breadth and depth in the intertwined topics of graphics, computational geometry, geometric modeling and data visualization.

CS 395T - Multi-scale Bio-Modeling and Visualization - Professor Chandrajit Bajaj

Biomedical modeling and visualization has roots in medical illustration and communication for the health sciences, with branches of application to mathematical modeling and computer simulation of artificial life. In this course we shall emphasize computational image processing, and modeling algorithms with emphasis on spatial realism, and the programmatic use of simulation and visualization to quantitatively depict "how things work" at the molecular, cellular, tissue, and organ level scales. Computational methods shall include multi-scale geometry representations, image filtering, contrast enhancement, segmentation, fusion, boundary and finite element meshing, spline interpolants and approximants and, their use in integral and differential equation solving, quadrature and cubature formulas, volumetric contouring, volumetric rendering, volumetric texture-based image and geometry composition, combinatorial, topological and integral/differential metric quantitation. Practical exercises on computational domain and physiological modeling and visualization at multiple scales, shall be drawn from cardiology (heart, cardiac tissue, myocytes,ion-channels), and neurology (brain, spinal cord, neurons, Schwann cells, neurotransmitters), and their interactions (synaptic transmission at the neuro-muscular junction).

EE397K-1: Engineering Applications in Genomics - Professor Sriram Vishwanath

PREREQUISITES

Undergraduate probability. None as far as molecular biology is concerned.

Molecular Evolution - Professsor Claus Wilke

Last taught: Fall 2006

This course introduces students into the basics of molecular evolution. Topics covered include protein and RNA folding, coalescent theory, models of sequence evolution, sequence alignment, methods to estimate evolutionary rates, statistical tests for neutrality, positive selection, and recombination, gene duplication, genome evolution, and more. The goal of the course is to give students a good working knowledge in the most-commonly used concepts and analysis methods of the field of molecular evolution. Parts of the course can be fairly mathematical, and a solid working knowledge in calculus and probability theory is helpful (but not a prerequisite).