A new curriculum is in development. For additional course options, please review the nanocourses available through the Bioinformatics Core Facility.
Current Computational Biology coursework includes:
Matlab Boot Camp
- Introduction to Matlab as an integrated environment for data processing and visualization, scientific computing, and modular programming
- The course covers the concepts of scripting and programming and will address fundamental issues of memory management and debugging. Parallels to other environments for scientific data handling such as Perl, Python, R, and programming languages such as C will also be discussed. The last day of the course is reserved for an introduction to the popular statistics package R.
- Data examples are borrowed from a variety of biomedical research areas.
- The objective of the course is for every student to build the foundation for advancing his/her research project by tailored computational data analysis solutions.
- This intensive one-week course consists of a combination of classroom lectures, guided exercises in small groups, and homework assignments. Lectures and guided exercises are held 9 a.m.–6 p.m. in classroom. Students then get homework for the evening and weekend that will deepen their understanding of the taught material.
- The course is open to incoming graduate students (first priority), MSTP students of all classes (second priority), and any biomedical researcher at UT Southwestern interested in programming and computational data processing. No prior experience with programming is required. Enrollment is limited to 50 students.
Core Curriculum – Introduction to Statistics
Spring – 16 week course
1 session per week
Course Director: Lindsay Cowell, Ph.D.
- Introduction to the fundamental principles of statistics and to the appropriate use and interpretation of statistics
- The course provides in depth discussions of statistical concepts, probability distributions, and statistical tests.
- The course also covers aspects of experimental design to optimize the power of statistical analyses.
- Problem sets are designed to deepen the understanding of the taught material and to showcase applications to biomedical research cases.
- The syllabus is organized around select chapters of Biostatistical Analysis, Fifth Edition, by Jerrold H. Zar.
- The course is part of the mandatory core course for all incoming graduate students.
Mathematical Foundations of Quantitative Biology
Fall – 8 week course
Course Director: Kimberly Reynolds, Ph.D.
- A course in the mathematical foundations of quantitative biology
- The goals of the class are to practice abstraction through mathematics, develop comfort with a practical toolkit of methods, understand the application of math in biology, and consolidate prior learning in Matlab
- Each week is oriented on a different mathematical topic. We will discuss:
- Differential equations
- Series, combinatorics, and distributions
- Entropy and information
- Probability theory
- Noise analysis
- Transform theory
- Linear algebra
- Multivariable calculus
- Learning is focused heavily on problem sets and applications. Per week there are two lecture sessions, and two problem sets. One problem set is basic and is distributed prior to the associated lecture to get students primed and oriented to the material. The second problem set is more complex both conceptually and technically, and is related to the second class session each week.
- The course is open to all graduate students and postdocs. Prior completion of the core course in statistics is required. Completion of the MATLAB bootcamp or previous experience with MATLAB is also required.
Machine Learning and Artificial Intelligence in Medicine
- This will be a project-focused course. Each student will be required to finish at least two projects. Basic data analysis and programming skills are prerequisites for this course.
- Course projects will cover different types of data and applications.
- In the first four weeks, the course will cover the concepts of Machine Learning and Artificial Intelligence in Medicine, an overview of different computational methods, project descriptions and assignments.
- In the following 12 weeks, students will discuss and present projects guided by the instructors.
Spring – 8 week course
2 sessions per week
Course Director: Milo Lin, Ph.D.
- Introduction to quantitative approaches to “complex” systems in biology
- The course comprises a mixture of didactic lectures that provide a review of basic concepts, theories, and tools of quantitative science, and also a number of case studies in which deep understanding of biological systems has emerged through the application of this approach. An overall theme is to define complexity in a more rigorous way and to learn about strategies to rationally address complexity.
- The course begins with the study of linear systems and the rich mathematical foundations for understanding and predicting their behaviors.
- The course then moves to non-linear systems: What makes them complex and difficult, and why is the mathematical treatment of these systems so much harder? We will explore several biological examples of non-linearity in fields ranging from structural biology to evolution, ending ultimately with a general definition of complexity in biology and an operational strategy for studying such systems.
- The syllabus includes a combination of analytical and computational exercises to solve as we go through the course; we will use MATLAB as our primary computing platform.
- The course is open to all graduate students and postdocs.
Advanced Data Analysis and Statistical Learning
- Introduction to methods for hypothesis testing and statistical inference, and statistical learning methods for prediction and classification
- In the first four weeks, the course will cover how to analyze different types of data, including analysis methods for continuous, categorical, survival, and time series data. Upon completion of the first four weeks, students should be able to think critically about data and apply appropriate statistical inference procedures to draw conclusions from such analyses.
- In the second four weeks, the course will discuss Bayesian statistics, image analysis and computational approaches for predictive modeling and data mining.
Statistical Analysis of Genetic Data
- This course focuses on statistical methods used in the analysis of genetic data. We will cover the principles underlying classical genetic study designs and discuss how these principles apply to the design and analysis of modern genetic studies, involving high-throughput DNA sequencing data.
- By the end of the course students will have acquired:
- Basic understanding of principles underlying genetic association and linkage studies
- Knowledge of common designs used in human genetic studies
- Basic understanding of statistical methods used in the analysis of human genetic studies
- Ability to perform simple analyses of genetic data
- Ability to understand research studies in human genetics
Genomics – Eukaryotic Genome Sequencing and Assembly: A Practical Exercise
Summer – 4 week course
Course Director: Nick Grishin, Ph.D.
- Hands-on course focused on sequencing and assembly of an animal genome
- The course begins with a theoretical overview of the entire process.
- The lab portion includes all steps of sequencing and sequence assembly. Each student gets a tissue sample (one species per student), performs DNA extraction, and prepares the library for Illumina sequencing.
- During the period the DNA is being sequenced, students study the computational methods of genome assembly from NGS reads and learn how to use relevant software. This knowledge is applied to the sequencing data from libraries made by the students.
- In lieu of a final exam, successful students prepare manuscripts describing their just sequenced genomes for publication.
- Prerequisites for the course are:
- Basic lab experience, i.e., familiarity with elementary DNA manipulation techniques; and
- Basic UNIX skills, e.g., listing directories, editing files, execution of programs by command lines.
- This is not an easy course. However, it is as close to real scientific project as it gets, and in addition to learning, should result in exciting new discoveries.
Computational Image Analysis Nanocourse
- An introduction to state-of-the-art computer vision methods to convert image data into quantitative information
- Topics covered include image enhancement and filtering, segmentation, object detection and tracking, colocalization analysis, morphological operators and machine learning approaches.
- Intensive four-day course, with topics covered in theory lectures and associated hands-on computer exercises using popular programs (ImageJ, CellProfiler and Matlab).
- The course objective is to provide biomedical scientists with the background to:
- Search for and evaluate existing image analysis software and
- Start devising their own image analysis pipeline/software.
- The course will also include an “image analysis therapy” session where the class can brainstorm about each other’s image analysis problems.
- The course is open to any interested person at UT Southwestern, provided they utilize imaging and are interested in computational image analysis for their research. Some background in mathematics and programming, e.g. by taking the Mathematical Foundations of Quantitative Biology course and the Matlab bootcamp, is a plus.
Seminar in Computational Biology, Work-in-Progress Series, Journal Club
1 session per week
- The Computational Biology (CB) Track includes a biweekly seminar featuring national leaders in broad areas of computational biology. The event is open to all members of the community. After the seminar a meet-the-speaker luncheon is offered to all attendees.
- Alternating with the seminar series students enrolled in CB track participate in a Works-in-Progress and Journal Club series where they present their work and discuss the literature.
- The Works-in-Progress seminars are open to the CB community, giving the students an ideal venue to practice their presentation skills and get critical feedback on their work. Additionally, there are talks by postdoctoral fellows, exposing the students to talks by more senior colleagues, and encouraging discussion among different lab members.
- The Journal Club is led by two faculty (rotating each year), with a theme each year based on the faculty’s expertise. As the CB community at UTSW is diverse, the goal of the journal club is to give the students a good foundation in the different CB fields through a discussion-based format that also promotes critical thinking.