Course Descriptions

A new curriculum is currently in development.

Computational and Systems Biology Coursework

Matlab Boot Camp

Summer – 1 week course
Daily 9 a.m.–11 p.m.
Course Directors: Gaudenz Danuser, Ph.D.; Andreas Doncic, Ph.D.Khuloud Jaqaman, Ph.D.

  • 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 (1st priority), MSTP students of all classes (2nd priority), and any biomedical researcher at UTSW 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

Fall – 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.

Introduction to Omics: Data Generation and Analysis

Fall – 4 week course
2 sessions per week
Course Directors: W. Lee Kraus, Ph.D., Ralf Kittler, Ph.D., and Gary Hon, Ph.D.

  • Introduction to the concepts of generation and analysis of high content/high complexity “omics” data sets and how information from diverse data sets is integrated to generate knowledge.
  • The course covers in overview format different types of high content data sets generated from a variety of ‘omics’ discovery platforms (i.e., genomics, proteomics, metabolomics, imaging, structure). These lectures include aspects of data preprocessing (focusing on quality control and reproducibility) and the basics of high content data set analyses. 
  • The course covers in overview format examples of strategies in data and analyses associated with the various discovery platforms.
  • Homework assignments expose students to data analysis and a variety of bioinformatics tools available on the internet.
  • The course is open to all graduate students and postdocs. Prior completion of the core course in statistics and the Matlab boot camp is recommended.
  • Many of the concepts taught in this course are discussed in greater depths in the spring and summer courses of the CSB track.  It is therefore an excellent platform for students to probe their interests and plan their further education in “omics” areas.

Mathematical Foundations of Quantitative Biology

Fall – 8 week course
Course Director: Kimberly Reynolds, Ph.D.

  • Review of the mathematical foundations of quantitative biology in preparation for the conceptual lectures in Quantitative Biology and other similar courses.
  • Week 1: Probability theory (basic rules, counting statistics, the binomial, Poisson, Gaussian, and Boltzmann distributions).
  • Week 2: Linear Algebra (Matrices, basic operations, the eigenvalue decomposition, the singular value decomposition)
  • Week 3: Ordinary differential equations (sequences and series, approximations, error propagation, solutions to ODE problems).
  • Week 4: Transform theory (Fourier, LaPlace, Hadamard transforms, solutions to systems of equations, sketching behavior).
  • Learning is focused heavily on problem sets and applications. Per week there is one lecture session, and two problem set sessions. 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 the subject of 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. Prior completion of the Matlab bootcamp is recommended

Quantitative Biology

Spring – 8 week course
2 sessions per week
Course Director: Rama Ranganathan, M.D., 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 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

Spring – 4 week course
2 sessions per week
Course directors: Yang Xie, Ph.D. and Guanghua Xiao, Ph. D.

  • Introduction to methods for hypothesis testing and statistical inference, and statistical learning methods for prediction and classification.
  • In the first two weeks, the course will cover how to analyze different types of data including analysis methods for continuous, categorical, survival, time series and spatial data. Upon completion of the first two 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 two weeks, the course will discuss statistical approaches for predictive modeling and data mining.

Machine Learning of Biological Data I

Spring – 4 week course
2 sessions per week
Course director: Tae Hyun Hwang, Ph. D.

  • Introduction to Machine Learning (ML) techniques and its applications in a wide range of biology and biomedicine. ML techniques have widespread use in biology to analyze large, heterogeneous, and usually noisy data sets (e.g., large genomic, epigenetic, phenotypic datasets) and extract meaningful information.
  • The course covers very basic concepts of ML of supervised learning (e.g., logistic regression, support vector machines, model selection and feature selection, ensemble methods) and unsupervised learning (e.g., clustering, k-means, non-negative matrix factorization) that are widely applied to biomedical research.
  • The course will include a project component with the opportunity to explore publicly available genomic data.
  • Each session explains the concept of one of the ML techniques followed by a practical application of the technique to real biological data. Students are welcome to bring their own data generated by their own labs.
  • Between sessions students get homework assignments to be solved individually or in groups.
  • The course use common open source software tools. Graduates of the course should be able to run ML algorithms in these software packaged to explore biological data.
  • The course is open to all graduate students and postdocs. Attendance by faculty and research staff is welcome, too. No prior experience with programming is required, but foundational training in statistics, as provided in the Core course: Introduction to Statistics is required.

Machine Learning of Biological Data II

Summer – 4 week course
2 sessions per week
Course director: Tae Hyun Hwang, Ph. D.

  • Advanced use of machine learning (ML) in diverse biomedical applications. Guided by student interest, the course covers topics in genome and microbiome data analysis, integration of diverse genomic data types, etc. and methods such as deep learning, sparse structure learning, and network analysis.
  • The course is taught as a guided journal club, with emphasis on framing problems in computational biology and computational medicine. Each session is divided into a brief introduction of the new topic in machine learning and computational biology by the instructor, followed by a critical discussion of relevant literature led by an assigned student.
  • The course contains a project component where students will apply under the instructor’s guidance a subset of the selected advanced methods. The students are welcome to bring their own data generated by their own labs.
  • Graduates of the course should be able to run ML algorithms and analyze biological data using open source tools. They also gain some experience in modifying these tools for particular data cases.
  • The course is open to all graduate students and postdocs. Attendance by faculty and research staff is welcome, too. A solid foundation in statistics and prior experience ML (attendance of the Machine Learning of Biological Data I) is required. Some experience in reading and editing program code is necessary. 


Summer – 4 week course
2 sessions per week
Course Director: Steven Patrie, Ph.D.

  • Intermediate level of instruction on tools, techniques, and informatics procedures applied in the fields of Proteomics and Metabolomics.
  • The course covers foundations associated with multidimensional analytical processing of proteomes/metabolomes including in-depth theoretical review of common sample processing, chromatographic and mass spectrometry (MS) tools/techniques.
  • The course provides hands on manipulation of MS and MSn data on peptides, proteins, post-translational modifications, and various metabolites.
  • The central piece of the course is an introduction to common informatics procedures and scoring methods to support high-throughput screens, particularly in the context of quantitative systems biology and/or clinical experiments. Informatics procedures for detailed analysis of protein microheterogeneity (i.e., proteoforms) are explored in detail.
  • The course is open to all students at UTSW. No prior background is required although experience with Matlab is beneficial.  The target audience for this class comprises students with combined interests in proteome/metabolome wetlab techniques, high-throughput screening with modern mass spectrometers, and quantitative/computational analysis on multidimensional datasets.

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.
  • Pre-requisites for the course are: 1) basic lab experience, i.e., familiarity with elementary DNA manipulation techniques; and 2) 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

September – 4 day course
Course director: Khuloud Jaqaman, Ph. D.

  • An introduction to state-of-the-art computer vision methods to convert image data into quantitative information.
  • Topics covered include image enhancement and segmentation, edge detection, object tracking, and colocalization analysis.
  • Intensive one-week course, with topics covered in theory lectures and associated hands-on computer exercises (mostly in Matlab).
  • The course objective is to provide biomedical scientists with the background to (1) search for and evaluate existing image analysis software and (2) start devising their own image analysis software.
  • The course is open to any interested person at UTSW, provided they have the mathematical and Matlab programming background. This background can be minimally acquired by attendance of the Matlab bootcamp and of the “Mathematical and Physical Foundations of Quantitative Biology” course.

Molecular Modeling

Summer – 2 week course
Course director: Junmei Wang, Ph. D.

  • The course will teach basic knowledge and techniques in modern molecular modeling and rational drug design. The concepts conveyed will prepare students to conduct molecular modeling studies for their own problems in the medical research.
  • Basic computer-aided drug design methods with emphases on molecular docking, pharmacophore modeling and virtual screening
  • Basic molecular simulation techniques and their applications in studying the structures, dynamics and functions of biomolecular systems
  • Fundamental concepts of electronic structure methods for both small and biomolecules
  • The course consists of a combination of classroom lectures, guided exercises and homework assignments. Main stream software packages for each topic are briefly introduced and guided exercises of using representative software packages are held in the classroom. Students are expected to work on real projects using the selected software packages (most of them are in the public domain) as homework to further deepen their understanding on the taught materials.
  • No particular requirements for attendance.

Next-Generation Sequencing Data Analysis Bootcamp

Summer – 1 week course
Daily 9 a.m.–5 p.m.
Course director: Tae Hyun Hwang, Ph. D.

  • The bootcamp course will introduce attendees with a strong biology background to the practice of analyzing high-throughput sequencing data from Illumina and other next-generation sequencing (NGS) platforms.
  • The course covers 1) basic Linux and other script languages for NGS analysis, 2) current NGS technologies, 3) NGS data format, 4) NGS quality control, alignment, visualization, 5) Post-processing and further analysis for processed data (e.g., somatic mutation, RNA-variants, differential expression detection, etc.) and practical NGS bioinformatics issues.
  • Some of analysis packages for pre- and post-processing tools and programming platforms (e.g., Perl, Python, R, etc.) are discussed.
  • Data examples are borrowed from a variety of biomedical research areas, and attendees have the opportunity to bring their own data for analysis.
  • 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 9AM – 5PM in classroom. Students then get homework for the evening that will deepen their understanding of the taught material
  • Graduates of the course should be able to map sequencing data to reference genomes and analyze the mapped sequencing data for variation, transcript prevalence (from mRNAseq data), and enriched genomic regions (from ChIP­seq data). In addition, they are trained in understanding sequencing data, finding potential problems and errors therein and interrogating data sets using bioinformatics tools.
  • The course is open to all graduate students and postdocs. Attendance by faculty and research staff is welcome, too. No prior experience with programming is required. 

Seminar in Computational Biology, Work-in-Progress Series, Journal Club

1 session per week

  • The Computational and Systems Biology (CSB) 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 CSB 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 CSB 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 CSB community at UTSW is diverse, the goal of the journal club is to give the students a good foundation in the different CSB fields through a discussion-based format that also promotes critical thinking.