The GSTP curriculum gives trainees a selection of courses in the genomic sciences and quantitative sciences areas. Physical and engineering courses are listed with the biological-genetics based courses under Genomic Sciences Courses. Computational / mathematical / statistical courses are listed under Quantitative Sciences Courses.
Required Courses
Genomic Science (Gen/Chem 626; required)
This course is designed to bring cutting-edge topics in the genomic sciences into the reach of traditionally “pure” chemistry, biology, engineering, computer science, and statistics students. Advanced lecture topics include single molecule systems, mass spectrometry, advanced microscopy technologies, spatial genomics, scSEQ approaches, micro-nano-fluidics, and CRISPR technologies, presented within the context of enabling large, high-dimensional datasets for analysis.
Responsible Conduct of Research (Biochem 729, Section 8).
All trainees take this course in the Fall semester of their first year.
The course is discussion-based and covers the 11 points defined by NIH for responsible conduct of research (RCR) training. The topics will be introduced largely through the use of case studies.
Genomic Sciences Program Seminars (Chem 923)
All trainees take this course all Fall and Spring semesters.
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Genomic Sciences Courses
Genomic Science (Gen/Chem 626; required)
This course is designed to bring cutting-edge topics in the genomic sciences into the reach of traditionally “pure” chemistry, biology, engineering, computer science, and statistics students. Advanced lecture topics include single molecule systems, mass spectrometry, advanced microscopy technologies, spatial genomics, scSEQ approaches, micro-nano-fluidics, and CRISPR technologies, presented within the context of enabling large, high-dimensional datasets for analysis.
Advanced Genomic and Proteomic Analysis (Gen 885)
With the availability of genome sequences and high-throughput techniques, organismal physiology can now be examined on a global scale by monitoring the behavior of all genes or proteins in a single experiment. This course will present modern techniques in genomics and proteomics, with particular focus on analyzing the data generated by these techniques.
Computational Modeling of Biological Systems (Bioch 570)
Introduction to the mathematical and computational tools needed to model biological systems spanning from molecules to ecosystems. Topics include protein folding and dynamics, gene regulation, biomolecular networks, and population dynamics. Teaches the fundamentals in quantitative thinking and analytical reasoning about complex biological systems
Principles of Human Diseases and Biotechnology (Bioch 550)
Biochemical and molecular analysis of selected human disease.
Clinical Cases in Medical Genetics (Gen 605)
The use of genetics in medicine has experienced significant growth over the past 50 years, identifying risk genes, and devising diagnostic tests and therapies based on this knowledge for specific clinical disorders. MDs and biomedical scientists will present lectures.
Human Genetics (Gen 565)
Principles, problems, and methods of human genetics. Surveys aspects of medical genetics, biochemical genetics, molecular genetics, cytogenetics, quantitative genetics, and variation as applied to humans.
Population Genetics (Gen 633)
Exploration of how genetic variation is influenced by mutation and recombination, population size changes and migration, and natural selection for or against new mutations. Prepares students for initiating research in the field.
Single Molecular Approaches to Biology (Bioch/Chem 918)
A combination of recent literature and original research presentations relating to the use of single molecule techniques in biochemistry including fluorescence microscopy, tethered particle motion, patch-clamping, cryo-electron microscopy, optical trapping, magnetic tweezers, and super resolution microscopy.
General Genetics (Gen 466)
Genetics in eukaryotes and prokaryotes. Includes Mendelian genetics, mapping, molecular genetics, genetic engineering, cytogenetics, quantitative genetics, and population genetics. Illustrative material includes viruses, bacteria, plants, fungi, insects, and humans.
Cell Signaling and Human Disease (CRB 701)
Landmark discoveries, as well as current knowledge and controversies in human health, with an emphasis on cancer biology, will be covered.
Biotechnology Operations (CRB 820)
Addresses issues related to the development and manufacture of products for human health, including medical devices and human therapeutics.
Quantitative Sciences Courses
Introduction to Bioinformatics (CS/BMI 576)
Algorithms for computational problems in molecular biology. Studies algorithms for problems such as: genome sequencing and mapping, pairwise and multiple sequence alignment, modeling sequence classes and features, phylogenetic tree construction, and gene-expression data analysis.
Statistical Methods for Bioscience I (Stat 571)
Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way ANOVA, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work.
Statistical Methods for Molecular Biology (Stat 877)
Develop statistical problems in gene mapping, high throughput -omics data analysis, phylogenetics and sequence analysis. Introduce ideas of key methods using published data. Statisticians learn statistical basis for research methodology.
Computational Network Biology (BMI 775)
Introduces networks as a powerful representation in many real-world domains including biology and biomedicine. Encompasses theory and applications of networks, also referred to as graphs, to study complex systems such as living organisms. Surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery. Enables hands-on experience in network biology by implementing computational projects.
Statistics in Human Genetics (BMI 826)
Statistical methods used in the analysis of human genetics and the human genome.
Bioinformatics for Microbiologists (Microbiol 657)
Provides a practical and fundamental introduction to sequence-based analysis focused on microbial systems. Emphasis on gaining a basic understanding of the principles of both classical and newer algorithms useful for bioinformatic analysis. Topics include: BLAST; RNA-seq analysis; transcriptional binding prediction; genome sequence assembly, analysis and annotation; and comparative genomics.
Mathematical Methods for Systems Biology (Bioch 609)
Provides a rigorous foundation for mathematical modeling of biological systems.
Advanced Bioinformatics (CS/BMI 776)
Advanced course covering computational problems in molecular biology. The course will study algorithms for problems such as: modeling sequence classes and features, phylogenetic tree construction, gene-expression data analysis, protein and RNA structure prediction, and whole-genome analysis and comparisons.
Data Science Programming I for Research (CS 319)
Introduction to Data Science programming using Python. In addition to a survey of programming basics (control flow and data structures), web scraping, database queries, and tabular analysis will be introduced. Projects will emphasize analyzing real datasets in a variety of forms and visual communication using plotting tools.
Responsible Conduct of Research (Biochem 729)
All trainees take this course during the Fall semester of their first year.
The course is discussion-based and covers the 11 points defined by NIH for responsible conduct of research (RCR) training. The topics will be introduced largely through the use of case studies.
Elective Courses
- Machine Learning: Intro to Artificial Intelligence (CS 540)
- Machine Learning (CS 760)
- Advanced Machine Learning (CS 761)
- Natural Language Processing: Natural Language and the Computer (CS 545)
- Natural Language and the Computer CS 769)
- Optimization: Intro to Optimization (CS 524)
- Linear Programming (CS 525), Tools & Environments for Optimization (CS 635)
- Integer Programming (CS 720)
Focus
Cellular and Molecular Biology
- Genomic Science (Gen 626)
- Statistical Methods for Bioscience I (Stat 571)
- Stat. Methods Mol Bio (Stat 877)
- Methods, Quantitative Biol (BME 780)
- Eukaryotic Molecular Biology (Bioch 620)
- Carcinogenesis and Tumor Cell Biology (Onc 7
- Fund. Stem Cell Regenerative Biol. (CRB 640)
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Biomedical Engineering
- Genomic Science (Gen 626)
- Stat. Meth. for Bioscience I (Stat 571)
- Bioinformatics for Biologists (Onc 778)
- Molecules, Cells and Systems (CBE 781)
- Cell Signals and Human Disease (Med 701)
- Cellular Biomanufacturing (CBE 562)
- Advanced Stem Cell Eng. (BME 602)
Chemical and Biological Engineering
- Genomic Sciences (Gen 626)
- Intro to Bioinformatics (BMI 576)
- Biochemical Engineering (CBE 560)
- Modeling Biological Systems (CBE 782)
- Physiol of Microorganisms (Microbiol 526)
- Prokaryotic Mol Biol (Microbio 612)
Financial Support
Predoctoral students must be in a Ph.D. program at the University of Wisconsin-Madison. All supported trainees must be permanent residents or U.S. citizens.
The grant from the National Human Genome Research Institute Support supports ten predoctoral and four postdoctoral research trainees each year. Traineeships are awarded one year at a time. Predoctoral traineeships generally awarded for a two- to three-year period; Postdoctoral traineeships generally awarded for a two-year period.
