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We strive not only to be at the cutting edge of systems biology research, but also to effectively pass on knowledge gained to developing researchers, ultimately furthering the understanding and innovation in this field. Thus,  A variety of courses meant to assist in this are offered at Vanderbilt University, including but not limited to:

Signal Measurement and Analysis (BME5600 or BME3600): Discrete time analysis of signals with deterministic and random properties and the effect of linear systems on these properties. Brief review of relevant topics in probability and statistics and introduction to random processes. Discrete Fourier transforms, harmonic and correlation analysis, and signal modeling. Implementation of these techniques on a computer is required. No credit for students who have earned credit for 3600. SPRING (3 units).

Foundation of Bioinformatics (BMIF 6310): This survey course introduces students to the experimental context and implementation of key algorithms in bioinformatics. The class begins with a review of basic biochemistry and molecular biology. The group will then focus on algorithms for matching and aligning biological sequences, given the context of molecular evolution. The emphasis will move from comparing sequences to the systems developed to enable high-throughput DNA sequencing, genome assembly, and gene annotation. Gene products will be the next focus as students consider the algorithms supporting proteomic mass spectrometry and protein structure inference and prediction. The informatics associated with transcriptional microarrays for genome-wide association studies will follow. Finally, the class will examine biological networks, including genetic regulatory networks, gene ontologies, and data integration. Formal training in software development is helpful but not required. Students will write and present individual projects. Undergraduates need the permission of the instructor to enroll. FALL. (3 units).

Methodological Foundations of Biomedical Informatics (BMIF 6315): In this course, students will develop foundational concepts of computation and analytical thinking that are instrumental in solving challenging problems in biomedical informatics. The course will use lectures and projects directed by co-instructors and guest lecturers. SPRING. (3 units).

Systems Biology (BMIF 7311): This survey course presents the student with the historical, conceptual, and technical foundations of systems biology as it relates to biomedical research using model systems as well as human disease. SPRING. (3 units)

Cancer Systems Biology (CANB 8347): This course introduces students to the field of Cancer Systems Biology, which aims to frame cancer as a complex biological system through multidisciplinary approaches linking biology, engineering, and computer science. It is designed to teach students how to apply “systems thinking” to the analysis and modeling of fundamental questions in cancer research. The course will provide an overview of basic concepts in systems biology, including complexity, systems dynamics, networks, evolution and game theory. A survey of mathematical, statistical and computational tools will empower students to apply these concepts to concrete cancer biology projects. Examples of class activities include: construction of gene or signaling networks using literature-based knowledge and existing databases; visualization of multidimensional data; and, basic programming workshops. There will be strong emphasis on designing “systems” experiments and interpreting results in a modern cancer research laboratory. (3 units).

Biophysical Models of Cancer (CANB 352A): The study of biophysical modeling in cancer biology, including models of DNA damage, avascular tumor growth, tumor cell motion and invasion, angiogenesis, transport within tumors, and therapy response. Prerequisites: MATH 2400 or MATH 2420/2610, one year calculus-based physics, or consent of instructor.


Quantitative Systems Biology in Single Cells (IGP 8002 MM2):  Technological advances have enabled the generation of large volumes of quantitative data in every field of the biomedical sciences that necessitates the pervasive use of computational analyses. In this module, we will introduce the subject of computational analysis in the context of systems biology, with a special focus on single-cell analysis. The student will learn basic programming concepts in R and Python and be exposed to advanced computational techniques with hands-on applications on datasets from key publications in the field. This module is not a literature-based course, and its intention is to exposed students to data analysis in a programming environment. Evaluation will be problem set-driven. Basic coding experience is recommended.