Course Descriptions

This course provides an introduction to computer systems and scientific computing environments to enable effective use of computational and data resources. The course assumes no prior computing experience and is broken into 3 component modules. These are:

• UNIX/Linux fundamentals
• Computer system architectures and applications in scientific computing
• Introduction to scientific programming in Python 3

Emphasis will be placed on real-world practicality by motivating study with examples and tasks relevant to bioinformatics, structural biology, imaging, and data science. The student will develop both

Credits: 3.0 | Fall

This course is a computer-science intensive program intended as a survey of algorithms – that is, computational methods used to solve appropriately defined problems, and their implementation on modern scientific computing hardware. Core to any modern discussion of algorithms is competency in one or more object-oriented programming languages, in addition to a deep dive into data structures, without which the discussion of practical algorithm implementation is not useful.

In this course, we use Python 3 as the core programming tool. The class is structured as 1.5 hours of lecture each week with a 1.5 hour lab component, for 12 total weeks.

Pre-requisites: Students must have significant, minimum scripting-level, programming experience with demonstrated productivity in one or more programming languages (python preferred). Exposure to object oriented programming and software engineering a plus, but not required for the motivated student.

If needed, programming prerequisites may be obtained by one or more of the following courses:

• Course #: BSR1015 Course Title: Computer Systems
• Course Title: Biomedical Software Engineering
• Course #: BSR1803 Course Title: Systems Biology: Biomedical Modeling
• Course #: BSR1010 Course Title: Introduction to R programming

Credits: 3.0 | Spring

Biomedical Science -Fall, is part 1 of a year long six-unit course that surveys a broad and comprehensive study of basic Molecular Cellular and Developmental Biology. The topics covered prepare students for both a career in biomedical research and for the advanced studies within the CAB, DSCB, GDS, IMM, and MIC multidisciplinary training area (MTAs). Biomedical Science is a required course for all first year students that intend to be members of these MTAs. The course is structured as a series of lectures; grade assessment is based on a mixture of in-class and take-home quizzes as well as one formal examination per unit.

Credits: 6 | Offered: Fall

Presents core molecular, cellular, and biochemical material within the context of physiology and pathophysiology of disease. There are five modules that make up the course: Introduction, Diabetes, Cancer, Renal Disease and Drug Abuse. The topics covered prepare students for advanced studies within the most quantitative MTAs of our PhD in Biomedical Sciences program.

Credits: 8.5 | Offered: Fall-Spring

This is a year-long, introductory core course divided into 4 separate units. Overall, the course will provide students with a rigorous foundation in the molecules, cells and circuits upon which nervous system function is based, how different brain systems are engaged to drive different behaviors and the nature of brain disorders that affect identified synapses, cells, circuits and systems.  Unit 1 covers Cellular and Molecular Neuroscience. Unit 2 covers Systems Neuroscience.

Credits: 7 | Offered: Fall

This course is required for all first-year graduate students, following NIH mandates. Specific topics for the eight 1 hour sessions:

• Research Misconduct
• Experimental design and data management practices
• Mentor and Trainee Responsibilities; Collaborative Research
• Conflicts of Interest; Intellectual property
• The Protection of Human Subjects
• (vi)The Welfare of Laboratory Animals
• Publication, authorship, and peer review
• Peer Review, the Grant Process, and Fiduciary Responsibility.

Each Session is ~45 minute lecture with 15 minutes of discussion.

Credits: 0.5 | Offered: Fall

This course is required for all first-year graduate students. Specific Topics for the eight 1 hour sessions: (i) Experimental Design; (II) Rigor at the bench; (iii) Validation of Biological and Chemical Reagents; (iv) Animal and Human Studies; (v) The importance and design of statistics; (vi) Data collection, storage and open science; (vii) Preparation of data for publication; and (viii) Review of NIH clearing house and discussion.

Credits: 0.5 Offered: Spring I.

This course is designed to train students, staff and faculty in commonly used methods to organize, mine and learn from data sets, especially those that are complex and large (big data). These methods include basic data concepts, classification, clustering, network inference and analysis and outlier/anomaly detection.  Students in teams will also be expected to conceive a relevant project at the beginning of the course and present their approach and results at the end.

Pre-requisites: Students must have significant, minimum scripting-level, programming experience with demonstrated productivity in one or more programming languages (python preferred, but R and Matlab acceptable). College-level mastery of basic mathematical and statistical knowledge of fundamental concepts should be obtained prior to starting class. Such concepts include basic calculus, linear algebra and probability distributions. If none of these pre-requisites are available, attending one or more of the following courses is required:

• Course #: BSR1015 Course Title: Computer Systems
• Course #: BSR1803 Course Title: Systems Biology: Biomedical Modeling
• Course #: BSR3400 Course Title: Introduction to Algorithms
• Course #: BSR1010 Course Title: Introduction to R programming

Credits: 3.0 | Spring