This course focuses on computational methods in cryo-EM, including three-dimensional ab-initio modelling, structure refinement, resolving structural variability of heterogeneous populations, particle picking, model validation, and resolution determination. Special emphasis is given to methods that play a significant role in many other data science applications. These comprise of key elements of statistical inference, image processing, optimization, and dimensionality reduction. The software packages RELION and ASPIRE are routinely used for class demonstration on both simulated and publicly available experimental datasets.

The cell biology of tissues is discussed covering the molecules and fundamental concepts in cell communication, adhesion, shape, division, and differentiation. How cells become different from one another in a developing organism is explored, focusing on important concepts and developmental strategies using model systems. Primary literature is used to introduce seminal work, classic and modern experimental approaches, and outstanding questions in cell and developmental biology. Students are expected to learn to read critically, think beyond the reading, and participate in presenting and discussing the materials.

Modern biology research increasingly relies on quantitative tools to make precise measurements of cell state. This course provides an introduction to the experimental techniques and computational methods that enable the quantitative study of biological systems. We start with an intro to programming using Python and we employ the learned skills to analyze proteomics and sequencing data for studying gene networks within and across species, modeling biochemical reactions to study the dynamics of gene and protein networks, and extracting information about the spatial organization of biological systems using fluorescence imaging.

We explore the molecular events leading to the onset and progression of human cancer. We review the central genetic and biochemical elements that make up the cell cycle, followed by a survey of the signal transduction pathways and checkpoints that regulate it. We discuss oncogenes, tumor suppressor and mutator genes that act in these pathways and review the role of viral oncogenes and their action on cells. We investigate the role of cancer stem cells and the interaction between tumor and the host environment. We explore specific clinical case studies in light of the molecular events underlying different cancers.

Students perform research in the laboratories of potential faculty advisors.

Satisfies the NIH mandate for training in the ethical practice of science. The course is discussion-based, and uses readings, videos, case studies and guest participants to examine basic ethical and regulatory requirements for the responsible conduct of research. Topics include: the nature of - and response to - research misconduct; collaborative research; protection of human and animal subjects; conflicts of interest and commitment; authorship, publication and peer review; mentorship; societal impacts of scientific research; diversity and inclusion in scientific research; and contemporary ethical issues in biomedical research.

A survey of modern neuroscience that covers experimental and theoretical approaches to understanding how the brain works. This semester builds on 501, focusing on how the circuits and systems of the brain give rise to cognition. The course covers the neural mechanisms responsible for vision, long-term memory, sleep, motor control, habits, decision making, attention, working memory, and cognitive control. How these functions are disrupted in neurodegenerative and neuropsychiatric disorders are also covered. This is the second term of a double-credit core lecture course required of all Neuroscience Ph.D. students.

This lab course introduces students to the variety of experimental and computational techniques and concepts used in modern cognitive neuroscience. Topics include functional magnetic resonance imaging, scalp electrophysiological recording, and computational modeling. In-lab lectures provide students with the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves, including student-designed experiments using these techniques. This is the second term of a double-credit core lab course required of all Neuroscience Ph.D. students.