This course focuses on the molecules and molecular assemblies that underlie cellular structure and function. Topics include protein structure and folding; ligand binding and enzyme catalysis; membranes, ion channels, and translocation; intracellular trafficking; signal transduction and cell-cell communication; and cytoskeleton assembly, regulation, and function. A major goal of the course is to increase proficiency in parsing and critically discussing papers from the primary literature.

This course focuses on the molecules and molecular assemblies that underlie cellular structure and function. Topics include macromolecules and their analysis, enzyme kinetics, molecular self-assembly, molecular motion, biomolecular phase transitions, trafficking and interfaces among others. A second focus is on methods and approaches, including imaging methods, force measurements, mass spectrometry, as well as structural biology. A major goal of the course is to increase proficiency in parsing and critically discussing papers from the primary literature.

Light microscopy is used in nearly all forms of biological research. For the past half century, these tools have gone from simple devices for magnifying the cellular world to very complex machines capable of seemingly defying the laws of physics. This course introduces light microscopy with an emphasis on practical applications for life scientists. The physics of light and image formation are covered in addition to a discussion of modern imaging modalities used in today's research. These include fluorescence, multiphoton, super resolution, single molecule, and force microscopy.

Advanced-level discussions of topics in prokaryotic and eukaryotic molecular biology and genetics. Emphasis is placed on original research papers and extensive reading together with critical thinking is required. Part I (weeks 1-8) topics include the genetic code, mutagenesis, chromosomes, DNA replication, recombination, repair, and transposition, and gene structure, function, and regulation in bacteriophage and bacteria. Part 2 (weeks 9-12) focuses on classical and modern genetic tools and the logical framework used to study gene function in cell, developmental, disease biology of multicellular model organisms.

Mandatory first-year graduate course consisting of participation in weekly MOL Butler seminar series and meetings with seminar speaker. Meetings may include a range of activities such as student-driven presentations and discussions relevant to the seminar talk, speaker's research publications, and career development.

Students will perform research in the laboratories of two faculty advisers.

Course focuses on the knowledge and skills necessary to be successful in a graduate program in biological science. This course helps students develop technical, leadership, and professional and executive skills. Topics include time management, effective communication, data management, managing reading load, oral research presentation, and critical reading of scientific literature.

Viruses are unique parasites of living cells and may be the most abundant, highest evolved life forms on the planet. The general strategies encoded by all known viral genomes are discussed using selected viruses as examples. A part of the course is dedicated to the molbio (tactics) inherent to these strategies. Another part introduces the biology of engagement of viruses with host defenses, what happens when virus infection leads to disease, vaccines and antiviral drugs, and the evolution of infectious agents and emergence of new viruses. These topics are intertwined with discussions of modern technologies that benefit the field of virology.

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).

A survey of modern neuroscience in lecture format, focusing on brain function from cells and the molecules they express to the function of circuits. The course emphasizes theoretical and computational/quantitative approaches. Topics include cellular neurophysiology, neuroanatomy, neural circuits and dynamics, cell fate decisions, neural development and plasticity, sensory systems, and molecular neuroscience. Students read and discuss primary literature throughout the course. This is one-half of a double-credit core course required of all Neuroscience Ph.D. students.

This laboratory course complements NEU 501A and introduces students to the variety of techniques and concepts used in modern neuroscience, from the point of view of experimental and computational/quantitative approaches. Topics include synaptic transmission and plasticity, two-photon imaging, central neuron activity patterns, optogenetic methods to control neural activity and student-designed special projects. In-lab lectures give students the background necessary to understand the scientific content of the labs but the emphasis is on the laboratory work. Second half of a double-credit core course required of all NEU Ph.D. students.

Close reading of published papers illustrating the principles, achievements, and difficulties that lie at the interface of theory and experiment in biology. Two important papers, read in advance by all students, will be considered each week; the emphasis will be on discussion with students as opposed to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, and maximum likelihood methods. A general tutorial on Matlab and specific tutorials for the four homework assignments will be available.

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. Both lectures and primary literature discussions are 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.

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.

This course is taught from the scientific literature. We begin the semester with several classic papers on protein folding. As the semester progresses, we read about protein structure, stability, and folding pathways. The latter part of the semester focuses on recent papers describing new research aimed toward the construction of novel proteins from "scratch." These papers cover topics ranging from evolution in vitro to computational and rational design. The course ends by discussing the possibility of creating artificial proteomes in the laboratory, and further steps toward synthetic biology.

Introduces students to computational issues involved in analysis and display of large-scale biological data sets. Algorithms covered will include clustering and machine learning techniques for gene expression and proteomics data analysis, biological networks, joint learning from multiple data sources, and visualization issues for large-scale biological data sets. No prior knowledge of biology or bioinformatics is required; an introduction to bioinformatics and the nature of biological data will be provided. In depth knowledge of computer science is not required, but students should have some understanding of programming and computation.

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.

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.

Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course surveys computational modeling and data analysis methods for neuroscience. Example topics are short-term memory and decision-making, population coding, modeling behavioral and neural data, and reinforcement learning. Classes are a mix of lectures from the professor, and presentations of research papers by the students. Two 90 minute lectures. Lectures in common between NEU 437/NEU 537. Graduate students carry out a semester-long project.