Molecular Biology

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 testing correction, performance evaluation), and machine learning methods which have been applied to biological problems (e.g., classification techniques, hidden Markov models, clustering).

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 molecular biology (the tactics) inherent in these strategies. Another part introduces the biology of engagement of viruses with host defenses, what happens when viral infection leads to disease, vaccines and antiviral drugs, and the evolution of infectious agents and emergence of new viruses. Prerequisite: MOL 214 or permission of instructor.

Within a broader context of historical, social, and ethical concerns, a survey of normal childhood development and selected disorders from the perspectives of the physician and the scientist. Emphasis on the complex relationship between genetic and acquired causes of disease, medical practice, social conditions, and cultural values. The course features visits from children with some of the conditions discussed, site visits, and readings from the original medical and scientific literature. Prerequisite: MOL 214. Two 90-minute classes and an evening 90-minute precept.

This lab 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 will include synaptic transmission, fluorescent and viral tracers, patch clamp recording in brain slices, optogenetic methods to control neural activity, and computational modeling approaches. In-lab lectures give students the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves. Second half of a double-credit core course required of all NEU Ph.D. students.

A survey of modern neuroscience in lecture format combining theoretical and computational/quantitative approaches. Topics include cellular neurophysiology, neuroanatomy, neural circuits and dynamics, neural development and plasticity, sensory systems, genetic model systems, and molecular neuroscience. This is one-half of a double-credit core course required of all Neuroscience Ph.D. students.

This lab course complements NEU 502A 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 electrophysiological recording, functional magnetic resonance imaging, psychophysics, and computational modeling. In-lab lectures give students the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves. Second half of a double-credit core course required of all Neuroscience Ph.D. students.

A survey of modern neuroscience in lecture format combining theoretical and computational/quantitative approaches. Topics include systems and cognitive neuroscience, perception and attention, learning and behavior, memory, executive function/decision-making, motor control and sequential actions. Diseases of the nervous system are considered. This is one-half of a double-credit core course required of all Neuroscience Ph.D. students.

How do seemingly simple organisms generate complex behaviors? Course will explore our current understanding of the genetic and neural basis for animal behavior, with an emphasis on cutting-edge research and model systems that are amenable to genetic manipulation. Each week students will discuss a new behavior with a focus on the underlying mechanisms; students will also lead discussions of primary literature. The goal of this course is to provide required background knowledge and critical thinking skills to move beyond the published literature to proposing original experiments. This effort will culminate in a final paper from each student.

Couse focusses 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 cellular communication; and cytoskeleton assembly, regulation, and function. A major goal of the course is to increase students' proficiency in parsing and critically discussing papers from the primary literature.

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, and linear and non-linear dimensionality reduction. The software packages RELION and ASPIRE are routinely used for class demonstration on both simulated and publicly available experimental datasets.

Advanced-level discussions of selected topics in prokaryotic and eukaryotic molecular biology. Emphasis is placed on original research papers and extensive reading together with critical thinking is required. Topics include the genetic code, mutagenesis, chromosome and chromatin structures, mechanisms of DNA replication, recombination, repair, and transposition, gene structure and function and mechanisms of gene regulation. Examples from bacteriophage, bacteria, lower and higher eukaryotes will be used to illustrate these different areas of molecular biology.

Modern biology increasingly relies on quantitative tools to precisely measure cellular states. This course aims to provide an introduction to the experimental techniques and computational methods that enable the quantitative study of biological systems. It focuses on generating and analyzing sequencing data for studying gene networks within/across species, modeling chemical reactions to study the dynamics of gene and protein networks, and extracting information about the spatial organization of biological systems using image processing. It also introduces Python programming, a versatile and powerful platform for scientific computing.

Course explores the molecular events that contribute to the onset and progression of human cancer. Reviews the central elements that make up the cell cycle, then surveys the signal transduction and checkpoint pathways that regulate and coordinate the cell cycle with other cellular events. Oncogenes, tumor suppressor genes and mutator genes will be discussed. Course then explores specific clinical case studies in light of the molecular events underlying different forms of cancer.

An introduction to the biophysics of nerve cells and synapses, the mathematical description of neural networks, and how neurons represent information. This course surveys computational modeling and data analysis methods for neuroscience and parallels some topics from 549, but from a computational perspective. Topics include representation of visual information, spatial navigation, short-term memory, and decision-making. Two 90 minute lectures, one laboratory. Lectures in common with MOL 437. Graduate students carry out and write up an in-depth semester-long project. Prerequisite: 410, or elementary knowledge of linear algebra.

Students perform research in the laboratories of two faculty advisers.

Students perform research in the laboratories of two faculty advisers.

Structures and properties of biological macromolecules. The forces and interactions that direct biological polymers to adapt particular 3-dimensional structures are discussed from both a structural and a thermodynamic perspective. Special emphasis is placed on recent experimental work probing the folding and stability of proteins as well as on the design of novel proteins.

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.

Medicinal chemistry, mechanisms of action, uses and abuses of drugs and botanical extracts that affect the central nervous system are examined. Relevant issues of mental health are addressed as well as implications for public health. The history and current public policies toward psychotropic drugs and natural products are discussed. Topics include pain management and opiate addiction, schizophrenia hallucinogens and anti-psychotics, anti-depressants and anxiolytics, mechanisms of addiction and withdrawal.

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. The first half of the course covers the molecular biology (the tactics) inherent in these strategies. The second half introduces the biology of engagement of viruses with host defenses, what happens when viral infection leads to disease, vaccines and antiviral drugs, and the evolution of infectious agents and emergence of new viruses.

Through case studies and class discussion, this course will examine the social framework for the public support of basic biomedical research, the rights and responsibilities of students and mentors in the conduct of research, and the significance of intellectual property. Course will also review regulations concerning research with human subjects and animals. The nature of, and response to, personal misconduct will be a principal focus. Course satisfies the mandate of the National Institutes of Health for training molecular biologists in the ethical practice of science.

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