This course will provide an introduction to genomics, epigenetics, and their application to problems in neuroscience. The rapid advances in genomic technology are in the process of revolutionizing how we conduct molecular biology research. These new techniques have given us an appreciation for the role that epigenetics modifications of the genome play in gene regulation, development, and inheritance. In this course, we will cover the biological basis of genomics and epigenetics, the basic computational tools to analyze genomic data, and the application of those tools to neuroscience. Through programming assignments and reading primary literature, the material will also serve to demonstrate important concepts in neuroscience, including the diversity of neural cell types, neural plasticity, the role that epigenetics plays in behavior, and how the brain is influenced by neurological and psychiatric disorders. Although the course focuses on neuroscience, the material is accessible and applicable to a wide range of topics in biology.

Some of the most serious public health problems we face today, from drug-resistant bacteria, to cancer, even covid, all arise from a fundamental property of living systems: their ability to evolve. Evolution permeates every system in flux and since Darwin’s theory of natural selection was first proposed, we have begun to understand how heritable differences in reproductive success drive the adaptation of living systems. This makes it intuitive and tempting to view evolution from an optimization perspective. However, genetic drift, trade-offs, constraints, and changing environ- ments, are among the many factors that may limit the optimizing force of natural selection. This tug-of-war between selection and drift, between the forces that produce variation in a population and the forces suppressing this variation, make the theory of evolution much more complex than previously thought and our understanding still far from complete.
The aim of this class is to provide an introduction into how biological systems are shaped by the forces and constraints driving evolutionary dynamics. I will also introduce population genetic theory as a lens for the understanding and interpretation of modern datasets. By the end of the course, you should have learned to appreciate the power of simple population genetic models, as well as the basic differences between idealized models and the data you might encounter in real life. The class is project-based and you will also work together to build your own models and explore open questions in evolutionary biology.

This course covers principles and applications of optical methods in the study of structure and function in biological systems. Topics to be covered include: absorption and fluorescence spectroscopy; interaction of light with biological molecules, cells, and systems; design of fluorescent probes and optical biosensor molecules; genetically expressible optical probes; photochemistry; optics and image formation; transmitted-light and fluorescence microscope systems; laser-based systems; scanning microscopes; electronic detectors and cameras: image processing; multi-mode imaging systems; microscopy of living cells; and the optical detection of membrane potential, molecular assembly, transcription, enzyme activity, and the action of molecular motors. This course is particularly aimed at students in science and engineering interested in gaining in-depth knowledge of modern light microscopy.

The purpose of this course is to review key cellular and molecular phenomenon in biological pathways with strong emphasis on latest experimental techniques used in applications including but not limited to disease diagnosis, therapeutics, large-scale genomic and proteomic analysis. Knowledge gained from this course will be both conceptual and analytical. Students will periodically write extensive research reports on select topics and give oral presentations on a select few, while critically analyzing primary literature.

This course will focus on advanced topics in Systems Immunology. Topics will include discussion of systems approaches, the general framework for applying approaches to biological problems, and specific immunologically relevant examples. The course is primarily intended to teach systems immunology techniques to graduate students in their second and third years so that they can adopt these approaches in their own research.

Comprehensive Immunology (2 credits)

This is a lecture course that will introduce the students to the fundamental concepts of modern immunology. The course will cover cells, tissues and organs of the immune system. Furthermore in-depth analysis of the development, activation, effector functions and regulation of immune response will be presented in this course.

Experimental Basis in Immunology (2 credits)

This course will expose the students to classical and contemporary literature in modern immunology. Emphasis will be on paper analysis and critical evaluation of primary data. This course will parallel the topics presented in comprehensive immunology lecture course which must be taken before or simultaneously with experimental basis of immunology.

Robotic scientific instruments are already used to decrease costs and increase reproducibility. Automated science and engineering take this one step further by leveraging Artificial Intelligence and Machine Learning to interpret data and select experiments in a closed-loop fashion. This emerging paradigm is motivated by the fact that most systems are too complex for humans to truly understand. Artificial Intelligence and Machine Learning can manage this complexity and find the most efficient paths to discovery and rational design by avoiding the costs of performing experiments where the outcome can already be predicted accurately.

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This course will focus on advanced topics in Systems Immunology. Topics will include discussion of systems approaches, the general framework for applying approaches to biological problems, and specific immunologically relevant examples. The course is primarily intended to teach systems immunology techniques to graduate students in their second and third years so that they can adopt these approaches in their own research.

By accurately controlling the movement of fluids at the microscale, microfluidics presents a unique opportunity to accurately establish mechanical and biochemical conditions that mimic the dynamic tissue environment in healthy and diseased tissue states. This course covers principles of biofluids, solid mechanics and mass transport that are applied for the design and analysis of biomedical microfluidic systems. Modeling of both healthy and diseased tissue microenvironment will be presented. Topics include tissue morphogenesis (e.g., epithelial layer and vascular pattern), tissue homeostasis (e.g., regeneration and wound healing) and cancer (angiogenesis and interactions with inflammatory cells). Lectures, in-class journal paper discussion and projects will focus on how microfluidic engineering can elucidate mechanistic understanding of cellular function and diseased tissues

Provides an in-depth look at modern algorithms used to process string data, particularly those relevant to genomics. The course will cover the design and analysis of efficient algorithms for processing enormous collections of strings. Topics will include string search; inexact matching; string compression; string data structures such as sux trees, sux arrays, and searchable compressed indices; and the Borrows-Wheeler transform. Applications of these techniques in genomics will be presented, including genome assembly, transcript assembly, whole-genome alignment, gene expression quantification, read mapping, and search of large sequence databases. No knowledge of biology is assumed; programming proficiency is required.

The course will focus on describing algorithms that work with strings and string-like data in a rigorous way. We will typically describe why the algorithms are correct and give proofs (sometimes abbreviated or sketched) for runtime. For each major topic, we will describe at least one application from genomics that motivates the developed algorithms. We will include examples from other application areas as well. We have the following objectives:

• Learn various algorithmic techniques and data structures for ecient processing of string data, including sux trees, sux arrays, Borrows-Wheeler transforms.
• Understand the why these algorithms and data structures work.
• Learn to apply and extend these algorithms and data structures.
• Learn about the practical application of these techniques, especially in genomics.
• At the end of this class, you should be familiar with much of the state-of-the-art in algorithms for strings, have familiarity with their use in practice, and have experience applying them to new problems.

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

The class will cover three aspects: the core representation, including Bayesian and Markov networks, and dynamic Bayesian networks; probabilistic inference algorithms, both exact and approximate; and learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong mathematical background to catch up and fully participate.

Some of the most serious public health problems we face today, from drug-resistant bacteria, to cancer, even covid, all arise from a fundamental property of living systems: their ability to evolve. Evolution permeates every system in flux and since Darwin’s theory of natural selection was first proposed, we have begun to understand how heritable differences in reproductive success drive the adaptation of living systems. This makes it intuitive and tempting to view evolution from an optimization perspective. However, genetic drift, trade-offs, constraints, and changing environ- ments, are among the many factors that may limit the optimizing force of natural selection. This tug-of-war between selection and drift, between the forces that produce variation in a population and the forces suppressing this variation, make the theory of evolution much more complex than previously thought and our understanding still far from complete.
The aim of this class is to provide an introduction into how biological systems are shaped by the forces and constraints driving evolutionary dynamics. I will also introduce population genetic theory as a lens for the understanding and interpretation of modern datasets. By the end of the course, you should have learned to appreciate the power of simple population genetic models, as well as the basic differences between idealized models and the data you might encounter in real life. The class is project-based and you will also work together to build your own models and explore open questions in evolutionary biology.

Automated science and engineering combines Robotics, Machine Learning, and Artificial Intelligence to accelerate the pace of discovery and rational design. This course introduces students to the Machine Learning and Artificial Intelligence algorithms that enable this emerging paradigm. Emphasis is placed on techniques for sequential analysis (i.e., model discovery and hypothesis generation), design of experiments, and optimization to maximize the return on research capital. Specific approaches will include Active Learning, Reinforcement Learning, and Bayesian Optimization. Examples of automated science and engineering from the literature will be studied.

Robotic scientific instruments are already used to decrease costs and increase reproducibility. Automated science and engineering take this one step further by leveraging Artificial Intelligence and Machine Learning to interpret data and select experiments in a closed-loop fashion. This emerging paradigm is motivated by the fact that most systems are too complex for humans to truly understand. Artificial Intelligence and Machine Learning can manage this complexity and find the most efficient paths to discovery and rational design by avoiding the costs of performing experiments where the outcome can already be predicted accurately.

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The overarching aim of this course is to teach evolutionary concepts and bioinformatic skills that are central to research in molecular, cell, developmental and microbiology. Evolutionary trees (phylogenies) model the evolutionary history of descent with modification from a shared ancestor, evolutionary relatedness, and patterns of divergence. Originally introduced to model species evolution, phylogenetics is now a primary tool for understanding the evolution of genes and proteins. Evolutionary models of protein evolution are of great practical importance because shared ancestry is a strong predictor of shared function. This assumption underlies many sequence‐based bioinformatics applications. Model organism research rests on the assumption that genes that share common ancestry (orthologs) perform the same function in related species. Reconstruction of evolutionary relationships in protein families is central to
identifying the appropriate target of study in an animal model.

The objective of the course to make the theory and practice of evolutionary bioinformatics
accessible to a broad biological audience and to accommodate students with a range of computational
backgrounds and skills. Students in 03‐327/727 acquire “tree thinking” skills required for critical interpretation of phylogenetic analyses and figures in the
literature; a detailed understanding of phylogenetic inference methods without relying upon formal mathematics; hands‐on experience working with sequence data repositories, bioinformatic tools for database retrieval, sequence analysis, and tree building; the knowledge required to apply those tools correctly to messy, genuine data sets, and the ability to think critically about abstract evolutionary models and evaluate alternate hypotheses in light of bioinformatic analyses. Students will acquire the knowledge and skills to carry out a phylogenetic analysis independently after completing the course.

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

The class will cover three aspects: the core representation, including Bayesian and Markov networks, and dynamic Bayesian networks; probabilistic inference algorithms, both exact and approximate; and learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong mathematical background to catch up and fully participate.

Note: The 700 level version of the course should be taken if available.

Computer modeling is playing an increasingly important role in chemical, biological and materials research. This course provides an overview of computational chemistry techniques including molecular mechanics, molecular dynamics, electronic structure theory and continuum medium approaches. Sufficient theoretical background is provided for students to understand the uses and limitations of each technique. An integral part of the course is hands on experience with state-of-the-art computational chemistry tools running on graphics workstations. 3 hrs. lec.

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

The class will cover three aspects: the core representation, including Bayesian and Markov networks, and dynamic Bayesian networks; probabilistic inference algorithms, both exact and approximate; and learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong mathematical background to catch up and fully participate.

Like all branches of physical science, physical virology encompasses a search for simplifying generalities. However, viruses display a kaleidoscopic diversity that imposes limits on any generalization and provides tremendous opportunity for discovery.

The course covers latest methods in biological physics as well as fundamentals in physics of DNA, protein self-assembly and membranes using viruses as a physical object. This course also provides introductory level biochemistry and molecular biology lectures so that students with any background can participate in the course. Being an interdisciplinary and up-to-date research field involving fundamental theory and numerous applications, the emerging field of physical virology is aimed to attract students from any of the natural science disciplines (physics, chemistry and biology).

Automated science and engineering combines Robotics, Machine Learning, and Artificial Intelligence to accelerate the pace of discovery and rational design. This course introduces students to the Machine Learning and Artificial Intelligence algorithms that enable this emerging paradigm. Emphasis is placed on techniques for sequential analysis (i.e., model discovery and hypothesis generation), design of experiments, and optimization to maximize the return on research capital. Specific approaches will include Active Learning, Reinforcement Learning, and Bayesian Optimization. Examples of automated science and engineering from the literature will be studied.

Robotic scientific instruments are already used to decrease costs and increase reproducibility. Automated science and engineering take this one step further by leveraging Artificial Intelligence and Machine Learning to interpret data and select experiments in a closed-loop fashion. This emerging paradigm is motivated by the fact that most systems are too complex for humans to truly understand. Artificial Intelligence and Machine Learning can manage this complexity and find the most efficient paths to discovery and rational design by avoiding the costs of performing experiments where the outcome can already be predicted accurately.

Website

Note: The 700 level version of the course should be taken if available.

Computer modeling is playing an increasingly important role in chemical, biological and materials research. This course provides an overview of computational chemistry techniques including molecular mechanics, molecular dynamics, electronic structure theory and continuum medium approaches. Sufficient theoretical background is provided for students to understand the uses and limitations of each technique. An integral part of the course is hands on experience with state-of-the-art computational chemistry tools running on graphics workstations. 3 hrs. lec.