Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• ENG BE 556: Optical Spectroscopic Imaging
  This introductory graduate-level course aims to teach students how electromagnetic waves and various forms of molecular spectroscopy can be used to study a complex biological system by pushing the physical limits on engineering system design.The course will cover fundamental concepts of optical spectroscopy and microscopy, followed by specific topics covering fluorescence-based , absorption-based, and scattering-based spectroscopic imaging. In addition, this course will provide in-depth discussions of linear and nonlinear spectroscopic imaging in the aspects of theory, instrumentation, image data analysis and enabling applications. Students will learn how to give a concise and informative presentation of a recent literature to the class. Students will be able to challenge their creativity in designing advanced imaging instrument of data analysis methods as part of their course assignments. The students will learn how to write and present a convincing proposal for the required final project to be designed by interdisciplinary teams formed among the students.
• ENG BE 560: Biomolecular Architecture
  Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho- lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed.
• ENG BE 561: DNA and Protein Sequence Analysis
  Fundamental concepts from molecular biology and molecular genetics are presented. Biological inferences are made from DNA and protein sequence data using mathematical and computer science techniques. Pairwise sequence comparative analyses and homolog identification are studied in detail. The dynamic programming algorithm is extended to deal with more general cases and is applied to RNA structure prediction. Additional topics include: multiple sequence alignment, and conserved sequence pattern recognition methods, phylogenetic tree reconstruction to study molecular evolution, methods of identifying coding regions in genomic data, algorithms to solve the fragment assembly problem of DNA sequencing, techniques for physical mapping, mathematical models and computations alogrithms for genetic regulation. An introduction to protein 3-dimensional structure predictions is also given.
• ENG BE 562: Computational Biology: Genomes, Networks, Evolution
  The algorithmic and machine learning foundations of computational biology, combining theory with practice are covered. Principles of algorithm design and core methods in computational biology, and an introduction of important problems in computational biology. Hands on experience analyzing large-scale biological data sets.
• ENG BE 564: Biophysics of Large Molecules
  The course considers the fundamental concepts of physical and mathematical description of polyatomic molecules and macromolecules on the basis of quantum and statistical mechanics. Special emphasis is given to molecular spectroscopy, the interaction of polyatomic molecules with electromagnetic radiation (visual light, ultraviolet and infrared radiation). Physics of macromolecules (or polymers) is treated in detail. Numerous biomedical applications of the fundamental concepts are considered including photosyntheses, molecular mechanism of vision, DNA damage under UV irradiation, structure of major biological molecules (proteins and nucleic acids).
• ENG BE 565: Molecular Biotechnology
  Covers the basic properties of biological macromolecules and assemblies including proteins, nucleic acids, and membranes. Among the topics covered are the forces that govern biological structures, how proteins act as catalysts, how membranes act to store energy, and how nucleic acids and proteins are synthesized in cells. Methods for manipulating the living cells to change their properties and to produce specific proteins of nucleic acids are detailed.
• ENG BE 566: DNA Structure and Function
  Physical structure and properties of DNA. The physical principles of the major experimental methods to study DNA are explained, among them: X-ray analysis, NMR, optical methods (absorption, circular dichroism, fluorescence), centrifugation, gel electrophoresis, chemical and enzymatic probing. Different theoretical models of DNA are presented, among them the melting (helix-coil) model, the polyelectrolyte model, the elastic-rod model, and the topological model. Theoretical approaches to treat the models, (e.g., the Monte Carlo method) are covered. Special emphasis is placed on DNA topology and DNA unusual structures and their biological significance. Major structural features of RNA are considered in parallel with DNA. The main principles of DNA-protein interaction are presented. the role of DNA and RNA structure in most fundamental biological proceses, replication, transcription, recombination, reparation, and translation is considered.
• ENG BE 567: Nonlinear Systems in Biomedical Engineering
  Introduction to nonlinear dynamical systems in biomedical engineering. Qualitative, analytical and computational techniques. Stability, bifurcations, oscillations, multistability, hysteresis, multiple time-scales, chaos. Introduction to experimental data analysis and control techniques. Applications discussed include population dynamics, biochemical systems, genetic circuits, neural oscillators, etc.
• ENG BE 568: Systems Biology of Human Disease
  This course will train students to apply or develop computational network, modeling, and machine learning concepts to probe into the systems biology of disease. The aim of this course is to cover general concepts in biological computing that provide the foundation of thinking computationally about anomalous behavior in biological systems that cause diseases. The course also aims to teach students to work in teams and develop the skills to plan and coordinate a scientific project. The course will cover computational frameworks, such as biological networks (including metabolic, regulatory, and signal transduction networks), micro array analysis, proteomic analysis, next generation sequencing, imaging, machine learning, probabilistic inference, genetics, pathway analysis, network and graph theory, and other technologies to medical diseases initially focusing on clincal problems such as cancer, diabetes, inflammation, and aging. The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics; however, students from other disciplines ranging from medicine to physics or computer science can attend the class with some prerequisites.
• ENG BE 569: Next Generation Sequencing
  The advent of high throughput sequencing is virtually changing biology and medicine. The technology enables us to catalog the entire functional parts list of living organisms from bacteria to human, develop and validate regulatory networks for controlling gene expression in systems biology models and develop novel biomarkers for personalized medicine that guide pharmacological treatments. In this course we will review the foundations of the field, starting from the biophysical foundations of current or emerging single molecule DNA sequencing techniques, through an introduction to the analytical tools to model and analyze NGS Data, and finally discussing clinical applications such as predicting drug response focusing on cancer. The course will involve bi-weekly homework assignments that include theoretical analysis and modeling, working with multiple analysis tools for NGS data including assembly, re-sequencing, alignments, RNA-seq, ChIP-seq, DNA methylation, mutation analysis and detection, copy number variation detection, and their applications to cancer.
• ENG BE 570: Introduction to Computational Vision
  Introductory course in biological visual neuroscience and computational vision. Provides a survey of the psychophysical, neuroanatomical and neurophysiological substrates of visual mechanisms underlying perception of visual motion, depth, objects, and space and of decision making mechanisms. Discussion of theoretical, explanatory, paradigms for these visual mechanisms. Topics addressed include psychophysics, methods from single cell recording physiology and low field potentials (LFP),multimodal imaging and computational modeling of various visual tasks and their modulation by attention. We will briefly address learning mechanisms and their relationship to brain plasticity. A term project is required for graduate credit.
• ENG BE 571: Introduction to Neuroengineering
  This course covers current and future neurotechnologies for analyzing the brain and for treating neurological and psychiatric diseases. It focuses on the biophysical, biochemical, anatomical principles governing the design of the current neurotechnologies, with a goal of encouraging innovations of new generation of therapies. Topics include basic microscopic and macroscopic architecture of the brain, the fundamental properties of individual neurons and ensemble neural networks, electrophysiology, DBS, TMS, various imaging methods, optical neural control technologies, optogenetics, neuropharmacology, and gene/stem-cell therapies. Discussions of related literatures and design projects will be involved. Enrollment is limited to 30 students. The course is open to MS, MEng, and PhD students, as well as qualified undergraduate seniors. This course meets with BE 771.
• ENG BE 575: Introduction to Neuroengineering
  This course covers existing and future neurotechnologies for analyzing brain signals and for treating neurological and psychiatric diseases. It focuses on the biophysical, biochemical, anatomical principles governing the design of current neurotechnologies, with a goal of encouraging innovations of a new generation of therapies. Topics include basic microscopic and macroscopic architecture of the brain, the fundamental properties of individual neurons and ensemble neural networks, electrophysiology, DBS, TMS, various imaging methods, optical neural control technologies, optogenetics, neuropharmacology, gene therapy, and stem-cell therapy. Discussions of related literatures and design projects will be involved. Meets with ENG BE 771.
• ENG BE 592: Biomed Elective
  
• ENG BE 593: ENG Elective
  
• ENG BE 594: Prof Elective
  
• ENG BE 595: Internatnl Crse
  
• ENG BE 600: Internatnl Crse
  
• ENG BE 601: Linear Algebra
  The first of four math modules designed to reinforce basic mathematical and computer programming concepts pertinent to graduate research in biomedical engineering. This course will emphasize the five cornerstones of applied linear algebra: Linear combinations, decompositions, orthogonality, metric, and linear transformations. Topics include LU and QR factorizations, finite difference methods for solving partial differential equations (PDEs), least squares, Fourier series and wavelets, solid mechanics, Markov chains, principal component analysis, and signal processing techniques. This course will provide the necessary linear algebra background needed to solve problems in BE 602, 603 and 604.
• ENG BE 602: Ordinary Differential Equations
  This math module will focus on four key ODE concepts: Linear dynamical systems, nonlinear conservative and excitable systems, discrete- time state machines, and generalized Fourier series solutions to Sturm- Liouville problems. Topics include: Filters, enzymatic networks, mechanical models for biomaterials, oscillators and limit cycles, phase- locked loops, nonlinear Leslie matrices, Legendre polynomials, Bessel functions, and a prelude to solving PDE problems associated with heat transfer, diffusion, and electrostatics. Prior exposure to linear algebra (BE 601 equivalent), and working knowledge of a programming language (Matlab, Python, etc.) is helpful.

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