Courses tagged with science
COSI 177a: Scientific Data Processing in Matlab (4) science
Introduces scientific computing using Matlab. Programming concepts such as data types, vectors, conditional execution, loops, procedural abstraction, modules, APIs are presented. The course will present scientific techniques relevant to computational science, with an emphasis on image processing. Usually offered every second year. (Brandeis)
CS 191 ab: Biomolecular Computation (9) science
This course investigates computation by molecular systems, emphasizing models of computation based on the underlying physics, chemistry, and organization of biological cells. We will explore programmability, complexity, simulation of, and reasoning about abstract models of chemical reaction networks, molecular folding, molecular self-assembly, and molecular motors, with an emphasis on universal architectures for computation, control, and construction within molecular systems. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton; physical limits of computation, reversibility, reliability, and the role of noise, DNA-based computers and DNA nanotechnology. Part a develops fundamental results; part b is a reading and research course: classic and current papers will be discussed, and students will do projects on current research topics. (Caltech)
CS 196 ab: Design and Construction of Programmable Molecular Systems (19) science
Part a: This course will introduce students to the conceptual frameworks and tools of computer science as applied to molecular engineering, as well as to the practical realities of synthesizing and testing their designs in the laboratory. In part a, students will design and construct DNA circuits and self-assembled DNA nanostructures, as well as quantitatively analyze the designs and the experimental data. Students will learn laboratory techniques including fluorescence spectroscopy and atomic force microscopy and will use software tools and program in Mathematica. Part b is an open-ended design and build project (Caltech)
CS 219 abc: Quantum Computation (9) science
The theory of quantum information and quantum computation. Overview of classical information theory, compression of quantum information, transmission of quantum information through noisy channels, quantum error-correcting codes, quantum cryptography and teleportation. Overview of classical complexity theory, quantum complexity, efficient quantum algorithms, fault-tolerant quantum computation, physical implementations of quantum computation. (Caltech)
CS 362: Computational Biology (6) science
Recent advances in high-throughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics. (Carleton)
COSC 16: Introduction to Computational Neuroscience (1) science
This course explores computational neuroscience, focusing on understanding how brains compute thought and reconstructing identified computations. Topics include anatomical circuit design, physiological operating rules, evolutionary derivation, mathematical analyses, and applications from robotics to medicine. (Dartmouth)
COSC 75: Introduction to Bioinformatics (1) science
Bioinformatics is broadly defined as the study of molecular biological information, and this course introduces computational techniques for the analysis of biomolecular sequence, structure, and function. While the course is application-driven, it focuses on the underlying algorithms and information processing techniques, employing approaches from search, optimization, pattern recognition, and so forth. The course is hands-on: programming lab assignments provide the opportunity to implement and study key algorithms. (Dartmouth)
COSC 86: Computational Structural Biology (1) science
Computational methods are helping provide an understanding of how the molecules of life function through their atomic-level structures, and how those structures and functions can be applied and controlled. This course will introduce the wide range of complex and fascinating challenges and approaches in computational structural biology, and will give hands-on experience applying and implementing some important methods. (Dartmouth)
CPS 363: Introduction to Bioinformatics (1) science
An introduction to the field of bioinformatics, addressing some of the important biology and computer science concepts related to it, with a focus on the computational aspects. Topics include a molecular biology primer, biological sequence alignments and analysis, gene mutation patterns, phylogenetic tree and construction algorithms, protein structures and functions, proteomics, application of basic machine learning algorithms, and other commonly used bioinformatics tools and resources. (F&M)
6.5160: Classical Mechanics: A Computational Approach (9) science
See description under subject 12.620. (MIT)
COS 455: Introduction to Genomics and Computational Molecular Biology (1) science
CS 47800: Introduction To Bioinformatics (3) science
Bioinformatics is broadly defined as the study of molecular biological information, targeting particularly the enormous volume of DNA sequence and functional complexity embedded in entire genomes. Topics will include understanding the evolutionary organization of genes (genomics), the structure and function of gene products (proteomics), and the dynamics of gene expression in biological processes (transcriptomics). Inherently, bioinformatics is interdisciplinary, melding various applications of computational science with biology. This jointly taught course introduces analytical methods from biology, statistics and computer science that are necessary for bioinformatics investigations. The course is intended for junior and senior undergraduates from various science backgrounds. Our objective is to develop the skills of both tool users and tool designers in this important new field of research. (Purdue)
COMP 485: Fundamentals of Medical Imaging I (3) science
This course will introduce basic principles of image acquisition, formation and processing of several medical imaging modalities such as X-Ray, CT, MRI, and US that are used to evaluate the human anatomy. The course also includes visits to a clinical site to gain experience with the various imaging modalities covered in class. (Rice)
COMP 486: Fundamentals of Medical Imaging II (3) science
This course focuses on functional imaging modalities used specifically in nuclear medicine such as Gamma cameras, SPECT, and PET imaging. The course will introduce the basic principles of image acquisition, formation, processing and the clinical applications of these imaging modalities and lays the foundations for understanding the principles of radiotracer kinetic modeling. A trip to a clinical site in also planned to gain experience with nuclear medicine imaging. (Rice)
CS 173A: Foundations of Computational Human Genomics (34) science
(Only one of 173A or 273A counts toward any CS degree program.) A coder's primer to Computational Biology through the most amazing 'source code' known: your genome. Examine the major forces of genome 'code development' - positive, negative and neutral selection. Learn about genome sequencing (discovering your source code from fragments); genome content: variables (genes), control-flow (gene regulation), run-time stacks (epigenomics) and memory leaks (repeats); personalized genomics and genetic disease (code bugs); genome editing (code injection); ultra conservation (unsolved mysteries) and code modifications behind amazing animal adaptations. Course includes primers on molecular biology and text processing. (Stanford)
CS 226: The Future of Mechanical Engineering (ME 228) (1) science
This seminar series provides an overview of current and emerging research topics in mechanical engineering and its application to engineering and scientific problems. The seminar is targeted at senior mechanical engineering undergraduates and mechanical engineering graduate students. Presenters will be selected external speakers who feature exciting and cutting-edge research of mechanical engineering. (Stanford)
CS 270: Modeling Biomedical Systems (BIOMEDIN 210) (3) science
At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. (Stanford)
CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214) (34) science
This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology. (Stanford)
CS 275: Translational Bioinformatics (34) science
Analytic and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. (Stanford)
CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (3) science
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. (Stanford)
CSE 185: Advanced Bioinformatics Laboratory (4) science
This course emphasizes the hands-on application of bioinformatics to biological problems. Students will gain experience in the application of existing software, as well as in combining approaches to answer specific biological questions. (UCSD)
CIS 1810: The Quantum and the Computer (1) science
This Freshman Seminar is designed to be a very introductory exposition about Quantum Computation and Quantum Information Science. There are no formal physics, mathematics, or computer science prerequisites. It is meant primarily for freshmen in SAS and Wharton, who have an itch to learn about a beautiful subject that intrinsically unites quantum physics, computation, and information science. The structure of the course will be lecture-based using small-team based exercises for evaluation. The enrollment will be limited to 20 students. Freshmen standing. (Penn)
CIS 3980: Quantum Computer and Information Science (1) science
The purpose of this course is to introduce undergraduate students in computer computer science and engineering to quantum computers (QC) and quantum information science (QIS). This course is meant primarly for juniors and seniors in Computer Science. No prior knowledge of quantum mechanics (QM) is assumed. Enrollment is by permission of the instructor. (Penn)
CIS 4360: Introduction to Computational Biology & Biological Modeling (1) science
The goal of this course is to develop a deeper understanding of techniques and concepts used in Computational Biology. Both theoretical and practical aspects of a range of methods will be covered. Theoretical aspects will include statistical analysis, modeling, and algorithm design. This course cannot provide a comprehensive survey of the field but focuses on a select core set of topics and data types. We will discuss the genome browser, alignment algorithms, classical and non-parametric statistics, pathway analysis, dimensionality reduction, GWAS, multiple testing and machine learning, with primary focus on biomedical data. UNIX, R and Python will be utilized to learn to execute big data analysis pipelines, including RNA-Seq and DNA-Seq. UNIX and R will be taught from first principles but programming experience in Python is expected. Students without prior experience with Python should consider taking PHYS 1100 before taking this class. You will be provided with a computational (cloud based) platform on which to do all programming and assignments. (Penn)
CIS 5350: Introduction to Bioinformatics (1) science
This course provides overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. Course material is aimed to address biological questions using computational approaches and the analysis of data. A basic primer in programming and operating in a UNIX environment will be presented, and students will also be introduced to Python R, and tools for reproducible research. This course emphasizes direct, hands-on experience with applications to current biological research problems. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing RNA, and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. The relevant principles underlying methods used for analysis in these areas will be introduced and discussed at a level appropriate for biologists without a background in computer science. (Penn)
CIS 5360: Fundamentals of Computational Biology (1) science
Introductory computational biology course designed for both biology students and computer science, engineering students. The course will cover fundamentals of algorithms, statistics, and mathematics as applied to biological problems. In particular, emphasis will be given to biological problem modeling and understanding the algorithms and mathematical procedures at the 'pencil and paper' level. That is, practical implementation of the algorithms is not taught but principles of the algorithms are covered using small sized examples. Topics to be covered are: genome annotation and string algorithms, pattern search and statistical learning, molecular evolution and phylogenetics, functional genomics and systems level analysis. (Penn)
CIS 5370: Biomedical Image Analysis (1) science
This course covers the fundamentals of advanced quantitative image analysis that apply to all of the major and emerging modalities in biological/biomaterials imaging and in vivo biomedical imaging. While traditional image processing techniques will be discussed to provide context, the emphasis will be on cutting edge aspects of all areas of image analysis (including registration, segmentation, and high-dimensional statistical analysis). Significant coverage of state-of-the-art biomedical research and clinical applications will be incorporated to reinforce the theoretical basis of the analysis methods. (Penn)
CMPU 353: Bioinformatics (1) science
DNA is the blueprint of life. Although it’s composed of only four nucleotide “letters” (A, C. T, G), the order and arrangement of these letters in a genome gives rise to the diversity of life on earth. (Vassar)
CS 313: Computational Biology (1) science
Many elegant computational problems arise naturally in the modern study of molecular biology. This course is an introduction to the design, implementation, and analysis of algorithms with applications in genomics. Topics include bioinformatic algorithms for dynamic programming, tree-building, clustering, hidden Markov models, expectation maximization, Gibbs sampling, and stochastic context-free grammars. Topics will be studied in the context of analyzing DNA sequences and other sources of biological data. Applications include sequence alignment, gene-finding, structure prediction, motif and pattern searches, and phylogenetic inference. Course projects will involve significant computer programming in Java. No biology background is expected. (Wellesley)
COMP 327: Evolutionary and Ecological Bioinformatics (1) science
Bioinformatic analysis of gene sequences and gene expression patterns has added enormously to our understanding of ecology and evolution. For example, through bioinformatic analysis of gene sequences, we can now reconstruct the evolutionary history of physiology, even though no traces of physiology exist in the fossil record. We can determine the adaptive history of one gene and all the gene's descendants. We can now construct the evolutionary tree of all of life. Bioinformatics is particularly promising for analysis of the ecology and biodiversity of microbial communities, since well over 99 percent of microorganisms cannot be cultured; our only knowledge of these organisms is through analysis of their gene sequences and gene expression patterns. For example, even when we cannot culture most of a microbial community, we can determine which metabolic pathways are of greatest significance through analysis of community-level gene expression. All these research programs are made accessible not only by breakthroughs in molecular technology but also by innovation in the design of computer algorithms. This course, team-taught by an evolutionary biologist and a computer scientist, will present how bioinformatics is revolutionizing evolutionary and ecological investigation and will present the design and construction of bioinformatic computer algorithms underlying the revolution in biology. Students will learn algorithms for reconstructing phylogeny, for sequence alignment, and for analysis of genomes, and students will have an opportunity to create their own algorithms. (Wesleyan)
CSCI 315: Computational Biology (1) science
This course will provide an overview of Computational Biology, the application of computational, mathematical, statistical, and physical problem-solving techniques to interpret the rapidly expanding amount of biological data. Topics covered will include database searching, DNA sequence alignment, clustering, RNA structure prediction, protein structural alignment, methods of analyzing gene expression, networks, and genome assembly using techniques such as string matching, dynamic programming, hidden Markov models, and statistics. (Williams)
CSCI 319: Integrative Bioinformatics, Genomics, and Proteomics Lab (1) science
What can computational biology teach us about cancer? In this lab-intensive experience for the Genomics, Proteomics, and Bioinformatics program, computational analysis and wet-lab investigations will inform each other, as students majoring in biology, chemistry, computer science, mathematics/statistics, and physics contribute their own expertise to explore how ever-growing gene and protein data-sets can provide key insights into human disease. In this course, we will take advantage of one well-studied system, the highly conserved Ras-related family of proteins, which play a central role in numerous fundamental processes within the cell. The course will integrate bioinformatics and molecular biology, using database searching, alignments and pattern matching, and phylogenetics to reconstruct the evolution of gene families by focusing on the gene duplication events and gene rearrangements that have occurred over the course of eukaryotic speciation. By utilizing high through-put approaches to investigate genes involved in the inflammatory and MAPK signal transduction pathways in human colon cancer cell lines, students will uncover regulatory mechanisms that are aberrantly altered by siRNA knockdown of putative regulatory components. This functional genomic strategy will be coupled with independent projects using phosphorylation-state specific antisera to test our hypotheses. Proteomic analysis will introduce the students to de novo structural prediction and threading algorithms, as well as data-mining approaches and Bayesian modeling of protein network dynamics in single cells. Flow cytometry and mass spectrometry may also be used to study networks of interacting proteins in colon tumor cells. (Williams)