Last Updated on December 26, 2022
The University of Washington is a world-class leader in the field of computational biology, with an emphasis on bioinformatics and genome research. This blog sets out to connect UW students with relevant companies and careers in these areas. Content is provided by current students and faculty affiliated with the Department of Genome Sciences.
Is there anything else you need to learn about University Of Washington Computational Biology? If so, you need not be concerned because the following article will provide the information to answer your questions.
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Biology is fast becoming an information science, with large databases and sophisticated algorithms now essential tools in the field. Allen School faculty and students collaborate with researchers in biology and medicine on a wide range of computational problems that will ultimately enable us to understand and augment complex biological systems. The Allen School and the University of Washington are at the forefront of exciting innovations at the intersection of computation and biology to advance scientific discovery, develop new diagnostics and therapeutics, and usher in a new era of personalized medicine. Learn more about our work by visiting the pages of our individual researchers and labs and explore some of our highlighted projects below.
Center for Neurotechnology
The Center for Neurotechnology focuses on the development of innovative neural devices and methods for engineering neuroplasticity in the brain and spinal cord. The goal is to revolutionize the treatment of people living with spinal cord injury, stroke and other debilitating neurological conditions by engineering devices that restore lost or injured connections in parts of the nervous system to improve, assist, and restore sensory and motor function. The center also focuses on the discovery of fundamental neuroscience an engineering principles with broader implications for the treatment of neurological diseases such as Parkinson’s and essential tremor.
Explainable Artificial Intelligence for Medicine and Science (AIMS) Lab
Members of the AIMS Lab develop explainable artificial intelligence for applications in health care and the life sciences. Working in partnership with biomedical researchers and clinicians in the UW School of Medicine and external institutions, the team aims to improve our understanding of the biology underlying diseases such as cancer and Alzheimer’s; enable physicians to target disease treatments based on a patient’s individual molecular profile; and to provide useful, interpretable predictions for a range of conditions and risk factors to improve patient outcomes.
Neural Systems Laboratory
The Neural Systems Laboratory aims to advance our understanding of the brain using computational models and simulations, and apply this knowledge to the task of building intelligent robotic systems and brain-computer interfaces (BCIs). The lab’s work combines data and techniques from a variety of fields, ranging from neuroscience and psychology, to machine learning and statistics. Members are focused on understanding probabilistic information processing and learning in the brain; building biologically-inspired robots that can learn through experience and imitation; and developing interfaces for controlling computers and robots using brain- and muscle-related signals.
CSE 427: Computational Biology Algorithmic and analytic techniques underlying analysis of large-scale biological data sets such as DNA, RNA, and protein sequences or structures, expression and proteomic profiling. Hands-on experience with databases, analysis tools, and genome markers. Applications such as sequence alignment, BLAST, phylogenetics, and Markov models. Prerequisite: CSE 312; CSE 332.CSE 428: Computational Biology Capstone Designs and implements a software tool or software analysis for an important problem in computational molecular biology. Prerequisite: CSE 312; CSE 331; CSE 332; recommended: CSE 427.CSE 487: Advanced Systems And Synthetic Biology Introduces advanced topics in systems and synthetic biology. Topics include advanced mathematical modeling; computational standards; computer algorithms for computational analysis; and metabolic flux analysis, and protein signaling pathways and engineering. Prerequisite: either BIOEN 401, BIOEN 423,E E 423, or CSE 486. Offered: jointly with BIOEN 424/E E 424; W.CSE 488: Laboratory Methods In Synthetic Biology Designs and builds transgenic bacterial using promoters and genes taken from a variety of organisms. Uses construction techniques including recombination, gene synthesis, and gene extraction. Evaluates designs using sequencing, fluorescence assays, enzyme activity assays, and single cell studies using time-lapse microscopy. Prerequisite: either BIOEN 423, E E 423, or CSE 486; either CHEM 142, CHEM 144, or CHEM 145. Offered: jointly with BIOEN 425/E E 425.CSE 490i: Neurobotics The field of Neurobotics lies at the intersection of robotics and medicine. It aims to build a robot-human closed loop system to alter the neural control of movement as a way to rehabilitate, assist, and enhance human motor control and learning capabilities. Typically, the primary target population is individuals with strokes, spinal cord injuries, traumatic brain injuries, and other injuries that inhibit daily activities. However, it could also target sports medicine, military, and entertainment applications. This course is an introductory design course in Neurobotics focusing on learning about human neural control of movement, using physiological signals as inputs, and controlling a mechanical device. Students will learn simple control laws, hands on experience and programming in controlling robots, and applying knowledge of human movements to move the robot. There is a design project competition at the end of quarter.
CSE 527: Computational Biology Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as network reconstruction and analysis, sequence analysis, regulatory analysis and genetic analysis. Techniques such as Bayesian networks, Gaussian graphical models, structure learning, expectation-maximization. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.CSE 528: Computational Neuroscience Introduction to computational methods for understanding nervous systems and the principles governing their operation. Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning. Prerequisite: elementary calculus, linear algebra, and statistics, or by permission of instructor. Offered: jointly with NEUBEH 528.CSE 529: Neural Control Of Movement: A Computational Perspe Systematic overview of sensorimotor function on multiple levels of analysis, with emphasis on the phenomenology amenable to computational modeling. Topics include musculoskeletal mechanics, neural networks, optimal control and Bayesian inference, learning and adaptation, internal models, and neural coding and decoding. Prerequisite: vector calculus, linear algebra, MATLAB, or permission of instructor. Offered: jointly with AMATH 533; W.
university of washington computational biology phd
Computational Molecular Biology
The Computational Molecular Biology (CMB) certificate program is the cooperative effort of ten outstanding research departments at the University of Washington and the Fred Hutchinson Cancer Research Center. The program not only allows enrolled students to receive formal recognition for their work, but also facilitates connections and provides a forum for the entire CMB community to showcase student and faculty work as well as to discuss open problems and trends.
- Graduate Certificate in Computational Molecular Biology
Program director/interdisciplinary group chair
- Su-In Lee, Professor, Department of Computer Science & Engineering and Department of Genome Sciences
Primary Staff Contact
- Brian Giebel, Academic Manager, Genome Sciences
Interdisciplinary Faculty Group Membership
The following are the core/voting Graduate Faculty members of the interdisciplinary group. For a complete list of faculty active in the program, see the program website.
- Thomas Daniel, Professor, Department of Biology
- Su-In Lee, Associate Professor, Department of Computer Science & Engineering and Department of Genome Sciences
- John E. Mittler, Professor, Department of Microbiology
- William Noble, Professor, Department of Genome Sciences
- Herbert Sauro, Professor, Department of Bioengineering
- Daniela Witten, Professor, Department of Statistics and Department of Biostatistics
university of washington bioinformatics masters
Master of Science in Biomedical Informatics
The certificate emphasizes the acquisition of biological and computational expertise by supplementing graduate students’ existing background with necessary training in molecular biology, genomics, and computer science. There is a clear need for the development of expertise to analyze the growing amount of biological data generated from genomic, phenotypic, environmental, and other sources. The goal of the certificate is to provide the coursework and a richer academic environment for graduate student to synthesize information across multiple disciplines. The certificate is aimed to prepare highly qualified graduate students who have rigorous multidisciplinary training in molecular biology, genomics, and computer science.
The certificate is aimed at graduate students in engineering, sciences, computer science, and agriculture, although students from other colleges may also find it valuable. The primary objective is to provide students an interdisciplinary training in bioinformatics. Our goal is to develop among the students a critical scientific understanding of bioinformatics, including the biological and computational aspects of algorithm development and implementation.
Admitted Masters or Ph.D. students under the advisement of WSU faculty, and post-graduate professionals who earned their degree in an appropriate field, are eligible to apply for the certificate program. Students who are eligible will notify their department’s graduate committee and their guidance committee of their interest in the certificate. Once the guidance committee has agreed that it is in the student’s best interest to pursue and complete the certificate, the student will apply to the Bioinformatics Certificate committee. The application will include a statement from the student’s advisor and graduate committee supporting the application. In this way, we hope to enhance the disciplinary degree.
For students to excel and get the most out of their participation in this certificate, we anticipate that students should have proficiency in the following: one year of calculus, coursework in probability and statistics (strongly advised as it is required for some courses). It is also advisable for students to have 1 year of computer programming (coursework or experience), but it is not required.
The Institute for Informatics (I2) is pleased to offer a master of science in biomedical informatics. The master’s degree program is administered through I2, and the degree is conferred through Washington University School of Medicine.
More information about our programs can be found on the Graduate Programs in Biomedical Informatics webpage.
Master of Science
- 36 units
- Two to five years for program completion
- Full-time and part-time options
- Three tracks offered:
- Translational bioinformatics
- Applied clinical informatics
- Population health
Core Courses: All Tracks
All students in this program will be expected to take the core courses listed below:
- BMI 5302 Introduction to Biomedical Informatics I (3 units)
- BMI 5303 Introduction to Biomedical Informatics II (3 units)
- BMI 5304 Introduction to Biomedical Data Science I (3 units)
- BMI 5305 Introduction to Biomedical Data Science II (3 units)
- BMI 5200 Biomedical Informatics Journal Club (2 units)
- CLNV 510 Ethical and Legal Issues in Clinical Research (2 units) or MSB 512 Ethics in Biostatistics and Data Science (2 units)
- Up to 10 units of research including capstone or thesis (3 units)
Students in the MS program will be expected to demonstrate completion of a scientific writing course by the time of graduation. Students who have taken the equivalent at other institutions may be excused from this course with permission of the program director. If this requirement has not been met, students will enroll in CLNV 529. This course will not count toward the 36 units required for graduation.
- CLNV 529 Scientific Writing and Publishing (2 units)