It is important to register for classes as soon as possible to ensure timely payment of academic fees as well as your first stipend payment. Berkeley registration for fall courses typically opens in mid July; UCSF registration typically opens in August. Check your CalCentral and UCSF student portal pages for enrollment dates. Check for emails from the administration at your home campus about how many units you must enroll in at each campus. Feel free to reach out to peer advisors with questions about what courses to sign up for, but the most important thing is to register for the required amount of units ASAP; you can easily add and drop courses throughout the beginning of the term. Additionally, don't worry if your desired Berkeley classes are full. Just register on the waitlist and make sure to show up for the first class sessions, as students are usually able to secure a spot in these classes even if they were full at registration.
Students are required to complete a breadth requirement (Area Requirements) and a depth requirement (Major and Minor Area). Students are to maintain a minimum GPA of 3.0. Some of the courses used to satisfy Area Requirements may also be counted toward the Major or Minor Areas, but they must be taken while enrolled as a student in the program.
- Area Requirements
- Major and Minor Requirements
- Exceptions
- Coursework Recommendations
- Sample Courses for Majors and Minors
- Examples of Major and Minor Titles
Area Requirements
Students must complete the Area Requirements listed below some time during their academic career in the program. Students may apply courses taken prior to entering the program (pending approval by their Graduate Adviser) or may be selected from appropriate offerings at Berkeley or UCSF. The Area Requirements Form must be updated annually with approving signatures to the Program Administrator on the Home Campus during the Spring Semester of the first two years.
| Area | Semester Units | Quarter Units |
|---|---|---|
| Anatomy, Physiology and Biology | 9 | 13.5 |
| Biochemistry, and Chemistry beyond General Chemistry | 3 | 4.5 |
| Engineering in a traditional discipline and Computer Science | 7 | 10.5 |
| Mathematics (beyond linear algebra and differential equations) and Statistics |
2 | 3.0 |
Major and Minor Requirements
Students must identify a Major and a Minor field in which the student will complete 16 Semester Units/24 Quarter Units and 8 Semester Units/12 Quarter Units respectively on a graded basis. This requirement is designed for students explore an area of interest in depth.
| Area | Semester Units | Quarter Units |
|---|---|---|
| Major Requirements | 16 | 24 |
| Minor Requirements | 8 | 12 |
For help in designing your Major and Minor Areas, here are a few resources:
- UCSF Course Catalog
- MTM (UCSF and UC Berkeley) BioE Courses
- UC Berkeley Classes Guide
- UC Berkeley BioE Courses
Exceptions
- Area Requirements does NOT refer to the major and minor; if you come in with a B.S. in bioengineering, you have probably already satisfied all of the Area Requirements.
- Students may apply courses taken prior to entering the program (pending approval by their Graduate Adviser) or may be selected from appropriate offerings at Berkeley or UCSF.
- Some of the courses used to satisfy Area Requirements may also be counted toward the Major or Minor areas (see next section), but they must be taken while enrolled as a student in the Program
- Students who already hold a Master’s Degree or professional degree may use courses from their prior degree program toward their Minor field with approval from the Head Graduate Adviser. In this case, the Major field must be in an area complementary to the student’s prior training.
- Up to six (6) units of undergraduate upper division courses may be applied towards the major area of study with approval from the Head Graduate Adviser.
- One course with the S/U option (rather than letter-graded) is acceptable for meeting the requirements of the Major Area if the student is able to present sufficient justification for the inclusion of the course and following approval from both the student’s Graduate Adviser and the Head Graduate Adviser.
Coursework Recommendations
See the Peer Advising’s compiled list of coursework recommendations. Please note that course availability changes every quarter and semester and some courses are only offered every two years. Furthermore, course numbers and term availability may vary.
Year | Campus | Semester/Quarter [may change!] | Research field | Secondary research field | Course number [may change!] | Course name | Professor | Notes | Rating (1-5) |
|---|---|---|---|---|---|---|---|---|---|
| 2022 | Berkeley | Fall | Statistics | PBHLTH 245 | Introduction to Multivariate Statistics | Lexin Li | Course material is useful, lectures very dry, HW not too challenging, no exam | 3 | |
| 2022 | Berkeley | Spring | Public Health | PBHLTH 260F | Infectious Disease Research in Developing Countries | Eva Harris | Really interesting course material for anyone in global health, covers ethics & logistics, readings for hw, project at the end, discussion based, lots of guest speakers | 5 | |
| 2022 | Berkeley | Spring | Public Health | PBHLTH 266B | Zoonotic Diseases | Peter Dailey | Fascinating material, discussion based with readings and guest lectures, project at the end, good if you are interested in infectious diseases & public health | 5 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | CMPBIO C231 | Introduction to Computational Molecular and Cell Biology | Ian Holmes | Lots of material covered in a short amount of time, useful topics & good overview of comp bio, homeworks were a bit unrelated to lectures at times, exam & project at the end and weekly hw, a little programming experience is helpful | 3 | |
| 2022 | Berkeley | Spring | BioMEMS/Nanotechnology | MCELLBI C205 | Modern Optical Microscopy for the Modern Biologist | Robert Betzig | Great class for widefield fluorescence and super resolution microscopy. The final project might be challenging, but you get a chance to use the Mosaic | 5 | |
| 2022 | Berkeley | Fall | Immunology | PBHLTH 263 | Public Health Immunology | Sarah Stanley | If Sarah Stanley is teaching, take this! She is amazing and explains immunology so well. I still use the notes and what I learned in this class. | 5 | |
| 2022 | UCSF | Spring | Computational Biology & Bioinformatics | PHARMGENOM 219 | Pharmacokinetic Modeling Minicourse | Rada Savic | Minicourses were great! I love the format. This one had no hw, just workshop in class. Very practical course to learn NONMEM & R modelling for pharmacokinetics | 4 | |
| 2022 | Berkeley | Spring | Tissue Engineering & Regenerative Medicine | MCB 250 | Advanced Immunology | David Raulet, Bill Sha, Michel Dupage | low workload but lots of material covered, heavy emphasis on molecular biology methods/techniques, especially great survey of adaptive immunity | 4 | |
| 2022 | Berkeley | Fall | Tissue Engineering & Regenerative Medicine | PbHlth 263 | Public Health Immunology | Amy Garlin, Stephen Popper | low workload, really great intro/primer to immunology but does not go into depth | 4 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | DATA C200 | Principles and Techniques of Data Science | Anthony Joseph | Intense workload, but a great introduction to Python, data visualization, and basic machine learning concepts. Workload consists of a weekly programming assignments, 1 midterm, 1 final project, and a final. Would highly recommend for anyone with minimal programming experience and an interest in computational techniques that could be applied to large datasets. | 5 | |
| 2022 | Berkeley | Spring | ENGIN 295 | Communications for Engineering Leaders | Thomas Fitzpatrick & Susan Houlihan | This is 1-unit class that's basically just two full days of public speaking workshops. Everyone in the class is so supportive, whether you are afraid of public speaking or someone who loves it! The class also encourages a lot of reflection about your career journey and goals. I would recommend taking it in the middle of your grad career, so you will have a better idea of your research directions but will still have time to implement the lessons and goals you gained from the course. | 5 | ||
| 2022 | Berkeley | Fall | BioMEMS/Nanotechnology | BIOENG 221 | Advanced BioMEMS and Bionanotechnology | Aaron Streets | This course had a midterm (maybe two? can't remember), a final project, and several problem sets. This class is a lot of work but you learn a lot from it, especially if you are interested in omics-based biotechnology. The project was probably the hardest part of the class because I got grouped with masters students that hardly did any work. Overall I would recommend to take this class if you're interested in single-cell sequencing or diagnostic type research. | 4 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | PBHLTH 245 | Introduction to Multivariate Statistics | Lexin Li | This is a super chill class that has like 5 fairly straightforward problem sets and then a final project that can be based on your current research or research from a rotation. Lexin is really sweet and helpful and its a good course to learn R for statistics and that basics of multivariate statistical tests, regression, and clustering. It is definitely an intro course, you won't find any intense derivations, stats or machine learning here. If you're looking for a slam dunk 4 semester units, this is the course for you. | 5 | |
| 2022 | Berkeley | Spring | Biophysics/Biomechanics | BIOENG C215 | Molecular Biomechanics and Mechanobiology of the Cell | Mohammad Mofrad | This class was a little weird because it was entirely over Zoom during the pandemic and Mofrad did not have a GSI. The class literally consisted of a journal club presentation and FOUR papers, three of which had a 10 page minimum, one was like a 6 page minimum I think. Those were the only assignments but it was honestly so much work. Mofrad is a really nice guy and I am sure the class is better in person and in a different format, but it was definitely not fun when I took it over Zoom. It’s a good class if you want to learn about protein modeling, nucleus, cytoskeleton , and membrane physics, and molecular biomechanics; I would only take it though if those things are heavily applicable to your research interests. | 2 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | DATA C200 | Principles and Techniques of Data Science | Joseph Gonzalez and Andrew Bray | This is an excellent class if you want to learn the basics of Python programming and data science. It teaches you how to think like a data scientist and I found that approach helpful when I applied it to bioinformatics. I would say this class is a must-take if you have no Python experience and are looking to gain skills in Python. There are a TON of problem sets, a midterm, a final, and a final project. This class is a lot of work, but also a lot of fun; make sure that you find a good partner for the final project. Don’t worry if your grade is terrible in the class, I think I got like a 40% on the final and still ended up with an A in the class because they curve it like crazy for grad students. Just be prepared that this class is all online and theres like 1000 students in the class; definitely take this class with cohort buddies if you can. Also lastly, there is not really any legit ML in this course but there is a lot of data cleaning, regression, and clustering. | 5 | |
| 2022 | Berkeley | Fall | Tissue Engineering & Regenerative Medicine | PBHLTH 263 | Public Health Immunology | Amy Garlin and Stephen Popper | This is a great course for learning the basics of immunology. It covers into decent detail both innate and adaptive immune systems. This class had two midterms and a final and I think that those were the only assignments, attendance was mandatory and you sometimes had to give little 5 minute group presentations in class about the material. Amy was an MD who was super duper sweet and knowledgeable, and Stephen was an HIV researcher/lecturer who was less helpful, haha. I think this is a must take if you are doing any immunoengineering or disease related research in which interactions with immune cells are important. Overall the class wasn't too hard and I learned a lot. | 4 | |
| 2022 | Berkeley | Spring | Neural Systems & Vision Science | NEUROSC C262 | Circuit and Systems Neurobiology | Dan Feldman and Yang Dan | Awesome course that dives deep into most corners of systems and circuit neuroscience. The class consists of student led and instructor moderated journal clubs on one day of the week and then instructor lectures on the other day of the week. You learn a lot about how circuit neuroscience hypotheses are derived, experiments are set up, and how confounding results are analyzed. Dan Feldman and Yang Dang are both super knowledgeable and were amazing at dissecting papers and helping the class think through why certain decisions were made in the papers we were reading. In the class you will have to present one 45 minute journal club presentation, one midterm, and then one final term paper of about 6 pages I think. You should definitely take this course if you are interested in anything related to circuit level or systems level neuroscience. | 5 | |
| 2022 | UCSF | Winter | Systems & Synthetic Biology | Genetics 200A | Principles of Genetics | David Toczyski | This was a good general overview of genetics and genetics techniques. The class is pretty chill and just consists of attending lectures, reading papers, and 4 small problem sets. Be prepared that this is a Tetrad course so like 90% of the class is in the tetrad program and they look down upon us humble bioengineers (jk the tetrad students are super nice and helpful). This class was most useful in helping me further understand how to effectively use and design driver lines, inducible promoters, and other genetic promoter systems. They go over yeast, bacteria, mouse, and human genetics. One of the coolest parts of this class were these mini groups where you and like 6 other students get to directly discuss papers with a couple of the UCSF professors for a few weeks. I think my professors were Barbara Panning and Su Guo, a there were like 8 other professors that did these mini groups. | 4 | |
| 2022 | Berkeley | Fall | Systems & Synthetic Biology | PMB220A | Microbial Genetics | Taga | Good amount of papers you will have to read within the 5 weeks of the course. But a good minicourse for background in microbial techniques/genetics. | 5 | |
| 2022 | Berkeley | Spring | Systems & Synthetic Biology | PMB220E | Microbial Physiology | John Coates | Focused on microbial metabolism, so may not be relevant to everyone. But definitely and interesting course and professor. | 5 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | BioEng C231 | Introduction to Computational Molecular and Cell Biology | Ian Holmes | The exams aren't very well written, and the time commitment each week is inconsistent. Still, amazing content! It's a class that teaches you the algorithms and methods applied in computational biology, NOT a class to tell you what tool to use for your data. | 5 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | PBHLTH C240C | Biostatistical Methods- Computational Statistics with Applications in Biology and Medicine | Jingshen Wang | When I first got to Berkeley, I struggled to find a statistics course that wasn't basic, but also wasn't going to leave me in the dust. This felt like the perfect intermediate course. I took it during zoomU, and I feel like the professor struggled with virtual classes. So I imagine that taking it in person would be slightly more difficult. | 4 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | BioEng 241 | Probabilistic Modeling in Computational Biology | Ian Holmes | Difficult content, but it's truly a "get out what you put in" kind of class. Because of the journal club portion, the class varies each iteration. Past themes were- 2021: computational epidemiology, 2022: machine learning for protein design. | 5 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | BioEng 245 | Introduction to Machine Learning for Computational Biology | Liana Lareau | Great primer with heavy focus on application. This would be a good course to take before the Intro ML course in the EECS department (CS289). | 4 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | EECS 289 | Introduction to Machine Learning | Jennifer Listgarten, Jitendra Malik | It'll kick your ass, but you'll come out with a strong mathematical and probabilistic intuition. It's very worthwhile if you're going to be designing your own ML methods. | 5 | |
| 2022 | Berkeley | Spring | Neural Systems & Vision Science | CS 289A | Intro to Machine Learning | Jonathan Shewchuk | This 'introductory' course can be pretty advanced. It may be worth taking Data 200C prior to this for a proper introduction to ML. | 5 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | CMPBIO 275 | Computational Biology Seminar/Journal Club | Rasmus Nielsen | You attend every seminar for the computational biology department, and get an extra hour at the end to chat with the speakers. When it's not a seminar week, you read a paper. I thought the journal club portion was kinda hit or miss. | 3 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | CMPBIO 293 | Doctoral Seminar | Nilah Ioannidis | It's a required seminar for the designated emphasis in computational biology and genomics. Super easy, mostly worked in the back of the class during lectures. | 3 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | CS 288 | Natural Language Processing | Dan Klein | Content-wise, Dan is an amazing lecturer, and the homework is very instructional. However, the workload was incredibly unreasonable (I put 50+ hours into the first HW assignment). Also, if you're outside the EECS department, you had to get 100 on the first HW assignment, or you'd be kicked out of the course. So if you can come into a class and code a full-blown LSTM in pytorch, without any mistakes... congrats, you're allowed to stay. Truthfully, this course took a big toll on my mental health and wellbeing... | 3 | |
| 2022 | Berkeley | Fall | Systems & Synthetic Biology | BIOE247 | Principles of Synthetic Biology | Adam arkin | High workload but great for building a foundation in synthetic Biology | 5 | |
| 2022 | Berkeley | Fall | Computational Biology & Bioinformatics | BIOENG C231 | Introduction to Computational Molecular and Cell Biology | Ian Holmes | Homework problems involve writing python in Jupyter notebooks and feel very useful, but (open note) exams are tough and involve learning tons of terms and facts, as well as being fast with probability and math. A lot of time commitment and tough exams, but I learned a lot and it seems like the grades given were good. | 3 | |
| 2022 | Berkeley | Spring | Systems & Synthetic Biology | BIOENG 235 | Frontiers in Microbial Systems Biology | Adam Arkin | The class and slides are fairly disorganized so it's hard to complete the homeworks and exams (lot of self teaching_, so I'm not sure how much I retained and felt lost a lot. But lectures are interesting and it is a low pressure class in terms of grades. | 2 | |
| 2022 | Berkeley | Fall | Machine Learning | CS 267 | Introduction to Machine Learning | Listgarden et al. | Super useful if you are going to use machine learning or deep learning in your research. Workload depends on prior proficiency, but should be doable. | 5 | |
| 2022 | Berkeley | Spring | HPC | CS 267 | Parallelism in Computing | The guy who wrote LAPACK | Even if you are well acquainted with C/C++, data structures, and basic optimizations such as loop unrolling, cache utilization, and intrinsics the course will be hard. If you don’t leech off of some of the exceptional CS students (it’s mostly group projects), it’s one of the hardest CS courses available period. It requires a significant time commitment even for people who do this for work/research due to the difficulty debugging parallel code with OpenMP, MPI, Cuda, etc. For one assignment, I had to spend 4 16-hour days to finish it up. You do learn a lot about those packages, basics of computer architecture, and code optimization/parallelism. | 4 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | DATA C100 | Principles and Techniques of Data Science | Joey Gonzalez | Very useful to get a good overview of python, pandas, and a little bit of machine learning. I went into this course with pretty minimal coding experience and learned more than any other class I've taken in grad school, both general principles/concepts and practical skills I constantly apply to my research and data analysis. Its an undergrad course, so the workload is significant (1-2 assignments per week, plus a few projects) and the class size is massive, but I still highly recommend it! | 5 | |
| 2022 | Berkeley | Fall | BioMEMS/Nanotechnology | BIOENG 221 | Advanced BioMEMS and Biotechnology | Streets | Content heavy class and does take a bit of time -- weekly hw and two exams and a final project. can be time consuming depending on how much of the content is review for you. I found it super helpful and interesting! | 4 | |
| 2022 | Berkeley | Fall | Tissue Engineering & Regenerative Medicine | BIOENG 202 | Cell Engineering | Conboy | Class goes over interesting content, not too much homework until the final project | 3 | |
| 2022 | Berkeley | Spring | Computational Biology & Bioinformatics | DATA C200 | Principles and Techniques of Data Science | Josh Hug, Lisa Yan | Good class, lot's of work super content heavy (weekly hw and optional labs, exams) but is good if you have some data science knowledge and would like a refresher. Kind of math heavy but I would recommend this if you are or aren't on the computational track. overall helpful. | 3 | |
| 2022 | UCSF | Winter | Tissue Engineering & Regenerative Medicine | BIOE 221 | Tissue Mechanobiology | J. Lotz, T. Alliston, V. Weaver | interesting content and lectures, no homework, final debate is a good review to incorporate class content | 4 | |
| 2021 | Berkeley | Fall | Other - Statistics | PH245 | Multivariate statistics | Lexin Li | Good basic stats class, very practical. Great professor who takes time for questions, introduces concepts with context and immediate applications. Good introduction in using R programming, no exam and one final project. | ||
| 2021 | Berkeley | Spring | Biomedical Imaging & Instrumentation | EE290P | Advanced topics in bioelectronics - Bioelectronic implants | Rikky Muller | Great class on electrically-active implanted medical devices; also learned a lot about FDA regulations for medical devices | ||
| 2021 | UCSF | Spring | Other - Statistics | Computational Biology & Bioinformatics | Nursing 291 | Applied Stat Methods For Longitudinal & Hierarchical Data | Longitudinal data analysis; hands-on experience of analyzing repeated measures data, no exams | ||
| 2021 | Berkeley | Spring | Biomedical Imaging & Instrumentation | BioE C265 | Principles of MRI | Moriel Vandsburger | Must-take for anyone doing MRI research. Not recommended otherwise as it's too specific otherwise - you're better suited to just taking BioE C261 instead. This course is a follow-up to BioE C261. Includes lab sections with MRI scanners. | ||
| 2021 | UCSF | Fall | Biomedical Imaging & Instrumentation | BI201 | Principles of MRI | Slightly shorter/less intense course on MRI as compared to Berkeley course (BioE265) | |||
| 2021 | Berkeley | Spring | Other - Biochemistry | MCB100B | Biochemistry | In-depth biochem course for biochem undergrad majors | |||
| 2021 | Berkeley | Fall | Other - Biochemistry | CHEM135 | Chemical biology | Evan Miller | Upper-level undergrad course; it was a great class to fulfill chemistry area requirement; valuable for understanding fundamentals of macromolecular charge and structure. Evan Miller was an excellent professor and has served on quals committees | ||
| 2021 | Berkeley | Spring | Computational Biology & Bioinformatics | CS289a | Introduction to Machine Learning | Jonathan Shewchuk | Jonathan Shewchuk was an incredible professor - lectured well and gave clear explanations (probably not as good with different professors - I attended a lecture with Prof Sahai and it was extremely confusing). Great theoretical overview of lots of machine learning topics. Great fundamentals of the math in ML. Quite a large homework load. It is a very difficult course, but I learned so much & got a good grasp of the mathematical basis behind different machine learning algorithms. | ||
| 2021 | UCSF | Winter | Tissue Engineering & Regenerative Medicine | Biomechanics | BioE221 | Tissue Mechanobiology | Tamara Alliston, Val Weaver, Jeff Lotz, Sophie Dumont, Aaron Fields | The professors are really excited to teach this class (so excited that they were willing to teach it in Mission Bay for us even though they're all based in Parnassus), it's a great overview of interesting topics that each professor is an expert in. | |
| 2021 | Berkeley | Fall | BioMEMS/Nanotechnology | BioE 221 | BioMEMS | Aaron Streets | It's a lot of work (homework most weeks, midterm, poster and final project), but you end up learning a lot about micro/nanofabrication techniques and applications! | ||
| 2021 | Berkeley | Fall | Tissue Engineering & Regenerative Medicine | Biomechanics | BioE 211 | Cell and Tissue Mechanotransduction | Sanjay Kumar | It's a great intro to a research field that lots of people in our program work in, and you learn lots of cool things about cells! | |
| 2021 | Berkeley | Spring | Systems & Synthetic Biology | Other - Genetics | MCB240 | Advanced Genetic Analysis | MCB tag team | Teaches you how to properly apply genetic concepts to experiments well (rather than just memorizing/learning biology jargon) | |
| 2021 | Berkeley | Spring | Other - Biology | Biomaterials | MCB250 | Advanced Immunology | Nice overview of the native and adaptive immune system. "The best class I took in grad school". Emphasizes thinking about experiment design to answer scientific questions. | ||
| 2021 | Berkeley | Fall | Biomaterials | BioMEMS/Nanotechnology | BioE C250 | Nanomaterials in Medicine | Phil Messersmith | Covers many material characterization techniques. In the first 1/3 of the semester, Phil typically lectures; the later 2/3 of the semester consist of journal clubs/lit-review type classes. Good practice for writing proposals; has a mock study section which is helpful when thinking about how fellowships are reviewed. | |
| 2021 | UCSF | Spring | Drug delivery systems & Pharmacogenomics | Chemistry 219 | Drug Discovery Minicourse | Michelle Arkin | Intensive minicourse over 5 weeks worth 6 UCSF units. Brought in different experts in drug development to teach different components of drug design | ||
| 2021 | UCSF | Spring | Biomedical Imaging & Instrumentation | Biophysics 210 | Light Microscopy | Delaine Larsen | Great course that includes both principles and hands-on experience with various forms of microscopy. Very useful course that directly improved my research | ||
| 2021 | UCSF | Spring | Tissue Engineering & Regenerative Medicine | Developmental an Stem Cell Biology 270 | Stem Cell Epigenetics Minicourse | Barbara Panning | Very interesting course about current topics in epigenetic regulation of stem cells. Barbara Panning was a great instructor. | ||
| 2021 | Berkeley | Fall | Systems & Synthetic Biology | BioE247 | Principles of synthetic biology | Adam Arkin | Covers a lot of useful synthetic biology related tools and current literature. Not an easy class since co-listed with undergrads, but you get a lot of good, detailed knowledge about the field) | ||
| 2021 | Berkeley | Fall | Systems & Synthetic Biology | BioE248 | Bioenergy and sustainable chemical synthesis | John Dueber | Great introduction to the field as a whole and how you can use synthetic biology and metabolic engineering in a number of fields. Not as detailed technically, but gives a great overview of the field and brings in a lot of guest speakers from industry that is great, class is easy but requires a decent bit of writing of guest reflections). | ||
| 2021 | Berkeley | Spring | Systems & Synthetic Biology | Other - Chemical Engineering | CHMENG 274 | Biomolecular engineering | Dave Schaffer | Very detailed from the chemistry side of entropy and enthalpy of proteins and cells, it goes into a lot of very technical detail about chemical engineering stuff and biology, also not an easy class, but you learn a lot) | |
| 2021 | Berkeley | Spring | Computational Biology & Bioinformatics | Other - Genomics | PlantBio220B | Genomics and computational biology | Deutschbauer | 5 week 1.5 credit class part of PMB grad program but you learn a lot about sequencing and how to assemble genomes) | |
| 2021 | Berkeley | Spring | Systems & Synthetic Biology | Other - Biochemistry | PlantBio220E | Microbial physiology | Hans Carlson | You learn alot about all different types of metabolism and how it works within cells - highly recommend the class, super easy, only two short assignments and Hans is great). | |
| 2021 | Berkeley | Fall | Neural Systems & Vision Science | Computational Biology & Bioinformatics | VisSci 265 | Intro to Neural Networks | Bruno Olhausen | ||
| 2021 | Berkeley | Spring | Other - Communication | Other - Leadership | ENG295 | Communications for Engineering Leaders | Susan Houlihan & Thomas Fitzpatrick | 2 day crash course in being an effective communicator | |
| 2021 | Berkeley | Fall | BioMEMS/Nanotechnology | EE247A | Intro to MEMS | Greenspun | Pretty solid intro to MEMS class, learned a lot, would come in handy if doing MEMS research | ||
| 2021 | Berkeley | Fall | Biomedical Imaging & Instrumentation | BioE C261 | Medical Imaging Signals & Systems | Steve Conolly | Good introduction to fundamental imaging principles with a focus on the characteristics that make images clinically relevant. Very useful background knowledge of the most common imaging modalities (no ultrasound and optics, however) that sets the foundation for future classes. Great for ensuring you have the mathematical/signal processing chops to characterize and understand imaging system theory. | ||
| 2021 | UCSF | Spring | Biomedical Imaging & Instrumentation | Other - Biochemistry | BioE 241 | Metabolism and Magnetic Resonance Spectroscopy | John Kurhanewitcz | Highly informative about MR spectroscopy and how to use it to study metabolism, with sessions in the NMR lab. | |
| 2021 | Berkeley | Fall | Computational Biology & Bioinformatics | COMPSCI 294-148 | Topics in machine learning, inverse problems, and data analysis in computation neuro and medical imaging / Deep unsupervised learning [this is a special topics course which may vary from semester to semester] | Miki Lustig & Chunlei Liu | If one wants to be serious about machine learning, you should take all your CS classes at berkeley, this one is of particularly high quality. Take with a buddy, these courses are a lot of work | ||
| 2021 | UCSF | Winter | Computational Biology & Bioinformatics | BMI 203 | Biocomputing Algorithms | Ryan Hernandez | Very thorough dive into designing algorithms, great for anyone doing computational biology method development | ||
| 2021 | UCSF | Winter | Drug delivery systems & Pharmacogenomics | BPS 272A | Advanced Drug Delivery | Frank Szoka | Interesting overview of drug delivery field, consists of different weekly speakers and a final project with a partner. Weekly speakers are interesting because they are typically leaders in the field, either in academia or start-up founders in industry. They provide a real, and first-hand perspective on drug delivery techniques. | ||
| 2021 | Berkeley | Spring | Computational Biology & Bioinformatics | EECS 227AT | Optimization Models in Engineering | Fundamentals of optimization, useful for any person adjacent to algorithms. Mathematical fundamentals surrounding any optimization problem, including but not limited to machine learning. | |||
| 2021 | Berkeley | Fall | BioMEMS/Nanotechnology | Biomedical Imaging & Instrumentation | EE240A | Analog Integrated Circuits | For if you are planning to do any silicon/wafer level design or work adjacent to it. Otherwise, still a good class to understand the limitations and design parameters around instrumentation and amplifier design. Warning: probably the heaviest work load of all the Berkeley courses I took. | ||
| 2021 | Berkeley | Fall | Biomaterials | BioE C208 | Biological Performance of Materials | Kevin Healy | Good recap/overview of biomaterials and their various uses | ||
| 2021 | UCSF | Winter | Other - Epidemiology | Epidemiology of Aging | Good class to back to clinical relevance of disease treatments, easy A | ||||
| 2021 | UCSF | Spring | Computational Biology & Bioinformatics | Other - Microbiology | BMS 270 | Genomics and NGS Applications in Microbiology | Charles Chiu | Great interactive coding class for all stages of coders (beginner to experienced) | |
| 2021 | UCSF | Spring | Systems & Synthetic Biology | BP 219? | Biophysics: Modularity in Biology Minicourse | Wendall Lim & Hana El-Samad | Great intro and exposure to modular systems and systems biology | ||
| 2021 | UCSF | Fall | Tissue Engineering & Regenerative Medicine | Other - Biology | Development & Stem Cell Biology 257 | Development & Stem Cell Biology | Sarah Knox and Julie Sneddon | Great class for intro to developmental biology and practice for proposal writing | |
| 2021 | Berkeley | Spring | Drug delivery systems & Pharmacogenomics | Other - Public Health | PBHLTH275 | Current Topics in Vaccinology | Lee Riley | Great primer on how vaccines work at a global health scale and how they are developed/tested | |
| 2021 | UCSF | Fall | Drug delivery systems & Pharmacogenomics | PHARMGENOM245A | Basic Principles of Pharmaceutical Sciences | Kathy Giacomini, Deanna Kroetz, Les Benet, and others | Really great primer on PK/PD and ADME topics making it a foundational pick for people interested in drug delivery/pharmaceutical engineering. ESSENTIAL for anyone interested in drug delivery, and you learn from THE experts in the field (Kathy Giacomini, Les Benet, and Deanna Krotz). Just the vocabulary you learn is so important when talking about PK/PD, and it'll make you sound like you know what you're talking about when you go on job interviews | ||
| 2021 | Berkeley | Fall | Neural Systems & Vision Science | Other - Biology | MCB C261 | Cellular and Developmental Neurobiology | Hillel Adesnik & Stephen Brohawn | Great discussion based course with lots of literature reading, good option for anyone interested in neurobiology at the cellular level | |
| 2021 | UCSF | Spring | Biomedical Imaging & Instrumentation | Computational Biology & Bioinformatics | BioE 245 | Machine Learning for Medical Imaging | Sri Nagarajan, Valentina Pedoia | Great course to start off or continue in machine learning for anyone in medical imaging, class broken up into unsupervised ML and supervised ML sections, covers both theoretical and practical applications | |
| 2021 | Berkeley | Spring | Computational Biology & Bioinformatics | CSC200A | Principles and Techniques of Data Science | Joey Gonzalez and Ani Adhikari | This class gives a very useful overview of data science and python. The homework is very practical and useful, and there is a lot of guidance so even with very little programming experience it is not too hard to learn. | ||
| 2021 | Berkeley | Fall | Other - Biology | Biomaterials | PBHLTH 263 | Immunology | Sarah Stanley | I recommend this course purely for Sarah Stanley as a professor! She is a very good lecturer and does a good job of teaching the main principles without getting too bogged down in the specific details. | |
| 2021 | Berkeley | Fall | Biomedical Imaging & Instrumentation | BioE 168L | Practical light microscopy | Dan Fletcher | Gave a hands-on approach to teaching the basics of optics with labs that demonstrated the fundamentals very well. | ||
| 2021 | Berkeley | Fall | Other - Biochemistry | MCB C100A / CHEM C130A | Biophysical Chemistry: Physical Principles and the Molecules of Life | Recommended to fulfill chemistry area requirement | |||
| 2021 | Berkeley | Fall | Other - Medical Devices | BioEng 252 | Clinical Needs-Based Therapy Solutions | Syed Hossainy | Class with guest speakers from the medical devices field (both academia and industry), and group projects related to the speaker topics | ||
| 2021 | Berkeley | Spring | Other - Entrepreneurship | BioEng 253 | Biotechnology Entrepreneurship | David Kirn | Class with guest speakers from all aspects of biotech entrepreneurship (R&D, legal, business, academia...) and a group project | ||
| 2021 | Berkeley | Spring | Other - Data Processing | Astron 250 | Python Computing for Data Science | Josh Bloom | Excellent, very practical overview of using Python tools for a variety of data processing tasks. Weekly homeworks take time but it's worth it; final project uses Python to do data processing for your own research | ||
| 2021 | Berkeley | Fall | Biomaterials | BioMEMS/Nanotechnology | MecEng C217 | Biomimetic Engineering | Robert Full | Fun class where you learn about biology-inspired engineering, and do a group project proposing a new biomimetic product idea | |
| 2021 | Berkeley | Fall | Biomedical Imaging & Instrumentation | CompSci 294 | Computational Imaging | Ren Ng | Covers various aspects of computational imaging and has a group project at the end. The math may seem intimidating at times, but it's OK, you can do it 🙂 Especially good if you plan to work on designing microscope- or camera-based systems |
Sample Courses for Majors and Minors
Here are some example courses taken by BioE students to satisfy the major and minor requirements set forth by the program. It is meant to be used as a starting point for students who would like suggestions on courses that fit their research interests. Please note that course availability changes every quarter and semester and some courses are only offered every two years. Furthermore, course numbers and term availability may vary.
Note that there are no set major or minor focus areas that you must follow, and no set courses that you must take to satisfy a particular major or minor, which allows for maximum flexibility in taking courses that help you with your research area. The rules are detailed as follows under “Major and Minor Areas” in the Grad Student Handbook.
See the Peer Advising’s compiled list of sample courses.
Topic | Campus | Course number [may change!] | Course name |
|---|---|---|---|
| Biomaterials | UCB | BioEng 211 | Cell and Tissue Mechanotransduction |
| Biomaterials | UCB | BioEng 221 | Advanced BioMEMS and Bionanotechnology |
| Biomaterials | UCB | BioEng 224 | Basic Principles of Drug Delivery |
| Biomaterials | UCB | BioEng 290A | Stem Cell Technologies |
| Biomaterials | UCB | BioEng C118 | Biological Performance of Materials |
| Biomaterials | UCB | BioEng C216 | Macromolecular Science in Biotechnology and Medicine |
| Biomaterials | UCB | BioEng C223 | Polymer Engineering |
| Biomaterials | UCB | Chem 271A-C | Chemical Biology |
| Biomaterials | UCB | ChmEng 295D | Development of Biopharmaceuticals |
| Biomaterials | UCB | ChmEng 295S | Introduction to Expermental Surface Chemistry |
| Biomaterials | UCB | MatSci C250 | Nanomaterials in Medicine |
| Biomaterials | UCB | MecEng C217 | Biomimetic Engineering |
| Biomaterials | UCB | PbHlth 263 | Public Health Immunology |
| Biomaterials | UCSF | BIOE 221 | Tissue Mechanobiology |
| Biomaterials | UCSF | BIOE 225B | Tissue and Organ Biology |
| Biomaterials | UCSF | BIOE 242 | Principles of Tissue Engineering |
| Biomaterials | UCSF | BPS 245A | Principles of Pharmaceutical Sciences |
| Biomaterials | UCSF | PSPG 245B | Enzymes and Reactions |
| Biomechanics | UCB | BioEng 102 | Biomechanics |
| Biomechanics | UCB | BioEng 210 | Cell Mechanics and the Cytoskeleton |
| Biomechanics | UCB | BioEng 211 | Cell and Tissue Mechanotransduction |
| Biomechanics | UCB | BioEng 263 | Principles of Molecular and Cellular Biophotonics |
| Biomechanics | UCB | BioEng C119 | Occupational Biomechanics |
| Biomechanics | UCB | BioEng C215 | Mechanobiology of the Cell |
| Biomechanics | UCB | BioEng C223 | Polymer Engineering |
| Biomechanics | UCB | Chem 270A | Advanced Biophysical Chemistry |
| Biomechanics | UCB | ChmEng 250 | Transport Processes |
| Biomechanics | UCB | MecEng 212 | Heat and Mass Transfer in Biology |
| Biomechanics | UCB | MecEng 214 | Structure Function Relationships |
| Biomechanics | UCB | MecEng 224 | Mechanical Behavior of Engineering Materials |
| Biomechanics | UCB | MecEng C176 | Orthopedic Biomechanics |
| Biomechanics | UCSF | BIOE 221 | Tissue Mechanobiology |
| Biomechanics | UCSF | BIOE 240 | Magnetic Resonance Imaging |
| Biomedical Imaging & Instrumentation | UCB | BioEng 110 | Biomedical Physiology for Engineers |
| Biomedical Imaging & Instrumentation | UCB | BioEng 115 | Cell Biology for Engineers |
| Biomedical Imaging & Instrumentation | UCB | BioEng 164 | Optics and Microscopy |
| Biomedical Imaging & Instrumentation | UCB | BioEng 165 | Introduction to Medical Imaging |
| Biomedical Imaging & Instrumentation | UCB | BioEng 221L | BioMEMS and Bionanotechnology Laboratory |
| Biomedical Imaging & Instrumentation | UCB | BioEng 263 | Principles of Molecular and Cellular Biophotonics |
| Biomedical Imaging & Instrumentation | UCB | BioEng 290 | Biomedical Imaging Signal and Systems II |
| Biomedical Imaging & Instrumentation | UCB | BioEng C117 | Structural Aspects of Biomaterials |
| Biomedical Imaging & Instrumentation | UCB | BioEng C213 | Fluid Mechanics of Biological Systems |
| Biomedical Imaging & Instrumentation | UCB | BioEng C261 | Medical Imaging Signals and Systems |
| Biomedical Imaging & Instrumentation | UCB | BioEng C265 | Principles of Magnetic Resonance Imaging |
| Biomedical Imaging & Instrumentation | UCB | CompSci 160 | User Interfaces |
| Biomedical Imaging & Instrumentation | UCB | CompSci 294 | Computational Imaging |
| Biomedical Imaging & Instrumentation | UCB | CompSci 294 | Design of Health Technology |
| Biomedical Imaging & Instrumentation | UCB | CompSci 294 | Visualization |
| Biomedical Imaging & Instrumentation | UCB | ElEng 218A | Introduction to Optical Engineering |
| Biomedical Imaging & Instrumentation | UCB | ElEng 225B | Digital Image Processing |
| Biomedical Imaging & Instrumentation | UCB | ElEng 227AT | Optimization Models in Engineering |
| Biomedical Imaging & Instrumentation | UCB | ElEng 236A | Quantum and Optical Electronics |
| Biomedical Imaging & Instrumentation | UCB | ElEng 240A | Analog Integrated Circuits |
| Biomedical Imaging & Instrumentation | UCB | ElEng 290 | Advanced Brain Imaging Methods |
| Biomedical Imaging & Instrumentation | UCB | ElEng 290C | Low-Powered Bioelectronics |
| Biomedical Imaging & Instrumentation | UCB | ElEng C249A | Introduction to Embedded Systems |
| Biomedical Imaging & Instrumentation | UCB | IntegBi 131 | Human Anatomy |
| Biomedical Imaging & Instrumentation | UCB | MecEng 109 | Heat Transfer |
| Biomedical Imaging & Instrumentation | UCB | MecEng 128 | Computer-Aided Mechanical Design |
| Biomedical Imaging & Instrumentation | UCB | MecEng 145L | Transducer Lab |
| Biomedical Imaging & Instrumentation | UCB | MecEng 212 | Heat and Mass Transfer in Biology |
| Biomedical Imaging & Instrumentation | UCB | MecEng 235 | Design of Microprocessor-Based Mechanical Systems |
| Biomedical Imaging & Instrumentation | UCB | Physics 137A | Quantum Mechanics |
| Biomedical Imaging & Instrumentation | UCB | Psych 201 | functional Magnetic Resonance Imaging |
| Biomedical Imaging & Instrumentation | UCB | Psych 214 | functional Magnetic Resonance Imaging Methods |
| Biomedical Imaging & Instrumentation | UCSF | ANATY 116 | Brain Anatomy |
| Biomedical Imaging & Instrumentation | UCSF | ANATY 116 | Gross Anatomy |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 230C | Introduction to Molecular Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 230C | Physics of Radiation Oncology |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 241 | Metabolism and Magnetic Resonance Spectroscopy |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 244 | Medical Imaging Processing |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 245 | Electromagnetic Neuroimaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 245 | Machine Learning Algorithms for Medical Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 247 | Introduction to MRI Systems & Hardware |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 270 | Translational Challenges of Medical Devices |
| Biomedical Imaging & Instrumentation | UCSF | BIOE 297 | MRI Programming |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 201 | Principles of Magnetic Resonance Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 202 | Physical Principles of CT, PET & SPECT Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 203 | Imaging Probes for Nuclear and Optical Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 204 | Principles of Diagnostic and Therapeutic Ultrasound |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 211 | MR Pulse Sequences |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 220 | Advanced Neurological Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 230 | Vascular Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 240 | Muscoskeletal Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 250 | Abdominal and Pelvic Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 260 | Image Processing and Analysis I |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 265 | Image Processing and Analysis II |
| Biomedical Imaging & Instrumentation | UCSF | BIOIM 270 | Cancer Imaging |
| Biomedical Imaging & Instrumentation | UCSF | BIOPHYS 201 | Cell Biophysics |
| Biomedical Imaging & Instrumentation | UCSF | BIOSTAT 183 | Statistical Analysis |
| Biomedical Imaging & Instrumentation | UCSF | BIOSTAT 208 | Biostatistical Methods |
| Biomedical Imaging & Instrumentation | UCSF | BMS 230 | Advanced Topics in Cancer Research |
| Biomedical Imaging & Instrumentation | UCSF | NEUROSCI 201C | Introduction to Systems and Behavioral Neuroscience |
| Biomedical Imaging & Instrumentation | UCSF | RAD/BIOIM 209 | Imaging Lab: MR, CT, PET, & SPECT |
| BioMEMS & Nanotechnology | UCB | BioEng 211 | Cell and Tissue Mechanotransduction |
| BioMEMS & Nanotechnology | UCB | BioEng 221 | Advanced BioMEMS and Bionanotechnology |
| BioMEMS & Nanotechnology | UCB | BioEng 221L | BioMEMS and Bionanotechnology Laboratory |
| BioMEMS & Nanotechnology | UCB | BioEng 251 | Micro/Nanofluidics for Bioengineering and Lab-On-A-Chip |
| BioMEMS & Nanotechnology | UCB | BioEng 263 | Principles of Molecular and Cellular Biophotonics |
| BioMEMS & Nanotechnology | UCB | BioEng C118 | Biological Performance of Materials |
| BioMEMS & Nanotechnology | UCB | BioEng C213 | Fluid Mechanics of Biological Systems |
| BioMEMS & Nanotechnology | UCB | BioEng C280 | Introduction to Nano-Science and Engineering |
| BioMEMS & Nanotechnology | UCB | ChmEng 250 | Transport Processes |
| BioMEMS & Nanotechnology | UCB | ElEng 143 | Microfabrication Technology |
| BioMEMS & Nanotechnology | UCB | ElEng 247 | Introduction to MEMS |
| BioMEMS & Nanotechnology | UCB | ElEng 290P | Brain-Machine Interface Systems |
| BioMEMS & Nanotechnology | UCB | ElEng C245 | Introduction to MEMS Design |
| BioMEMS & Nanotechnology | UCB | MCellBi 230 | Advanced Cell Biology |
| BioMEMS & Nanotechnology | UCB | MecEng 290L | Introduction to Nanobiology |
| BioMEMS & Nanotechnology | UCB | MecEng C217 | Biomimetic Engineering |
| BioMEMS & Nanotechnology | UCB | NSE C203 | Nanoscale Fabrication |
| BioMEMS & Nanotechnology | UCSF | EPI 260 | Development and Approval of Drugs and Devices |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng 143 | Computational Methods |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng 144 | Protein Informatics |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng 231 | Introduction to Computational Molecular Cell Biology |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng 247 | Principles of Synthetic Biology |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng 290B | Advanced Topics in Bioinformatics |
| Computational Biology, Bioinformatics, & Genomics | UCB | BioEng C218 | Stem Cells and Directed Organogenesis |
| Computational Biology, Bioinformatics, & Genomics | UCB | Chem 220A | Thermodynamics and Statistical Mechanics |
| Computational Biology, Bioinformatics, & Genomics | UCB | Chem 220B | Statistical Mechanics |
| Computational Biology, Bioinformatics, & Genomics | UCB | CompSci 281A | Statistical Learning Theory |
| Computational Biology, Bioinformatics, & Genomics | UCB | CompSci 289A | Introduction to Machine Learning |
| Computational Biology, Bioinformatics, & Genomics | UCB | CompSci 294 | Deep Unsupervised Learning |
| Computational Biology, Bioinformatics, & Genomics | UCB | CompSci 294 | Special Topics in Deep Learning |
| Computational Biology, Bioinformatics, & Genomics | UCB | ElEng 290S | Machine Learning for Sequential Decision Making |
| Computational Biology, Bioinformatics, & Genomics | UCB | ElEng 290T | High-Dimensional Data with Low-Dimensional Models |
| Computational Biology, Bioinformatics, & Genomics | UCB | Engin 231 | Mathematical Methods in Engineering |
| Computational Biology, Bioinformatics, & Genomics | UCB | MCellBi 206 | Physical Biochemistry |
| Computational Biology, Bioinformatics, & Genomics | UCB | IndEng 290 | Data-X |
| Computational Biology, Bioinformatics, & Genomics | UCB | MCellBi 240 | Advanced Genetic Analysis |
| Computational Biology, Bioinformatics, & Genomics | UCB | MecEng 120 | Computational Biology Across Multiple Scales |
| Computational Biology, Bioinformatics, & Genomics | UCB | PbHlth 240C | Computational Biostatistics |
| Computational Biology, Bioinformatics, & Genomics | UCB | PlantBi 200B | Genomics and Computational Biology |
| Computational Biology, Bioinformatics, & Genomics | UCB | Stat 141C | Statistics for Bioinformatics |
| Computational Biology, Bioinformatics, & Genomics | UCB | Stat 260 | Probabilistic Modeling in Genomics |
| Computational Biology, Bioinformatics, & Genomics | UCB | Stat C245C | Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine |
| Computational Biology, Bioinformatics, & Genomics | UCSF | BIOE 245 | Machine Learning Algorithms for Medical Imaging |
| Computational Biology, Bioinformatics, & Genomics | UCSF | BIOMEDINF 203 | Introduction to Biocomputing Algorithms |
| Computational Biology, Bioinformatics, & Genomics | UCSF | BIOMEDINF 206 | Statistical Methods for Bioinformatics |
| Computational Biology, Bioinformatics, & Genomics | UCSF | BMS 270 | Genomics and NGS Applications in Microbiology (minicourse) |
| Computational Biology, Bioinformatics, & Genomics | UCSF | BMS 270 | Genomics and NGS Applications in Microbiology (minicourse) |
| Computational Biology, Bioinformatics, & Genomics | UCSF | EPI 266 | Mathematical Modeling of Infectious Diseases |
| Computational Biology, Bioinformatics, & Genomics | UCSF | PHYSIOL 220 | Essential Immunology and Immunopathology |
| Drug Delivery Systems & Pharmacogenomics | UCB | BioEng 224 | Basic Principles of Drug Delivery |
| Drug Delivery Systems & Pharmacogenomics | UCB | BioEng C216 | Macromolecular Science in Biotechnology and Medicine |
| Drug Delivery Systems & Pharmacogenomics | UCB | BioEng C223 | Polymer Engineering |
| Drug Delivery Systems & Pharmacogenomics | UCB | MatSci 251 | Polymer Surfaces and Interfaces |
| Drug Delivery Systems & Pharmacogenomics | UCB | MatSci C250 | Nanomaterials in Medicine |
| Drug Delivery Systems & Pharmacogenomics | UCB | MCellBi 150 | Molecular Immunology |
| Drug Delivery Systems & Pharmacogenomics | UCSF | BIOPHYS 202 | Biophysical Methods |
| Drug Delivery Systems & Pharmacogenomics | UCSF | CHEM 241 | Molecular Thermodynamics |
| Drug Delivery Systems & Pharmacogenomics | UCSF | PSPG 245A-C | Basic Principles of Pharmaceutical Science |
| Drug Delivery Systems & Pharmacogenomics | UCSF | PSPG 271 | Advanced Pharmacokinetics |
| Drug Delivery Systems & Pharmacogenomics | UCSF | PSPG 272A | Advanced Drug Delivery |
| Neural Systems Engineering & Vision Science | UCB | BioEng C261 | Medical Imaging Signals and Systems |
| Neural Systems Engineering & Vision Science | UCB | CompSci 281A | Statistical Learning Theory |
| Neural Systems Engineering & Vision Science | UCB | CompSci 289A | Introduction to Machine Learning |
| Neural Systems Engineering & Vision Science | UCB | CompSci 294 | Deep Unsupervised Learning |
| Neural Systems Engineering & Vision Science | UCB | CompSci 294 | Special Topics in Deep Learning |
| Neural Systems Engineering & Vision Science | UCB | ElEng 129 | Neural and Non-Linear Information Processing |
| Neural Systems Engineering & Vision Science | UCB | ElEng 225A | Digital Signal Processing |
| Neural Systems Engineering & Vision Science | UCB | ElEng 225D | Audio Signal Processing in Humans and Machines |
| Neural Systems Engineering & Vision Science | UCB | ElEng 290 | Advanced Brain Imaging Methods |
| Neural Systems Engineering & Vision Science | UCB | ElEng 290P | Brain-Machine Interface Systems |
| Neural Systems Engineering & Vision Science | UCB | ElEng 290S | Machine Learning for Sequential Decision Making |
| Neural Systems Engineering & Vision Science | UCB | ElEng 290T | High-Dimensional Data with Low-Dimensional Models |
| Neural Systems Engineering & Vision Science | UCB | IntegBi 245 | Neuroanatomy |
| Neural Systems Engineering & Vision Science | UCB | MCellBi 160 | Neuroscience |
| Neural Systems Engineering & Vision Science | UCB | MCellBi 163 | Mammalian Neuroanatomy |
| Neural Systems Engineering & Vision Science | UCB | MCellBi 166 | Biophysical Neurobiology |
| Neural Systems Engineering & Vision Science | UCB | MCellBi 261 | Cellular and Developmental Neurobiology |
| Neural Systems Engineering & Vision Science | UCB | MCellBi 262 | Advanced Topics in Systems Neurobiology |
| Neural Systems Engineering & Vision Science | UCB | MCellBi C261 | Advanced Cell Neurobiology |
| Neural Systems Engineering & Vision Science | UCB | Neurosc 201A | Basic Concepts in Cell and Molecular Neuroscience |
| Neural Systems Engineering & Vision Science | UCB | Neurosc C262 | Circuit and Systems Neurobiology |
| Neural Systems Engineering & Vision Science | UCB | VisSci 212B | Visual Neurophysiology |
| Neural Systems Engineering & Vision Science | UCB | VisSci 212C | Spatial Vision and Machine Vision |
| Neural Systems Engineering & Vision Science | UCB | VisSci 298 | Computational Neuroscience |
| Neural Systems Engineering & Vision Science | UCSF | BIOE 245 | Machine Learning Algorithms for Medical Imaging |
| Neural Systems Engineering & Vision Science | UCSF | NEUROSCI 201C | Introduction to Systems and Behavioral Neuroscience |
| Neural Systems Engineering & Vision Science | UCSF | PHYS 248 | Analysis of Neural and Behavioral Data |
| Systems & Synthetic Biology | UCB | BioEng 233 | Genetic Devices |
| Systems & Synthetic Biology | UCB | BioEng 235 | Frontiers in Microbial Systems Biology |
| Systems & Synthetic Biology | UCB | BioEng 244 | Introduction to Protein Informatics |
| Systems & Synthetic Biology | UCB | BioEng 244L | Introduction to Protein Informatics Lab |
| Systems & Synthetic Biology | UCB | BioEng 247 | Principles of Synthetic Biology |
| Systems & Synthetic Biology | UCB | BioEng 248 | Bioenergy and Sustainable Chemical Synthesis: Metabolic Engineering and Synthetic Biology Approaches |
| Systems & Synthetic Biology | UCB | BioEng 263 | Principles of Molecular and Cellular Biophotonics |
| Systems & Synthetic Biology | UCB | BioEng C129 | Protein Engineering |
| Systems & Synthetic Biology | UCB | ChmEng 274 | Biomolecular Engineering |
| Systems & Synthetic Biology | UCB | CompSci 289A | Introduction to Machine Learning |
| Systems & Synthetic Biology | UCB | ElEng 222 | Nonlinear Systems and Controls |
| Systems & Synthetic Biology | UCB | ElEng 227 | Optimization Models and Applications |
| Systems & Synthetic Biology | UCB | Engin 231 | Mathematical Methods in Engineering |
| Systems & Synthetic Biology | UCB | MCellBi 148 | Microbial Genetics and Genomics |
| Systems & Synthetic Biology | UCB | MCellBi C112 | General Microbiology |
| Systems & Synthetic Biology | UCB | PbHlth 263 | Public Health Immunology |
| Systems & Synthetic Biology | UCB | PlantBi 200B | Genomics and Computational Biology |
| Systems & Synthetic Biology | UCB | PlantBi 200B | Microbial Physiology |
| Systems & Synthetic Biology | UCB | PlantBi 220D | Cell Structure and Function |
| Systems & Synthetic Biology | UCB | PlantBi 222 | Biochemistry of Biofuels |
| Systems & Synthetic Biology | UCB | Stat 131A | Introduction to Probability and Statistics for Life Scientists |
| Systems & Synthetic Biology | UCSF | BIOCHEM 210 | Protein Homeostasis Network in Normal and Disease States (minicourse) |
| Systems & Synthetic Biology | UCSF | BIOMEDINF 203 | Introduction to Biocomputing Algorithms |
| Systems & Synthetic Biology | UCSF | BIOMEDINF 206 | Statistical Methods for Bioinformatics |
| Systems & Synthetic Biology | UCSF | BIOPHYS 201A | Macromolecular Structure and Interactions |
| Systems & Synthetic Biology | UCSF | BIOPHYS 206 | Complex Biological Systems |
| Systems & Synthetic Biology | UCSF | BMS 270 | Genomics and NGS Applications in Microbiology (minicourse) |
| Systems & Synthetic Biology | UCSF | BMS 270 | The CRISPR Revolution (minicourse) |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng 113 | Stem Cell Technologies |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng 116 | Cell and Tissue Engineering |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng 211 | Cell and Tissue Mechanotransduction |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng 290A | Stem Cell Technologies |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng 290H | Bio-Inspired Nanoscience and Nanomaterials |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng C117 | Structural Aspects of Biomaterials |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng C118 | Biological Performance of Materials |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng C216 | Macromolecular Science in Biotechnology and Medicine |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng C218 | Stem Cells and Directed Organogenesis |
| Tissue Engineering & Regenerative Medicine | UCB | BioEng C223 | Polymer Engineering |
| Tissue Engineering & Regenerative Medicine | UCB | IntegBi 131 | Anatomy |
| Tissue Engineering & Regenerative Medicine | UCB | IntegBi 245 | Neuroanatomy |
| Tissue Engineering & Regenerative Medicine | UCB | MatSci C250 | Nanomaterials in Medicine |
| Tissue Engineering & Regenerative Medicine | UCB | MCellBi 130 | Cell Biology |
| Tissue Engineering & Regenerative Medicine | UCB | MCellBi 150 | Molecular Immunology |
| Tissue Engineering & Regenerative Medicine | UCB | MCellBi C260 | Introduction to Neurobiology |
| Tissue Engineering & Regenerative Medicine | UCB | MecEng 280A | Introduction to the Finite Element Method |
| Tissue Engineering & Regenerative Medicine | UCB | MecEng C176 | Orthopedic Biomechanics |
| Tissue Engineering & Regenerative Medicine | UCSF | BIO 183 | Introduction to Statistical Analysis |
| Tissue Engineering & Regenerative Medicine | UCSF | BIOE 221 | ECM |
| Tissue Engineering & Regenerative Medicine | UCSF | BIOE 221 | Tissue Mechanobiology |
| Tissue Engineering & Regenerative Medicine | UCSF | BIOE 225B | Tissue and Organ Biology |
| Tissue Engineering & Regenerative Medicine | UCSF | BIOE 242 | Principles of Tissue Engineering |
| Tissue Engineering & Regenerative Medicine | UCSF | BIOIM 204 | Principles of Diagnostic and Therapeutic Ultrasound |
| Tissue Engineering & Regenerative Medicine | UCSF | CELLBIO 245 | Cell Biology |
Examples of Major and Minor Titles
We would also like to emphasize that this list of sample titles is just for inspiration; it is not an exhaustive list, and students are able to design their own major and minor. The rules for doing so are detailed as follows under “Major and Minor Areas” in the Grad Student Handbook.
See the Peer Advising’s compiled list of example titles for major and minor areas.
Examples of major/minor titles |
|---|
| Bioinstrumentation |
| Biomaterials |
| Biomechanics |
| Biomedical Circuits/Devices |
| BioMEMS & Medical Devices |
| BioMEMS for Cellular Biology |
| Bio-nanotechnology |
| Biophysics + Computational biology |
| Biotechnology |
| Cellular & Molecular Engineering |
| Chemical Engineering |
| Communication, Education, and Advocacy in STEM |
| Computational Methods |
| Computational Neuroscience |
| Computational Statistics for Medical Imaging |
| Data Analysis and Statistics |
| Data Processing in Medicine & Biology |
| Drug Delivery |
| Engineering Devices and Materials for Biology and Medicine |
| Immunology/Vaccinology |
| Machine Learning |
| Machine Learning for Neuroprosthetics |
| Medical Imaging |
| Medical Imaging: Instrumentation and Algorithms |
| Medical Imaging: Physiology and Diseases |
| Molecular Biology |
| MRI Acquisition and Analysis |
| Neuroscience Imaging, Device and Data Analysis |
| Protein Biochemistry |
| Public Health Applications |
| Single-Cell Analysis |
| Structural biology |
| Structural Biology |
| Synthetic Biology |
| Systems/Computational Biology |
| Tissue Engineering |
| Translational Bioengineering Entrepreneurship |