The PhD curriculum for our trainees consists of formal instruction to provide the intellectual framework for conducting research.
Biomedical Informatics Core: We described the courses that are fundamental to the BMI program.
- Informatics in Clinical Environments (MED 265):1 Students are introduced to the basics of healthcare systems and clinical information needs through direct observation and classroom discussion. Students are introduced to medical language, disease processes, and health care practices to provide context prior to direct patient observation at primary, specialty, emergency, and inpatient sites in conjunction with clinical faculty affiliated with the training program. Students examine how clinicians use history-taking, physical examination and diagnostic testing to establish diagnoses and prognoses. Medical decision-making is introduced in the context of available informatics tools and clinical documentation and communication processes. Post-observation classroom discussions encourage students to think critically of the processes they observed and formulate hypotheses about how informatics solutions can modify the processes.
- Modeling Clinical Data and Knowledge for Computation (MED 267): This course describes existing methods for representing and communicating biomedical knowledge. The course describes existing health care standards and modeling principles required for implementing data standards, including biomedical ontologies, standardized terminologies, and knowledge resources.
1 Students with a clinical background will replace MED 265 with an additional course: Bioinformatics Applications to Human Disease (MED 263).
Bioinformatics Core: The core courses provide foundations in the biological basis of human health and disease and the statistical discovery of medical knowledge from biological experimentation. These classes are taken during the first year.
- Bioinformatics II (BENG 202): Introduction to methods for sequence analysis, applications to genome and proteome sequences, and protein structure and sequence-structure analysis.
- Principles of Biomedical Informatics (MED 264): students are introduced to the fundamental principles of BMI and to the problems that define modern healthcare. The extent to which BMI can address healthcare problems is explored. Topics covered include structuring of data, computing with phenotypes, integration of molecular, image and other non-traditional data types into electronic medical records, clinical decision support systems, biomedical ontologies, data and communication standards, data aggregation, and knowledge discovery.
- Bioinformatics IV (MATH 283): Analysis of modern genomic data, sequence analysis, gene expression/functional genomics analysis, and gene mapping/applied population genetics. The course focuses on statistical modeling and inference.
For the fourth core class, choose one of the following. In the even that a student completes two or more of these with suitable grades, one will count as core and the other(s) as electives.
- Algorithms in Computational Biology (CSE 280A): (Formerly CSE 206B) The course focuses on algorithmic aspects of modern bioinformatics and covers the following topics: computational gene hunting, sequencing, DNA arrays, sequence comparison, pattern discovery in DNA, genome rearrangements, molecular evolution, computational proteomics, and others. Prerequisites: CSE202 preferred or consent of instructor.
- Algorithms for Biological Data Analysis (ECE 208): This course introduces a series of general algorithmic techniques but uses computational evolutionary biology as the context. The course motivates each algorithmic concept using a specific biological application related to evolution and focuses the discussion on specific types of (big) data available in modern biological studies. Note: The instructor and the BISB program are in the process of getting approval from the Graduate Council to introduce this as a course and to allow it as a core option. While we await approval, the course is offered under a temporary course number, ECE 286, by Prof. Siavash Mirarab, with the title "Algorithms for Biological Data Analysis." The course code ECE 286 may be used by other special topics courses as well, so be sure to enroll in the correct one.
- Genomics, Proteomics, and Network Biology (Bioinformatics III, BENG 203/CSE283): This is core in the BISB track. In the BMI track, it may be taken as the 4th core class or as an elective. Anotating genomes, characterizing functional genes, profiling, reconstructioning pathways. Prerequisites: Pharm 201, BENG 202/CSE282, or consent of instructor.
Seminars: All students in years 1 and 2 must take both seminars in fall, winter, and spring quarters.
Ethics: All students must take one of the two ethics courses by the end of second year. However, funding sources may require that it be taken first year, so we recommend taking it the first year.
- Scientific Ethics (SOMI 226): see below description
- Ethics in Scientific Research (BIOM 219): Overview of ethical issues in scientific research, conflicts of interest; national, statewide and campus issues and requirement; ethical issues in publications; authorship; retention of research records; tracing of research records; attribution; plagiarism; copyright considerations; primary, archival and meeting summary publications; ethical procedures and policies; NIH, NSF, California and UC San Diego; case studies and precedents in ethics.
Research and Teaching: During the academic year, all students must be enrolled in the appropriate research course for their level. Students typically do three rotations in year 1 (BNFO 298) and then do research units (BNFO 299) with their thesis advisor in years 2 and later. BNFO 299 units may be varied to meet the full-time enrollment requirement of 12 units per quarter in fall, winter, and spring.
- Teaching Assistantship (TA) (BNFO 500): Students will be a TA for two quarters during second or third year. To prepare for this teaching, students will receive training through the Center for Teaching Development at UCSD.
- Research Rotation (BNFO 298): Taken each quarter during first year to help determine the thesis adviser.
- Graduate Research (BNFO 299): Independent work by graduate students engaged in research and writing theses. S/U grades only. May be taken for credit fifteen times.
Electives: Students must take a total of 16 units of elective courses. The two BMI core courses (MED 265 & MED 267) count as 4 units of the total. An additional 4 units must be from the CS series, and an additional 4 from the BMI series. The final 4 units can be taken from any series. Please check the BISB page for descriptions of all possible electives.
Formal Progress to Degree
There are three formal evaluations that students must complete prior to being awarded a PhD degree: (1) the Qualifying Examination, (2) the Advancement to Candidacy Examination, and (3) the Final Examination. All evaluations are described below.
- Qualifying Examination: This examination must be passed prior to the end of the student’s second year of study. The written portion of the exam consists of the student preparing an NIH or NSF-style research proposal. This proposal is then defended in an oral examination. Once the student passes the oral portion of the exam, the student is deemed to be qualified for advancing into PhD thesis research.
- Advancement to PhD Candidacy: Upon completion of formal course requirements, each student is required to take a written and oral qualifying examination that admits the student to the candidacy of the PhD Program. The exam is administered by the dissertation committee, which consists of five faculty members.
- Final Examination: All students defend their thesis in a final oral examination.
How to Apply
Application for admission to graduate studies is made directly through the Bioinformatics and Systems Biology program. Please consult the program's website for application details:
To be considered for the NLM fellowship, in addition to submitting
your application and documentation to the degree program of your choice,
please send the following to dbmifellowship at ucsd dot edu:
Statement- explaining why you are a good candidate for the fellowship
and what you hope to accomplish as an NLM trainee, the specific kind of
research and topics you are interested in studying and what your goals
are after completing the fellowship.
- A current and up to date CV; and
- In the body of your email please indicate which degree program you are applying to.