Proposal for a New Interdepartmental Graduate

Degree Program in Bioinformatics (M.S. and Ph.D.)

To be offered by the Interdepartmental Degree Committee in Bioinformatics

University of California, Los Angeles

Submitted by: Christopher Lee, Department of Chemistry and Biochemistry

Parag Mallick, Department of Chemistry and Biochemistry

Matteo Pellegrini, Department of Molecular, Cell, and Developmental Biology

Eleazar Eskin, Department of Computer Science

January 30, 2007

Table of Contents

Section 1. Introduction........................................................................................................ 5

Section 1-1. Aims and objectives of the program...................................................... 5

Section 1-2. Historical development of the field and historical development of the departmental strength in the field........................................................................................................................... 6

Section 1-3. The timetable for development of the program..................................... 9

1-3a. Specific timing................................................................................................... 9

1-3b. Consistency of enrollment projections with the campus enrollment plan......... 10

1-3c. Reduction of enrollments in programs in order to accommodate the proposed program, if any 10

Section 1-4. Relationship of the proposed program to existing programs on campus and the Campus Academic Plan. If the program is not in the campus academic plan, why is it important that it be begun now?.............................................................................................................................. 10

1-4a. Could the curriculum be offered just as effectively within an existing structure (e.g., as a pathway with an existing major program)?........................................................................................... 11

1-4b. Overlap between the proposed curriculum and the curricula of other units on this campus 12

1-4c. Effect of the proposed program on undergraduate programs offered by the sponsoring department(s) 12

Section 1-5. Interrelationship of the program with other University of California programs 13

1-5a. Possibility of cooperation or competition with other programs within the University 13

1-5b. Differences from other similar programs within the University and other California institutions 13

1-5c. Letters of evaluation from chairs of departments in related fields from the UC system and outside. 16

Section 1-6. Department or group which will administer the program................. 16

Section 1-7. Plan for evaluation of the program by the offering department and campuswide 16

Section 1-8. Evidence that the different participating disciplines contribute to the total program in such a way that the student cannot achieve necessary knowledge without substantial study in two or more established departments............................................................................................. 16

Section 2. Program............................................................................................................ 17

Section 2-1. Undergraduate preparation for admission......................................... 17

2-1a. Field examinations and/or other pre-qualifying examinations.......................... 17

2-1b. Qualifying examinations—written and/or oral................................................. 17

2-1c. Relationship of master’s and doctor’s programs.............................................. 17

2-1d. Special preparation for careers in teaching....................................................... 17

2-1e. Other admissions requirements........................................................................ 17

Section 2-2. Foreign language requirement.............................................................. 18

Section 2-3. Program of Study................................................................................... 18

2-3a. Specific fields of emphasis............................................................................... 18

2-3b. Plan(s): Masters I and/or II; Doctorate............................................................ 19

2-3c. Unit requirements............................................................................................. 19

2-3d. Required and recommended courses, including teaching requirement............. 20

2-3e. Requirements for licensing or certification....................................................... 20

Section 2-4. Field examinations—written and/or oral............................................. 20

Section 2-5. Qualifying examinations—written and/or oral................................... 20

Section 2-6. Thesis and/or dissertation expectations............................................... 20

Section 2-7. Final examination requirements........................................................... 21

Section 2-8. Special requirements over and above Graduate Division minimum requirements 21

Section 2-9. Relationship of masters and doctoral programs................................. 21

Section 2-10. Special preparation for careers in teaching....................................... 21

Section 2-11. Student sample program for each year.............................................. 21

2-12. Normative time from matriculation for degree (assuming student has no deficiencies and is enrolled full-time)....................................................................................................................... 22

2-12a. Normative lengths of time for pre-candidacy and candidacy periods............. 22

2-12b. Other incentives to support expeditious times-to-degree................................ 22

Section 3. Projected Need.................................................................................................. 22

Section 3-1. Student demand for the program........................................................ 22

3-1a. Documentation of demand for program with three to five years of enrollment and admissions statistics from this or other institutions, or data on rate of student inquiries............................. 23

3-1b. Evidence that this demand will be stable and long-lasting................................ 23

3-1c. For new programs that are extensions of existing disciplines, enrollment statistics from related courses to demonstrate demand................................................................................................... 24

3-1d. Statistics or other documentation of need......................................................... 24

Section 3-2. Opportunities for placement of graduates........................................... 24

3-2a. Placement records of other UC programs in the field in recent years............... 24

3-2b. Demonstration of a strong market for program graduates by listing recent job listings, employer surveys, assessments of future job growth............................................................................... 24

Section 3-3. The importance of the new program to the discipline........................ 27

Section 3-4. Ways in which the program will meet the needs of society................ 28

Section 3-5. Relationship of the program to research and/or professional interests of the faculty 28

3-5a. Core faculty...................................................................................................... 28

3-5b. Associate faculty.............................................................................................. 29

Section 3-6. Differentiation of the proposed program from existing UC and California independent university programs, and from similar programs proposed by other UC campuses 30

Section 4. Staff.................................................................................................................. 30

4-1a. List of program faculty, their ranks, their highest degree and other professional qualifications, and a citation of no more than five recent publications (abbreviated curricula vitae); Data concerning faculty limited to that information pertinent to the committee’s evaluation of faculty qualifications....... 30

4-1b. Comments from chairs of departments with graduate programs and/or faculty closely related to or affected by the proposed program........................................................................................... 30

4-1c. For participating faculty members outside of the sponsoring department, copies of letters indicating their interest in the program (critical for interdisciplinary programs).................................. 30

Section 4-2. Organizational Structure....................................................................... 31

Section 4.3 Sources of available fellowship and assistantship support................. 32

Section 5. Courses.............................................................................................................. 32

Section 5-2. Elective Bioinformatics Courses........................................................... 32

The following supporting courses are offered in related fields: Statistics 100A (or equivalent preparation) is required as a prerequisite for Chemistry CM260A. CS 31 (or equivalent programming skills) is required for Chemistry C260BL (Bioinformatics Algorithms Laboratory). The Program in Computing offers a range of courses (PIC 10ABC, PIC 20AB, PIC 60, PIC 110) that are also very useful for bioinformatics students. The many departments of the Medical School and Life Sciences offer a wide range of coursework on biology that is highly relevant to bioinformatics students.................................................................. 35

Section 5-3. Program degree requirements................................................................ 35

Section 6. Resource Requirements and Enrollment Plans.................................................... 35

Section 6-1. Methods for funding.............................................................................. 35

6-1a. FTE faculty...................................................................................................... 35

6-1b. Staff FTE......................................................................................................... 36

6-1c. Library acquisitions.......................................................................................... 36

6-1d. Computing costs.............................................................................................. 36

6-1e. Equipment—inventory of current equipment and future needs........................ 37

6-1f. Space and other capital facilities—inventory of current facilities and future requirements 37

6-1g. Other operating costs (technical and administrative staff, supplies and expenses, lab maintenance and other facilities) and description of current staffing levels and future requirements.............. 37

Section 6-2. Projected doctoral enrollments for the first five years........................ 37

Section 7. Graduate Student Support.................................................................................. 37

Section 7-1. Strategy for meeting support needs..................................................... 37

Section 7-2. Current availability of faculty grants to support graduate students and funding trends in agencies expected to provide future research or training grants............................. 39

Section 7-3. Other extramural resources likely to provide graduate student support, or internal fellowships and other institutional support made available to the program......... 39

Section 7-4. Campus fund-raising initiatives that will contribute to support of graduate students 40

Section 7-5. Graduate student support table listing maximum number of students projected and sources of support for the first six years of the program...................................................... 40

Section 8. Changes in Senate Regulations........................................................................... 41

Section 9. Abstract............................................................................................................. 41

Section 10. Departmental Commitment to Proposed Program............................................ 41

Tables

Table 1. Sample Student Program for the First Year........................................................... 22

Table 2. Enrollments of Bioinformatics Core Courses ....................................................... 26

Table 3. Present and Proposed Courses for the Program .................................................... 32

Table 4. Projected Doctoral Enrollments for the First Five Years........................................ 38

Table 5. Graduate Student Support.................................................................................... 40

Appendices

Appendix A. Summary of Information Required by the California Postsecondary Education Commission

Appendix B. Letters of Support from Comparable California Programs

Appendix C. Brief Curricula Vitae of Program Faculty

Appendix D. Letters of Support from UCLA Departments

Appendix E. Letters of Interest from Participating Faculty Members

Appendix F. Catalog Descriptions of All Required and Recommended Courses

Appendix G. Program Degree Requirements

Section 1. Introduction

Section 1-1. Aims and objectives of the program

We propose to create an Interdepartmental Program (IDP) in Bioinformatics composed of faculty from 14 departments, so that it is possible for students to apply for and receive graduate training in bioinformatics at UCLA. This will strengthen faculty research in the Divisions of Life and Physical Sciences and the Schools of Medicine and Engineering, help train the next generation of scientists in a field of rapidly growing prominence, attract new sources of funding to the UCLA campus, aid faculty recruitment and retention, and drive growth at UCLA.

Our main goals are:

• To provide integrated graduate training for biologists, computer scientists and mathematicians that will enable them to invent new kinds of bioinformatics throughout their future careers.
• To create a strong, collaborative community of bioinformatics research groups at UCLA that unites faculty from many departments, and fosters leading-edge research in this highly interdisciplinary field. An excellent graduate program is a critical success factor for world-class research and for attracting superb faculty.
• To make major contributions to bioinformatics research, through the development of new methodologies, new analyses of the human genome and other genomics data, and discoveries that directly affect human health.

Bioinformatics can be defined broadly as the study of the inherent structure of biological information. Some of this inherent structure is very obvious (e.g., codon usage biases that reveal protein coding regions), while others are less obvious but still immediately fruitful (e.g., how transcriptional regulatory sites give rise to “programs” of gene expression), while others are profound long-term challenges (e.g., how the genome encodes the capabilities of the human mind). Bioinformatics is the marriage of biology and the information sciences. Long term, this is a profound intellectual project. Fortunately, it is producing immediately valuable results now, e.g.:

• Statisticians have invented analyses of DNA microchip results (expression measurements of all 30,000 human genes simultaneously) that can distinguish different types of tumors with dramatically different treatment requirements, which previously were hard to differentiate clinically.
• Evolutionary biologists have developed bioinformatics analyses of genome sequence data that reveal the precise pathways by which dangerous pathogens (like HIV) evolve drug resistance, and how to slow the evolution of multi-drug resistance.
• Computer scientists have created powerful new ways for mapping brain functions automatically from standard imaging data.

Bioinformatics is of central importance to biomedical research in the 21^st century (see Section 1-2 below), and to the economy of California. By training both Bioinformatics M.S. and Bioinformatics Ph.D. scientists from a variety of backgrounds, the proposed IDP will contribute directly to the skilled workforce that California’s biotechnology and software companies require for success. The IDP’s research may also give rise to new technologies and new companies. Indeed, many of the faculty have already done so in the past.

The proposed IDP will provide an academic home for bioinformatics at UCLA that will bring many different efforts together for the first time. Examples of current bioinformatics research conducted by the core faculty include:

• The analysis of gene and protein sequences to reveal protein evolution and alternative splicing
• The development of computational approaches to study and predict protein structure to further our understanding of function
• The analysis of mass spectrometry data to, for example, understand the connection between phosphorylation and cancer
• The development of computational methods to utilize expression data to reverse engineer gene networks in order to more completely model cellular biology
• The study of population genetics and its connection to human disease

UCLA has already established a strong record of bioinformatics research and graduate training (see Section 1-2 below). In 1999 the faculty established a graduate core curriculum in bioinformatics, which has been offered continuously since that time (see Section 3-1a), demonstrating the faculty’s commitment to collaborative teaching and to long-term development of an integrated bioinformatics program. These initiatives have been recognized by a large number of awards of multi-investigator Project and Training grants in bioinformatics from NIH, NSF, DOE and other funding sources. These many disparate efforts need a strong graduate program to make them cohesive, successful, and competitive in the long term.

The establishment of the Bioinformatics IDP will allow UCLA to overcome the limitations of the current situation, in which no single program brings together bioinformatics students. Specifically, we expect to resolve these existing weaknesses:

• Prospective bioinformatics graduate students do not know which program to apply to at UCLA, since neither departmental graduate programs nor ACCESS specifically recruit and train them.
• Graduate students at UCLA who are conducting bioinformatics research currently take diverse courses that do not necessarily cover the core training in bioinformatics that we believe they need and that we propose here.
• Relative to other UC schools that already have established bioinformatics graduate programs (see Section 1-5 below), the lack of such a program at UCLA places UCLA at a disadvantage in competing for the top bioinformatics students, and this impacts the ability of our faculty to obtain funding in this area.

The creation of the Bioinformatics IDP at UCLA will allow us to overcome all of these limitations.

Section 1-2. Historical development of the field and historical development of the departmental strength in the field

Intellectually, the proposed IDP draws strength from one of the most fundamental trends now visible in the life sciences. Over the last fifty years, biology has had one major theme—the marriage of biology and physical chemistry (symbolized by the omnipresence of “molecular biology” throughout the life sciences). Its ultimate expression—the Human Genome Project—has now been completed, setting the stage for a new era. Over the next fifty years, the most important engine of discovery will be the marriage of biology with the information sciences, notably computer science.

The reason for this is the revolutionary technological change in the biological sciences. It has always been a truism that biology is information. However, in the past biologists lacked the experimental and computational tools to deal with the true complexity of biology’s information dimension. Instead of being able to see and analyze the whole “computer program” of a cell or organism, biologists were limited to breaking the computer down to its smallest components and measuring their activity one gene at a time. This is akin to trying to understand a computer’s program by attaching a voltmeter to a single component inside it. While this reductionist program has been incredibly productive in terms of discovering new components, it traditionally was often difficult to put these pieces back together again in a way that could predict or explain the behavior of the entire system.

Over the last ten years, a wave of revolutionary technologies such as genomics has changed this situation dramatically. A marriage of molecular biology, engineering and computer science has increased experimental bandwidth—the ability to read out the program of information or activities in cells—by up to 10,000 times what was possible before this automation. Where previously a research project would sequence a gene, now it can sequence the entire genome. Where previously an experiment would measure the expression of one gene, now it can easily and rapidly measure the expression of all genes in the genome. The dramatic completion of the human genome five years before its planned deadline was only the most visible example of this universal trend. Already genome sequences of more than four hundred different organisms have been completed. Many waves of genomics technologies are spreading to new areas of biology, such as proteomics, and this process is accelerating.

These technologies have created an incredible avalanche of new experimental data, driving the need for bioinformatics. Genomics produces raw data; bioinformatics interprets its meaning. Completion of the human genome, for example, led to a strange anticlimax: once we had the data, it became obvious that we could understand very little of what it means. The answers to this problem—and there are already many—come from bioinformatics.

Already, bioinformatics has gained enormous importance and prominence in the human genome project and many other areas of biology, because of a simple shift in the “rate-limiting step” of biological research. Previously, just getting experimental data was difficult and slow. Now, biologists are awash in huge amounts of experimental data (to cite just one example, over 400 fully sequenced genomes); now the real challenge lies in analyzing their meaning. This is bioinformatics.

A key feature of this new field is its strongly interdisciplinary character. As we will document in Section 1-8, there is no one department at UCLA that could conceivably cover the range of topics—from Bayesian statistics to database design to protein function—that come up in nearly every bioinformatics research project. Statistics matter. Mathematical models matter. Computational algorithms and software engineering matter. Genetics matters. Molecular biology matters. Protein structure matters. There is no escaping the problem of interdisciplinary training in bioinformatics. The proposed IDP would provide an academic solution to this problem at UCLA for the first time.

UCLA has made seminal contributions to bioinformatics research. UCLA’s strengths can be divided into several categories: