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., statistical patterns that reveal crucial functional regions such as genes), while others are less obvious but still immediately fruitful (e.g., how regulatory sequences 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.
about the program

Bioinformatics Seminars

Bioinformatics Resources

• UCLA Bioinformatics Community Site (http://bioinfo.mbi.ucla.edu)
• UCLA Bioinformatics Blog site (http://bioinformatics.ucla.edu): Wordpress MultiUser blog server. This has several advantages:
• Wordpress interface for blogging, choosing a visual design for your blog, collaboration of multiple users on one blog etc.
• you can choose your own address for your blog (e.g. "thinking.bioinformatics.ucla.edu")
• built-in support for LaTeX equations in your blog
• To obtain a login or other questions about this site, please contact Tom Holton (holton@mbi.ucla.edu).
• UCLA Bioinformatics Discussion forum site (http://bioinformatics.ucla.edu/forum)
• UCLA Bioinformatics User Facility (http://my.bioinformatics.ucla.edu)
• Institute for Genomics and Proteomics (http://www.doe-mbi.ucla.edu)
• Center for Computational Biology (http://cms.loni.ucla.edu/CCB/)
• Dept. of Human Genetics (http://www.genetics.ucla.edu/)

Open Positions in Bioinformatics

04/23/2008 - Software Project Manager

The Python Graph Database Framework (Pygr) project seeks an experienced software project manager on a part-time, contract basis, to manage our software development and release process.

Tasks include:
- work with developers to plan development and release schedules.
- write and implement detailed release cycle plans, including automated testing, user testing, and product release.
- manage team members working on development and testing tasks using issue tracking and project management tools.

Requirements:
- at least three years of prior software development experience in Python and / or C / C++.
- prior experience in software project management for successful software products.
- a thorough understanding of software project and release cycle management, from both extensive experience and thorough knowledge of "best practices" analysis.
- strong familiarity with open source software development tools such as version control, issue tracking, and automated test suites.
- ability to work effectively with an open source developer team in an academic environment.

The initial position will be offered as a specified project contract, but it is possible that it could evolve into a full-time employee position.

About the Pygr project:
The Python Graph Database Framework provides a powerful and extensive set of scientific analysis tools, with an applications focus on bioinformatics. It combines pure Python code, and high-performance C / Pyrex code in specific cases that require the highest possible performance. This open source project is based in the UCLA Institute of Genomics and Proteomics. For more details, see the project website:
http://www.bioinformatics.ucla.edu/pygr

Information for Applicants to the Bioinformatics Program

The Bioinformatics IDP has been approved at the UCLA campus level, and is now undergoing review at the UCOP system-wide level. The IDP proposal was approved by the UCLA Faculty Executive Committee at its Apr. 6, 2007 meeting. The UCLA Graduate Council approved the proposal at its Dec. 14, 2007 meeting. The proposal was approved by the UCLA Executive Vice-Chancellor on Jan. 23, 2008, and transmitted to the UC Office of the President. It is currently under review by the UC Coordinating Council on Graduate Affairs. No student inquiries can be considered until final approval is received.

Expected undergraduate preparations for the program fall into three major categories:

• Bioinformatics and computational biology major: an increasing number of universities are offering undergraduate majors in bioinformatics and computational biology. (UCLA itself offers undergraduate bioinformatics study via its Computational and Systems Biology major). This represents an ideal undergraduate preparation for the program, because it demonstrates the applicant's performance in each of the essential subject areas-biology, mathematics, and computer science.


• Double major in biology and computer science or mathematics: in our view, the single greatest difficulty of bioinformatics is its interdisciplinary character. Most undergraduates only have strong preparation in one subject area, making it difficult to evaluate their likely performance in other areas that are essential for bioinformatics. Thus, we also encourage applications from students who have double majored in biology plus a quantitative science, preferably computer science or mathematics.


• Single majors with strong evidence of interdisciplinary skills: in some cases we will admit exceptionally strong students from a single traditional major. In this case the student's academic record and research experiences must show clear evidence of ability in other areas essential for bioinformatics, outside of his/her major. In particular, non-biology majors must demonstrate strong performance in relevant biology coursework. Similarly, biology majors must demonstrate strong quantitative skills in computer science and mathematics.

Example: The UCLA Computational and Systems Biology major with Specialization in Bioinformatics provides an example template of appropriate undergraduate preparation for the program, albeit with a strongly engineering background.

http://www.cs.ucla.edu/~cyber/bioinformatics.htm

Other undergraduate preparations that are more biology-oriented are equally valid. During this period of rapid change in bioinformatics and the early development of bioinformatics curricula, no fixed formula for appropriate preparation can be enforced. Flexibility and case-by-case evaluation of a student's demonstrated skills and interests are essential.

Additionally, the program will place a strong emphasis on applicants' bioinformatics research experience. Currently, most students entering UCLA via various departments to pursue bioinformatics, have extensive bioinformatics research experience. Success in bioinformatics research, and strong letters of recommendation from bioinformatics faculty advisors, provide the program with clear evidence of a student's ability to combine the interdisciplinary skills necessary for bioinformatics. This is very important, and will continue to be a vital consideration for admissions."

Bioinformatics Program Faculty

Core Faculty

Associate Faculty

Courses

Bioinformatics Core Curriculum

UCLA's Bioinformatics Core Curriculum has been offered continuously since 1999. It functions not as a monolithic, comprehensive bioinformatics curriculum, but instead defines the hard core of what you must know to invent new kinds of bioinformatics. It teaches the shared concepts, language and skills which bioinformaticists must have to operate in a collaborative, inter-disciplinary mode. Our goal is to train students who can speak the language of statistical inference, computational complexity, network analysis and data mining. The core curriculum teaches a common vocabulary and set of concepts so people can communicate and collaborate. Beyond this, our main goal is to leave flexibility for students to create a bioinformatics training tailored to their individual interests and background.

Important: In 2007-8 the bioinformatics core courses were reorganized significantly, including new courses and course prerequisite requirements. The following guidelines will hopefully help you find the right course(s) for your interests!

New Course Choices: intro course vs. bioinformatics core sequence

For biology students interested in an introduction to genomics and bioinformatics tools, we've created a new course: MCDB 172 "Genomics and Systems Biology". This course does not require programming or statistics prerequisites, and was first offered in Fall 2007 by Prof. Matteo Pellegrini.

For students who want an in-depth introduction to bioinformatics theory and methods, we've expanded the bioinformatics core course sequence and changed its prerequisite requirements. Chem 160A/260A "Introduction to Bioinformatics" (offered by C. Lee in Fall 2007), Chem 160B/260B "Advanced Algorithms in Bioinformatics" (offered by P. Mallick in Winter 2008), and Statistics 254 "Statistical Methods in Computational Biology" (offered by Q. Zhou) require statistics and programming prerequisites:

1. one of Statistics 100A or 110A or Math 170A or Biostatistics 100A or 110A
2. AND one of PIC 60 or Computer Science 180. Beginning 2007-8, the bioinformatics core courses require understanding algorithms and data structures, and involve programming projects.

Note: these prerequisites are required. Stats 100A is offered during Summer session and both Fall and Spring quarters.

Course Schedule

Fall 07

• MCDB 172 "Genomics and Systems Biology". This course does not require programming or statistics prerequisites.
• Chem 160A/260A: "Introduction to Bioinformatics". This course requires statistics and programming prerequisites (see above).

Winter 08

• Chem 160B/260B "Advanced Algorithms in Bioinformatics". This course requires 160A/260B as a prerequisite.
• Statistics 254 "Statistical Methods in Computational Biology". This course requires Stats 100A as a prerequisite.

Spring 08

• Computer Science 124/224 "Computational Genetics". This course introduces students to current quantitative understanding of human genetics and prepare them to computational interdisciplinary research in the genetics. Topics include: introduction to genetics, human population history, linkage analysis, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genotyping technologies. The computational techniques discussed include techniques from statistics and computer science. The course is intended for both undergraduate and graduate students in engineering as well as students from the biological sciences and medical school.

Past Courses

• Chem 160A/260A: last taught Fall 06.
• M298 Genome Evolution seminar.

Present and proposed courses, with planned instructors, and supporting courses offered in related fields

Required Courses:

Chemistry 202. Bioinformatics Interdisciplinary Research Seminar. (2)
Seminar, two hours; discussion, two hours. Concrete examples of how biological questions about genomics data map to and are solved by methodologies from other disciplines, including statistics, computer science, and mathematics. May be repeated for credit. S/U or letter grading.

Chemistry M252. Seminar: Advanced Methods in Computational Biology. (2)
(Same as Human Genetics M252.) Seminar, one hour; discussion, one hour. Designed for advanced graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies. S/U or letter grading.

Chemistry CM260A. Introduction to Bioinformatics and Genomics. (4)
(Formerly numbered CM260.) (Same as Human Genetics M260A.) Lecture, three hours; discussion, one hour. Recommended requisite: Statistics 100A or 110A. Genomics and bioinformatics results and methodologies, with emphasis on concepts behind rapid development of these fields. Focus on how to think genomically via case studies showing how genomics questions map to computational problems and their solutions. Concurrently scheduled with course C160A. S/U or letter grading.

Chemistry C260B. Algorithms in Bioinformatics and Systems. (4)
Lecture, 4 hours; laboratory, 4 hours. Enforced requisite: C160A or C260A with a grade of C- or better. Recommended: Statistics 100A and 110A and PIC 32 and 60. Development and application of computational approaches to biological questions. Understanding of mechanisms for determining statistical significance of computationally derived results. Students will develop a foundation for innovative work in Bioinformatics and Systems Biology. Concurrently scheduled with course C160B. S/U or letter grading.

Chemistry 260BL. Advanced Bioinformatics Computational Laboratory. (2)
Laboratory, 4 hours. Enforced requisite: CM260A. Co-requisite: C260B. This course will focus on the development and application of computational approaches to ask and answer biological questions. Students completing the course should be able to implement a variety of bioinformatics and systems biology algorithms. Correspondingly, they should have an appreciation for the advantages and disadvantages of different algorithmic methods for studying biological questions. Furthermore, students should gain a preliminary understanding of how to compute the statistical significance of their results, a process which may involve writing an estimation or sampling program. The course will focus on development of a conceptual understanding of implementation of bioinformatics algorithms and give students a foundation for how to do innovative work in these fields. Material will be drawn from specific, relevant biological problems and will closely parallel 260B. As a complement to 260, students will gain experience in observing the impact of computational complexity of an algorithm in computing a solution. S/U or letter grading. Bioinformatics Interdepartmental Graduate Program Proposal page 50

Statistics M254. Statistical Methods in Computational Biology. (4)
(Same as Biomathematics M271.) Lecture, three hours; discussion, one hour. Preparation: elementary probability concepts. Requisite: course 100A. Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics. Letter grading. (To be multiple-listed in future as Chemistry 260C.)

Elective Courses:

Students must take at least 12 units of elective courses, of which at most one can be an undergraduate course, from the following areas:

Genomics Concentration Area:
Biostatistics M272. Theoretical Genetic Modeling . (4)
(Formerly numbered M237A.) (Same as Biomathematics M207A and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading.

Biostatistics M278. M278. Statistical Analysis of DNA Microarray Data. (4)
(Formerly numbered 278.) (Same as Human Genetics M278.) Lecture, three hours. Requisite: course 200C. Instruction in use of statistical tools used to analyze microarray data. Structure corresponds to analytical protocol an investigator might follow when working with microarray data. S/U or letter grading.

Chemical Engineering 246. Systems Biology: Intracellular Network Identification and Analysis. (4)
Lecture, four hours; outside study, eight hours. Requisites: course CM245, Life Sciences 1, 2, 3, 4, Mathematics 31A, 31B, 32A, 33B. Systems approach to intracellular network identification and analysis. Transcriptional regulatory networks, protein networks, and metabolic networks. Data from genome sequencing, large-scale expression analysis, and other high-throughput techniques provide bases for systems identification and analysis. Discussion of gene-metabolic network synthesis. Letter grading.

Chemistry 269C. Nucleic Acid Structure and Catalysis. (2)
Lecture, five hours; discussion, two hours. Requisites: courses 153A, 153B, 153C, 156. Threedimensional structure of DNA and RNA. Sequence-specific recognition of DNA and RNA. RNAcatalyzed processes, including self-splicing and peptide bond formation. Letter grading.

Computer Science 224. Computational Genetics (4)
(Same as Human Genetics CM224.) Lecture, three hours; discussion, one hour; outside study, eight hours. Preparation: one statistics course and familiarity with any programming language. Designed for undergraduate and graduate engineering students, as well as students from biological sciences and medical school. Introduction to current quantitative understanding of human genetics and computational interdisciplinary research in genetics. Topics include introduction to genetics, human population history, linkage analysis, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genotyping technologies. Computational techniques include those from statistics and computer science. Concurrently scheduled with course CM124. Letter grading. Bioinformatics Interdepartmental Graduat e Program Proposal  page 51

Ecology and Evolutionary Biology C275. Computational Biology. (4)
Lecture, three hours; laboratory, one hour. Requisites: Life Sciences 1, 4. Introduction to computational biology. Topics include statistical and mathematical analysis, computer simulation, use of Internet for remote databases, and connection to supercomputers, with emphasis on biological applications and individual or group projects. Concurrently scheduled with course C159.

Human Genetics 236A. Advanced Human Genetics. (4)
(Formerly numbered 236.) Lecture, three hours. Requisites: courses CM248, CM253. Advanced topics in human genetics related to Mendelian disease, molecular genetics, and relevant technologies. Topics include cytogenetics, genomics, proteomics, positional cloning, bioinformatics, gene therapy, and developmental genetics. Reading materials include original research papers and reviews. Letter grading.

Human Genetics 236B. Advanced Human Genetics. (4)
Lecture, three hours. Requisites: courses 236A, CM248, CM253. Advanced topics in human genetics related to complex genetic traits and common diseases, with emphasis on biostatistics and mathematical modeling. Reading materials include original research papers and reviews. Letter grading.

Human Genetics C244. Genomic Technology. (4)
Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Survey of key technologies that have led to successful application of genomics to biology, with focus on theory behind specific genome-wide technologies and their current applications. Concurrently scheduled with course C144. S/U or letter grading.

Pathology 255. Mapping and Mining Human Genomes. (3)
Lecture, three hours. Basic molecular genetic and cytogenetic techniques of gene mapping. Selected regions of human genomic map scrutinized in detail, particularly gene families and clusters of genes that have remained linked from mouse to human. Discussion of localizations of disease genes. S/U or letter grading.

Statistics 165. Statistical Methods and Data Mining. (4)
Lecture, three hours. Requisite: course 100A. Introduction and overview of up-to-date statistical methods in microarray analysis designed for students in biostatistics, statistics, and human genetics who are interested in technology and statistical analysis of microarray experiments. Useful for biology students with basic statistical training who are interested in understanding logic underlying many statistical methods. P/NP or letter grading.

Proteomics Concentration Area:

Chemical Engineering 246. Systems Biology: Intracellular Network Identification and Analysis. (4)
Lecture, four hours; outside study, eight hours. Requisites: course CM245, Life Sciences 1, 2, 3, 4, Mathematics 31A, 31B, 32A, 33B. Systems approach to intracellular network identification and analysis. Transcriptional regulatory networks, protein networks, and metabolic networks. Data from genome sequencing, large-scale expression analysis, and other high-throughput techniques provide bases for systems identification and analysis. Discussion of gene-metabolic network synthesis. Letter grading.

Chemistry M230B. Structural Molecular Biology. (4)
Bioinformatics Interdepartmental Graduat e Program Proposal  page 52 (Same as Molecular, Cell, and Developmental Biology M230B.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 3C, Physics 6C. Selected topics from principles of biological structure; structures of globular proteins and RNAs; structures of fibrous proteins, nucleic acids, and polysaccharides; harmonic analysis and Fourier transforms; principles of electron, neutron, and X-ray diffraction; optical and computer filtering; three-dimensional reconstruction. S/U or letter grading.

Chemistry 256N. Seminar: Research in Biochemistry: Advanced Topics in Structural Biology. (2)
Seminar, three hours. Advanced study and analysis of current topics in biochemistry. Discussion of current research and literature in research specialty of faculty member teaching course. S/U grading.

Chemistry 256S. Seminar: Research in Biochemistry: Proteome Bioinformatics. (2)
Seminar, three hours. Advanced study and analysis of current topics in biochemistry. Discussion of current research and literature in research specialty of faculty member teaching course. S/U grading.

Chemistry 266. Proteomics and Protein Mass Spectrometry. (3)
Lecture, two hours. Essential technologies and concepts practiced in proteomics-based research, including methods for protein separation and display, protein quantitation, and protein identification. Emphasis on fundamentals of protein mass spectrometry. S/U or letter grading.

Chemistry 269A. Protein Structure. (2)
Lecture, five hours; discussion, two hours. Requisites: courses 153A, 153B, 153C, 156. Threedimensional structure of proteins. Forces that stabilize structure of soluble and membrane proteins. Kinetics of protein folding and role of chaperones. Prediction of protein structure from sequence. Letter grading.

Molecular Evolution and Comparative Genomics Concentration Area:

Biomathematics M211. Mathematical and Statistical Phylogenetics. (4)
(Same as Human Genetics M211.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B, Mathematics 170A. Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data. S/U or letter grading.

Biostatistics M272. Theoretical Genetic Modeling . (4)
(Formerly numbered M237A.) (Same as Biomathematics M207A and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading.

Ecology and Evolutionary Biology M231. Molecular Evolution. (4)
(Same as Earth and Space Sciences M217.) Lecture, two hours; discussion, two hours. Series of advanced topics in molecular evolution, with special emphasis on molecular phylogenetics. Topics may include nature of the genome, neutral evolution, molecular clocks, concerted evolution, molecular systematics, statistical tests, and phylogenetic algorithms. Themes may vary from year to year. May be repeated for credit. S/U or letter grading. Bioinformatics Interdepartmental Graduat e Program Proposal  page 53

Ecology and Evolutionary Biology C235. Population Genetics. (4)
Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Strongly recommended: course 100, Mathematics 31A, 31B. Basic principles of genetics of population, dealing with genetic structure of natural populations and mechanisms of evolution. Equilibrium conditions and forces altering gene frequencies, polygenic inheritance, molecular evolution, and methods of quantitative genetics. Concurrently scheduled with course C135. Letter grading. Molecular, Cell, and Developmental Biology C222A. Advanced Topics in Cell and

Molecular Biology. Molecular Evolution. (2)
Lecture, two hours. Requisites: courses 100 or C139 or M140, 144, Life Sciences 4. Current developments in the field of molecular evolution. Constructing evolutionary trees at molecular level; formal testing of evolutionary hypotheses using sequencing data. Original research proposal required. Letter grading.

Molecular, Cell, and Developmental Biology 292. Seminar: Molecular Evolution. (2)
Discussion, three hours. Detailed analysis of current understanding of evolution of molecular sequences and structures.

Neuroinformatics Concentration Area:

Biomedical Engineering M217. Biomedical Imaging. (4)
(Same as Electrical Engineering M217.) Lecture, three hours; laboratory, two hours; outside study, seven hours. Requisite: Electrical Engineering 114D or 211A. Mathematical principles of medical imaging modalities: X-ray, computed tomography, positron-emission tomography, single photon emission computed tomography, magnetic resonance imaging. Topics include basic principles of each imaging system, image reconstruction algorithms, system configurations and their effects on reconstruction algorithms, specialized imaging techniques for specific applications such as flow imaging. Letter grading.

Biomedical Physics 208A. Medical Physics Laboratory: Medical Imaging. (4)
Discussion, two hours; laboratory, four hours. Requisite: course 205. Hands-on experience performing acceptance testing and quality control checks of imaging equipment such as fluoroscopy, digital subtraction angiography, mammography, ultrasound, magnetic resonance imaging, computed tomography, and computed radiography.

Biomedical Physics 210. Principles of Medical Image Processing. (4)
Lecture, three hours; discussion, one hour. Requisite: course 209. Study of image representation, computational structures for imaging, linear systems theory, image enhancement and restoration, image compression, segmentation, and morphology. Special topics include visualization techniques, three-dimensional modeling, computer graphics, and neural net applications. Laboratory projects apply concepts developed in class.

Biomedical Physics 214. Medical Image Processing Systems. (4)
Lecture, three hours; discussion, one hour. Requisites: courses 209, 210. Advanced image processing and image analysis techniques applied to medical images. Discussion of approaches to computer-aided diagnosis and image quantitation, as well as application of pattern classification techniques (neural networks and discriminant analysis). Examination of problems from several imaging modalities (CT, MR, CR, and mammography).

Biomedical Physics M266. Advanced Magnetic Resonance Imaging. (4)
Bioinformatics Interdepartmental Graduat e Program Proposal  page 54 (Same as Neuroscience M267 and Psychiatry M266.) Lecture, four hours. Starting with basic principles, presentation of physical basis of magnetic resonance imaging (MRI), with emphasis on developing advanced applications in biomedical imaging, including both structural and functional studies. Instruction more intuitive than mathematical. Letter grading.

Biomedical Physics M285. Functional Neuroimaging: Techniques and Applications. (4)
(Same as Psychiatry M285.) In-depth examination of activation imaging, including PET and MRI methods, data acquisition and analysis, experimental design, and results obtained thus far in human systems. Strong focus on understanding technologies, how to design activation imaging paradigms, and how to interpret results. Laboratory visits and design and implementation of a functional MRI experiment. S/U or letter grading.

Neuroscience CM272. Neuroimaging and Brain Mapping. (4)
(Same as Physiological Science M272 and Psychology M213.) Lecture, three hours. Requisites: courses M201, M202. Theory, methods, applications, assumptions, and limitations of neuroimaging. Techniques, biological questions, and results. Brain structure, brain function, and their relationship discussed with regard to imaging. Concurrently scheduled with course C172. Letter grading.

Statistics 233. Statistical Methods in Biomedical Imaging. (4)
Lecture, three hours. Requisite: course 100A. Brief review of common general statistical techniques. Advanced statistical methods for analysis of medical imaging, integration, visualization, interrogation, and interpretation of imaging and nonimaging metadata. S/U or letter grading.

Computer Science Concentration Area:

Biomedical Engineering 220. Introduction to Medical Informatics. (2)
Lecture, two hours; outside study, four hours. Designed for graduate students. Introduction to research topics and issues in medical informatics for students new to field. Definition of this emerging field of study, current research efforts, and future directions in research. Key issues in medical informatics to expose students to different application domains, such as information system architectures, data and process modeling, information extraction and representations, information retrieval and visualization, health services research, telemedicine. Emphasis on current research endeavors and applications. S/U grading.

Biomedical Engineering 223A-223B-223C. Programming Laboratories for Medical Informatics I, II, III. (4-4-4)
Lecture, two hours; laboratory, two hours. Designed for graduate students. Programming laboratories to support coursework in other medical informatics core curriculum courses. Exposure to programming concepts for medical applications, with focus on basic abstraction techniques used in image processing and medical information system infrastructures (HL7, DICOM). Letter grading. 223A. Integrated with course 226 to reinforce concepts presented with practical experience. Projects focus on understanding medical networking issues and implementation of basic protocols for health care environment, with emphasis on use of DICOM. 223B. Requisite: course 223A. Integrated with courses 224A and 227 to reinforce concepts presented with practical experience. Projects focus on medical image manipulation and decision support systems. 223C. Requisite: course 223B. Integrated with courses 224B and 225 to reinforce concepts presented with practical experience. Projects focus on medical image storage and retrieval.

Biomedical Engineering 226. Medical Knowledge Representation. (4)
Bioinformatics Interdepartmental Graduat e Program Proposal  page 55 Seminar, four hours; outside study, eight hours. Designed for graduate students. Issues related to medical knowledge representation and its application in health care processes. Topics include data structures used for representing knowledge (conceptual graphs, frame-based models), different data models for representing spatio-temporal information, rule-based implementations, current statistical methods for discovery of knowledge (data mining, statistical classifiers, and hierarchical classification), and basic information retrieval. Review of work in constructing ontologies, with focus on problems in implementation and definition. Common medical ontologies, coding schemes, and standardized indices/terminologies (SNOMEF, UMLS, MeSH, LOINC). Letter grading.

Biomedical Engineering 228. Medical Decision Making. (4)
Lecture, four hours; outside study, eight hours. Designed for graduate students. Overview of issues related to medical decision making. Introduction to concept of evidence-based medicine and decision processes related to process of care and outcomes. Basic probability and statistics to understand research results and evaluations, and algorithmic methods for decision-making processes (Bayes theorem, decision trees). Study design, hypothesis testing, and estimation. Focus on technical advances in medical decision support systems and expert systems, with review of classic and current research. Introduction to common statistical and decision-making software packages to familiarize students with current tools. S/U grading.

Computer Science 224. Computational Genetics (4)
(Same as Human Genetics CM224.) Lecture, three hours; discussion, one hour; outside study, eight hours. Preparation: one statistics course and familiarity with any programming language. Designed for undergraduate and graduate engineering students, as well as students from biological sciences and medical school. Introduction to current quantitative understanding of human genetics and computational interdisciplinary research in genetics. Topics include introduction to genetics, human population history, linkage analysis, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genotyping technologies. Computational techniques include those from statistics and computer science. Concurrently scheduled with course CM124. Letter grading.

Computer Science 249. Current Topics in Data Structures. (2 to 12)
Lecture, four hours; outside study, eight hours. Review of current literature in an area of data structures in which instructor has developed special proficiency as a consequence of research interests. Students report on selected topics. May be repeated for credit with consent of instructor. Letter grading.

Computer Science M276A. Pattern Recognition and Machine Learning. (4)
(Formerly numbered 276A.) (Same as Statistics M231.) Lecture, three hours. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.

Computer Science M296A. Advanced Modeling Methodology for Dynamic Biomedical Systems. (4)
(Same as Biomedical Engineering M296A and Medicine M270C.) Lecture, four hours; outside study, eight hours. Requisite: Electrical Engineering 141 or 142 or Mathematics 115A or Mechanical and Aerospace Engineering 171A. Development of dynamic systems modeling Bioinformatics Interdepartmental Graduat e Program Proposal  page 56 methodology for physiological, biomedical, pharmacological, chemical, and related systems. Control system, multicompartmental, noncompartmental, and input/output models, linear and nonlinear. Emphasis on model applications, limitations, and relevance in biomedical sciences and other limited data environments. Problem solving in PC laboratory. Letter grading.

Computer Science M296D. Introduction to Computational Cardiology. (4)
(Same as Biomedical Engineering M296D.) Lecture, four hours; outside study, eight hours. Requisite: course M186B. Introduction to mathematical modeling and computer simulation of cardiac electrophysiological process. Ionic models of action potential (AP). Theory of AP propagation in one-dimensional and two-dimensional cardiac tissue. Simulation on sequential and parallel supercomputers, choice of numerical algorithms, to optimize accuracy and to provide computational stability. Letter grading.

Math and Statistics Concentration Area:

Biomathematics M203. Stochastic Models in Biology. (4)
(Same as Human Genetics M203.) Lecture, four hours. Requisite: Mathematics 170A or equivalent experience in probability. Mathematical description of biological relationships, with particular attention to areas where conditions for deterministic models are inadequate. Examples of stochastic models from genetics, physiology, ecology, and a variety of other biological and medical disciplines. S/U or letter grading.

Biomathematics 210. Optimization Methods in Biology. (4)
Lecture, four hours. Preparation: undergraduate mathematical analysis and linear algebra; familiarity with programming language such as Fortran or C. Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics. S/U or letter grading.

Biomathematics M211. Mathematical and Statistical Phylogenetics. (4)
(Same as Human Genetics M211.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B, Mathematics 170A. Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data. S/U or letter grading.

Biostatistics M234. Applied Bayesian Inference. (4)
(Same as Biomathematics M234.) Lecture, three hours; discussion, one hour; laboratory, one hour. Requisites: courses 115 (or Statistics 100C), 200A. Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods. S/U or letter grading.

Biostatistics M272. Theoretical Genetic Modeling . (4)
(Formerly numbered M237A.) (Same as Biomathematics M207A and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading. Bioinformatics Interdepartmental Graduat e Program Proposal  page 57

Biostatistics M278. M278. Statistical Analysis of DNA Microarray Data. (4)
(Formerly numbered 278.) (Same as Human Genetics M278.) Lecture, three hours. Requisite: course 200C. Instruction in use of statistical tools used to analyze microarray data. Structure corresponds to analytical protocol an investigator might follow when working with microarray data. S/U or letter grading.

Statistics 165. Statistical Methods and Data Mining. (4)
Lecture, three hours. Requisite: course 100A. Introduction and overview of up-to-date statistical methods in microarray analysis designed for students in biostatistics, statistics, and human genetics who are interested in technology and statistical analysis of microarray experiments. Useful for biology students with basic statistical training who are interested in understanding logic underlying many statistical methods. P/NP or letter grading.

Statistics M231. Pattern Recognition and Machine Learning. (4)
(Formerly numbered 231.) (Same as Computer Science M276A.) Lecture, three hours. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.

Statistics 234. Statistics and Information Theory. (4)
Lecture, three hours. Preparation: introductory probability theory course. While data compression and transmission are fundamental problems in information theory, field provides insights into fundamentally statistical problems of estimation, prediction, and model selection. Even new concepts of randomness emerge from this line of research. S/U or letter grading.

Students with gaps in their previous training are allowed to take, with the approval of their academic adviser, appropriate undergraduate courses. However, these undergraduate courses may not be applied toward course requirements for a graduate degree in the program.

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.

About the Bioinformatics program

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., statistical patterns that reveal crucial functional regions such as genes), while others are less obvious but still immediately fruitful (e.g., how regulatory sequences 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 huge 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 21st 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.

Contact Us

Bioinformatics Student Affairs Office

Jia-Qing Zhao, Student Affairs Officer
2124 Life Sciences Building
621 Charles E. Young Drive South
Box 951606
Los Angeles, CA 90095-1606
Tel: (310) 794-4256
email: jzhao@lifesci.ucla.edu

Bioinformatics Department Chair

Dr. Christopher J. Lee
601A Boyer Hall
BOX 951569,
Los Angeles, CA 90095-1569
Tel: (310) 825-7374
Fax: (310) 206-7286
email: leec@chem.ucla.edu