Bioinformatics faculty

The Bioinformatics Interdepartmental Ph.D. Program is one of ten Home Areas within the UCLA Graduate Programs in Bioscience (GPB).  Home Areas consist of faculty and students with shared interest in research areas and approaches. Each interdepartmental Home Area is aligned with a Ph.D.-granting program, provides in depth, cutting-edge training, and offers access to a wide variety of exceptional faculty mentors. Interdisciplinary Ph.D. program with integrated one-year core curriculum, over 50 elective courses, and over 20 core bioinformatics faculty spanning life & physical sciences, the Schools of Medicine and Engineering.

What is the UCLA Bioinformatics Ph.D. Program?

We offer integrated doctoral training for students interested in working at the interface of computer science, biology, and mathematics to address the fundamental challenges of contemporary genomic-scale research.  Our interdisciplinary Ph.D. program consists of an integrated one-year core curriculum, research rotations, over 50 elective courses, and faculty mentors spanning biology, mathematics, engineering, and medicine.

What is Bioinformatics?

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.

UCLA Bioinformatics History

UCLA has a strong record of bioinformatics research and graduate training. In 1999 the faculty established a graduate core curriculum in bioinformatics, which has been offered continuously since that time, and recently has been greatly expanded, 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.

The Bioinformatics IDP provides an academic home for bioinformatics at UCLA that brings together the many different disciplines that this field requires. 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

Research strengths

The program involves over 45 core bioinformatics faculty leading research in:

  • Prediction of protein structure, function, interaction networks
  • Transcriptomics via RNAseq
  • Epigenomics (high-throughput methylation profiling)
  • Genome-wide association for disease genes
  • Stochastic network inference and modeling
  • Population genomics
  • Bayesian phylogenetics and comparative genomics
  • Genome evolution
  • Algorithmic development for high-throughput data-mining


  • Core facilities for high-throughput technologies
    • Expression profiling
    • Genomic-scale genotyping
    • Next generation sequencers (e.g. pyrosequencing)
    • Chemical informatics (diversity library screening)
    • Proteomics (e.g. NMR, mass spectroscopy)
  • Core facilities for advanced computing: The Center for Computational Biology provides one of the largest computational Grid clusters in Southern California.
  • Institute for Pure and Applied Mathematics
  • Single campus that integrates schools of Medicine, Engineering, Life Sciences, Mathematical and Physical Sciences, and Public Health.
  • Extensive fellowship support