Center for Computational Biology

Northern California Computational Symposium

On Saturday October 12, 2019, UC Davis will be hosting the Northern California Computational Symposium, which will highlight current research and opportunities in the field of computational biology. To learn more about the symposium, click here.

C. Titus Brown

Our lab tackles questions surrounding biological data analysis, data integration, and data sharing. Our primary interest is in genomic, transcriptomic, and metagenomic sequence analysis. In brief,

  • the lab is the primary developer of the khmer software, for faster and more efficient sequence analysis of high-throughput sequencing data.
  • we run quite a bit of training in data-intensive biology.
  • we coordinate training and communication for the NIH-funded Data Commons Pilot Phase.

Contact information

Population Health and Reproduction

University of California, Davis

Davis, CA, 95616

Email: ctbrown@ucdavis.edu

Website

Patrice Koehl

The ongoing transformation of biology to a quantitative discipline raises as many opportunities as challenges. The many -omics projects (genomics, proteomics, transcriptomics, metabolomics, to only name a few) allow us to map and identify all components of a living cell at the molecular level, from both a physical and functional standpoint. New technologies such as high resolution time-lapse microscopy and micro-scale devices have vastly enhanced our abilities to study the mechanics of biomolecules, cells, and tissues, giving us hope that we will be able to unravel the fundamentals of life. In addition to these technological advances, computational methods are playing an ever growing role in biology. As physical models improve and greater computational power becomes available, simulation of complex biological processes will become increasingly tractable. The challenges however come in analyzing and interpreting the vast amount of data generated from these disciplines. We need new methods for extracting knowledge from data, as well as new simulation methods that allow us to implement this knowledge into holistic models that will enable understanding. This needs have been the major drive in my scientific career. Specifically, we develop:

  • New algorithms for clustering post-genomic data, derived from statistics.
  • Powerful geometric methods for processing image data and for comparisons of 3D-images in a high-throughput manner.
  • Efficient and robust solvers of partial differential equations to understand the solvation and thermodynamics of drug targets.
  • In addition, many ideas have been developed to understand the rich information contained in sequence data, making use of phylogenetic information, as well as of the geometric and structural properties of macromolecules.

Contact information

Room 4319, Genome Center, GBSF
451 East Health Sciences Drive
University of California
Davis, CA 95616

Email: koehl@cs.ucdavis.edu

Website

Jie Peng

My lab focuses on the following:

  • Graphical Models
  • High Dimension Inference
  • Functional Data Analysis
  • Statistical Genomics
  • Neuroimaging

Contact information

Department of Statistics

University of California, Davis

Davis, CA, 95616

Email: jiepeng@ucdavis.edu

Website

Gerald Quon

The QUON-titative bio lab is broadly interested in applying machine learning towards problems in the following areas:

Genetics of human disease

Several projects in the lab focus on building models to predict how genetic variation ultimately impacts disease risk through different layers of biological complexity, including molecules (e.g. gene regulation, gene expression, chromatin accessibility) and cell- and organ-level phenotypes measurable through live-cell imaging or MRIs. Our goals are two-fold: to understand the mechanism of action through which genetic variation modulates our risk of different diseases and traits, as well as to identify actionable therapeutic targets. We have published a number of papers on characterizing the genetics of obesity and Alzheimer’s disease, and have more recently focused on psychiatric and other mental disorders.

Models of cell population dynamics

We are developing neural network-based models of population-level behavior of single cells. Sequencing technologies are routinely used to generate high resolution, high dimensional snapshots of individual cells. However, they historically are poor at capturing cell population-level behavior as well as time-varying behavior, because cells are sacrificed before sequencing. On the other hand, imaging-based technologies can be used to observe e.g. transcriptional behavior over time of a limited number of genes. Here we combine imaging-based and sequencing-based methodologies to model how cells interact and influence each other’s transcriptome. Shown in the figure above is an illustration of one such project, where we used single cell genomics data on non-malignant cells cultured either alone (S1) or co-cultured with malignant cells (S1 co-cultured with T4-2), to predict that S1 gene regulation of the ERK pathway is disrupted by proximity to T4-2 cells. This is evident both at the gene regulatory network level, as well as through reporter assays of individual genes.

Neurogenomics

Our understanding of the dynamics of molecular events in the cell has been rapidly increasing, thanks to the many ways in which DNA sequencing has been repurposed to study different aspects of gene regulation. The relationship of gene regulation to cellular-level phenotypes and events, however, is poorly understood, in large part because they are hard to jointly measure experimentally. Several projects in the lab aim to bridge this gap by building quantitative neural network-based models to characterize how gene expression and electrophysiological/morphological properties of neurons co-vary, in order to then understand how changes in gene regulation caused by disease also change properties of neurons.

Contact information

Genome and Biomedical Sciences Facility Office Number 4331

Email: gquon@ucdavis.edu

Website

Ian Korf

We are interested in structure and function in genomic sequence. Specifically, our research seeks to build better models of eukaryotic genes by investigating the individual components that define gene structure. Our research employs a combination of computational modeling, comparative genomics, and experimental molecular biology.

Contact information

UC Davis Genome Center

Email: ifkorf@ucdavis.edu

Website

David Rocke

  • Statistical analysis of gene expression, proteomics, and metabolomics data
  • Radiation biology: effects of low and moderate dose radiation on human skin
  • Biomedical statistics
  • Wound healing

Contact information

David M. Rocke
Division of Biostatistics
Med Sci 1C, Room 140B
University of California, Davis
One Shields Avenue, Davis, CA, 95616

Email: dmrocke@ucdavis.edu

Website

Sharon Aviran

Our lab develops novel computational methods for inferring RNA dynamics from experiments and theory, with applications ranging from basic research to biomolecular engineering and synthetic biology.

Contact information

Department of Biomedical Engineering
Genome Center UC Davis

Office: 2319 GBSF

Email: saviran (at) ucdavis (dot) edu

Website

Fereydoun Hormozdiari

Our lab is at the intersection of computer science and genomics. We specialize in developing novel computational algorithms for analyzing biological data. The primary objective of the lab is to develop novel combinatorial algorithms and machine learning methods to study genomes and discover biomolecular causes of complex disorders.

Contact information

UC Davis Genome Center

451 Health Sciences Drive

Office: Room 4339

Davis, CA 95616

Email: fhormozd@ucdavis.edu

Website

Annual Halloween Symposium

Come to the Genome and Biomedical Center on the UC Davis campus on October 31, 2019 for computational biology talks, posters, and fun Halloween events. Please check the UC Davis Genome Center website for times.