Quantitative Foundations of Computational Biology

Key to Computational Biology are the approaches and algorithms for processing, analyzing and modeling knowledge, information and data, that are relevant to research questions from across the life sciences. The development of mathematical and computational methods with probabilistic, statistical, combinatorial, or heuristic foundations continue to drive innovation in Bioinformatics and Systems Biology.

Benjamin Smarr

Faculty Status
Active
Title
Assistant Professor
Title 2
Assistant Professor
Email
bsmarr@ucsd.edu
Track(s)
Bioinformatics and Systems Biology
Department
Brief Research Description
What information do biological timeseries hold? What biodynamic interactions generate these signals?
Lab Description

I'm new to UCSD, so my lab is still taking shape. My research focuses on time series analysis in biological systems, with an emphasis on practical information extraction for translational applications. Currently the main project is TemPredict, in which we have gathered 50K people worth of wearable device data, coupled with over 1 million daily symptom reports. Collection is ongoing, as is the addition of 20K antibody tests to key participants to improve classification certainty from symptom and diagnosis self-reports. This massive data object is being used to identify signs of COVID-19 onset, progression, and recovery, as well to compare COVID-19 progression profiles with other conditions that arose over 2020 in these individuals. We believe this is the largest public data set of time series physiology data ever accumulated, so there are many additional uses for the creative physiology and data-minded student or collaborator.

Kit Curtius

Faculty Status
Active
Title
Assistant Professor
Email
kitcurtius@gmail.com
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Brief Research Description
Mathematical models of cancer evolution, optimization of cancer screening and personalized surveillance, epigenetic aging, translational risk prediction tools

Niema Moshiri

Faculty Status
Active
Title
Assistant Teaching Professor
Email
niema@ucsd.edu
Phone
(619) 993-1642
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Brief Research Description
Computational HIV epidemiology, phylogenetics, models of sequence and tree evolution, Massive Adaptive Interactive Text (MAIT) development
Lab Description

We are interested in developing scalable methods for performing epidemiological analyses of large viral (primarily HIV) sequence and phylogenetic datasets. Topics of interest include large-scale phylogenetic analyses, developing novel models of sequence and tree evolution, performing epidemiological simulation experiments, and developing methods for predicting epidemic outcomes.

Ludmil Alexandrov

Faculty Status
Active
Title
Assistant Professor
Title 2
Assistant Professor
Email
l2alexandrov@ucsd.edu
Phone
(858) 246-2747
Track(s)
Bioinformatics and Systems Biology
Department 2
Brief Research Description
Using large-scale omics data to study mutational processes causing human cancer, identify potential cancer prevention strategies, and develop novel approaches for targeted cancer treatment.

Jill Mesirov

Faculty Status
Active
Title
Professor
Title 2
Associate Vice Chancellor, Computational Health Sciences
Title 3
Professor of Medicine
Email
jmesirov@ucsd.edu
Phone
(858) 534-5096
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
Algorithms and analytic methodologies for pattern recognition and discovery with applications to cancer genomics, to better diagnose, stratify, and treat patients. Development of biologist-friendly biomedical software tools.
Lab Description

Research in the Mesirov Lab focuses on cancer genomics applying machine-learning methods to functional data derived from patient tumors. The lab analyzes these molecular data to determine the underlying biological mechanisms of specific tumor subtypes, to stratify patients according to their relative risks of relapse, and to identify candidate compounds for new treatments. The overall goal is to treat patients as individuals specific to their tumors. Importantly, the lab is committed to the development of practical, accessible software tools to bring these methods to the general biomedical research community.

Graham McVicker

Faculty Status
Active
Title
Assistant Professor
Email
gmcvicker@salk.edu
Phone
(858) 453-4100
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
Human genetic variation, chromatin and gene regulation in immune cells
Lab Description

The McVicker laboratory aims to understand how chromatin state and organization are encoded by the human genome. Our approach to this problem is to exploit naturally occurring human genetic variation to identify sequence variants that disrupt chromatin function. We are currently focused on chromatin within immune cells and we are also interested in how variants that affect chromatin and gene regulation lead to disease risk. The problems that we work on often require the development of sophisticated computational and statistical methods that can extract subtle signals from noisy experimental data.

Siavash Mirarab

Faculty Status
Active
Title
Assistant Professor
Email
smirarabbaygi@ucsd.edu
Phone
858-822-6245
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
Computational biology, large scale phylogenetics, estimation of species trees from gene trees (phylogenomics), large scale multiple sequence alignment, and metagenomic analysis using phylogenetic placement.
Lab Description

Our lab specializes in reconstruction of evolutionary histories (phylogenies) from large scale datasets and applications of phylogenetic analyses to downstream analyses. Large-scale datasets include those with many genes and those with many species, and we focus on high accuracy and scalability at the same time. Many projects in this area are available, some of which are described below, but students can contact me to start on other projects as well.

Debashis Sahoo

Faculty Status
Active
Title
Assistant Professor
Phone
(858) 246-1803
Track(s)
Bioinformatics and Systems Biology
Department
Brief Research Description
Systems Biology of normal and cancer tissues, Boolean analysis of biological systems, Computational models of stem cell differentiation, Sequencing data analysis

Saket Navlakha

Faculty Status
Active
Title
Assistant Professor
Email
navlakha@salk.edu
Phone
(858) 453-4100 x2247
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
Algorithms in nature, biological networks and systems biology, information processing and computation in biological systems
Lab Description

We are interested in the interface of theoretical computer science and systems biology. By thinking computationally about the goals, constraints, and algorithmic strategies used by biological systems, we hope to advance both computer science (by developing new bio-inspired algorithms) and biology (by raising testable hypotheses and developing theory and models to predict system behavior).