Computational Biology, Systems Immunology and Bioinformatics
Our group brings together statisticians, computational scientists and mathematical modelers to develop next-generation analytical frameworks for complex immunological, virological and infectious disease data. We develop computational tools that are both biologically informed and statistically robust, and scale to high-dimensional data. Our work includes the development of advanced approaches such as generative AI, with a strong emphasis on integrative analysis and systems immunology. A core research area is immune-receptor analysis, where we quantify immunological responses across multiple biological scales. We work closely with domain experts to transform large-scale datasets into discoveries in immunology and pathogens. Key partners include the HVTN and VISC.
Computational immunology and bioinformatics faculty include Drs. Allan DeCamp, Michael Duff, Andrew Fiore‑Gartland, Elena Giorgi, John Huddleston, Yunda Huang, Ollivier Hyrien, Holly Janes, Michal Juraska, Li Li, Koshlan Mayer‑Blackwell, J.T. McCrone, Evan Newell, Kellie MacPhee and April Randhawa.
Phylodynamics of Human Pathogens
Phylodynamic research uncovers the evolutionary and epidemiological drivers of pathogen transmission from mutations present in sequenced isolates. The rapid growth in sequencing capacity provides an exciting opportunity to better understand the host and viral factors that contribute to outbreaks. Our work on the Nextstrain platform aims to make these methods and insights accessible to researchers and public health officials to inform real-time genomic epidemiology. Nexstrain investigators’ novel approaches to understanding pathogen and immune system dynamics have enabled the characterization of the Ebola virus’ natural history (and by extension, its reservoir), and informed influenza vaccine strain selection.
Phylodynamics faculty include Drs. Elena Giorgi, John Huddleston and J.T. McCrone.
Infectious Disease Prevention Trials
Our faculty are global leaders in the field of statistical trial design for the development of safe and effective vaccines, non-vaccine preventions and therapies to combat infectious diseases and infection-related cancers. We also carry out rigorous and reproducible analyses of study data and develop innovative quantitative methods that emerge directly from the research. Areas of expertise include translating scientific questions into study objectives, optimizing study endpoints, selecting trial populations, designing statistical analyses, crafting approaches to interim analysis, and conducting interim trial reporting and final study analyses. Partnering with BBE’s SCHARP, our faculty lead statistical and data management centers for clinical research networks including the HVTN, HPTN, VISC, and the IDCRC.
Our faculty include Drs. Elizabeth Brown, Allan deCamp, Dobromir Dimitrov, Deborah J Donnell, Paul Edlefsen, Leigh Fisher, Andrew Fiore-Gartland, Youyi Fong, Fei Gao, Peter Gilbert, Elena Giorgi, Peter Guarino, Brett Hanscom, Tzu-Jung Huang, Ying Huang, Yunda Huang, Aaron Hudson, Holly Janes, Michal Juraska, Li Li, Laura Matrajt, Kellie MacPhee, Koshlan Mayer-Blackwell, Laina Mercer, Zoe Moodie, Mia Moore, April Randhawa, Tim Skalland, Jean de Dieu Tapsoba, Emily Voldal and Bo Zhang.
Population Impact Mathematical Modeling
The goal of the Mathematical Modeling Group is to improve the effectiveness and efficiency of efforts to combat infectious disease epidemics. In support of this goal, we design and execute simulation studies and cost-effectiveness analyses that utilize the best available data to characterize the changing dynamics of epidemics, identify key factors that drive transmission, and project impact and support targeting of interventions to reduce infection, disease and onward transmission. The HIV epidemic is a special area of emphasis.
Using rigorous quantitative approaches, our faculty assess the potential impact of vaccine, antiviral, behavioral and policy strategies and develop, refine and validate mathematical modeling tools that inform decision-making regarding the development and deployment of these strategies. We provide analytical support for clinical trial design, including simulation-based guidance for interim and final analyses, and translate clinical trial findings into real-world public health insights that enable application across diverse populations and settings.
Mathematical modelers among the BBE faculty include Drs. Dobromir Dimitrov, Debroah Donnell, Michael Duff, John Huddleston, Kellie MacPhee, Laura Matrajt, Bryan Mayer, J.T. McCrone, and Mia Moore.
Biomarker Development and Evaluation
Our biomarker development and evaluation research involves deeply collaborative work between BBE immunologists and virologists and the University of Washington Biostatistics and Statistics departments. Biomarker research runs the gamut from basic laboratory research, such as building bioassay data-analysis pipelines, to population sciences research on the statistical design and analysis of clinical trials and epidemiological studies.
The biomarker work integrates methodological research — including research on causal inference, machine learning, and systems immunology for high-dimensional biomarkers — with quantitative science leadership for landmark clinical trials and other infectious disease studies under the HPTN, the HVTN and VISC.
Faculty who work on biomarker development and evaluation include Drs. Paul Edlefsen, Andrew Fiore‑Gartland, Leigh Fisher, Youyi Fong, Peter Gilbert, Tzu‑Jung Huang, Ying Huang, Aaron Hudson, Holly Janes, Michal Juraska, Bryan Mayer, Koshlan Mayer‑Blackwell, Laina Mercer, Zoe Moodie, April Randhawa and Bo Zhang.
Pharmacokinetic Modeling
Pharmacokinetics (PK) modeling entails the use of mathematical and computational models to describe and predict how a drug, such as an anti-HIV monoclonal antibody, distributes across the body over time. Our research has focused on understanding how intrinsic and extrinsic factors may impact antibody PK. We aim to develop optimal antibody regimens that reduce the likelihood of HIV acquisition across different global populations while being cost-effective and operationally feasible. Our collaborators include the HVTN and CAVD.
Faculty who work on PK modeling include Drs. Allan deCamp, Dobromir Dimitrov, Brett Hanscom, Yunda Huang, Ollivier Hyrien, Laura Matrajt, Bryan Mayer and Mia Moore.
General Statistical Methodology
Clinical trials evaluating novel interventions often generate data that challenge standard analytical approaches and require careful methodological adaptation. BBE statisticians develop rigorous, modern statistical approaches that enable valid and interpretable inference. Much of this work combines information across studies and advanced data-fusion statistical methods. BBE faculty have also developed regression methodology to address missing potential outcomes in vaccine efficacy trials and identified a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. Additional contributions include testing non-regular threshold regression models.
Faculty with a focus on general statistical methodology include Drs. Elizabeth Brown, Tracy Dong, Deborah J Donnell, Paul Edlefsen, Leigh Fisher, Youyi Fong, Fei Gao, Peter Gilbert, Peter Guarino, Brett Hanscom, Tzu-Jung Huang, Ying Huang, Aaron Hudson, Ollivier Hyrien, Li Li, Laina Mercer, Zoe Moodie, Emily Voldal and Bo Zhang.