research

Gene regulatory networks

Cells decide which genes to switch on by recruiting transcription factors to the regulatory regions of their targets. I build methods and software to infer genome-scale gene regulatory networks (GRNs) from multi-omic data, and to study how they rewire across tissues, conditions, and disease — work implemented in the Network Zoo and made queryable through the GRAND database.

Representative work on GRN inference:

• The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks — Genome Biology (2023)
• gpuZoo: cost-effective estimation of gene regulatory networks using the GPU — NAR Genomics and Bioinformatics (2022)
• GRAND: a database of gene regulatory network models across human conditions — Nucleic Acids Research (2021)

Multiscale dynamical models

Biological systems run on many scales at once — fast molecular kinetics, slower metabolic fluxes, and whole-body physiology. I develop dynamical and constraint-based models that bridge these scales, from genome-scale metabolic models to whole-body and pharmacokinetic models of drug response. Much as a Fourier series builds a complex signal from simple harmonics oscillating at different frequencies, a multiscale model composes a system’s behavior from processes acting at different rates.

Representative work:

• Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes — Nature Computational Science (2021)
• Predicting gastrointestinal drug effects using contextualized metabolic models — PLoS Computational Biology (2019)
• Model-based dietary optimization for late-stage, levodopa-treated, Parkinson’s disease patients — npj Systems Biology and Applications (2016)