Charlotte Merzbacher

This paper introduces a differentiable dFBA simulator for Bayesian inference over microbial metabolic models using OD600 measurements, enabling scalable analysis of fermentation data.

This paper introduces gsMOBO, a computational method that uses genome-scale metabolic models and Bayesian optimization to design cost-effective and productive culture media by optimizing nutrient combinations across high-dimensional spaces.

This paper introduces Flux Cone Learning, a framework for predicting gene deletion phenotypes, showing improved accuracy over existing methods in various organisms.

The paper develops a method integrating kinetic pathway models with genome-scale metabolic models to simulate dynamic host-pathway interactions in Escherichia coli, enabling faster predictions of metabolite dynamics under genetic perturbations.

This paper uses self-supervised learning to create low-dimensional representations of genome-scale metabolic flux data, improving cell type classification and comparison across organisms.

This paper introduces a Bayesian optimization method for designing multiscale biological circuits, demonstrating its effectiveness on gene circuits with nonlinearities and multiple scales.

The paper evaluates normalization methods for DNA methylation data to improve BMI prediction across cohorts when projecting new data onto a reference panel.