Jakob Ruess

The paper develops methods to infer parameters of stochastic biochemical models from single-cell perturbation data using likelihood approximations and Kalman filters, showing their effectiveness with high-frequency measurements.

The paper demonstrates that stochastic modeling of single-cell processes can predict population-level behavior of synthetic circuits without parameter adjustment.

This paper introduces a light-tunable differentiation system in yeast to create and control stable microbial consortia with custom functionalities.

MicroMator enables reactive microscopy experiments by integrating real-time data with machine learning for single-cell control and recombination.