Michael P. May

Exploiting Intrinsic Noise for Heterogeneous Cell Control Under Time Delays and Model Uncertainties

This paper shows that noise-exploiting controllers can effectively manage multi-cell systems with time delays and model uncertainties, enabling distinct control of multiple cells with a single input.

Using mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments

This paper proposes a hybrid computational pipeline combining mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments capable of resolving multiple mRNA species.

Optogenetic Maxwell Demon to Exploit Intrinsic Noise and Control Cell Differentiation Despite Time Delays and Extrinsic Variability

This paper extends previous work on Optogenetic Maxwell Demons to analyze the robustness of noise-based control under system volume changes, time delays, and model uncertainties.

Biochemical noise enables a single optogenetic input to control identical cells to track asymmetric and asynchronous reference signals

The paper demonstrates that a single optogenetic input can control identical cells to track asymmetric and asynchronous reference signals by cycling through different stochastic controllers.

Exploiting noise, nonlinearity, and feedback to differentially control multiple synthetic cells with a single optogenetic input

This paper demonstrates how noise and feedback can be used to control multiple synthetic cells with a single optogenetic input through a stochastic SIMO controller.

Computational design and interpretation of live-cell, single-RNA translation experiments

The paper develops a computational model to simulate single-RNA translation dynamics and evaluates experimental assays for estimating elongation kinetics.