Control

Computation

Learning

We study how genetic circuits, circuit syntax, and circuit context can be used to enact feedback control.

We experiment with biological nonlinearities in living cells to realize novel computation.

We build models to represent computation, adaptation, and memory in living cells.


Recent News

December 2022: Charles Johnson posted a new two-part paper to arXiv on using heterogeneous mixtures of new and old classes of dictionary functions to approximate Koopman operators. [PDF]


September 2022: Our paper with Dr. Sai Pushpak Nandanoori and Dr. Subhrajit Sinha on data-driven methods for learning phase spaces of nonlinear systems has been accepted in the Journal of Nonlinear Science! Congratulations Sai and Subhrajit! [PDF]


July 2022: Prof. Yeung gave an invited tutorial at the SIAM Life Sciences meeting in Pittsburgh. [PDF]


June 2022: Aqib Hasnain posted a new paper on discovering biosensors for novel compounds in Pseudomonas fluorescens using data-driven methods to bioRxiv.


January 2022: Our collaborative paper on modeling whole-cell transcriptional response is out in Bioinformatics with Dr. Mohammed Eslami of Netrias! [PDF]


December 2021: Jamiree published a paper in Mathematics to analyze stability of time-varying nonlinear systems with the method of Koopman [PDF].


September 2021: Prof. Yeung published a paper in the Royal Society Interface showcasing dynamical structure functions for debugging and verifying functionality of genetic circuits [PDF].