| Moment-based inference predicts bimodality in transient gene expression C Zechner, J Ruess, P Krenn, S Pelet, M Peter, J Lygeros, H Koeppl Proceedings of the National Academy of Sciences 109 (21), 8340-8345, 2012 | 233 | 2012 |
| Designing experiments to understand the variability in biochemical reaction networks J Ruess, A Milias-Argeitis, J Lygeros Journal of The Royal Society Interface 10 (88), 20130588, 2013 | 65 | 2013 |
| Shaping bacterial population behavior through computer-interfaced control of individual cells R Chait, J Ruess, T Bergmiller, G Tkačik, CC Guet Nature communications 8 (1), 1-11, 2017 | 61 | 2017 |
| Iterative experiment design guides the characterization of a light-inducible gene expression circuit J Ruess, F Parise, A Milias-Argeitis, M Khammash, J Lygeros Proceedings of the National Academy of Sciences 112 (26), 8148-8153, 2015 | 57 | 2015 |
| Moment estimation for chemically reacting systems by extended Kalman filtering J Ruess, A Milias-Argeitis, S Summers, J Lygeros The Journal of chemical physics 135 (16), 10B621, 2011 | 41 | 2011 |
| Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks J Ruess, J Lygeros ACM Transactions on Modeling and Computer Simulation (TOMACS) 25 (2), 1-25, 2015 | 26 | 2015 |
| Adaptive moment closure for parameter inference of biochemical reaction networks S Bogomolov, TA Henzinger, A Podelski, J Ruess, C Schilling International Conference on Computational Methods in Systems Biology, 77-89, 2015 | 18 | 2015 |
| Adaptive moment closure for parameter inference of biochemical reaction networks C Schilling, S Bogomolov, TA Henzinger, A Podelski, J Ruess Biosystems 149, 15-25, 2016 | 12 | 2016 |
| Identifying stochastic biochemical networks from single-cell population experiments: A comparison of approaches based on the Fisher information J Ruess, J Lygeros 52nd IEEE Conference on Decision and Control, 2703-2708, 2013 | 12 | 2013 |
| Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study F Parise, J Lygeros, J Ruess Frontiers in Environmental Science 3, 42, 2015 | 11 | 2015 |
| Estimating information in time-varying signals SA Cepeda-Humerez, J Ruess, G Tkačik PLoS computational biology 15 (9), e1007290, 2019 | 10 | 2019 |
| To quarantine, or not to quarantine: A theoretical framework for disease control via contact tracing D Lunz, G Batt, J Ruess Epidemics 34, 100428, 2021 | 8 | 2021 |
| Approximating the solution of the chemical master equation by combining finite state projection and stochastic simulation A Hjartarson, J Ruess, J Lygeros 52nd IEEE Conference on Decision and Control, 751-756, 2013 | 8 | 2013 |
| Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space J Ruess The Journal of chemical physics 143 (24), 12B636_1, 2015 | 6 | 2015 |
| Grey-box techniques for the identification of a controlled gene expression model F Parise, J Ruess, J Lygeros 2014 European Control Conference (ECC), 1498-1503, 2014 | 6 | 2014 |
| Molecular noise of innate immunity shapes bacteria-phage ecologies J Ruess, M Pleška, CC Guet, G Tkačik PLoS computational biology 15 (7), e1007168, 2019 | 5 | 2019 |
| To isolate, or not to isolate: a theoretical framework for disease control via contact tracing D Lunz, G Batt, J Ruess medRxiv, 2020 | 4 | 2020 |
| Optimal control of an artificial microbial differentiation system for protein bioproduction E Weill, V Andréani, C Aditya, P Martinon, J Ruess, G Batt, F Bonnans 2019 18th European Control Conference (ECC), 2663-2668, 2019 | 4 | 2019 |
| Moment-based methods for the analysis and identi cation of stochastic models of biochemical reaction networks J Ruess ETH Zurich, 2014 | 4 | 2014 |
| Beyond the chemical master equation: stochastic chemical kinetics coupled with auxiliary processes D Lunz, G Batt, J Ruess, JF Bonnans PLoS Computational Biology 17 (7), e1009214, 2021 | 3 | 2021 |
