| Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks C Briat, A Gupta, M Khammash Cell Systems 2 (1), 15-26, 2016 | 442 | 2016 |
| A universal biomolecular integral feedback controller for robust perfect adaptation SK Aoki, G Lillacci, A Gupta, A Baumschlager, D Schweingruber, ... Nature 570 (7762), 533-537, 2019 | 332 | 2019 |
| A scalable computational framework for establishing long-term behavior of stochastic reaction networks A Gupta, C Briat, M Khammash PLoS computational biology 10 (6), e1003669, 2014 | 118 | 2014 |
| Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks C Briat, A Gupta, M Khammash Journal of The Royal Society Interface 15 (143), 20180079, 2018 | 89* | 2018 |
| A finite state projection algorithm for the stationary solution of the chemical master equation A Gupta, J Mikelson, M Khammash The Journal of Chemical Physics 147 (15), 154101, 2017 | 76 | 2017 |
| Adaptive hybrid simulations for multiscale stochastic reaction networks B Hepp, A Gupta, M Khammash The Journal of chemical physics 142 (3), 2015 | 58 | 2015 |
| Noise Induces the Population-Level Entrainment of Incoherent, Uncoupled Intracellular Oscillators A Gupta, B Hepp, M Khammash Cell Systems 3 (6), 521-531, 2016 | 37 | 2016 |
| Unbiased estimation of parameter sensitivities for stochastic chemical reaction networks A Gupta, M Khammash SIAM Journal on Scientific Computing 35 (6), A2598-A2620, 2013 | 31 | 2013 |
| Universal structural requirements for maximal robust perfect adaptation in biomolecular networks A Gupta, M Khammash Proceedings of the National Academy of Sciences 119 (43), e2207802119, 2022 | 29 | 2022 |
| DeepCME: A deep learning framework for computing solution statistics of the chemical master equation A Gupta, C Schwab, M Khammash PLoS computational biology 17 (12), e1009623, 2021 | 29 | 2021 |
| A hidden integral structure endows absolute concentration robust systems with resilience to dynamical concentration disturbances D Cappelletti, A Gupta, M Khammash Journal of the Royal Society Interface 17 (171), 20200437, 2020 | 27 | 2020 |
| An efficient and unbiased method for sensitivity analysis of stochastic reaction networks A Gupta, M Khammash Journal of The Royal Society Interface 11 (101), 20140979, 2014 | 26 | 2014 |
| An antithetic integral rein controller for bio-molecular networks A Gupta, M Khammash 2019 IEEE 58th Conference on Decision and Control (CDC), 2808-2813, 2019 | 23 | 2019 |
| Sensitivity analysis for stochastic chemical reaction networks with multiple time-scales A Gupta, M Khammash | 23 | 2014 |
| Decimal expansion of 1/p and subgroup sums A Gupta, B Sury Integers: Electronic Journal of Combinatorial Number Theory 5 (1), 1-11, 2005 | 22 | 2005 |
| Determining the long-term behavior of cell populations: A new procedure for detecting ergodicity in large stochastic reaction networks A Gupta, M Khammash IFAC World Congress 2014 19 (1), 1711-1716, 2013 | 20 | 2013 |
| Computational identification of irreducible state-spaces for stochastic reaction networks A Gupta, M Khammash SIAM Journal on Applied Dynamical Systems 17 (2), 1213-1266, 2018 | 18* | 2018 |
| The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data M Manceau, A Gupta, T Vaughan, T Stadler Journal of theoretical biology 509, 110400, 2021 | 17 | 2021 |
| The occurrence birth–death process for combined-evidence analysis in macroevolution and epidemiology J Andréoletti, A Zwaans, RCM Warnock, G Aguirre-Fernández, ... Systematic Biology 71 (6), 1440-1452, 2022 | 16 | 2022 |
| Frequency spectra and the color of cellular noise A Gupta, M Khammash Nature communications 13 (1), 4305, 2022 | 16 | 2022 |
Mustafa KhammashDepartment of Biosystems Science and Engineering, ETH ZurichDirección de correo verificada de bsse.ethz.ch