| In silico feedback for in vivo regulation of a gene expression circuit A Milias-Argeitis, S Summers, J Stewart-Ornstein, I Zuleta, D Pincus, ... Nature biotechnology 29 (12), 1114-1116, 2011 | 330 | 2011 |
| Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth A Milias-Argeitis, M Rullan, SK Aoki, P Buchmann, M Khammash Nature communications 7 (1), 12546, 2016 | 253 | 2016 |
| An optogenetic platform for real-time, single-cell interrogation of stochastic transcriptional regulation M Rullan, D Benzinger, GW Schmidt, A Milias-Argeitis, M Khammash Molecular cell 70 (4), 745-756. e6, 2018 | 134 | 2018 |
| 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 | 76 | 2013 |
| 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 | 73 | 2015 |
| Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast A Litsios, DHEW Huberts, HM Terpstra, P Guerra, A Schmidt, K Buczak, ... Nature cell biology 21 (11), 1382-1392, 2019 | 72 | 2019 |
| 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), 2011 | 46 | 2011 |
| Attaining mean square boundedness of a marginally stable stochastic linear system with a bounded control input F Ramponi, D Chatterjee, A Milias-Argeitis, P Hokayem, J Lygeros IEEE Transactions on Automatic Control 55 (10), 2414-2418, 2010 | 39 | 2010 |
| Stochastic dynamics of genetic networks: modelling and parameter identification E Cinquemani, A Milias-Argeitis, S Summers, J Lygeros Bioinformatics 24 (23), 2748-2754, 2008 | 39 | 2008 |
| Assessment of the interaction between the flux‐signaling metabolite fructose‐1, 6‐bisphosphate and the bacterial transcription factors CggR and Cra B Bley Folly, AD Ortega, G Hubmann, S Bonsing‐Vedelaar, HJ Wijma, ... Molecular Microbiology 109 (3), 278-290, 2018 | 28 | 2018 |
| Dynamic single-cell NAD(P)H measurement reveals oscillatory metabolism throughout the E. coli cell division cycle Z Zhang, A Milias-Argeitis, M Heinemann Scientific Reports 8 (1), 2162, 2018 | 26 | 2018 |
| Inference of the high-level interaction topology between the metabolic and cell-cycle oscillators from single-cell dynamics S Özsezen, A Papagiannakis, H Chen, B Niebel, A Milias-Argeitis, ... Cell systems 9 (4), 354-365. e6, 2019 | 21 | 2019 |
| TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle P Guerra, LAPE Vuillemenot, YB van Oppen, M Been, A Milias-Argeitis Journal of Cell Science 135 (18), jcs260378, 2022 | 20 | 2022 |
| Local identification of piecewise deterministic models of genetic networks E Cinquemani, A Milias-Argeitis, S Summers, J Lygeros Hybrid Systems: Computation and Control: 12th International Conference, HSCC …, 2009 | 18 | 2009 |
| Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks A Milias-Argeitis, S Engblom, P Bauer, M Khammash Journal of The Royal Society Interface 12 (113), 20150831, 2015 | 17 | 2015 |
| Systematic In Vivo Characterization of Fluorescent Protein Maturation in Budding Yeast P Guerra, LA Vuillemenot, B Rae, V Ladyhina, A Milias-Argeitis ACS synthetic biology 11 (3), 1129-1141, 2022 | 14 | 2022 |
| Parameter inference for stochastic single-cell dynamics from lineage tree data I Kuzmanovska, A Milias-Argeitis, J Mikelson, C Zechner, M Khammash BMC systems biology 11, 1-13, 2017 | 14 | 2017 |
| Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate A Gupta, A Milias-Argeitis, M Khammash Journal of the Royal Society Interface 14 (132), 2017 | 14 | 2017 |
| The timing of Start is determined primarily by increased synthesis of the Cln3 activator rather than dilution of the Whi5 inhibitor A Litsios, P Goswami, HM Terpstra, C Coffin, LA Vuillemenot, M Rovetta, ... Molecular biology of the cell 33 (5), rp2, 2022 | 13 | 2022 |
| Elucidation of genetic interactions in the yeast GATA-factor network using Bayesian model selection A Milias-Argeitis, AP Oliveira, L Gerosa, L Falter, U Sauer, J Lygeros PLoS computational biology 12 (3), e1004784, 2016 | 13 | 2016 |
