| Phase separation provides a mechanism to reduce noise in cells A Klosin, F Oltsch, T Harmon, A Honigmann, F Jülicher, AA Hyman, ... Science 367 (6476), 464-468, 2020 | 254 | 2020 |
| 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 | 251 | 2012 |
| Dynamics of CTCF-and cohesin-mediated chromatin looping revealed by live-cell imaging M Gabriele, HB Brandão, S Grosse-Holz, A Jha, GM Dailey, C Cattoglio, ... Science 376 (6592), 496-501, 2022 | 225 | 2022 |
| Digital quantification of proteins and mRNA in single mammalian cells C Albayrak, CA Jordi, C Zechner, J Lin, CA Bichsel, M Khammash, S Tay Molecular cell 61 (6), 914-924, 2016 | 178 | 2016 |
| Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings C Zechner, M Unger, S Pelet, M Peter, H Koeppl Nature methods 11 (2), 197-202, 2014 | 150 | 2014 |
| Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge AL Tarca, M Lauria, M Unger, E Bilal, S Boue, K Kumar Dey, J Hoeng, ... Bioinformatics 29 (22), 2892-2899, 2013 | 131 | 2013 |
| Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation C Briat, C Zechner, M Khammash ACS synthetic biology 5 (10), 1108-1116, 2016 | 91 | 2016 |
| Molecular circuits for dynamic noise filtering C Zechner, G Seelig, M Rullan, M Khammash Proceedings of the National Academy of Sciences 113 (17), 4729-4734, 2016 | 86 | 2016 |
| Autocrine signaling is a key regulatory element during osteoclastogenesis P Kopesky, K Tiedemann, D Alkekhia, C Zechner, B Millard, B Schoeberl, ... Biology open 3 (8), 767-776, 2014 | 68 | 2014 |
| Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments C Zechner, H Koeppl Plos Computational Biology 10 (12), e1003942, 2014 | 50 | 2014 |
| Cell-free gene expression dynamics in synthetic cell populations DT Gonzales, N Yandrapalli, T Robinson, C Zechner, TYD Tang ACS synthetic biology 11 (1), 205-215, 2022 | 48 | 2022 |
| Stochasticity and determinism in cell fate decisions C Zechner, E Nerli, C Norden Development 147 (14), dev181495, 2020 | 38 | 2020 |
| Accounting for extrinsic variability in the estimation of stochastic rate constants H Koeppl, C Zechner, A Ganguly, S Pelet, M Peter International Journal of Robust and Nonlinear Control 22 (10), 1103-1119, 2012 | 37 | 2012 |
| Uniform gene expression in embryos is achieved by temporal averaging of transcription noise LC Stapel, C Zechner, NL Vastenhouw Genes & development 31 (16), 1635-1640, 2017 | 27 | 2017 |
| Stochastic reaction networks in dynamic compartment populations L Duso, C Zechner Proceedings of the National Academy of Sciences 117 (37), 22674-22683, 2020 | 23 | 2020 |
| Control of Hox transcription factor concentration and cell-to-cell variability by an auto-regulatory switch DK Papadopoulos, K Skouloudaki, Y Engström, L Terenius, R Rigler, ... Development 146 (12), dev168179, 2019 | 23 | 2019 |
| Reconstructing dynamic molecular states from single-cell time series L Huang, L Pauleve, C Zechner, M Unger, AS Hansen, H Koeppl Journal of The Royal Society Interface 13 (122), 20160533, 2016 | 21 | 2016 |
| Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population L Bronstein, C Zechner, H Koeppl Methods 85, 22-35, 2015 | 19 | 2015 |
| Regularized variational bayesian learning of echo state networks with delay&sum readout D Shutin, C Zechner, SR Kulkarni, HV Poor Neural Computation 24 (4), 967-995, 2012 | 19 | 2012 |
| Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations C Zechner, S Pelet, M Peter, H Koeppl 2011 50th IEEE Conference on Decision and Control and European Control …, 2011 | 17 | 2011 |
Heinz KoepplTechnische Universität Darmstadt, Dept. Electrical Engineering and Dept. BiologyDirección de correo verificada de bcs.tu-darmstadt.de
