A.L. Moor and C. Zechner, Dynamic Information Transfer in Stochastic Biochemical Networks. arXiv preprint arXiv:2208.04162, 2022.
E. Nerli, J. Kretzschmar, T. Bianucci, M. Rocha-Martins, C. Zechner, and C. Norden, Deterministic and probabilistic fate decisions co-exist in a single retinal lineage. bioRxiv, 2022.
published / in press
H.J. Wiederanders, A.L. Moor, and C. Zechner. Automated Generation of Conditional Moment Equations for Stochastic Reaction Networks. in International Conference on Computational Methods in Systems Biology. 2022. Springer.
D.T. Gonzales, N. Yandrapalli, T. Robinson, C. Zechner, and T.D. Tang, Cell-Free Gene Expression Dynamics in Synthetic Cell Populations. ACS Synth Biol, 2022. 11(1): p. 205-215.
M. Gabriele, H.B. Brandao, S. Grosse-Holz, A. Jha, G.M. Dailey, C. Cattoglio, T.S. Hsieh, L. Mirny, C. Zechner, and A.S. Hansen, Dynamics of CTCF- and cohesin-mediated chromatin looping revealed by live-cell imaging. Science, 2022. 376(6592): p. 496-501. See feature on F1000.
C.A. Weber and C. Zechner, Drops in cells. Physics Today, 2021. 74(6): p. 38-43.
T. Pietzsch, L. Duso, and C. Zechner, Compartor: a toolbox for the automatic generation of moment equations for dynamic compartment populations. Bioinformatics, 2021. 37(17): p. 2782-2784.
A.S. Hansen and C. Zechner, Promoters adopt distinct dynamic manifestations depending on transcription factor context. Mol Syst Biol, 2021. 17(2): p. e9821.
L. Duso, T. Bianucci, and C. Zechner. Shared antithetic integral control for dynamic cell populations. in 2021 60th IEEE Conference on Decision and Control (CDC). 2021. IEEE.
C. Zechner, E. Nerli, and C. Norden, Stochasticity and determinism in cell fate decisions. Development, 2020. 147(14): p. dev181495.
A. Klosin, F. Oltsch, T. Harmon, A. Honigmann, F. Jülicher, A.A. Hyman, and C. Zechner, Phase separation provides a mechanism to reduce noise in cells. Science, 2020. 367(6476): p. 464-468. See features on F1000 and PhysicsToday.
D.T. Gonzales, C. Zechner, and T.-Y.D. Tang, Building synthetic multicellular systems using bottom–up approaches. Current Opinion in Systems Biology, 2020. 24: p. 56-63.
L. Duso and C. Zechner, Stochastic reaction networks in dynamic compartment populations. Proceedings of the National Academy of Sciences, 2020. 117(37): p. 22674-22683.
M. Bahadorian, C. Zechner, and C.D. Modes, Gift of gab: Probing the limits of dynamic concentration-sensing across a network of communicating cells. Physical Review Research, 2020. 2(2): p. 023403.
D.K. Papadopoulos, K. Skouloudaki, Y. Engström, L. Terenius, R. Rigler, C. Zechner, V. Vukojević, and P. Tomancak, Control of Hox transcription factor concentration and cell-to-cell variability by an auto-regulatory switch. Development, 2019. 146(12): p. dev168179.
D.T. Gonzales, T.D. Tang, and C. Zechner. Moment-based analysis of biochemical networks in a heterogeneous population of communicating cells. in 2019 IEEE 58th Conference on Decision and Control (CDC). 2019. IEEE.
L. Duso and C. Zechner. Path mutual information for a class of biochemical reaction networks. in 2019 IEEE 58th Conference on Decision and Control (CDC). 2019. IEEE.
L. Duso and C. Zechner, Selected-node stochastic simulation algorithm. The Journal of chemical physics, 2018. 148(16): p. 164108.
L.C. Stapel, C. Zechner, and N.L. Vastenhouw, Uniform gene expression in embryos is achieved by temporal averaging of transcription noise. Gene Dev, 2017. 31(16): p. 1635-1640.
J. Ruess, H. Koeppl, and C. Zechner, Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability. The Journal of chemical physics, 2017. 146(12): p. 124122.
I. Kuzmanovska, A. Milias-Argeitis, J. Mikelson, C. Zechner, and M. Khammash, Parameter inference for stochastic single-cell dynamics from lineage tree data. Bmc Syst Biol, 2017. 11(1): p. 1-13.
C. Zechner, G. Seelig, M. Rullan, and M. Khammash, Molecular circuits for dynamic noise filtering. Proceedings of the National Academy of Sciences, 2016. 113(17): p. 4729-4734.
C. Zechner and M. Khammash. A molecular implementation of the least mean squares estimator. in 2016 IEEE 55th Conference on Decision and Control (CDC). 2016. IEEE.
C. Zechner and M. Khammash. Approximate filtering distributions for characterizing input-output behavior of biochemical networks. in 2016 European Control Conference (ECC). 2016. IEEE.
L. Studer, L. Paulevé, C. Zechner, M. Reumann, M.R. Martinez, and H. Koeppl. Marginalized continuous time Bayesian networks for network reconstruction from incomplete observations. in Proceedings of the AAAI Conference on Artificial Intelligence. 2016.
L. Huang, L. Pauleve, C. Zechner, M. Unger, A.S. Hansen, and H. Koeppl, Reconstructing dynamic molecular states from single-cell time series. Journal of The Royal Society Interface, 2016. 13(122): p. 20160533.
C. Briat, C. Zechner, and M. Khammash, Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation. ACS synthetic biology, 2016. 5(10): p. 1108-1116.
C. Albayrak, C.A. Jordi, C. Zechner, J. Lin, C.A. Bichsel, M. Khammash, and S. Tay, Digital quantification of proteins and mRNA in single mammalian cells. Mol Cell, 2016. 61(6): p. 914-924.
L. Bronstein, C. Zechner, and H. Koeppl, Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population. Methods, 2015. 85: p. 22-35.
C. Zechner, F. Wadehn, and H. Koeppl, Sparse learning of markovian population models in random environments.IFAC Proceedings Volumes, 2014. 47(3): p. 1723-1728.
C. Zechner, M. Unger, S. Pelet, M. Peter, and H. Koeppl, Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings. Nat Methods, 2014. 11(2): p. 197-202.
C. Zechner and H. Koeppl, Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments.Plos Comput Biol, 2014. 10(12): p. e1003942.
C. Zechner, Stochastic biochemical networks in random environments: probabilistic modeling and inference. 2014, ETH Zürich.
P. Kopesky, K. Tiedemann, D. Alkekhia, C. Zechner, B. Millard, B. Schoeberl, and S.V. Komarova, Autocrine signaling is a key regulatory element during osteoclastogenesis. Biology open, 2014. 3(8): p. 767-776.
C. Zechner, S. Deb, and H. Koeppl. Marginal dynamics of stochastic biochemical networks in random environments. in 2013 European Control Conference (ECC). 2013. IEEE.
A.L. Tarca, M. Lauria, M. Unger, E. Bilal, S. Boue, K. Kumar Dey, J. Hoeng, H. Koeppl, F. Martin, and P. Meyer, Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics, 2013. 29(22): p. 2892-2899.
P. Nandy, M. Unger, C. Zechner, K.K. Dey, and H. Koeppl, Learning diagnostic signatures from microarray data using L1-regularized logistic regression. Systems Biomedicine, 2013. 1(4): p. 240-246.
C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter, J. Lygeros, and H. Koeppl, Moment-based inference predicts bimodality in transient gene expression. Proceedings of the National Academy of Sciences, 2012. 109(21): p. 8340-8345.
C. Zechner, P. Nandy, M. Unger, and H. Koeppl. Optimal variational perturbations for the inference of stochastic reaction dynamics. in 2012 IEEE 51st IEEE conference on decision and control (CDC). 2012. IEEE.
D. Shutin, C. Zechner, S.R. Kulkarni, and H.V. Poor, Regularized variational bayesian learning of echo state networks with delay&sum readout. Neural Computation, 2012. 24(4): p. 967-995.
P. Nandy, M. Unger, C. Zechner, and H. Koeppl, Optimal perturbations for the identification of stochastic reaction dynamics. IFAC Proceedings Volumes, 2012. 45(16): p. 686-691.
H. Koeppl, C. Zechner, A. Ganguly, S. Pelet, and M. Peter, Accounting for extrinsic variability in the estimation of stochastic rate constants. Int J Robust Nonlin, 2012. 22(10): p. 1103-1119.
C. Zechner, S. Pelet, M. Peter, and H. Koeppl. Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations. in 2011 50th IEEE Conference on Decision and Control and European Control Conference. 2011. IEEE.
C. Zechner and D. Shutin. Bayesian learning of echo state networks with tunable filters and delay&sum readouts. in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. 2010. IEEE.
T. Thurner and C. Zechner. Phase-based algorithm for 2D displacement estimation of laser speckle patterns. in 2008 IEEE Instrumentation and Measurement Technology Conference. 2008. IEEE.