Publications

preprints

Hansen, A. S., & Zechner, C. (2019). Promoters adopt distinct dynamic manifestations depending on transcription factor context. BioRxiv, 650762.

published / in press

Duso, L., & Zechner, C. (2020). Stochastic reaction networks in dynamic compartment populations. PNAS, 202003734; DOI: 10.1073/pnas.2003734117.

Bahadorian, M., Zechner C. & Modes, C. (2020) The gift of gab: probing the limits of dynamic concentration-sensing across a network of communicating cells. Phys. Rev. Research 2, 023403.

Klosin, A., Oltsch, F., Harmon, T., Honigmann, A., Jülicher, F., Hyman, A. A., & Zechner, C. (2020). Phase separation provides a mechanism to reduce noise in cells. Science,  367 (6476), 464-468. See features on F1000 and PhysicsToday.

Gonzales, D. T., Tang, T. Y., & Zechner, C. (2019). Moment-based analysis of biochemical feedback circuits in a population of chemically interacting cells, IEEE CDC 2019ArXiv Preprint ArXiv:1905.02053.

Duso, L., & Zechner, C. (2019). Path mutual information for a class of biochemical reaction networks, IEEE CDC 2019ArXiv Preprint ArXiv:1904.01988.

Papadopoulos, D. K., Skouloudaki, K., Engström, Y., Terenius, L., Rigler, R., Zechner, C., … Tomancak, P. (2019). Control of Hox transcription factor concentration and cell-to-cell variability by an auto-regulatory switch. Development146(12), dev168179.

Duso, L., & Zechner, C. (2018). Selected-node stochastic simulation algorithm. The Journal of Chemical Physics148(16), 164108.

Ruess, J., Koeppl, H., & Zechner, C. (2017). Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability. The Journal of Chemical Physics146(12), 124122.

Stapel, L. C., Zechner, C., & Vastenhouw, N. L. (2017). Uniform gene expression in embryos is achieved by temporal averaging of transcription noise. Genes & Development31(16), 1635–1640.

Previous work

Kuzmanovska, I., Milias-Argeitis, A., Mikelson, J., Zechner, C., & Khammash, M. (2017). Parameter inference for stochastic single-cell dynamics from lineage tree data. BMC Systems Biology11(1), 52.

Albayrak, C., Jordi, C. A., Zechner, C., Lin, J., Bichsel, C. A., Khammash, M., & Tay, S. (2016). Digital quantification of proteins and mRNA in single mammalian cells. Molecular Cell61(6), 914–924.

Zechner, C., Seelig, G., Rullan, M., & Khammash, M. (2016). Molecular circuits for dynamic noise filtering. Proceedings of the National Academy of Sciences113(17), 4729–4734.

Studer, L., Paulevé, L., Zechner, C., Reumann, M., Martinez, M. R., & Koeppl, H. (2016). Marginalized continuous time Bayesian networks for network reconstruction from incomplete observations. Thirtieth AAAI Conference on Artificial Intelligence.

Briat, C., Zechner, C., & Khammash, M. (2016). Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation. ACS Synthetic Biology5(10), 1108–1116.

Huang, L., Pauleve, L., Zechner, C., Unger, M., Hansen, A. S., & Koeppl, H. (2016). Reconstructing dynamic molecular states from single-cell time series. Journal of The Royal Society Interface13(122), 20160533.

Zechner, C., & Khammash, M. (2016). A molecular implementation of the least mean squares estimator. 2016 IEEE 55th Conference on Decision and Control (CDC), 5869–5874.

Zechner, C., & Khammash, M. (2016). Approximate filtering distributions for characterizing input-output behavior of biochemical networks. 2016 European Control Conference (ECC), 1818–1823.

Bronstein, L., Zechner, C., & Koeppl, H. (2015). Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population. Methods85, 22–35.

Zechner, C., Unger, M., Pelet, S., Peter, M., & Koeppl, H. (2014). Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings. Nature Methods11(2), 197.

Zechner, C., Wadehn, F., & Koeppl, H. (2014). Sparse learning of Markovian population models in random environments. IFAC Proceedings Volumes47(3), 1723–1728.

Kopesky, P., Tiedemann, K., Alkekhia, D., Zechner, C., Millard, B., Schoeberl, B., & Komarova, S. V. (2014). Autocrine signaling is a key regulatory element during osteoclastogenesis. Biology Open3(8), 767–776.

Zechner, C. (2014). Stochastic biochemical networks in random environments: probabilistic modeling and inference. ETH Zürich.

Zechner, C., & Koeppl, H. (2014). Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments. Plos Computational Biology10(12), e1003942.

Nandy, P., Unger, M., Zechner, C., Dey, K. K., & Koeppl, H. (2013). Learning diagnostic signatures from microarray data using L1-regularized logistic regression. Systems Biomedicine1(4), 240–246.

Tarca, A. L., Lauria, M., Unger, M., Bilal, E., Boue, S., Kumar Dey, K., … others. (2013). Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics29(22), 2892–2899.

Zechner, C., Deb, S., & Koeppl, H. (2013). Marginal dynamics of stochastic biochemical networks in random environments. 2013 European Control Conference (ECC), 4269–4274.

Zechner, C., Ruess, J., Krenn, P., Pelet, S., Peter, M., Lygeros, J., & Koeppl, H. (2012). Moment-based inference predicts bimodality in transient gene expression. Proceedings of the National Academy of Sciences109(21), 8340–8345.

Koeppl, H., Zechner, C., Ganguly, A., Pelet, S., & Peter, M. (2012). Accounting for extrinsic variability in the estimation of stochastic rate constants. International Journal of Robust and Nonlinear Control22(10), 1103–1119.

Shutin, D., Zechner, C., Kulkarni, S. R., & Poor, H. V. (2012). Regularized variational bayesian learning of echo state networks with delay&sum readout. Neural Computation24(4), 967–995.

Zechner, C., Nandy, P., Unger, M., & Koeppl, H. (2012). Optimal variational perturbations for the inference of stochastic reaction dynamics. 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 5336–5341.

Nandy, P., Unger, M., Zechner, C., & Koeppl, H. (2012). Optimal perturbations for the identification of stochastic reaction dynamics. IFAC Proceedings Volumes45(16), 686–691.

Zechner, C., Pelet, S., Peter, M., & Koeppl, H. (2011). Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations. 2011 50th IEEE Conference on Decision and Control and European Control Conference, 5837–5843.

Zechner, C., & Shutin, D. (2010). Bayesian learning of echo state networks with tunable filters and delay&sum readouts. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998–2001.

Thurner, T., & Zechner, C. (2008). Phase-based algorithm for 2D displacement estimation of laser speckle patterns. 2008 IEEE Instrumentation and Measurement Technology Conference, 2173–2178.