Mission. Chemical reactions serve as central units for cellular information processing and control. However, reaction chemistry inside cells is “noisy”, leading to significant variability in the molecular constitution of living systems. How can we reconcile the large degree of stochasticity in intracellular chemistry with the high degree of spatiotemporal control that is required for forming and maintaining a complex multicellular organism? In the Zechner lab, we merge signal and control theory with statistical physics to address this question. We develop effective computational methods to analyze stochastic biological processes across scales, and to reverse-engineer their dynamical features from experimental data. We apply these techniques to different biological systems in collaboration with experimentalists at the MPI-CBG and elsewhere.
Control of cellular noise via subcellular compartmentalization. In this project, we study the role of biomolecular condensates to control intracellular noise. We have recently provided a first proof-of-principle of this idea , showing that protein concentration noise can be strongly reduced when the protein partitions into condensates. Based on these findings we are now exploring the generality of this concept in the context of cellular information processing and feedback control . To this end, we merge statistical physics with control theory to understand the statistical constraints of chemical pathways in condensed, non-equilibrium environments. We complement our theoretical work with experiment in close collaboration with the Hyman lab.
 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.
 C.A. Weber and C. Zechner, Drops in cells. Physics Today, 2021. 74(6): p. 38-43.
Dynamics of chromatin looping and its role in transcriptional regulation. Loop extrusion has been proposed as a mechanism to compartmentalize chromatin into topologically associating domains (TADs), thereby facilitating interactions between promoters and enhancers. In collaboration with the Hansen and Mirny labs at MIT, we use statistical modelling and super-resolution live-cell imaging to understand the dynamics of chromatin looping and its role in transcription regulation. We have recently developed a rigorous statistical method to infer loop contact frequencies and lifetimes from noisy time-series data . Our long-term goal is to use these approaches to establish a quantitative link between the dynamics of chromatin looping and transcription.
 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.
Stochastic models of compartmentalised biochemical systems. Most established approaches to model stochastic intracellular processes are based on the assumption that biochemical reactions take place in dilute and well-mixed environments. Many biochemical processes, however, are organized within compartments such as endosomes or liquid condensates. The interplay between stochastic reaction- and compartmental dynamics gives rise to challenging multiscale problems, for which effective solutions have been lacking. Our goal is to develop mathematical and computational methods to fill this gap.
 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.
 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.
Bottom-up approaches to study noise in cell communities. Cell-to-cell communication allows cells to behave collectively in the presence of population and environmental noise. Understanding the interplay between noise and cell-to-cell communication is an important problem, which is challenging to address in living systems. In collaboration with the Tang lab, we use artificial cell-like compartments in combination and stochastic modeling to study how communication affects noise in a minimal gene expression system.
Collaborators: Tang lab
 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.
Signal processing by gene promoters. Cells encode different environmental stresses and signals in the temporal activation dynamics of transcription factors. In this project, we use Bayesian inference in combination with high-throughput single-cell gene expression data to understand how different gene promoters decode these dynamic transcription factor profiles and convert them into distinct gene expression responses.
 A.S. Hansen and C. Zechner, Promoters adopt distinct dynamic manifestations depending on transcription factor context. Mol Syst Biol, 2021. 17(2): p. e9821.