Research

Stochasticity in intracellular processes causes substantial heterogeneity across genetically identical cells. Yet, biological systems manage to act in a highly robust and well-orchestrated manner when this is needed, for instance during embryonic development. How can we reconcile this high degree of randomness observed in individual cells with the high degree of spatiotemporal organization that is required for a cell to develop into a fully formed organism? In the Zechner lab, we develop theory and computational approaches to explore this question in collaboration with our experimental partners at the MPI-CBG and abroad.

On the methodological side, we focus on the development of mathematical methods to study stochastic processes in cells and tissues. Recently, we have been putting special emphasis on approaches that bridge between different scales of biological organization (e.g., molecules / compartments / cells). Furthermore, we work on statistical / data-driven approaches to infer biological processes from single-cell experimental data.


Recent projects

Klosin & Oltsch et al. Science (2020)

Noise buffering by liquid compartments. Compartmentalization of molecules via liquid-liquid phase separation provides a potential way to reduce concentration fluctuations inside cells. In this collaboration with the Hyman and Jülicher labs, we use statistical physics, stochastic modeling and quantitative single-cell experiments to study how liquid-liquid phase separation affects noise in biochemical systems.

Team: Florian, Hari, Hyman & Jülicher labs


Data-driven modelling of the early endosomal network.

Data-driven modelling of the early endosomal network. Endosomes are small dynamical vesicles responsible for the uptake, transport and sorting of molecules in eukaryotic cells. Upon internalization at the plasma membrane, the endosomes collectively process their molecular content by undergoing fusion and fission events, which are regulated in a complex and dynamic fashion. In collaboration with the Zerial lab, we develop a data-driven approach to reverse-engineer the dynamic features of the early endosomal network from experimental snapshot data.

Team: Lorenzo, Zerial lab


Gene expression in a population of artificial cell-like compartments.

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.

Team: David, Tang lab


Signal processing by gene networks.

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.

Team: Chris, Hansen lab @ MIT