Current work on this topic focuses on investigating both precise transcription factor sensing schemes and information-theoretic inference schemes. I'm interested in optimal network architectures or optimal decision pathways, also more abstractly. Biologically, I am interested in how different enhancer and regulatory architectures process transcription factor signals well, as well as comparing these architectures with models. 

Current projects on this topic focus on modeling sub-cellular spatial structure. I'm interested quantitatively evaluating the impacts of this structure on sensing precision. Theoretically, I want to do so also using a stochastic thermodynamics and derivation of known noise bounds. On the biological side, I am interested in individual manifestations of such spatial structure, also in the context of liquid-liquid phase separation.

Current projects on this topic focus on identifying good information-theoretic quantifiers for data, as well as developing coarse-graining, compression and optimization schemes. Theoretically, I'm interested in finding limits or bounds for simple model systems that we can compare biological systems with; these can be found for a specific question or by developing compression schemes for specific signals. On the biological side, I am interested in investigating quantifiers for data that can efficiently tell us how systems develop.