Image from Auconi et al., Phys Rev Letters (2017)

The intuition of causation is so fundamental that almost every research study in life sciences refers to this concept. However, a widely accepted formal definition of causal influence between observables is still missing. In the framework of linear Langevin networks without feedback (linear response models) we propose a measure of causal influence based on a new decomposition of information flows over time. We discuss its main properties and we compare it with other information measures like the transfer entropy. We are currently unable to extend the definition of causal influence to systems with a general feedback structure and nonlinearities.

The central aim of my project is to improve our understanding how the cell cycle progression is regulated in budding yeast (Saccharomyces cerevisiae) at the levels of transcription and translation. I am developing single-RNA tools to image subcellular localization, spatio-temporal dynamics and translation of Cyclin B2 (CLB2), which is the mRNA of a crucial regulator of mitotic entry.
Regulation of cell cycle progression in budding yeast is robust even though there are few mRNAs per regulatory gene and only hundreds of any regulatory protein⁠. The low numbers should make the system susceptible to molecular noise. One factor aiding robustness is thought to be the presence of two cell-cycle oscillators that can drive its progression, within certain limits, independently: Both, a transcription factor network and a 'master kinase' (cyclin dependent kinase, CDK) can trigger the waves of transcription characterizing the cell cycle.
In my project, I aim to investigate the robustness of the latter system, the CDK oscillator, with particular regards to the mitotic entry.