Research

Single-cell activity readouts for nutrient-sensing pathways

Eukaryotic cells possess signaling pathways that regulate cell growth in response to a multitude of inputs such as nutrients and various stressors. Time-lapse microscopy can provide a wealth information on the dynamics of growth-regulatory pathways in response to perturbations. This information can in turn help elucidate the complex structure of growth-regulatory pathways and their crosstalk. However, monitoring the activity of these pathways via microscopy requires single-cell activity readouts, which are still lacking. 

We are therefore working on establishing and validating single-cell readouts for the activity of TOR Complex 1 (TORC1) and Protein Kinase A (PKA), two major and evolutionarily conserved regulators of biomass accumulation in budding yeast (Saccharomyces cerevisiae). To this end, we study the regulation mechanisms of endogenous TORC1 and PKA targets, and also explore the use of mammalian proteins as orthogonal readouts.

Relevant publication: [Vuillemenot et al. 2022]

Monitoring the activity of nutrient-sensing pathways during the budding yeast cell cycle

Besides responding to external conditions, nutrient-sensing pathways are known to respond to internal cues, such as metabolic fluxes and feedback signals generated by downstream processes. Dynamic changes in the activity of nutrient-sensing pathways during the cell cycle could therefore underlie the oscillatory growth dynamics that has been recently observed in both budding yeast and mammalian cells. However, cell cycle-resolved measurements of pathway activity are still missing.

Using budding yeast (Saccharomyces cerevisiae) as a model, we investigate the activity TORC1 and PKA during the cell cycle of individual cells in variety of conditions using time-lapse fluorescence microscopy. 

Relevant publication: [Guerra et al. 2021]

Optogenetic tools for understanding TORC1 signal transduction

Despite the progress achieved in the last two decades, the dynamic regulation of the TORC1 signaling pathway in response to nutrient and stress stimuli is not completely understood. The study TORC1 dynamics is complicated by the presence of compensatory feedback mechanisms, which can mask the effect of genetic perturbations such as deletions or mutations of pathway components. Systems that can dynamically perturb the TORC1 pathway will be instrumental for the elucidation of TORC1 dynamics. An excellent tool for this purpose is provided by optogenetics, a technique that uses genetically encoded photo-sensitive protein domains to dynamically regulate protein activity in living cells.

To understand how the different TORC1 pathway components (upstream regulators and downstream effectors) contribute to its dynamic responses, we develop optogenetic systems that can dynamically perturb the activity of these components. By exploring the endogenous regulation mechanisms of key TORC1 pathway nodes, we gain insights that can be used in optogenetic tool development.

Relevant publication: [Novarina et al. 2021]

Computational methods for mathematical models of heterogeneous cell populations 

A critical challenge in the development and analysis of mathematical models of biochemical networks is that these model often include several sources of uncertainty. For example, model parameters are imprecisely known from experiments, while the large heterogeneity observed in cell populations needs to be accounted for with the introduction of parameter distributions and other stochastic effects. In such cases, it is often desired to quantify the resulting uncertainty in model predictions in order to analyze model robustness of the model, understand how experimental outcomes are affected by population variability, and to eventually design experiments that will minimize model uncertainty. 

We are creating computational methods which can efficiently address questions related model uncertainty, such as global sensitivity analysis and uncertainty propagation via moments or distributions. 

Relevant publications: [Kurdyaeva and Milias-Argeitis, 2018], [Kurdyaeva and Milias-Argeitis, 2021], [Kurdyaeva and Milias-Argeitis, 2021]

Statistical and machine learning-based tools for the processing of single-cell data

Time-lapse microscopy has become indispensable for systems biology research, as it provides a wealth of information on cellular process dynamics at the single-cell level. While large volumes of time-lapse microscopy data can be easily generated, processing the resulting microscopy movies to extract biological insights can still be challenging and time-consuming. Using machine learning and statistical tools, we aim to facilitate the processing of large volumes of single-cell microscopy data. 

Relevant publication: [Kruitbosch et al., 2021]