bioRxiv Science⌕ Search

Biology subjects

Enserink, J.

Publications and source records attributed to Enserink, J..

2 recordsLinked to original sources

Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway

Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phospho-regulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and to prevent pheromone-associated cell death.

cell biology↗

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in-vitro drug combination experiments

The effect of cancer therapies is often tested pre-clinically via in-vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

bioinformatics↗