Ensemble modelling is used to study the yeast high osmolarity glycerol (HOG) pathway, a prototype for eukaryotic mitogen-activated kinase signalling systems. The best fit model provides new insights into the function of this system, some of which are then experimentally validated.

The main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production.The transcriptional response rather serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity after adaptation.A fast negative feedback of activated Hog1 on the upstream signalling branches serves to stabilise the adaptation response by preventing oscillatory behaviour.Two parallel redundant signalling branches elicit a more robust and swifter adaptation than a single branch alone, at least for low osmotic shock. This notion could be corroborated by dedicated measurements of single-cell volume recovery for the wild-type and single-branch mutants.

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

One goal of systems biology is to understand how genome-encoded parts interact to produce quantitative phenotypes. The Alpha Project is a medium-scale, interdisciplinary systems biology effort that aims to achieve this goal by understanding fundamental quantitative behaviors of a prototypic signal transduction pathway, the yeast pheromone response system from Saccharomyces cerevisiae. The Alpha Project distinguishes itself from many other systems biology projects by studying a tightly-bounded and well-characterized system that is easily modified by genetic means, and by focusing on deep understanding of a discrete number of important and accessible quantitative behaviors. During the project, we have developed tools to measure the appropriate data and develop models at appropriate levels of detail for studying a number of these quantitative behaviors. We also have developed transportable experimental tools and conceptual frameworks for understanding other signaling systems. In particular, we have begun to interpret system behaviors and their underlying molecular mechanisms through the lens of information transmission, a principal function of signaling systems. The Alpha Project demonstrates that interdisciplinary studies that identify key quantitative behaviors and measure important quantities, in the context of well-articulated abstractions of system function and appropriate analytical frameworks, can lead to deeper biological understanding. Our experience may provide a productive template for system biology investigations of other cellular systems.

This unit describes a method to quantify, from sets of microscope images, various cellular parameters from individual cells, and includes procedures to track cells over time. For example, the user can measure cell volume, total and subcellular localization (nuclear, plasma membrane) of fluorescence for multiple fluorescence channels. This method uses the image processing capabilities of Cell-ID (Gordon et al., 2007) and data analysis by the statistical programming framework R, both open source software packages. The first step for successful cytometry entails acquiring at least one set of images for each field of cells. Each set is composed of one purposefully defocused transmission image (sometimes referred to as brightfield, or BF) that will be used to locate each cell, and one fluorescence image for each of the color channels to be analyzed. Images may be conventional wide-field epifluorescence or confocal microscopy images. Cell-ID processes the images and outputs a tab-delimited file with information extracted from each cell, for each time point and each fluorescence channel. Finally, the user analyzes the data using R (R-Development-Team, 2008), which we have supplemented with a package tailored to analyze Cell-ID output.

Haploid Saccharomyces cerevisiae yeast cells use a prototypic cell signaling system to transmit information about the extracellular concentration of mating pheromone secreted by potential mating partners. The ability for cells to respond distinguishably to different pheromone concentrations depends on how much information about pheromone concentration the system can transmit. Here we show that the MAPK Fus3 mediates fast-acting negative feedback that adjusts the dose-response of downstream system response to match that of receptor-ligand binding. This “dose-response alignment”, defined by a linear relationship between receptor occupancy and downstream response, can improve the fidelity of information transmission by making downstream responses corresponding to different receptor occupancies more distinguishable and reducing amplification of stochastic noise during signal transmission. We also show that one target of the feedback is a novel signal-promoting function of the RGS protein Sst2. Our work suggests that negative feedback is a general mechanism used in signaling systems to align dose-responses and thereby increase the fidelity of information transmission.