Modelling the dynamics of the yeast pheromone pathway

Kofahl, Bente; Klipp, Edda

doi:10.1002/yea.1122

Cited by 108 publications

(115 citation statements)

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“…Previously, quantitative computational simulation proved to support understanding of regulatory systems [5][6][7][8][9][10] , characterizing threshold properties and bistability 11,12 , characteristic times 13,14 and feedback effects 12,[15][16][17][18][19] . The model presented here comprises receptor stimulation, the HOG signaling pathway, activation of gene expression, adaptation of cellular metabolism, glycerol accumulation and a thermodynamic description of the control of volume and osmotic pressure.…”

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confidence: 99%

Integrative model of the response of yeast to osmotic shock

et al. 2005

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Integration of experimental studies with mathematical modeling allows insight into systems properties, prediction of perturbation effects and generation of hypotheses for further research. We present a comprehensive mathematical description of the cellular response of yeast to hyperosmotic shock. The model integrates a biochemical reaction network comprising receptor stimulation, mitogen-activated protein kinase cascade dynamics, activation of gene expression and adaptation of cellular metabolism with a thermodynamic description of volume regulation and osmotic pressure. Simulations agree well with experimental results obtained under different stress conditions or with specific mutants. The model is predictive since it suggests previously unrecognized features of the system with respect to osmolyte accumulation and feedback control, as confirmed with experiments. The mathematical description presented is a valuable tool for future studies on osmoregulation in yeast and-with appropriate modifications-other organisms. It also serves as a starting point for a comprehensive description of cellular signaling.Osmoregulation encompasses active processes with which cells monitor and adjust osmotic pressure and control shape, turgor and relative water content. Even individual cells in multicellular organisms respond to osmotic changes, and strategies of cellular adaptation are conserved from bacteria to human 1 . The yeast Saccharomyces cerevisiae is a suitable model system to study osmoregulation and a substantial amount of information is available on osmotic shockinduced signal transduction, control of gene expression and accumulation of osmolytes 2 .Osmoregulation is a homeostatic process, though commonly studied as a response to osmotic shock. Central to yeast osmotic adaptation is the high osmolarity glycerol (HOG) signaling system 2,3 (Fig. 1). S. cerevisiae monitors osmotic changes through the plasma membrane-localized sensor histidine kinase Sln1. Under ambient conditions, Sln1 is active and inhibits signaling. Upon loss of turgor pressure, Sln1 is inactivated 4 resulting in activation of a mitogen-activated protein (MAP) kinase cascade and phosphorylation of the MAP kinase Hog1. Active Hog1 accumulates in the nucleus where it affects gene expression. Two HOG target genes encode enzymes in glycerol production. Hence, activation of Hog1 stimulates the production of glycerol, which serves as an osmolyte to increase intracellular osmotic pressure. Glycerol accumulation is also controlled by rapid closing of the aquaglyceroporin Fps1, which is an osmolarity-regulated glycerol channel. Hog1 activation and Hog1-dependent transcriptional stimulation are transient processes, indicating rigorous feedback control. Several protein phosphatases are known as negative regulators of the pathway. Although the overall organization of the systems is well characterized, open questions concern the mechanisms underlying activation and deactivation of the system, feedback control and the causal relationship between different events in...

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Integrative model of the response of yeast to osmotic shock

et al. 2005

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“…To show the suitability of the P system formalism to model cell signalling networks, we have decided to convert the comprehensive yeast pheromone pathway model by Kofahl and Klipp [6] into our framework. The pathway consists of different modules: the G-protein-coupled receptor (M 1 , corresponding to receptor activation and G-protein cycle in [6]), formation of the scaffold protein (M 2 ), MAPK cascade (M 3 ), Fus3 phosphorylation cycle (M 4 ), and responses of the cell to the activation of the pathway (not modelled here).…”

Section: Determinismmentioning

confidence: 99%

“…The pathway consists of different modules: the G-protein-coupled receptor (M 1 , corresponding to receptor activation and G-protein cycle in [6]), formation of the scaffold protein (M 2 ), MAPK cascade (M 3 ), Fus3 phosphorylation cycle (M 4 ), and responses of the cell to the activation of the pathway (not modelled here).…”

Section: Determinismmentioning

confidence: 99%

“…While most parts of the model given in [6] were directly adapted, a few slight changes had to be introduced. Concentrations, which were given in nM in the original, were converted into molecule numbers.…”

Section: Ste20mentioning

confidence: 99%

“…In order to compensate for this effect on the reaction kinetics, these were extended by the volume V i of each module as a normalisation constant. Values for all parameters k ij appearing in the reaction kinetics are not given here, but can be calculated from the data given in [6]. In accordance with the original model, reaction kinetics consist of mass-action formulations, which have to be rounded in order to yield integer values as results.…”

Section: Ste20mentioning

confidence: 99%

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A Protein Substructure Based P System for Description and Analysis of Cell Signalling Networks

2006

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Abstract. The way how cell signals are generated, encoded, transferred, modified, and utilised is essential for understanding information processing inside living organisms. The tremendously growing biological knowledge about proteins and their interactions draws a more and more detailed image of a complex functional network. Considering signalling networks as computing devices, the detection of structural principles, especially modularisation into subunits and interfaces between them, can help to seize ideas for their description and analysis. Algebraic models like P systems prove to be appropriate to this. We utilise string-objects to carry information about protein binding domains and their ligands. Embedding these string-objects into a deterministic graph structured P system with dynamical behaviour, we introduce a model that can describe cell signalling pathways on a submolecular level. Beyond questions of formal languages, the model facilitates tracing the evolutionary development from single protein components towards functional interacting networks. We exemplify the model by means of the yeast pheromone pathway.

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