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Sackmann2006 - mating pheromone response pathway of S.cerevisiae

ABSTRACT: Sackmann2006 - mating pheromone response pathway of S.cerevisiae This model is described in the article: Application of Petri net based analysis techniques to signal transduction pathways Sackmann A, Heiner M, Koch I. BMC Bioinformatics. 2006 Nov 2;7:482. Abstract: BACKGROUND: Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. METHODS:We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. RESULTS:We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. CONCLUSION:The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. This model is hosted on BioModels Database and identified by: MODEL1403040000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Jens Einloft

PROVIDER: MODEL1403040000 | BioModels | 2014-03-05

REPOSITORIES: BioModels

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Application of Petri net based analysis techniques to signal transduction pathways.

Sackmann Andrea A Heiner Monika M Koch Ina I

BMC bioinformatics 20061102

<h4>Background</h4>Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any ex ...[more]

PMID: 17081284

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Project description:Fission yeast cells undergo sexual differentiation in response to nitrogen starvation. In this process haploid M and P cells first mate to form diploid zygotes, which subsequently enter meiosis and sporulate. Prior to mating, M cells secrete M-factor and P-cells secrete P-factor, and these diffusible mating pheromone can activate a signal transduction pathway in the opposite cell type. The pheromone response orchestrates mating and is also required for entry into meiosis. Here we use DNA microarrays to identify genes that are induced by M-factor in P cells and by P-factor in M-cells. The use of a cyr1 genetic background allowed us to study pheromone stimulation independently of nitrogen starvation. We identified a total of 163 genes that were consistently induced more than two-fold by pheromone stimulation. As expected, there was a substantial overlap between the genes induced by M- and P-factor. Surprisingly, we found that pheromone control extended to genes fulfilling their function well beyond the point of entry into meiosis, including numerous genes required for meiotic recombination. A direct comparison of the M- and P-factor induced expression pattern allowed us to identify cell-type specific transcripts, including three new M-specific genes and one new P-specific gene. Finally, our results support the notion that Ste11 is the key transcription factor activated by pheromone signalling. Keywords: dose response Gene expression profiling experiment in which gene expression in pheromone treated cultures was compared to that of untreated control cells. All cultures were growing exponentially in MSL minimal medium. S. pombe h+ cyr1- cells treated with M-factor mating pheromone for 2, 4 and 6 hrs respectively, were compared to non-treated cells. Similarly, S. pombe h- sax2- cyr1- cells treated with P-factor mating pheromone for 2, 4 and 6 hrs, were compared to non-treated cells. For identiofication of cell-type specific gene expression, h+ cyr1- cells were compaired to h+ sxa2- cyr1- cells. This was done both before pheromone treatment (0h/0h) and after 6 hours of pheromone treatment (6h/6h). RNA was extracted and subjected to cDNA expression profiling analysis using the established protocols (Xue et al, Yeast 21, 25-39, 2004).Data normalized with 'Lowess' per chip per spot normalization.

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Sackmann2006 - mating pheromone response pathway of S.cerevisiae

Publications

Application of Petri net based analysis techniques to signal transduction pathways.

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