Grand amphithéâtre de l'INRIA à Montbonnot
| 14h00 - 15h00 |
Andreas Kremling, Technische Universität München Mathematical models for carbohydrate uptake and sensing in bacteria More information on the Andreas Kremling website |
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| 15h00 - 15h30 | Pause café |
| 15h30 - 16h30 |
Sylvie Reverchon, CNRS/UCBL/INSA/BayercropScience
Delphine Ropers, INRIA |
| 16h30 - 17h00 | Discussion générale |
Andreas Kremling, Technische Universität München
Mathematical models for carbohydrate uptake and sensing in bacteria
Carbohydrate uptake systems are both transport systems and sensory systems that allow cells to sense their environmental conditions and to adjust the control systems according to the needs. In the talk, various mathematical models for Escherichia coli and Pseudomonas putida are introduced and the insights are summarized. To describe carbohydrate uptake in Escherichia coli a very detailed model was introduced [1] that comprises uptake and metabolism of six carbohydrate, including the description of global signalling, signal processing and gene expression. The model was validated by fitting kinetic parameters against a very comprehensive experimental data base (18 experiments with 5 different strains). The model describes expression of 17 key enzymes, 38 enzymatic reactions and the dynamic behaviour of more than 50 metabolites. In contrast to a model that was published very recently by Nishio et al. [2] on the same topic, our model shows a high predictive character and is able to forecast experiments with high accuracy [3].
Based on the results and the insights with the detailed model, a simplified version of the model was used to analyse a specific functionality, here, characteristics of the sensory system. We could show that the sensory system maps the specific uptake rate of different carbohydrates to the degree of phosphorylation of the protein EIIA. Moreover, we could show that a feed-forward-loop, the activation of the pyruvate kinase by a metabolite from the upper part of the glycolysis is an important structural element that guarantees robustness with respect to kinetic parameters and with respect to the experimental data [4,5]. The reduced model was extended to describe also uptake of the carbon acid acetate with good accuracy.
To analyse the observation that cross talk can occur between two branches of the PTS in Pseudomonas putida a steady-state model was set up to infer the flow of phosphoryl groups between the two branches. The model describes available data of the state of phosphorylation of PtsN, one of the PTS proteins in different environmental conditions and different strain variants [6]. Furthermore, data from flux balance analysis was used to determine some of the kinetic parameters of the involved reactions. Interestingly, modelling the system proposed that during growth on the PTS substrate fructose, about 80% of the required phosphoryl groups for fructose uptake via the PTSFru are provided by the PTSNtr, i.e. the N-branch of the system. This result is rather unexpected and gives rise to new questions on the biological relevance of the cross talk between the two systems and its implementation in the overall metabolism.
References
- Bettenbrock et al., 2006, J. Biol. Chem. 281.
- Nishio et al, 2008, Mol. Sys. Biol. 4
- Kremling et al., 2009, FEBS J 276
- Kremling et al., 2007, BMC Systems Biology 1.
- Kremling et al., 2008, Bioinformatics 24
- Kremling et al., 2011, submitted
Sylvie Reverchon, CNRS-UCBL-INSA-BayercropScience
Regulatory networks controlling the expression of the main virulence genes of Dickeya dadantii
Regulatory networks enable bacteria to adapt to almost every environmental niche on earth. Regulation is achieved by a network of interactions among diverse types of molecules including DNA, RNA, proteins and metabolites. The primary role of regulatory networks in bacteria is to control the response to environmental changes, such as nutritional status and environmental stress. A complex organization of networks allows the organism to coordinate and integrate multiple environmental signals. During infection process, pathogenic bacteria are faced with significant variations in their environmental conditions. Transition from saprophytic lifestyle to infectious lifestyle is therefore accompanied by a spatiotemporal shuffling of transcriptional expression patterns.
We use Dickeya dadantii as model pathogen to identify the regulatory networks involved in infection and survival in hostile conditions. D. dadantii is an enterobacterial plant pathogen. Its virulence relies on its ability to secrete plant cell wall degrading enzymes that are responsible for soft-rot symptoms. Similarly to many bacterial pathogens, D. dadantii virulence genes are expressed in a concerted manner and culminate when bacterial multiplication slows. Our results reveal that several parameters (chromatin structure, metabolic fluxes, genetic regulations) are used by the bacterium to adapt to changes in the environmental conditions during infection. Architecture of the regulatory networks controlling the expression of the main virulence genes of Dickeya will be presented.
Delphine Ropers, INRIA
Modelling the gene expression machinery in Escherichia coli
The adaptation of the physiology of the enterobacterium E. coli to environmental fluctuations involves system-wide changes of gene expression. This reprogramming of the cell takes place at two different levels: on a global scale through the adjustment of the level and activity of the components of the gene expression machinery (RNA polymerase and ribosome); and locally through the adjustment of the concentrations of regulators specifically coordinating the cell response to the new environmental conditions.
Classical studies in bacterial physiology have shown the close interactions between these two levels of regulation in the adaptive response of bacteria. Most work in systems biology has focused on gene networks with transcription factors and other specific regulators, whereas the modeling of the gene expression machinery and its interactions with other regulatory mechanisms has received less attention until now. In this presentation, I will give an overview of current work on the modeling of the gene expression machinery and present recent experimental and modeling results from our group.
This session is sponsored by Atelier Modélisation at Cluster Environnement
