MOSES

SysMO is a European transnational funding and research initiative on "Systems Biology of Microorganisms".

The goal pursued by SysMO was to record and describe the dynamic molecular processes going on in unicellular microorganisms in a comprehensive way and to present these processes in the form of computerized mathematical models.

Systems biology will raise biomedical and biotechnological research to a new quality level and contribute markedly to progress in understanding. Pooling European research

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Investigation of dynamics of the central metabolism (glycolysis, PPP, anaplerotic reactions, purines) of yeast Saccharomyces cerevisiae in anaerobic conditions

Steady state metabolic fluxes and metabolite concentrations of yeast Saccharomyces cerevisiae in anaerobic chemostat at D=0.1 h-1

This investigation examines the effect of benzoic acid, a food preservative, on the ATP levels of yeast cells.

This study aims to establish the optimum conditions and assay methods for measuring ATP levels

To see changes in ATP levels in cells with induced ABC transporters. Cells with Pdr12 pump by 10 mM benzoic acid are used.

ATP levels of cells stressed with higher concentrations of benzoic acid (30 mM and 50 mM).

Effect of benzoate treatment (high concentrations) on ATP levels and Pdr12 expression after pretreatment of cells with low concentrations of benzoic acid.

Cell survival was determined under different benzoic acid concentrations

The steady state anaerobic culture (D = 0.1 h-1) was pertrubed by sudden increase of the extracellular glucose up to 1 g/L and both extra- and intracellular transient metabolite concentrations were measured

Steady state fluxes in yeast Saccharomyces cerevisiae in anaerobic chemostat at D=0.1 h-1

Steady state concentrations of metabolites in yeast Saccharomyces cerevisiae in anaerobic chemostat at D=0.1 h-1

experimentally measured extracellular fluxes in yeast Saccharomyces cerevisiae in anaerobic glucose limited chemostat (D=0.1 h-1) on minimal medium

Steady state concentrations of extracellular metabolites in yeast Saccharomyces cerevisiae in anaerobic chemostat at D = 0.1 h-1 on minimal medium

Biomass weight during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Dynamics of extracellular metabolites (glc, pyr, suc, lac, gly, ac, etoh, fum, mal, cit, including loss of akg, g3p, 2pg, 3pg, r5p, f6p, g6p, 6pg) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Dynamics of intracellular metabolites (pyr, suc, fum, mal, akg, pep, g3p, 2pg, 3pg, cit, r5p, f6p, g6p, 6pg, ATP, ADP, AMP, UTP, GTP, inosine, NAD+, IMP, UDP, NADP+, CTP, AdenyloSuccinate, NADPH, trehalose) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines,

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Dynamics of macromolecules (total RNA) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

We will compare two different procedures to extract ATP from yeast cells: Standard kit procedure (hot Tris/EDTA) and Serrano procedure (cold perchloric acid). In addition we have tested different condition as it turned out that some are important.

Cellular size and granularity (measured by FACS) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

The dynamic model describes response of yeast metabolic network on metabolic perturbation (i.e. glucose-pulse). One compartmental ODE-based model of yeast anaerobic metabolism includes: glycolysis, pentose phosphate reactions, purine de novo synthesis pathway, purine salvage reactions, redox reactions and biomass growth. The model describes metabolic perturbation of steady state growing cells in chemostat.

Monitoring of partial pressure of CO2 in off-gas. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Dynamics of intracellular metabolites (pyr, suc, fum, mal, akg, pep, g3p, 2pg, 3pg, cit, r5p, f6p, g6p, 6pg, ATP, ADP, AMP, UTP, GTP, inosine, NAD+, IMP, UDP, NADP+, CTP, AdenyloSuccinate, NADPH, trehalose) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines,

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Dynamics of extracellular metabolites (glc, pyr, suc, lac, gly, ac, etoh, fum, mal, cit, including loss of akg, g3p, 2pg, 3pg, r5p, f6p, g6p, 6pg) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Cellular size and granularity (measured by FACS) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Dynamics of macromolecules (total RNA) during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Biomass weight during glucose pulse. Glucose pulse was performed in anaerobically growing yeast Saccharomyces cerevisiae in steady state chemostat (D = 0.1 h-1) and transent concentrations of the extra- and intracellular metabolites from central carbon metabolism (e.g. glycolysis, PPP, glycerol, purines, etc) were measured.

Steady state concentrations of intracellular metabolites in yeast Saccharomyces cerevisiae anaerobic chemostat at D = 0.1 h-1 on minimal medium. All metabolite concentrations are in mmol/L(CV).

Steady state concentrations of extracellular metabolites in yeast Saccharomyces cerevisiae anaerobic chemostat at D = 0.1 h-1 on minimal medium. All metabolite concentrations are in mmol/L(R) except CO2, which is in parts of the partial pressure.

Steady state metabolic fluxes measured in glucose-limited chemostat of Saccharomyces cerevisiae at D = 0.1 h-1 growing on minimal medium. Fluxes are: glucose, ethanol, glycerol, acetate, succinate, pyruvate, lactate, citrate, malate, a-ketoglutarate, fumarate

Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material:

TNF and MMP7 concentration upgrade of the models

All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF

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Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material:

TNF and MMP7 concentration upgrade of the models

All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF

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This is a model about a ROS network that exhibits five design principles, and has been calibrated so as to predict quantitatively various steady state concentrations. 10191125.

Instructions

RUN the model for steady state.

For the Menadione experiment set the initial concentration of 'Menadione' species to experimental dosing i.e. 100 000 nM (0.1 mM) and make the simulation type "reaction" for both the species i.e. 'Menadione' and 'Menadione_internal'. Then run for 24 hr i.e. 1500 minutes approx.

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Model that can be used to obtain the figures of Abudulikemu et al 2018:

Abudukelimu, A., Barberis, M., Redegeld, F.A., Sahin, N., and Westerhoff, H.V. (2018). Predictable Irreversible Switching Between Acute and Chronic Inflammation. Front Immunol 9, 1596.

(Abudulikemu et al 2000 (also 2018) Standard model of acute mode Figure 32.

Particularly figure 2 of of Abudulikemu et al 2020 in press

The principles of Stealthy Engineering (Adamczyk et al.: Biotechnology Journal 2012; 7(7):877-83) are illustrated in this model by emulating a cross engineering intervention between L. lactis and S. cerevisiae.

The case study consists of replacing the native glucose uptake system of L. lactis with that native to the yeast S. cerevisiae. A modified version of Hoefnagel et al.’s model of L. lacrtis’ central metabolism was used as starting point. The total functional replacement of the PTS with the

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The model includes glycolysis, pentosephosphate pathway, purine salvage reactions, purine de novo synthesis, redox balance and biomass growth. The network balances adenylate pool as opened moiety.

Assay methodologies for individual glycolytic isoenzymes from the Mendes Group, University of Manchester, UK

This is the general protocol for the glycolytic enzyme measurements. Detailed Information for each Enzyme can be found in the SOP: Assays for measuring the activities of the individual glycolytic isoenzymes of Saccharomyces cerevisiae

SOP for growing yeast in anaerobic conditions

Biomass sampling - SOP for sampling of biomass

Metabolic perturbations - SOP for metabolic perturbations (i.e. glucose pulse)

Yeast strains used in the project

General protocol for measuring the kinetic parameters of the purified glycolytic enzymes from Saccharomyces cerevisiae - SOP for measuring the kinetic parameters of the purified glycolytic isoenzymes

Sample preparation - SOP for sampling, preparation of cell-free extracts, and determination of total extracted protein

Sample preparation - SOP for sampling, preparation of cell extracts, and general assay set-up

The kit is used for the quantitative detection of ATP in yeast cells by luciferase driven

bioluminescence. ATP extraction is done by boiling cells in TE buffer.

This protocol is designed to prepare sufficient amounts of high-quality total

RNA from the yeast saccharomyces cerevisiae grown in liquid culture for

analysis on microarrays.

Talk given by Maksim Zakhartsev (Hohenheim University, Stuttgart, Germany, member of MOSES, ZucAt and ExtremoPharm projects)