Saccharomyces cerevisiae

The main objective of SynBio4Flav is to go further in the standardization of high complexity synthetic biological parts, and to demonstrate the development of a standardized, and systematic platform for flavonoids production based on synthetic microbial consortia (SMCs).

Increasing temperature in the European wine producing regions is having a negative impact on this key sector. Climate change results in a lack of balance between technological and phenolic ripening of wine grapes and, as a consequence, alcohol increase in wines. This trend is of great concern for the European wine industry because it has a negative impact on wine quality, becomes a hurdle for international trade, and jeopardizes compatibility of moderate wine consumption with a healthy lifestyle. ...

Microbial strains used in biotechnological industry need to produce their biotechnological products at high yield and at the same time they are desired to be robust to the intrinsic nutrient dynamics of large-scale bioreactors, most noticeably to transient limitations of carbon sources and oxygen. The engineering principles for robustness of metabolism to nutrient dynamics are however not yet well understood. The ROBUSTYEAST project aims to reveal these principles for microbial strain improvement ...

D-xylose is a major component of lignocellulose and is after D-glucose the most abundant monosaccharide on earth. However, D-xylose cannot be naturally utilised by several industrially relevant microorganisms. On the way to a strong bio-based economy in Europe, this widely available feedstock has to be made accessible for the sustainable microbial synthesis of value-added chemical building blocks to be used in a broad range of applications. The project aims at engineering Corynebacterium glutamicum ...

Systems Biology studies the properties and phenotypes that emerge from the interaction of biomolecules where such properties are not obvious from those of the individual molecules. By connecting fields such as genomics, proteomics, bioinformatics, mathematics, cell biology, genetics, mathematics, engineering and computer sciences, Systems Biology enables discovery of yet unknown principles underlying the functioning of living cells. At the same time, testable and predictive models of complex ...

GMO free systems optimization of wine yeast for wine production by massive scale directed evolution

MOSES (Micro Organism Systems biology: Energy and Saccharomyces cerevisiae) develops a new Systems Biology approach, which is called 'domino systems biology'. It uses this to unravel the role of cellular free energy ('ATP') in the control and regulation of cell function. MOSES operates though continuous iterations between partner groups through a new systems-biology driven data-management workflow. MOSES also tries to serve as a substrate for three or more other SYSMO programs.

Gene interaction networks and models of cation homeostasis in Saccharomyces cerevisiae

Dear SEEK users, this Assay is just an example Excel sheet for intracellular metabolites concentration measurements performed using cell culture growing in chemostat

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.

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, ...

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.

A boolean network was created using booleannet (after experimenting with Squad and CellNetAnalyzer). This network can be simulated and visualized using additional software components that will be part of the pyMantis CMS that is developed by the Translucent project.

A interaction network analysis tool (currently based on the BioGrid - PSICQUIC web services) was created that helps to discover interactions of Yeast proteins. The tool will at some point be freely available on the www as part of the pyMantis CMS created within the Translucent project.

2D Gels in wild type and mutants grown or incubated under several conditions: starvation, potassium limiting or different growth phases.

Elucidation of protein networks involved in the regulation of cation homeostasis using protein interaction datasets.

Development of bioinformatic tools to investigate the role of transcription factors and 14-3-3 proteins in the regulation of genes involved in cation homeostasis.

The potassium influx after the addition of a certain amount of KCl to a potassium free medium, followed by the injection of glucose will be measured by using the MIFE and FLISE technique. This reveals a time course of potassium. Also the external potassium concentrations will be measured.

In parallel with the potassium influx the efflux of protons is monitored by measuring the external proton concentration changes with MIFE or FLISE.

Further fluxes (ammonium, chloride, calcium) will be measured dependent on the capacities of the MIFE/FLISE technique.

The external potassium changes will be monitored by the MIFE and FLISE technique. This allows an estimation of internal potassium changes by determining an initial concentration.

The external pH changes will be monitored by the MIFE and FLISE technique. This allows an estimation of internal pH changes by determining an initial pH. pH changes will be also determined by using green fluorescent protein dyes. Relating the proton efflux and the change of internal pH allows an estimate of the proton buffering capacity.

The consensus GEM for Saccharomyces cerevisiae, version 8.3.3, maintained on https://github.com/SysBioChalmers/yeast-GEM.

This SBML file uses the RAM extension and contains a minimal genome scaled model for Saccharomyces cerevisiae. The model is based of Yeast 6.06 and was published first in A.-M. Reimers Thesis "Understanding metabolic regulation and cellular resource allocation through optimization".

PGK-GAPDH model yeast kouril7

PGK yeast Fig1a

PGK yeast with/without recycling

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson4) predicts the steady-state ...

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.

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson1) predicts the limit ...

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson2) predicts the damped ...

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson3) predicts the steady-state ...

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez1) is the basis kinetic model derived from that published by Teusink et al., 2000 (PMID: 10951190).

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez2) is an oscillating version of the basis kinetic model (dupreez1) derived from that published by Teusink et al., 2000 (PMID: 10951190).

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez3) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in oscillating yeast cultures reported by Richard et al., 1996 (PMID: 8813760).

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez4) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in oscillating yeast cultures reported by Richard et al., 1996a (PMID: 8813760) as well as the rapid synchronization following the mixing of two yeast cultures that oscillate 180 degrees out of ...

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez5) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes the amplitude bifurcation of oscillating yeast cultures in a CSTR setup reported by Hynne et al., 2001 (PMID: 11744196).

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez6) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in cell free extracts of oscillating yeast cultures reported by Das and Busse, 1991 (PMCID: 1260073).

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez7) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes the fluorescence signal of NADH in oscillating yeast cultures reported by Nielsen et al., 1998 (PMID: 17029704).