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 in biotechnological applications using a systems biology approach. This will contribute to establishing evolutionary optimization protocols for making microbial production strains robust against dynamic nutrient conditions.
The consortium will study the robustness of Saccharomyces cerevisiae in experiments during the dynamics associated with two cyclic nutrient transitions that are each of major relevance to industry: repeated cycles of glucose and ethanol growth and of aerobic and anaerobic growth. We shall monitor the physiological changes during the evolutionary adaptation of yeast to those transitions, using laboratory-evolution in lab-scale bioreactors (chemostat mode). By combining this data with computational modelling we shall identify the metabolic features that make yeast robust to these industrially relevant condition cycles. The theoretical and computational approaches that the consortium will develop involve optimisation methods applicable to metabolism transiting from one steady state to the next via dynamic regulation.
We shall iterate experiments and modelling to improve our models given experimental data, to identify new measurements critical to improve our understanding, and to finally identify key regulatory mechanisms for a robust metabolism of S. cerevisiae, given changes in glucose, ethanol and oxygen concentrations. The robustness of metabolic regulation under dynamic conditions will be evaluated from the kinetic models, and the regulatory interactions that confer such robustness will be determined
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The main objective of the ERANET proposal Systems Biology Applications - ERASysAPP (app = application = translational systems biology) is to promote multidimensional and complementary European systems biology projects, programmes and research initiatives on a number of selected research topics. Inter alia, ERASysAPP will initiate, execute and monitor a number of joint transnational calls on systems biology research projects with a particular focus on applications - or in other words so called ...
A small model representing the core carbon network in each cell. For more detail on the model creation see . The model is written in SBML using the RAM extension for use in deFBA. Compatible python software for simulation can be found at https://tinyurl.com/yy8xu4v7
 S. Waldherr, D. A. Oyarzún, A. Bockmayr. Dynamic optimization of metabolic networks coupled with gene expression. In: Journal of Theoretical Biology, 365(0): 469 - 485.
This model was created to showcase all functions of the SBML extension RAM. The model can be unr in deFBA with the python software deFBA-Python. The software is freely available at at https://tinyurl.com/yy8xu4v7
A minimal model showing the core of resource allocation models as it can either be invested in enzymatic machinery or single biomass components with the best yield. The model is written in SBML using the RAM extension for use in deFBA. Compatible python software for simulation can be found at https://tinyurl.com/yy8xu4v7
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".
Submitter: Henning Lindhorst
Model type: Stoichiometric model
Model format: SBML
Environment: Not specified
Organism: Saccharomyces cerevisiae
Investigations: No Investigations
Studies: No Studies
Assays: No Assays
Metabolic networks with gene expression are researched under very different banners with different techniques. For example, there are the dynamic enzyme-cost Flux Balance Analysis (deFBA) , conditional Flux Balance Analysis , Metabolism and Expression models (ME models) , Resource Balance Analysis , etc. At their core, these methods can all understood as Resource Allocation Models (RAM) and while investigating their potential and their results, we encountered the problem of sharing ...
Date Published: 13th Jun 2017
Publication Type: Not specified