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Steffen Waldherr

About Steffen Waldherr:
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SEEK ID: https://fairdomhub.org/people/589

Location:  Germany

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ROBUSTYEAST (Otto-von-Guericke University Magdeburg)

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ROBUSTYEAST

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
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Programme: ERASysAPP

Public web page: Not specified

Carbon Core deFBA Model
ROBUSTYEAST

A small model representing the core carbon network in each cell. For more detail on the model creation see [1]. 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

[1] 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.

Creators: Henning Lindhorst, Steffen Waldherr

Submitter: Henning Lindhorst

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Modeling metabolic networks including gene expression and uncertainties
ROBUSTYEAST

Abstract (Expand)

Constraint based methods, such as the Flux Balance Analysis, are widely used to model cellular growth processes without relying on extensive information on the regulatory features. The regulation is … instead substituted by an optimization problem usually aiming at maximal biomass accumulation. A recent extension to these methods called the dynamic enzyme-cost Flux Balance Analysis (deFBA) is a fully dynamic modeling method allowing for the prediction of necessary enzyme levels under changing environmental conditions. However, this method was designed for deterministic settings in which all dynamics, parameters, etc. are exactly known. In this work, we present a theoretical framework extending the deFBA to handle uncertainties and provide a robust solution. We use the ideas from multi-stage nonlinear Model Predictive Control (MPC) and its feature to represent the evolution of uncertainties by an exponentially growing scenario tree. While this representation is able to construct a deterministic optimization problem in the presence of uncertainties, the computational cost also increases exponentially. We counter this by using a receding prediction horizon and reshape the standard deFBA to the short-time deFBA (sdeFBA). This leads us, along with further simplification of the scenario tree, to the robust deFBA (rdeFBA). This framework is capable of handling the uncertainties in the model itself as well as uncertainties experienced by the modeled system. We applied these algorithms to two case-studies: a minimal enzymatic nutrient uptake network, and the abstraction of the core metabolic process in bacteria.

Authors: Henning Lindhorst, Sergio Lucia, Rolf Findeisen, Steffen Waldherr

Date Published: 13th Jun 2017

Publication Type: Not specified

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