Henning Lindhorst

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.

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".

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) [1], conditional Flux Balance Analysis [2], Metabolism and Expression models (ME models) [3], Resource Balance Analysis [4], 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
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