Flexible Nets to Improve GEM Cell Factories by Combining Kinetic and Proteomics Data

Author(s): Jorge Lázaro, Jorge Júlvez, Jürgen Zanghellini
Abstract: Alzheimer’s disease is expected to reach a prevalence of 152 million people worldwide caused by the aggregation of amyloid β-proteins leading to apoptosis of neurons and loss of cognitive function. Although there is no effective treatment for this disease, molecules such as scyllo-inositol have been shown to be promising. Bacillus subtilis has been proposed as a suitable organism for the production of scyllo-inositol. Metabolic computational models have proven useful in the prediction of the production of a metabolite. However, most genome-scale metabolic models lack detailed parameters and tend to overestimate the production of a metabolite with respect to the consumption of medium resources. In order to reduce the solution space and, hence, obtain a more realistic model, additional constraints from experimental data can be added to the model. This work exploits the modeling capabilities of Flexible Nets to model the production of scyllo-inositol in a genome-scale metabolic model of Bacillus subtilis that has been previously enriched with proteomic and enzymatic data. We assess how these constraints limit the scyllo-inositol production to more realistic levels. Moreover, the integration of different types of constraints allowed us to uncover which one of them limits the production of scyllo-inositol for a given growth rate.
Organisation(s): Department of Analytical Chemistry
External organisation(s): Universidad de Zaragoza
Volume: 14971
Pages: 137-154
No. of pages: 18
DOI: https://doi.org/10.1007/978-3-031-71671-3_11
Publication date: 2024
Peer reviewed: Yes
Austrian Fields of Science 2012: 106005 Bioinformatics, 104027 Computational chemistry
Keywords
ASJC Scopus subject areas: Theoretical Computer Science, General Computer Science
Portal url: https://ucrisportal.univie.ac.at/en/publications/0bc890cb-bdd6-4516-89fe-571086a03bc7

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