Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 13, Issue 7, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005444
Keywords
-
Funding
- Ecole Polytechnique Federale de Lausanne (EPFL)
- Swiss National Science Foundation
- European Union's Horizon research and innovation programme [686070]
- MicroscapesX within SystemsX.ch
- Swiss Initiative for Systems Biology
- RobustYeast within ERA via SystemsX.ch
Ask authors/readers for more resources
Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these consistently-reduced models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available