NDLI: Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation

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Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation

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Molecular BioSystems Vol - 9 Issue - 3

Molecular BioSystems Vol - 9 Issue - 2

Molecular BioSystems Vol -8

Molecular BioSystems Vol -7

Molecular BioSystems Vol -6

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Molecular BioSystems Vol -4

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Molecular BioSystems Vol -2

Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation

Content Provider	Royal Society of Chemistry (RSC)
Author	Tong, Wei Cao, Zhe Zhang, Jiyuan Chen, Zhen Bai, Xue Wang, Rong Liu, Siqi Wang, Quanhui
Copyright Year	2013
Abstract	The integration of omic data with metabolic networks has been demonstrated to be an effective approach to elucidate the underlying metabolic mechanisms in life. Because the metabolic pathways of Thermoanaerobacter tengcongensis (T. tengcongensis) are incomplete, we used a 1-13C-glucose culture to monitor intracellular isotope-labeled metabolites by GC/MS and identified the gap gene in glucose catabolism, Re-citrate synthase. Based on genome annotation and biochemical information, we reconstructed the metabolic network of glucose metabolism and amino acid synthesis in T. tengcongensis, including 253 reactions, 227 metabolites, and 236 genes. Furthermore, we performed constraint based modeling (CBM)-derived robustness analysis on the model to study the dynamic changes of the metabolic network. By perturbing the culture temperature from 75 to 55 °C, we collected the bacterial growth rates and differential proteomes. Assuming that protein abundance changes represent metabolic flux variations, we proposed that the robustness analysis of the CBM model could decipher the effect of proteome change on the bacterial growth under perturbation. For approximately 73% of the reactions, the predicted cell growth changes due to such reaction flux variations matched the observed cell growth data. Our study, therefore, indicates that differential proteome data can be integrated with metabolic network modeling and that robustness analysis is a strong method for representing the dynamic change in cell phenotypes under perturbation.
Starting Page	713
Ending Page	722
Page Count	10
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	9
Issue Number	4
Journal	Molecular BioSystems
DOI	10.1039/c3mb25278g
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Metabolic network Flux (metabolism) Proteome Catabolism Glucose Metabolism Thermoanaerobacter Gap gene DNA annotation Protein
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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