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Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.

Jerby L, Shlomi T, Ruppin E - Mol. Syst. Biol. (2010)

Bottom Line: The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions.The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46).The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. livnatje@post.tau.ac.il

ABSTRACT
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

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Related in: MedlinePlus

The diagram illustrates the function of the model-building algorithm (MBA). The algorithm is given tissue-specific core reactions sets (CH and CM) as input and reconstructs a tissue model containing all of the CH reactions, as many as possible CM reactions, and a minimal set of other generic model reactions that are required for obtaining overall model consistency.
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f1: The diagram illustrates the function of the model-building algorithm (MBA). The algorithm is given tissue-specific core reactions sets (CH and CM) as input and reconstructs a tissue model containing all of the CH reactions, as many as possible CM reactions, and a minimal set of other generic model reactions that are required for obtaining overall model consistency.

Mentions: On this background, we present in this study, to the best of our knowledge, the first computational approach for a rapid generation of genome-scale tissue-specific models. The method relies on integrating the previously reconstructed generic human models with a variety of high-throughput molecular ‘omics' data, including transcriptomic, proteomic, metabolomic, and phenotypic data, as well as literature-based knowledge, characterizing the tissue in hand (Figure 1). Hence, it can be readily used to quite rapidly build and use a large array of human tissue-specific models. The resulting model satisfies stoichiometric, mass-balance, and thermodynamic constraints. It serves as a functional metabolic network that can then be used to explore the metabolic state of a tissue under various genetic and physiological conditions, simulating enzymatic inhibition or drug applications through standard constraint-based modeling methods, without requiring additional context-specific molecular data.


Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.

Jerby L, Shlomi T, Ruppin E - Mol. Syst. Biol. (2010)

The diagram illustrates the function of the model-building algorithm (MBA). The algorithm is given tissue-specific core reactions sets (CH and CM) as input and reconstructs a tissue model containing all of the CH reactions, as many as possible CM reactions, and a minimal set of other generic model reactions that are required for obtaining overall model consistency.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2964116&req=5

f1: The diagram illustrates the function of the model-building algorithm (MBA). The algorithm is given tissue-specific core reactions sets (CH and CM) as input and reconstructs a tissue model containing all of the CH reactions, as many as possible CM reactions, and a minimal set of other generic model reactions that are required for obtaining overall model consistency.
Mentions: On this background, we present in this study, to the best of our knowledge, the first computational approach for a rapid generation of genome-scale tissue-specific models. The method relies on integrating the previously reconstructed generic human models with a variety of high-throughput molecular ‘omics' data, including transcriptomic, proteomic, metabolomic, and phenotypic data, as well as literature-based knowledge, characterizing the tissue in hand (Figure 1). Hence, it can be readily used to quite rapidly build and use a large array of human tissue-specific models. The resulting model satisfies stoichiometric, mass-balance, and thermodynamic constraints. It serves as a functional metabolic network that can then be used to explore the metabolic state of a tissue under various genetic and physiological conditions, simulating enzymatic inhibition or drug applications through standard constraint-based modeling methods, without requiring additional context-specific molecular data.

Bottom Line: The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions.The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46).The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. livnatje@post.tau.ac.il

ABSTRACT
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

Show MeSH
Related in: MedlinePlus