2015•Journal of Mechanics in Medicine and BiologyRequires access

NETWORK APPROACHES FOR ANALYSIS AND MODELING OF THE HUMAN METABOLISM

Gabriele Fontanarosa, Giulia Menichetti, Enrico Giampieri, Gastone Castellani, Giovanni Martinelli, Daniel Remondini

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Abstract

We describe a novel approach for metabolic network reconstruction in order to switch from the full reaction-metabolite scheme to a more synthetic description at a pathway level. The network thus obtained retains much information of the original model, allowing easier graphical visualizations and multiscale modeling. We apply our approach to the state-of-the-art database of human metabolic network (Recon2): our approach allows different ranking of the network elements based on its topology and on Markov dynamics induced by network structure.

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What this paper is about

We describe a novel approach for metabolic network reconstruction in order to switch from the full reaction-metabolite scheme to a more synthetic description at a pathway level. The network thus obtained retains much information of the original model, allowing easier graphical visualizations and multiscale modeling. We apply our approach to the state-of-the-art database of human metabolic network (Recon2): our approach allows different ranking of the network elements based on its topology and on Markov dynamics induced by network structure.

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Available abstract

We describe a novel approach for metabolic network reconstruction in order to switch from the full reaction-metabolite scheme to a more synthetic description at a pathway level. The network thus obtained retains much information of the original model, allowing easier graphical visualizations and multiscale modeling. We apply our approach to the state-of-the-art database of human metabolic network (Recon2): our approach allows different ranking of the network elements based on its topology and on Markov dynamics induced by network structure.

Key concepts: Metabolic network, Computer science, Network topology, Markov chain, Ranking (information retrieval), Network model, Data mining, Network analysis

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