Community Detection in E. coli Metabolic Network using Markov Stability and Constrain-based Modelling
Gabriel Bosque, Jesús Picó, Mariano Beguerisse Díaz, Mauricio Barahona and Diego A. Oyarzún
Community detection is a main branch in the network analysis field.
It is used to detect groups of individuals with similar properties among large structures of linked element.
Community detection has been widely used in economy, ecology, sociology and many other disciplines.
Biological networks, especially at the molecular level, such as metabolic, protein interaction, signaling or regulatory networks have been subject to network analysis.
On the other hand, constrain-based modelling represents a very popular approach to model metabolic behavior in cells in steady state1.
Reactions and metabolites are displayed in stoichiometry matrices and after a series of constrains are imposed a set of feasible solutions for the cell flux distribution are obtained.
In this work we apply techniques of community detection to the metabolic network of the bacteria E. coli.
These techniques are Markov Stability2 and Role-Based Similarity3.
The bacterial network used was obtained from a reconstruction of the core E. coli metabolic network4.
We explore the different representations of adjacency matrix in a given metabolic network and how constrain-based modelling can support additional information to the pure topology, such as reaction stoichiometry, directionality and flux.
Then we analyze how different are the detected communities for each representation and how different levels of information define and detect particular communities.
Additionally we compare the particular community distributions of each representation of the metabolic network to the classic well-studied biochemical pathways and how they relate and interconnect.
References
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& Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245– 248 (2010). 2. Beguerisse-Díaz, M., Garduño-Hernández, G., Vangelov, B., Yaliraki, S. N.
& Barahona, M. Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots. ArXiv13116785 Phys. (2013). 3. Beguerisse-Díaz, M., Vangelov, B.
& Barahona, M. Finding role communities in directed networks using Role-Based Similarity, Markov Stability and the Relaxed Minimum Spanning Tree. ArXiv13091795 Phys. Q-Bio 937–940 (2013). 4. Orth, J. D., Palsson, B. Ø.
& Fleming, R. M. T. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide. EcoSal Plus 1, (2010).