Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.
BACKGROUND
The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism.


RESULTS
Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.
Jeongah
J
Yoon
Yaguang
Y
Si
Ryan
R
Nolan
Kyongbum
K
Lee
D017667:Adipocytes / Q000378:metabolism*; D000465:Algorithms; D003198:Computer Simulation; D050356:Lipid Metabolism / Q000502:physiology*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome