Exploring metabolism flexibility in complex organisms through quantitative study of precursor sets for system outputs

BMC Systems Biology

Jérémie Bourdon²  Anne Siegel¹  Jaap Van Milgen³  Sophie Lemosquet³  Oumarou Abdou-Arbi¹ 

[1] INRIA, Campus de Beaulieu, 35042 Rennes Cedex, France;LINA, UMR 6241, Université de Nantes, Nantes, France;Agrocampus Ouest, UMR1348 Pegase, F-35000 Rennes, France

关键词: Nutritional model;    Yield variability;    Flux distributions exploration;    Flux Balance Analysis;   

Others  :  1141550
DOI  :  10.1186/1752-0509-8-8

 received in 2013-04-16, accepted in 2013-10-01,  发布年份 2014

【 摘 要 】

Background

When studying metabolism at the organ level, a major challenge is to understand the matter exchanges between the input and output components of the system. For example, in nutrition, biochemical models have been developed to study the metabolism of the mammary gland in relation to the synthesis of milk components. These models were designed to account for the quantitative constraints observed on inputs and outputs of the system. In these models, a compatible flux distribution is first selected. Alternatively, an infinite family of compatible set of flux rates may have to be studied when the constraints raised by observations are insufficient to identify a single flux distribution. The precursors of output nutrients are traced back with analyses similar to the computation of yield rates. However, the computation of the quantitative contributions of precursors may lack precision, mainly because some precursors are involved in the composition of several nutrients and because some metabolites are cycled in loops.

Results

We formally modeled the quantitative allocation of input nutrients among the branches of the metabolic network (AIO). It corresponds to yield information which, if standardized across all the outputs of the system, allows a precise quantitative understanding of their precursors. By solving nonlinear optimization problems, we introduced a method to study the variability of AIO coefficients when parsing the space of flux distributions that are compatible with both model stoichiometry and experimental data. Applied to a model of the metabolism of the mammary gland, our method made it possible to distinguish the effects of different nutritional treatments, although it cannot be proved that the mammary gland optimizes a specific linear combination of flux variables, including those based on energy. Altogether, our study indicated that the mammary gland possesses considerable metabolic flexibility.

Conclusion

Our method enables to study the variability of a metabolic network with respect to efficiency (i.e. yield rates). It allows a quantitative comparison of the respective contributions of precursors to the production of a set of nutrients by a metabolic network, regardless of the choice of the flux distribution within the different branches of the network.

【 授权许可】

   
2014 Abdou-Arbi et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

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