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Systemic modelling of metabolic flexibility: toward a better understanding of adaptive responses to environmental challenges

We used three modelling formalisms for a better understanding and representation of cell metabolism in livestock animals. These formalisms helped in describing and understanding metabolic flexibility when animals are facing environmental constraints, particularly nutritional challenges, and shed light on the key role of flexibility at the cell level on the adaptive capacities of these animals.

Behavior of a dynamic system in the face of short- and long-term disturbances © Masoomeh Taguipoor
Updated on 07/18/2017
Published on 07/17/2017

Context and challenge:

A better representation of the biological mechanisms underlying the adaptive responses of animals to environmental challenges is required to propose new management strategies for a sustainable production. To this end, dynamical and mechanistic models have been used to investigate the adaptive responses at the animal scale. Our objective has been to expand on this approach by describing the underlying mechanisms responsible for animal adaptation, taking into account the key role of metabolic flexibility at the cell level. Because networks linking genes, enzymes and(or) metabolites are very complex, systemic modelling is an appropriate approach to describe, understand, and predict metabolic behavior, especially in presence of external perturbations. To illustrate the potential of modelling to describe metabolic flexibility and its role on the global response of a tissue or organ (represented as a “super cell”), three case studies relevant to different nutritional situations were considered (Taghipoor et al., 2016a).


The modelling formalism to be applied depends on the research question and the desired outputs (predictions), experimental data, and size of the subsystem to be considered.  

Graph theory. This topological approach made it possible to reconstruct large (genome-) scale metabolic networks in mammals. Based on this formalism, (Gondret et al. 2014) identified a reasonable set of upstream regulators of lipid and glucose metabolism in tissues of pigs fed iso-energetic diets with contrasted nutrient and energy sources. To do so, many possible relationships between regulated genes and biochemical reactions from databases were confronted with experimental data obtained in those tissues. 

Flux balance analysis (FBA).This approach analyses the metabolic network in terms of the stoichiometric matrix and, in particular, it describes the metabolic flux in a stationary state. This formalism was used to study the role of metabolic flexibility of the mammary gland in dairy cows to maintain lactose synthesis of milk in response to different nutrient supplies and uptakes. The definition of different objective functions allowed to highlight the possible distributions of nutrients in the different metabolic pathways (in terms of carbon) to explain changes in milk composition (Abdou Arbi et al. 2014).

Kinetics of biochemical reactions. Dynamical modelling is a powerful tool to observe the time-related evolution of a limited number of elements in the metabolic network. This formalism was used to describe the synergy between three enzymatic pathways responsible for storing energy (lipids and glycogen), and their hierarchy of actions to maintain intracellular energy equilibrium when cells are facing changes in the intensity and frequency of glucose uptake (Taghipoor et al., 2016b). 


We propose to investigate an intermediate modeling approach combining different formalisms (flux-balance analysis and dynamical modelling) at different levels of biological information (cell to animal) to represent the time-related evolution of large metabolic networks.


Taghipoor M., Lemosquet S., van Milgen, J., Siegel A., Sauvant D., Gondret, F. (2016a). Modélisation de la flexibilité métabolique : vers une meilleure compréhension des capacités adaptatives de l’animal. INRA Productions Animales 29(3): 201‐216.

Taghipoor M., van Milgen, J., Gondret, F. (2016b). A systemic approach to explore the flexibility of energy stores at the cellular scale: Examples from muscle cells. Journal of Theoretical Biology 404: 331‐341. doi: 10.1016/j.jtbi.2016.06.014

Gondret F., Vincent A., Houee‐Bigot M., Siegel A., Lagarrigue S., Louveau I., Causeur D. (2016). Molecular alterations induced by a high‐fat high‐fiber diet in porcine adipose tissues: variations according to the anatomical fat location. BMC Genomics 17: 120. doi: 10.1186/s12864‐016‐2438‐3

Abdou‐Arbi O., Lemosquet S., van Milgen J., Siegel A., Bourdon J. (2014). Exploring metabolism flexibility in complex organisms through quantitative study of precursor sets for system outputs. BMC Systems Biology 8:8. doi:10.1186/1752‐0509‐8‐8