Subcellular metabolic organization in the context of dynamic energy budget and biochemical systems theories

Abstract

The dynamic modelling of metabolic networks aims to describe the temporal evolution of metabolite concentrations in cells. This area has attracted increasing attention in recent years owing to the availability of high-throughput data and the general development of systems biology as a promising approach to study living organisms. Biochemical Systems Theory (BST) provides an accurate formalism to describe biological dynamic phenomena. However, knowledge about the molecular organization level, used in these models, is not enough to explain phenomena such as the driving forces of these metabolic networks. Dynamic Energy Budget (DEB) theory captures the quantitative aspects of the organization of metabolism at the organism level in a way that is nonspecies-specific. This imposes constraints on the sub-organismal organization that are not present in the bottom-up approach of systems biology. We use in vivo data of lactic acid bacteria under various conditions to compare some aspects of BST and DEB approaches. Due to the large number of parameters to be estimated in the BST model, we applied powerful parameter identification techniques. Both models fitted equally well, but the BST model employs more parameters. The DEB model uses similarities of processes under growth and no-growth conditions and under aerobic and anaerobic conditions, which reduce the number of parameters. This paper discusses some future directions for the integration of knowledge from these two rich and promising areas, working top-down and bottom-up simultaneously. This middle-out approach is expected to bring new ideas and insights to both areas in terms of describing how living organisms operate.

Dynamic energy budget theory restores coherence in biology

“…Vinga et al (2010) use DEB theory for a top-down approach to understand the dynamic behaviour of metabolites. The individual level controls the size of the reserve fluxes that are associated with assimilation, dissipation and growth: _ J EA , _ J ED and _ J EG .…”

“…The DEB model for glycolysis in Lactococcus lactis uses much less parameters than a comparable BST model Vinga et al (2010), while it better catches the differences between growth under aerobic and anaerobic conditions. DEB rate parameters depend on temperature.…”

Biochemical Systems Theory: A Review

“…Searson and colleagues used a hybrid S-system model for the analysis of fedbatch data [360]. Vinga and colleagues discussed advantages of integrating BST models with aspects of dynamic energy budget theory, a modeling framework that represents certain constraints on the organization of metabolic network at the organismic level in a nonspecies speci�c manner [170]. While many studies have described the distribution of a radioisotope label in network systems at steady state, Voit and colleagues developed models to capture the exact dynamics of this distribution upon an input of radioactive material [680,681].…”

“…As one might expect, this formulation permits greater �exibility than ISRN Biomathematics 9 the corresponding power-law representation , ) = ⋅ but requires more parameter values. Vinga and colleagues compared BST with the concepts of dynamic energy budget theory and proposed a combined approach that used aspects of both [170]. Her group also presented a comprehensive comparison of several modeling frameworks, including BST, �ux balance analysis (FBA), and Petri nets [171].…”

“…Alternatives to evolutionary algorithms included leastsquares regression [170,293,294], Kalman �lter methods [295], and neural networks [203,296,297]. Yet other approaches to the same problem employed interval and Newton-�ow analysis [298][299][300][301][302].…”