Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA

Abstract

The computational analysis of phototrophic growth using constraint-based optimization requires to go beyond current time-invariant implementations of flux-balance analysis (FBA). Phototrophic organisms, such as cyanobacteria, rely on harvesting the sun’s energy for the conversion of atmospheric CO2 into organic carbon, hence their metabolism follows a strongly diurnal lifestyle. We describe the growth of cyanobacteria in a periodic environment using a new method called conditional FBA. Our approach enables us to incorporate the temporal organization and conditional dependencies into a constraint-based description of phototrophic metabolism. Specifically, we take into account that cellular processes require resources that are themselves products of metabolism. Phototrophic growth can therefore be formulated as a time-dependent linear optimization problem, such that optimal growth requires a differential allocation of resources during different times of the day. Conditional FBA then allows us to simulate phototrophic growth of an average cell in an environment with varying light intensity, resulting in dynamic time-courses for all involved reaction fluxes, as well as changes in biomass composition over a diurnal cycle. Our results are in good agreement with several known facts about the temporal organization of phototrophic growth and have implications for further analysis of resource allocation problems in phototrophic metabolism.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

“…To this end, we assemble and numerically evaluate an autocatalytic genome-scale model of cyanobacterial growth, based on a highquality metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. Our model significantly improves upon previous computational analyses of diurnal phototrophic growth (14,(16)(17)(18) and takes recent developments in constraintbased analysis into account (19)(20)(21)(22). Our approach is closely related to resource balance analysis (23,24) and dynamic enzymecost flux balance analysis (25), as well as integrated metabolism and gene expression (ME) models (21,26), but explicitly accounts for the properties of diurnal phototrophic growth.…”

“…Building upon previous works (1,6,15,18,23,25,26), our approach is based on the fact that growth is inherently autocatalytic: The cellular machinery to sustain metabolism is itself a product of metabolism. Our foci have therefore been the net stoichiometric and energetic implications of diurnal growth on the de novo synthesis of proteins and other cellular macromolecules.…”

“…We are primarily interested in diurnal dynamics, and hence a timescale of several hours. Following the arguments of Rügen et al (18) and Waldherr et al (25), we therefore assume that internal metabolites are in quasi-steady state. The resulting computational model then consists of the stoichiometric network reconstruction, the synthesis reactions for cellular macromolecules, and the abundance constraints for noncatalytic macromolecules, as well as the capacity constraints induced by the elements of M (t).…”

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

“…DFBA also provides a suitable framework for multi-scale metabolic modelling, where the interplay of different cell types and tissues is taken into account [69]. More recently, DFBA has been extended to metabolic networks coupled with gene expression of the corresponding enzymes, where it incorporated constraints on resource allocation [70,71]. -FBA and nonlinearity.…”

Network Analysis and Modeling in Systems Biology

“…Second, the FBG can be readily used to quantify metabolic robustness via graph statistics upon node (e.g., reaction) removal (Smart, Amaral, and Ottino 2008). Third, the proposed approach can be extended to include dynamic adaptations of metabolic activity, for example, by using dynamic extensions of FBA (Mahadevan, Edwards, and Doyle 2002;Rügen, Bockmayr, and Steuer 2015;Waldherr, Oyarzún, and Bockmayr 2015), or by incorporating static (Colijn et al 2009) and time-varying (Oyarzún 2011) enzyme concentrations. Fourth, the FBG could provide a novel route for robustness analysis of FBA solutions (Gudmundsson and Thiele 2010).…”