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

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.

Network reduction in metabolic pathway analysis: elucidation of the key pathways involved in the photoautotrophic growth of the green alga Chlamydomonas reinhardtii.

Metabolic pathway analysis aims at discovering and analyzing meaningful routes and their interactions in metabolic networks. A major difficulty in applying this technique lies in the decomposition of metabolic flux distributions into elementary mode or extreme pathway activity patterns, which in general is not unique. We propose a network reduction approach based on the decomposition of a set of flux vectors representing adaptive microbial metabolic behavior in bioreactors into a minimal set of shared pathways. Several optimality criteria from the literature were compared in order to select the most appropriate objective function. We further analyze photoautotrophic metabolism of the green alga Chlamydomonas reinhardtii growing in a photobioreactor under maximal growth rate conditions. Key pathways involved in its adaptive metabolic response to changes in light influx are identified and discussed using an energetic approach.

A model-based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae Chlamydomonas reinhardtii.

A constraint-based modeling approach was developed to investigate the metabolic response of the eukaryotic microalgae Chlamydomonas reinhardtii under photoautotrophic conditions. The model explicitly includes thermodynamic and energetic constraints on the functioning metabolism. A mixed integer linear programming method was used to determine the optimal flux distributions with regard to this set of constraints. It enabled us, in particular, to highlight the existence of a light-driven respiration depending on the incident photon flux density in photobioreactors functioning in physical light limitation.