  Cyanobacteria are an integral part of the Earth's biogeochemical cycles and a
promising resource for the synthesis of renewable bioproducts from atmospheric
CO2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal
rhythm of light availability. As yet, however, insight into the stoichiometric
and energetic constraints of cyanobacterial diurnal growth is limited. Here, we
develop a computational platform to evaluate the optimality of diurnal
phototrophic growth using a high-quality genome-scale metabolic reconstruction
of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate
phototrophic growth as a self-consistent autocatalytic process and evaluate the
resulting time-dependent resource allocation problem using constraint-based
analysis. Based on a narrow and well defined set of parameters, our approach
results in an ab initio prediction of growth properties over a full diurnal
cycle. In particular, our approach allows us to study the optimality of
metabolite partitioning during diurnal growth. The cyclic pattern of glycogen
accumulation, an emergent property of the model, has timing characteristics
that are shown to be a trade-off between conflicting cellular objectives. The
approach presented here provides insight into the time-dependent resource
allocation problem of phototrophic diurnal growth and may serve as a general
framework to evaluate the optimality of metabolic strategies that evolved in
photosynthetic organisms under diurnal conditions.
