Abstract
Phototrophic organisms such as cyanobacteria utilize the sun’s energy to convert atmospheric carbon dioxide into organic carbon, resulting in diurnal variations in the cell’s metabolism. Flux balance analysis is a widely accepted constraint-based optimization tool for analyzing growth and metabolism, but it is generally used in a time-invariant manner with no provisions for sequestering different biomass components at different time periods. Here we present CycleSyn, a periodic model of Synechocystis sp. PCC 6803 metabolism that spans a 12-hr light/12-hr dark cycle by segmenting it into 12 Time Point Models (TPMs) with a uniform duration of two hours. The developed framework allows for the flow of metabolites across TPMs while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. The 12 TPMs allow for the incorporation of time-dependent constraints that capture the cyclic nature of cellular processes. Imposing bounds on reactions informed by temporally-segmented transcriptomic data enables simulation of phototrophic growth as a single linear programming (LP) problem. The solution provides the time varying reaction fluxes over a 24-hour cycle and the accumulation/consumption of metabolites. The diurnal rhythm of metabolic gene expression driven by the circadian clock and its metabolic consequences is explored. Predicted flux and metabolite pools are in line with published studies regarding the temporal organization of phototrophic growth in Synechocystis PCC 6803 paving the way for constructing time-resolved genome-scale models (GSMs) for organisms with a circadian clock. In addition, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to temporally separate photosynthesis from oxygen-sensitive nitrogen fixation is also explored using the developed model formalism.
Highlights
Flux balance analysis (FBA) has become a popular tool to analyze the metabolic function of organisms [1]
Varying gene expression levels over the 24hr cycle implies that the corresponding metabolic fluxes would vary significantly and the biomass precursor production be dynamically shaped as the cumulative contribution by metabolism over 24 hours
Diurnal oscillations in Cyanobacteria have been the focus of many studies [6,17,53] but those have mainly been concentrated on the associated transcriptomic changes or a subset of its entire metabolism
Summary
Phototrophic organisms such as cyanobacteria harvest the sun’s energy to convert atmospheric CO2 into organic carbon, due to which their metabolism is heavily influenced by light availability. Presence of two distinct metabolic phases–a light-dependent anabolic phase tailored around the synthesis of storage compounds and metabolic precursors and a light-absent catabolic period that metabolizes the previously manufactured compounds to release energy in the absence of an external energy source Due to these considerations, the analysis of phototrophic growth using constraint-based optimization methods is insufficient and needs to be extended beyond time-invariant descriptions. Our approach enables us to account for temporal metabolic shifts tailored around light availability while still allowing for the use of the pseudo steady-state assumption used in conventional flux balance analysis. This is achieved by exploiting the large difference in timescales between metabolic reactions and cell growth. Our study lays the foundation for subsequent analysis of systems with temporal variations in metabolism using a constraint-based optimization approach
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