Abstract
BackgroundCyanobacteria and other phototrophic microorganisms allow to couple the light-driven assimilation of atmospheric text {CO}_{2} directly to the synthesis of carbon-based products, and are therefore attractive platforms for microbial cell factories. While most current engineering efforts are performed using small-scale laboratory cultivation, the economic viability of phototrophic cultivation also crucially depends on photobioreactor design and culture parameters, such as the maximal areal and volumetric productivities. Based on recent insights into the cyanobacterial cell physiology and the resulting computational models of cyanobacterial growth, the aim of this study is to investigate the limits of cyanobacterial productivity in continuous culture with light as the limiting nutrient.ResultsWe integrate a coarse-grained model of cyanobacterial growth into a light-limited chemostat and its heterogeneous light gradient induced by self-shading of cells. We show that phototrophic growth in the light-limited chemostat can be described using the concept of an average light intensity. Different from previous models based on phenomenological growth equations, our model provides a mechanistic link between intracellular protein allocation, population growth and the resulting reactor productivity. Our computational framework thereby provides a novel approach to investigate and predict the maximal productivity of phototrophic cultivation, and identifies optimal proteome allocation strategies for developing maximally productive strains.ConclusionsOur results have implications for efficient phototrophic cultivation and the design of maximally productive phototrophic cell factories. The model predicts that the use of dense cultures in well-mixed photobioreactors with short light-paths acts as an effective light dilution mechanism and alleviates the detrimental effects of photoinhibition even under very high light intensities. We recover the well-known trade-offs between a reduced light-harvesting apparatus and increased population density. Our results are discussed in the context of recent experimental efforts to increase the yield of phototrophic cultivation.
Highlights
Cyanobacteria and other phototrophic microorganisms allow to couple the light-driven assimilation of atmospheric CO2 directly to the synthesis of carbon-based products, and are attractive platforms for microbial cell factories
We can build upon an established theory of the light-limited chemostat, originally developed by Huisman et al [23] and later refined by other authors [18, 30, 31]. These previous analyses, were almost all based on phenomenological growth models, such as the Monod or Haldane-type equation, and only few works, such as the computational analysis of He et al [20], explicitly integrate intra- and extracellular information to achieve a better understanding of bioreactor productivities
The paper is organized as follows: in the first two sections, we briefly introduce a model of the light-limited chemostat
Summary
Cyanobacteria and other phototrophic microorganisms allow to couple the light-driven assimilation of atmospheric CO2 directly to the synthesis of carbon-based products, and are attractive platforms for microbial cell factories. There have been significant efforts to obtain a better quantitative understanding of the photosynthetic productivity of cyanobacterial growth in photobioreactors [8, 43] Despite this progress, there remains a need for an improved computational framework to better understand the physiological acclimation of cyanobacteria in a heterogeneous light environment typically encountered in dense cultures. In this respect, we can build upon an established theory of the light-limited chemostat, originally developed by Huisman et al [23] and later refined by other authors [18, 30, 31]. These previous analyses, were almost all based on phenomenological growth models, such as the Monod or Haldane-type equation, and only few works, such as the computational analysis of He et al [20], explicitly integrate intra- and extracellular information to achieve a better understanding of bioreactor productivities
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