A dynamic growth model of Dunaliella salina : Parameter identification and profile likelihood analysis

Microalgae production in a biofilm photobioreactor

“…A few studies predicted the specific absorption coefficient in response to, e.g., incident light (Takache et al 2012), nitrogen availability (Fachet et al 2014), biomass concentration (Bechet et al 2014a), biomass yield on light , or by the ratio of realized to potential photosynthetic electron flow (Geider et al 1996). However, the current models did not provide an appropriate correlation to describe the dependency of a x,λ in our case.…”

“…Changes in the light direction can result in a longer light path through the reactor. In literature, similar strategies to improve the Lambert-Beer Law were reported and include: introducing a scattering correction factor for microalgae , including scattering by gas bubbles , including a backscattering coefficient (Fachet et al 2014), or including an extinction coefficient determined for the actual photobioreactor and microalgae suspension that is used which thus includes both light absorption and scattering (Bechet et al 2014a). In all three examples, the light gradient correction factor is included in the exponent of the Lambert-Beer Law equation.…”

Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina

“…In scenario A -D, biomass production occurred without any formation of β-carotene as a side product, meaning that the β-carotene flux Car14 is always 0 mmol/(g dw • h) ( Table 1). Since the objective function did only include the biomass growth (μ) under nitrogenreplete conditions it is biologically plausible that βcarotene formation was suppressed in the flux scenarios A -D. As described by [6] and [11] oversaturating light conditions and nutrient repletion led only to moderate βcarotene accumulation, whereas oversaturating light combined with nutrient stress is the most potent inducer of secondary carotenoids in D. salina.…”

“…The construction of dynamic-kinetic growth models using ordinary differential equations (ODEs) is a wellestablished formalism in bioprocess engineering. These models allow for prediction of biomass growth, nutrient uptake and metabolite production and enable the identification of bottlenecks in the process setup for both lab-scale and large-scale outdoor cultivation systems [9][10][11]. Simplified growth models are robust and computationally inexpensive.…”

“…Since D. salina is the richest known source of natural β-carotene, a metabolic network model is highly beneficial to fully exploit the biotechnological potential of this alga. So far, for D. salina some metabolic profiling information is available [31,32], and the first growth models have recently been created [11,33,34]. In addition, the genome of D. salina has been released (http://genome.jgi.…”