Dynamic modelling of Haematococcus pluvialis photoinduction for astaxanthin production in both attached and suspended photobioreactors

Zhang, Dongda; Wan, Minxi; Rio‐Chanona, Ehecatl Antonio del; Huang, Jianke; Wang, Weiliang; Li, Yuanguang; Vassiliadis, Vassilios S.

doi:10.1016/j.algal.2015.11.019

Cited by 56 publications

(32 citation statements)

References 49 publications

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“…Most importantly, there is a thriving international research interest, development and deployment of microalgae based sustainable and environmentally friendly technologies, by the health sector. This is with the aim of producing specialist high‐value products such as lutein (Xie, ), C‐phycocyanin (del Rio‐Chanona et al, ), and astaxanthin (Zhang, ), for which traditional synthesis routes and refinery methods from existing non‐renewable sources are expensive, energy intensive, and of low efficiency (Capelli et al, ; Yen et al, ).…”

Section: Introductionmentioning

confidence: 99%

An efficient model construction strategy to simulate microalgal lutein photo‐production dynamic process

Rio‐Chanona

Fiorelli

Zhang

et al. 2017

Biotech & Bioengineering

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Lutein is a high-value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever-increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper-parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long-term dynamic bioprocess simulation in both real-time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses. Biotechnol. Bioeng. 2017;114: 2518-2527. © 2017 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

An efficient model construction strategy to simulate microalgal lutein photo‐production dynamic process

Rio‐Chanona

Fiorelli

Zhang

et al. 2017

Biotech & Bioengineering

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“…While nitrates are essential for biomass growth, high nitrate concentrations can severely supress the accumulation of biolipid (Mata et al, ). Consequently, the nitrate consumption rate was modeled using an adopted form of the the Monod model (Equation ), commonly used to simulate nutrient consumption (Pruvost, Degrenne, Titica, & Legrand, ; Zhang et al, ).

\frac{d N}{d t} = - μ_{N} \cdot \frac{N}{N + K_{N}} \cdot X

where

N

is culture nitrate concentration (mg L −1 ),

K_{N}

is half‐velocity coefficient (mg L −1 ), and

μ_{N}

is maximum specific nitrate uptake rate (mg g −1 h −1 ).…”

Section: Materials and Modeling Methodologymentioning

confidence: 99%

“…Furthermore, photons in a PBR are either absorbed by microalgal biomass or scattered by bubbles, causing the local light intensity to diminish along the light transmission direction in the reactor. To take this light attenuation into account, a modified form of the Lambert‐Beer law has been proposed and has been widely utilized in recent studies, as shown in Equation (Zhang et al, ).

μ_{m} (I) = μ_{M} \cdot \frac{I}{I + k_{s} + \frac{I^{2}}{k_{i}}}

I (z) = I_{0} \cdot exp [- (α \cdot X + β) \cdot z]

where

μ_{M}

is maximum specific growth rate (h −1 ),

I

is light intensity (µmol m −2 s −1 ),

k_{s}

and

k_{i}

are light saturation term (µmol m −2 s −1 ) and light inhibition term (µmol m −2 s −1 ) for cell growth, respectively,

I_{0}

is incident light intensity (µmol m −2 s −1 ),

α

is cell absorption coefficient (m 2 g −1 ),

β

is bubble scattering coefficient (m −1 ),

z

is the distance from light source (m), and

L

is the width of the PBR (m).…”

Section: Materials and Modeling Methodologymentioning

confidence: 99%

“…While nitrates are essential for biomass growth, high nitrate concentrations can severely supress the accumulation of biolipid (Mata et al, 2010). Consequently, the nitrate consumption rate was modeled using an adopted form of the the Monod model (Equation (3)), commonly used to simulate nutrient consumption (Pruvost, Degrenne, Titica, & Legrand, 2009;Zhang et al, 2016).…”

Section: Nitrate Consumptionmentioning

confidence: 99%

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Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Rio‐Chanona

Liu

Wagner

et al. 2017

Biotech & Bioengineering

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Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.

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“…Researches on H.pluvialis have taken place over several decades, mostly focused on cell differentiation [4], astaxanthin metabolism[5], cultivation methods [6] and astaxanthin extraction technology[7]. Continuous efforts were made to improve its cellular growth and the level of astaxanthin accumulation through traditional mutagenesis of H.pluvialis [8]; however there are no reports of phenotypic modification through genetic engineering methods.…”

Section: Introductionmentioning

confidence: 99%

Biolistic transformation of Haematococcus pluvialis with constructs based on the flanking sequences of its endogenous alpha tubulin gene

Yuan

Zhang

et al. 2018

Preprint

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18The complete sequence information of the alpha tubulin (tub) genes was obtained from 19 both Haematococcus pluvialis NIES144 and SCCAP K0084. , Putative transcriptional 20 elements and polyadenylation signals were identified respectively in their 5' and 3' 21 flanking regions. Three selection cassettes of tub/aadA, tub/hyr and tub/ble with 3 22 different antibiotic-resistant genes fused between the 5' and 3' flanking sequences of the 23 tub gene were constructed and utilized for biolistic transformation of H.pluvialis. 24 Antibiotic resistant transformants were obtained in the bombardments with the tub/aadA 25 cassette in 2 strains. It was found that, the foreign tub/aadA DNA could be completely 26 transferred and inherited in their genome through non-homologous recombination. 27 Moreover, transcripts of the insert and spectinomycin resistance were identified. 28 Transformation efficiencies up to 3×10 -5 per μg DNA could be obtained in H.pluvialis 29 NIES144 or SCCAP K0084 through utilization of a culture with a high percentage of 30 flagellate cells and by optimizing bombarding protocol. The presented selection marker 31 and optimized transforming procedures in this report should strengthen the platform 32 technology for genetical manipulation and modification of H.pluvialis. 33 34 35 36 37 38 39 3 40 41 Astaxanthin (3, 3'-dihydroxy-ß-carotene-4, 4'-dione) is the strongest antioxidant in the 42 nature, and has been widely used as an additive in health care products and cosmetics [1]. 43 Haematococcus pluvialis has attracted great attention as its richest biological source. 44 Currently, H.pluvialis cultivation has been commercialized to produce astaxanthin 45 powder, which can be utilized as feedstocks or as additives in aquacultural, 46 pharmaceutical and nutraceutical products [2]. H.pluvialis has been considered as an 47 organism for the production of recombinant pharmaceutical proteins[3]. 48 Researches on H.pluvialis have taken place over several decades, mostly focused on 49 cell differentiation [4], astaxanthin metabolism[5], cultivation methods [6] and 50 astaxanthin extraction technology[7]. Continuous efforts were made to improve its 51 cellular growth and the level of astaxanthin accumulation through traditional mutagenesis 52 of H.pluvialis [8]; however there are no reports of phenotypic modification through 53 genetic engineering methods. To date, only 4 researchers have reported stable genetic 54 transformation of H.pluvialis. Steinbrenner & Gerhard Sandmann in 2006 [9] 55 transformed H.pluvialis with a mutated phytoene desaturase (pds) gene conferring 56 resistance to norflurazon, using a biolistic method. Sharon-Gojman et al. in 2015[10] 57 optimized this vector system by shortening the promoter sequence from 2000 base pairs 58 (bp) to 1000 bp. And an agrobacterium-mediated method using a plant-general plasmid 59 pCAMBIA1301 could transform H.pluvialis, with a foreign hptII gene been identified in 60 the transformants [11]. Successful chloroplast transformation was also achieved thr...

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Dynamic modelling of Haematococcus pluvialis photoinduction for astaxanthin production in both attached and suspended photobioreactors

Cited by 56 publications

References 49 publications

An efficient model construction strategy to simulate microalgal lutein photo‐production dynamic process

An efficient model construction strategy to simulate microalgal lutein photo‐production dynamic process

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Biolistic transformation of Haematococcus pluvialis with constructs based on the flanking sequences of its endogenous alpha tubulin gene

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