HEAD OF THE GROUP

Prof. Dr.-Ing. Kai Sundmacher
Prof. Dr.-Ing. Kai Sundmacher
Phone: +49 391 6110-351
Fax: +49 391 6110-353
Room: N. 309
Links: Publications

Team leaders

Dr. Techn. Liisa Rihko-Struckmann
Dr. Techn. Liisa Rihko-Struckmann
Phone: +49 391 6110 318
Room: N. 316
Links: CV

Researchers

Dr.-Ing. Melanie Fachet
Dr.-Ing. Melanie Fachet
Phone: +49 391 6110 273
Room: N 2.10
Dipl.-Ing. Kristin Ludwig
Phone: +49 391 6110 437
Room: N 2.10

Biological Production Systems

Header image 1448439156

Biochemical Production Systems using Photosynthetic Organismus

Photosynthetic microorganisms such as green microalgae are innovative cell factories for the environmentally benign production of many bio-based chemicals. Such microorganisms are capable to synthesize a wide range of macromolecules, e.g. lipids, carbohydrates, proteins and valuable secondary metabolites from sunlight, carbon dioxide and inorganic nutrients at higher volumetric and areal productivities compared to the terrestrial crops. As examples for photosynthetic systems, we have focused our research so far on β-carotene producing green microalga Dunaliella salinaand Phaeodactylum tricornutum, a siliceous diatom.  

From the systems-theoretical point of view, modeling and control of micro-organic cultivations is a very challenging task: biological cell populations represent a complex dynamic system hierarchically organized on multiple temporal and spatial scales, of which we have only limited mechanistic knowledge (Fig. 1). Additionally, biological systems exhibit strong variability in the phenotype, e.g. metabolite dynamics, which renders difficult experimental probing for model identification [1]. Given the hierarchical multi-scale structure of a micro-organic cultivation, the Elementary Process Functions Methodology (EPF) is ideally suited for the optimal design of bioreactors. Therefore, we develop computational models to describe cellular metabolism and bioreactor dynamics. On the plant level, we aim to find optimized operational states, which includes the interlinked processes, e.g. cultivation, cell flocculation, harvesting, extraction and downstream processing. 

For the support of the computational modelling and optimization, new experimental methods are required. We have developed and applied several quantitative techniques including e.g. High Performance Liquid Chromatography (HPLC), Fourier Transformed Infrared Spectroscopy (FTIR) and Flow Cytometry (FC) to analyse and characterize the photosynthetic cell populations. The flow cytometry was found to be a powerful tool for the algal cell population characterization, e.g. cell vitality and neutral lipid fluorescence (see Fig. 2 [2]). The nitrogen limitation and oversaturating light induced distinct responses in the cells. The homogeneous population distribution split into two heterogeneous subpopulations for the cell vitality and neutral lipid fluorescence (see Fig. 3 [2])

Supported by the novel experimental methodologies we have developed a comprehensive computational model of D. salina, which enables robust design optimization. The modeling approach is based on dynamic flux balance analysis (DFBA) and includes regulatory models to predict the accumulation of pigment molecules. Typically, in the flux balance analysis (FBA the intracellular dynamics is assumed to be fast compared to the extracellular dynamics. For photosynthetic organisms that undergo fast environmental fluctuations the quasi-steady state approximation of the conventional FBA approach is not justifiable. Indeed, dynamic intracellular accumulation and consumption are essential in the cell metabolism. Therefore, in our current formulation we introduced intracellular dynamic states. The DFBA model applied for the first time for photosynthetic microorganisms consists of two main components, a metabolic model of the microalga and a dynamic model of the photobioreactor environment [3]. 

The accuracy of the model predictions is validated through independent experimental. In our fed-batch optimization study we could identify conditions where the biomass and β-carotene density were increased by factors of about 2.5 and 2.1, respectively (see Fig. 4 [3]). 

On the plant level, an overall process model based on mass and energy balances was used to compare alternative optimal routes from the individual cell to the final product [4]. Primarily, we focused on the optimization of the biomass harvesting unit for potential reduction of operation cost, since on the one side this is a very challenging step given the low density and fragility of D. salina. On the other side, the optimal choice of a downstream route depends on the desired product. For that reason we investigated chemical and physical flocculation as an innovative ß-carotene harvesting approach for D. salina [5].Furthermore, we have identified the importance to valorize all macromolecular fractions in algal biomass, not only the main product, e.g. the lipophilic ß-carotene in D. salina. Here we aim to develop simple heterogeneously catalyzed processes for the valorization of the remnant fractions. The algal carbohydrates are the main cell constituents in the remnant and can be successfully converted to key renewable molecules, e.g. 5-hydroxy methyl furfural and levulinic acid [6]. 

On the plant level, we developed an overall process model based on mass and energy balances to compare alternative optimal processing routes from the individual cell to the final product. By integrating real experimental results and economic aspects, the process model could reveal strengths and bottlenecks of the individual optimized processing steps. Primarily, we focused on the optimization of the biomass harvesting unit for potential reduction of operation cost, since on the one side this is a very challenging step given the low density and fragility of D. salina. On the other side, the optimal choice of a downstream route depends on the desired product [4]. For that reason we investigated chemical and physical flocculation as an innovative ß-carotene harvesting approach for microalgae (see Figure 4) in comparison to the conventional method of centrifugation. A reliable comparison of the different harvesting methods based on operation cost and energy demand of the overall process was achieved by integrating all experimental results into the process model [5]. This systems approach can be transferred to assess other process units, e.g. extraction of ß-carotene or post-processing by liquefaction of residual biomass. In consequence, the developed concept provides a general platform to evaluate microalgal process methods in a reliable manner on plant scale.

Figure 1: Multi-scale representation of an algal-based production system. The different length and time scales can be associated to the molecular, phase, process unit and plant level. Zoom Image

Figure 1: Multi-scale representation of an algal-based production system. The different length and time scales can be associated to the molecular, phase, process unit and plant level.

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Figure 2: Growth model of Dunaliella salina. (b)  Identifiability analysis of 4 model parameters based on the profile likelihood (black solid lines c2). Blue solid lines represent the profile likelihood level at fixed wN,max. The red dotted line represents the critical c2 value at significance level a = 0.05. The green asterisk indicates the best parameter estimate. Figure from [2]. Zoom Image

Figure 2: Growth model of Dunaliella salina. (b)  Identifiability analysis of 4 model parameters based on the profile likelihood (black solid lines c2). Blue solid lines represent the profile likelihood level at fixed wN,max. The red dotted line represents the critical c2 value at significance level a = 0.05. The green asterisk indicates the best parameter estimate. Figure from [2].

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Fig. 2: Effect of abiotic stress type on the cell growth of D. salina. a) Scatter plot of the algal cell population: cell size (FSC) vs. chlorophyll fuorescence (FL3) under the low light b) Cell density growth curves for the three investigated cultivation conditions; LL - low light, HL - high light, HL-ND - high light and nitrogen depletion. Zoom Image

Fig. 2: Effect of abiotic stress type on the cell growth of D. salina. a) Scatter plot of the algal cell population: cell size (FSC) vs. chlorophyll fuorescence (FL3) under the low light b) Cell density growth curves for the three investigated cultivation conditions; LL - low light, HL - high light, HL-ND - high light and nitrogen depletion.

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Fig. 3: Effect of abiotic stress type on the cell vitality of the population. a) Histograms for samples with a high (green, LL) or a low cell vitality (red, HL-ND). b) Time series of the cell vitality in the culture. LL - low light, HL - high light, HL-ND - high light and nitrogen depletion. Zoom Image

Fig. 3: Effect of abiotic stress type on the cell vitality of the population. a) Histograms for samples with a high (green, LL) or a low cell vitality (red, HL-ND). b) Time series of the cell vitality in the culture. LL - low light, HL - high light, HL-ND - high light and nitrogen depletion.

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Fig. 4: Dynamics of the optimized fed-batch run: Simulations results (lines), experimental data (symbols) and error bars. Detailed model description in [3]. Zoom Image

Fig. 4: Dynamics of the optimized fed-batch run: Simulations results (lines), experimental data (symbols) and error bars. Detailed model description in [3].

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Sources:

[1] Fachet, M.; Flassig, R.; Rihko-Struckmann, L.; Sundmacher, K.: Carotenoid Production Process Using Green Microalgae of the Dunaliella Genus: Model-Based Analysis of Interspecies Variability. Industrial and Engineering Chemistry Research 56 (45), pp. 12888 - 12898 (2017)

[2] Fachet, M.; Hermsdorf, D.; Rihko-Struckmann, L.; Sundmacher, K.: Flow cytometry enables dynamic tracking of algal stress response: A case study using carotenogenesis in Dunaliella salina. Algal Research (13), pp. 227 - 234 (2016)

[3] Flassig, R.; Fachet, M.; Höffner, K.; Barton, P. I.; Sundmacher, K.: Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae. Biotechnology for Biofuels 9 (165), pp. 1 - 12 (2016)

[4] Pirwitz, K.; Flassig, R.; Rihko-Struckmann, L.; Sundmacher, K.: Energy and operating cost assessment of competing harvesting methods for D. salina in a ß-carotene production process. Algal Research 12, pp. 161 - 169 (2015)

[5] Pirwitz, K.; Rihko-Struckmann, L.; Sundmacher, K.: Comparison of flocculation methods for harvesting Dunaliella. Bioresource Technology 196, pp. 145 - 152 (2015)

[6] Rihko-Struckmann, L.; Molnar, M.; Pirwitz, K.; Fachet, M.; McBride, K.; Zinser, A.; Sundmacher, K.: Recovery and Separation of Carbohydrate Derivatives from the Lipid Extracted Alga Dunaliella by Mild Liquefaction. ACS Sustainable Chemistry & Engineering 5 (1), pp. 588 - 595 (2017)

 
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