A review on machine learning approaches for microalgae cultivation systems.

Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.

Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study.

Accurate prediction of microalgae growth is crucial for understanding the impacts of light dynamics and optimizing production. Although various mathematical models have been proposed, only a few of them have been validated in outdoor cultivation. This study aims to investigate the use of machine learning algorithms in microalgae growth modeling. Outdoor cultivation data of Phaeodactylum tricornutum in flat-panel airlift photobioreactors for 50 days were used to compare the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) with traditional models, namely Monod and Haldane. The results indicate that the machine learning models outperform the traditional models due to their ability to utilize light history as input. Moreover, the LSTM model shows an excellent ability to describe the light acclimation effect. Last, two potential applications of these models are demonstrated: 1) use as a biomass soft sensor and 2) development of an optimal harvest strategy for outdoor cultivation.

Selection of a suitable photosynthetically active microalgae strain for the co-cultivation with mammalian cells.

Preventing hypoxic zones in 3D bioprinted mammalian cell-laden constructs using an internal oxygen supply could enable a more successful cultivation both in vitro and in vivo. In this study, the suitability of green microalgae as photosynthetic oxygen generators within bioprinted constructs was evaluated by defining and investigating important parameters for a successful co-culture. First, we assessed the impact of light-necessary for photosynthesis-on two non-light adapted mammalian cell types and defined red-light illumination and a temperature of 37°C as essential factors in a co-culture. The four thermotolerant microalgae strains Chlorella sorokiniana, Coelastrella oocystiformis, Coelastrella striolata, and Scenedesmus sp. were cultured both in suspension culture and 3D bioprinted constructs to assess viability and photosynthetic activity under these defined co-culture conditions. Scenedesmus sp. proved to be performing best under red light and 37°C as well as immobilized in a bioprinted hydrogel based on alginate. Moreover, the presence of the antibiotic ampicillin and the organic carbon-source glucose, both required for mammalian cell cultures, had no impact on bioprinted Scenedesmus sp. cultures regarding growth, viability, and photosynthetic activity. This study is the first to investigate the influence of mammalian cell requirements on the metabolism and photosynthetic ability of different microalgal strains. In a co-culture, the strain Scenedesmus sp. could provide a stable oxygenation that ensures the functionality of the mammalian cells.

Optimized Protocol for Microalgae DNA Staining with SYTO9/SYBR Green I, Based on Flow Cytometry and RSM Methodology: Experimental Design, Impacts and Validation.

Multiple fluorochromes are extensively used to investigate different microalgal aspects, such as viability and physiology. Some of them can be used to stain nucleic acids (DNA). Well-known examples are SYBR Green I and SYTO 9, the latter of which offers several advantages, especially when combined with flow cytometry (FCM)­a powerful method for studying microalgal population heterogeneity and analyzing their cell cycles. However, the effects of these dyes on the microalgae cell physiology have not been fully elucidated yet. A statistical experimental design, using response surface methodology (RSM) with FCM was applied in this study to optimize the DNA staining of a non-conventional microalgae, Chromochloris zofingiensis, with SYBR Green I and SYTO 9, and to optimize the variables affecting staining efficiency, i.e., the dye concentration, incubation time and staining temperature. We found that none of these factors affects the staining efficiency, which was not less than 99.65%. However, for both dyes, the dye concentration was shown to be the most significant factor causing cell damage (p-values: 0.0003; <0.0001) for SYBR Green I and SYTO 9, respectively. The staining temperature was only significant for SYTO 9 (p-value: 0.0082), and no significant effect was observed regarding the incubation time for both dyes. The values of the optimized parameters (0.5 µM, 05 min and 25 °C) for SYTO 9 and (0.5 X, 5 min and 25 °C) for SYBR Green I resulted in the maximum staining efficiency (99.8%; 99.6%), and the minimum damaging effects (12.86%; 13.75%) for SYTO 9 and SYBR Green I, respectively. These results offer new perspectives for improving the use of DNA staining fluorochromes and provides insights into their possible side effects on microalgae.

Strain Development in Microalgal Biotechnology-Random Mutagenesis Techniques.

Microalgal biomass and metabolites can be used as a renewable source of nutrition, pharmaceuticals and energy to maintain or improve the quality of human life. Microalgae's high volumetric productivity and low impact on the environment make them a promising raw material in terms of both ecology and economics. To optimize biotechnological processes with microalgae, improving the productivity and robustness of the cell factories is a major step towards economically viable bioprocesses. This review provides an overview of random mutagenesis techniques that are applied to microalgal cell factories, with a particular focus on physical and chemical mutagens, mutagenesis conditions and mutant characteristics.

The Assessment of the Real-Time Radiative Properties and Productivity of Limnospira platensis in Tubular Photobioreactors.

The development of tools to predict the photobioreactors' (PBRs) productivity is a significant concern in biotechnology. To this end, it is required to know the light availability inside the cultivation unit and combine this information with a suitable kinetic expression that links the distribution of radiant energy with the cell growth rate. In a previous study, we presented and validated a methodology for assessing the radiative properties necessary to address the light distribution inside a PBR for varying illuminating conditions through the cultivation process of a phototrophic microorganism. Here, we sought to utilise this information to construct a predictive tool to estimate the productivity of an autotrophic bioprocess carried out in a 100 [L] tubular photobioreactor (TPBR). Firstly, the time-dependent optical properties over ten batch cultures of L. platensis were calculated. Secondly, the local volumetric rate of photon absorption was assessed based on a physical model of the interaction of the radiant energy with the suspended biomass, together with a Monte Carlo simulation algorithm. Lastly, a kinetic expression valid for low illumination conditions has been utilised to reproduce all the cultures' experimentally obtained dry weight biomass concentration values. Taken together, time-dependent radiative properties and the kinetic model produced a valuable tool for the study and scaling up of TPBRs.

Modeling and Optimizing the Effect of Light Color, Sodium Chloride and Glucose Concentration on Biomass Production and the Quality of Arthrospira platensis Using Response Surface Methodology (RSM).

Arthrospira platensis (Spirulina) biomass is a valuable source of sustainable proteins, and the basis for new food and feed products. State-of-the-art production of Spirulina biomass in open pond systems only allows limited control of essential process parameters, such as light color, salinity control, or mixotrophic growth, due to the high risk of contaminations. Closed photobioreactors offer a highly controllable system to optimize all process parameters affecting Spirulina biomass production (quantity) and biomass composition (quality). However, a comprehensive analysis of the impact of light color, salinity effects, and mixotrophic growth modes of Spirulina biomass production has not been performed yet. In this study, Response Surface Methodology (RSM) was employed to develop statistical models, and define optimal mixotrophic process conditions yielding maximum quantitative biomass productivity and high-quality biomass composition related to cellular protein and phycocyanin content. The individual and interaction effects of 0, 5, 15, and 30 g/L of sodium chloride (S), and 0, 1.5, 2, and 2.5 g/L of glucose (G) in three costume-made LED panels (L) where the dominant color was white (W), red (R), and yellow (Y) were investigated in a full factorial design. Spirulina was cultivated in 200 mL cell culture flasks in different treatments, and data were collected at the end of the log growth phase. The lack-of-fit test showed that the cubic model was the most suitable to predict the biomass concentration and protein content, and the two-factor interaction (2FI) was preferred to predict the cellular phycocyanin content (p > 0.05). The reduced models were produced by excluding insignificant terms (p > 0.05). The experimental validation of the RSM optimization showed that the highest biomass concentration (1.09, 1.08, and 0.85 g/L), with improved phycocyanin content of 82.27, 59.47, 107 mg/g, and protein content of 46.18, 39.76, 53.16%, was obtained under the process parameter configuration WL4.28S2.5G, RL10.63S1.33G, and YL1.00S0.88G, respectively.

Think outside the box: 3D bioprinting concepts for biotechnological applications - recent developments and future perspectives.

3D bioprinting - the fabrication of geometrically complex 3D structures from biocompatible materials containing living cells using additive manufacturing technologies - is a rapidly developing research field with a broad range of potential applications in fundamental research, regenerative medicine and industry. Currently, research into 3D bioprinting is mostly focused on new therapeutic concepts for the treatment of injured or degenerative tissue by fabrication of functional tissue equivalents or disease models, utilizing mammalian cells. However, 3D bioprinting also has an enormous potential in biotechnology. Due to the defined spatial arrangement of biologically active (non-mammalian) cells in a biomaterial matrix, reaction compartments can be designed according to specific needs, or co-cultures of different cell types can be realized in a highly organized manner to exploit cell-cell interactions. Thus, 3D bioprinting technology can enable new biotechnological concepts, for example, by implementing perfusion systems while protecting shear sensitive cells or performing cascaded bioreactions. Here, we review the use of 3D bioprinting to manufacture defined 3D microenvironments for biotechnological applications using bacteria, fungi, microalgae, plant cells and co-cultures of different cell types. We discuss recent approaches to apply 3D bioprinting in biotechnological applications and - as it is a particular challenge - concepts for the real-time monitoring of the physiological state, growth and metabolic activity of the embedded cells in 3D bioprinted constructs. With these insights, we outline new applications of 3D bioprinting in biotechnology, engineered living materials and space research.

Microalgae wastewater treatment: Biological and technological approaches.

Current global environmental issues raise unavoidable challenges for our use of natural resources. Supplying the human population with clean water is becoming a global problem. Numerous organic and inorganic impurities in municipal, industrial, and agricultural waters, ranging from microplastics to high nutrient loads and heavy metals, endanger our nutrition and health. The development of efficient wastewater treatment technologies and circular economic approaches is thus becoming increasingly important. The biomass production of microalgae using industrial wastewater offers the possibility of recycling industrial residues to create new sources of raw materials for energy and material use. This review discusses algae-based wastewater treatment technologies with a special focus on industrial wastewater sources, the potential of non-conventional extremophilic (thermophilic, acidophilic, and psychrophilic) microalgae, and industrial algae-wastewater treatment concepts that have already been put into practice.

Additive Biotech-Chances, challenges, and recent applications of additive manufacturing technologies in biotechnology.

The diversity and complexity of biotechnological applications are constantly increasing, with ever expanding ranges of production hosts, cultivation conditions and measurement tasks. Consequently, many analytical and cultivation systems for biotechnology and bioprocess engineering, such as microfluidic devices or bioreactors, are tailor-made to precisely satisfy the requirements of specific measurements or cultivation tasks. Additive manufacturing (AM) technologies offer the possibility of fabricating tailor-made 3D laboratory equipment directly from CAD designs with previously inaccessible levels of freedom in terms of structural complexity. This review discusses the historical background of these technologies, their most promising current implementations and the associated workflows, fabrication processes and material specifications, together with some of the major challenges associated with using AM in biotechnology/bioprocess engineering. To illustrate the great potential of AM, selected examples in microfluidic devices, 3D-bioprinting/biofabrication and bioprocess engineering are highlighted.

Green bioprinting: extrusion-based fabrication of plant cell-laden biopolymer hydrogel scaffolds.

Plant cell cultures produce active agents for pharmaceuticals, food and cosmetics. However, up to now process control for plant cell suspension cultures is challenging. A positive impact of cell immobilization, such as encapsulation in hydrogel beads, on secondary metabolites production has been reported for several plant species. The aim of this work was to develop a method for bioprinting of plant cells in order to allow fabrication of free-formed three-dimensional matrices with defined internal pore architecture for in depth characterization of immobilization conditions, cell agglomeration and interactions. By using extrusion-based 3D plotting of a basil cell-laden hydrogel blend consisting of alginate, agarose and methylcellulose (alg/aga/mc), we could demonstrate that bioprinting is applicable to plant cells. The majority of the cells survived plotting and crosslinking and the embedded cells showed high viability and metabolic activity during the investigated cultivation period of 20 d. Beside its compatibility with the plant cells, the novel alg/aga/mc blend allowed fabrication of defined 3D constructs with open macropores both in vertical and horizontal direction which were stable under culture conditions for several weeks. Thus, Green Bioprinting, an additive manufacturing technology processing live cells from the plant kingdom, is a promising new immobilization tool for plant cells that enables the development of new bioprocesses for secondary metabolites production as well as monitoring methods.

Light-field-characterization in a continuous hydrogen-producing photobioreactor by optical simulation and computational fluid dynamics.

Externally illuminated photobioreactors (PBRs) are widely used in studies on the use of phototrophic microorganisms as sources of bioenergy and other photobiotechnology research. In this work, straightforward simulation techniques were used to describe effects of varying fluid flow conditions in a continuous hydrogen-producing PBR on the rate of photofermentative hydrogen production (rH2 ) by Rhodobacter sphaeroides DSM 158. A ZEMAX optical ray tracing simulation was performed to quantify the illumination intensity reaching the interior of the cylindrical PBR vessel. 24.2% of the emitted energy was lost through optical effects, or did not reach the PBR surface. In a dense culture of continuously producing bacteria during chemostatic cultivation, the illumination intensity became completely attenuated within the first centimeter of the PBR radius as described by an empirical three-parametric model implemented in Mathcad. The bacterial movement in chemostatic steady-state conditions was influenced by varying the fluid Reynolds number. The "Computational Fluid Dynamics" and "Particle Tracing" tools of COMSOL Multiphysics were used to visualize the fluid flow pattern and cellular trajectories through well-illuminated zones near the PBR periphery and dark zones in the center of the PBR. A moderate turbulence (Reynolds number = 12,600) and fluctuating illumination of 1.5 Hz were found to yield the highest continuous rH2 by R. sphaeroides DSM 158 (170.5 mL L(-1) h(-1) ) in this study.

Hydrogen production by Rhodobacter sphaeroides DSM 158 under intense irradiation.

To identify optimal hydrogen production conditions using growing cultures of Rhodobacter sphaeroides DSM 158 the effects of varying the reactor's volumetric power input (0.01-1.4kWm(-3)) and irradiation intensity (5-2500Wm(-2)) were investigated in batch and continuous production modes. Irradiation intensity had a greater effect on hydrogen production than volumetric power input. Hydrogen production and photofermentative biomass formation were maximized by irradiation at 2250Wm(-2) with a volumetric power input of 0.55kWm(-3). The bacterial dry weight (2.64gL(-1)) and rate of hydrogen production (195mLL(-1)h(-1)) achieved under these conditions were greater than any that have previously been reported for batch-mode hydrogen production by R. sphaeroides. Continuous mode experiments (D=0.1h(-1)) yielded a bacterial dry weight, hydrogen production rate, productivity and hydrogen yield of 2.35±0.18gL(-1), 165±6.2mLL(-1)h(-1), 3.96LL(-1)d(-1) and 36.6%, respectively.