Microalgae harvesting for wastewater treatment and resources recovery: A review.

Microalgae-based wastewater treatment has been conceived to obtain reclaimed water and produce microalgal biomass for bio-based products and biofuels generation. However, microalgal biomass harvesting is challenging and expensive, hence one of the main bottlenecks for full-scale implementation. Finding an integrated approach that covers concepts of engineering, green chemistry and the application of microbial anabolism driven towards the harvesting processes, is mandatory for the widespread establishment of full-scale microalgae wastewater treatment plants. By using nature-based substances and applying concepts of chemical functionalization in already established harvesting methods, the costs of harvesting processes could be reduced while preventing microalgae biomass contamination. Moreover, microalgae produced during wastewater treatment have unique culture characteristics, such as the consortia, which are primarily composed of microalgae and bacteria, that should be accounted for prior to downstream processing. The aim of this review is to examine recent advances in microalgal biomass harvesting and recovery in wastewater treatment systems, considering the impact of consortia variability. The costs of available harvesting technologies, such as coagulation/flocculation, coupled to sedimentation and differential air flotation, are provided. Additionally, promising technologies are discussed, including autoflocculation, bioflocculation, new filtration materials, nanotechnology, microfluidic and magnetic methods.

New strategy for bioplastic and exopolysaccharides production: Enrichment of field microbiomes with cyanobacteria.

Seven photosynthethic microbiomes were collected from field environmental samples to test their potential in polyhydroxybutyrate (PHB) and exopolysaccharides (EPS) production, both alternatives to chemical-based polymers. Microscope observations together with microbial sequence analysis revealed the microbiome enrichment in cyanobacteria after culture growth under phosphorus limitation. PHB and EPS production were studied under three culture factors (phototrophy, mixotrophy and heterotrophy) by evaluating and optimizing the effect of three parameters (organic and inorganic carbon and days under light:dark cycles) by Box-Behnken design. Results showed that optimal conditions for both biopolymers synthesis were microbiome-dependent; however, the addition of organic carbon boosted PHB production in all the tested microbiomes, producing up to 14 %dcw PHB with the addition of 1.2 g acetate·L-1 and seven days under light:dark photoperiods. The highest EPS production was 59 mg·L-1 with the addition of 1.2 g acetate·L-1 and four days under light:dark photoperiods. The methodology used is suitable for enriching microbiomes in cyanobacteria, and for testing the best conditions for bioproduct synthesis for further scale up.

Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes.

The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes.

The potential of wastewater grown microalgae for agricultural purposes: Contaminants of emerging concern, heavy metals and pathogens assessment.

In the coming years, the use of microalgal biomass as agricultural biofertilizers has shown promising results. The use of wastewater as culture medium has resulted in the reduction of production costs, making microalgae-based fertilizers highly attractive for farmers. However, the occurrence of specific pollutants in wastewater, like pathogens, heavy metals and contaminants of emerging concern (CECs), such as pharmaceuticals and personal care products may pose a risk on human health. This study presents an holistic assessment of the production and use of microalgal biomass grown in municipal wastewater as biofertilizer in agriculture. Results showed that pathogens and heavy metals concentrations in the microalgal biomass were below the threshold established by the European regulation for fertilizing products, except for cadmium. Regarding CECs, 25 out of 29 compounds were found in wastewater. However, only three of them (hydrocinnamic acid, caffeine, and bisphenol A) were found in the microalgae biomass used as biofertilizer. Agronomic tests were performed for lettuce growth in greenhouse. Four treatments were studied, comparing the use of microalgae biofertilizer with a conventional mineral fertilizer, and also a combination of both of them. Results suggested that microalgae can help reducing the mineral nitrogen dose, since similar fresh shoot weights were obtained in the plants grown with the different assessed fertilizers. Lettuce samples revealed the presence of cadmium and CECs in all the treatments including both negative and positive controls, which suggests that their presence was not linked to the microalgae biomass. On the whole, this study revealed that wastewater grown microalgae can be used for agricultural purposes reducing mineral N need and guaranteeing health safety of the crops.

Monitoring PHB production in Synechocystis sp. with hyperspectral images.

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies. The aim of this study was to investigate the application of hyperspectral technologies to monitor cyanobacteria population growth and PHB production. We have established a ground-breaking measurement method able to discern spectral reflectance changes from light emitted to cyanobacteria in different phases. All in all, enabling to distinguish between cyanobacteria growth phase and PHB accumulation phase. Furthermore, first tests of classification algorithms used for machine learning and image recognition technologies had been applied to automatically recognize the different cyanobacteria species from a complex microbial community containing cyanobacteria and microalgae cultivated in pilot-scale photobioreactors (PBRs). We have defined three main indicators for monitoring PHB production: (i) cyanobacteria specific-strain density, (ii) differentiate between growth and PHB-accumulation and (iii) chlorosis progression. The results presented in this study represent an interesting alternative for traditional measurements in cyanobacteria PHB production and its application in pilot-scale PBRs. Although not directly determining the amount of PHB production, they would give insights on the undergoing processes.

2D printed multicellular devices performing digital and analogue computation.

Much effort has been expended on building cellular computational devices for different applications. Despite the significant advances, there are still several addressable restraints to achieve the necessary technological transference. These improvements will ease the development of end-user applications working out of the lab. In this study, we propose a methodology for the construction of printable cellular devices, digital or analogue, for different purposes. These printable devices are designed to work in a 2D surface, in which the circuit information is encoded in the concentration of a biological signal, the so-called carrying signal. This signal diffuses through the 2D surface and thereby interacts with different device components. These components are distributed in a specific spatial arrangement and perform the computation by modulating the level of the carrying signal in response to external inputs, determining the final output. For experimental validation, 2D cellular circuits are printed on a paper surface by using a set of cellular inks. As a proof-of-principle, we have printed and analysed both digital and analogue circuits using the same set of cellular inks but with different spatial topologies. The proposed methodology can open the door to a feasible and reliable industrial production of cellular circuits for multiple applications.

Two-Component Biosensors: Unveiling the Mechanisms of Predictable Tunability.

Many studies have been devoted to the engineering of cellular biosensors by exploiting intrinsic natural sensors. However, biosensors rely not only on input detection but also on an adequate response range. It is therefore often necessary to tune natural systems to meet the demands of specific applications in a predictable manner. In this study, we explored the customizability of two-component bacterial biosensors by modulating the main biosensor component, i.e., the receptor protein. We developed a mathematical model that describes the functional relationship between receptor abundance and activation threshold, sensitivity, dynamic range, and operating range. The defined mathematical framework allows the design of the genetic architecture of a two-component biosensor that can perform as required with minimal genetic engineering. To experimentally validate the model and its predictions, a library of biosensors was constructed. The good agreement between theoretical designs and experimental results indicates that modulation of receptor protein abundance allows optimization of biosensor designs with minimal genetic engineering.

Plug-and-Play Multicellular Circuits with Time-Dependent Dynamic Responses.

Synthetic biology studies aim to develop cellular devices for biomedical applications. These devices, based on living instead of electronic or electromechanic technology, might provide alternative treatments for a wide range of diseases. However, the feasibility of these devices depends, in many cases, on complex genetic circuits that must fulfill physiological requirements. In this work, we explored the potential of multicellular architectures to act as an alternative to complex circuits for implementation of new devices. As a proof of concept, we developed specific circuits for insulin or glucagon production in response to different glucose levels. Here, we show that fundamental features, such as circuit's affinity or sensitivity, are dependent on the specific configuration of the multicellular consortia, providing a method for tuning these properties without genetic engineering. As an example, we have designed and built circuits with an incoherent feed-forward loop architecture (FFL) that can be easily adjusted to generate single pulse responses. Our results might serve as a blueprint for future development of cellular devices for glycemia regulation in diabetic patients.