Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity

Production of high-amylose starch with low digestibility in a green marine microalga Tetraselmis subcordiformis by delaying high-bicarbonate induction

A photoautotrophic green marine microalga Tetraselmis subcordiformis was applied to sustainably produce high-amylose starch (HAS) from CO2 via high-bicarbonate (HB) induction. Delaying the addition of HB from Day 0 to Day 3 overcome the HB stress and significantly improved photosynthetic activity, resulting in 7.2-times higher starch productivity and the production of HAS with the total starch content of 21.81 % and amylose/amylopectin ratio of 1.51 obtained on Day 6, which represented the highest HAS levels among the photoautotrophic microalgae reported hitherto. The HB treatment improved the activity of granule-bound starch synthase and decreased starch branching enzymes, soluble starch synthase and amylase activity, leading to the formation of HAS by enhancing amylose production with simultaneously attenuating amylopectin biosynthesis. HB induction mainly changed the structure of amylose with increased chain lengths rather than amylopectin, and the ordered structures and degree of branching were also reduced. The produced HAS under HB induction (H-MS) was a kind of very small granular starch with A-type crystalline. It was identified to have a high proportion of short-chain amylopectin and elongated amylose that enabled low gelatinization temperatures (56-69 °C) and digestibility (containing 55.58 % of resistant starch). These features manifested an excellent prospect for application in sustainable food industry.

Synthetic Biology Strategies and Tools to Modulate Photosynthesis in Microbes

The utilization of photosynthetic microbes, such as cyanobacteria and microalgae, offers sustainable solutions to addressing global resource shortages and pollution. While these microorganisms have demonstrated significant potential in biomanufacturing, their industrial application is limited by suboptimal photosynthetic efficiency. Synthetic biology integrates molecular biology, systems biology, and engineering principles to provide a powerful tool for elucidating photosynthetic mechanisms and rationally optimizing photosynthetic platforms. This review summarizes recent advancements in regulating photosynthesis in cyanobacteria and microalgae via synthetic biology, focusing on strategies to enhance light energy absorption, optimize electron transport chains, and improve carbon assimilation. Furthermore, we discuss key challenges in translating these genetic modifications to large-scale bioproduction, highlighting specific bottlenecks in strain stability, metabolic burden, and process scalability. Finally, we propose potential solutions, such as AI-assisted metabolic engineering, synthetic microbial consortia, and next-generation photobioreactor designs, to overcome these limitations. Overall, while synthetic biology holds great promise for enhancing photosynthetic efficiency in cyanobacteria and microalgae, further research is needed to refine genetic strategies and develop scalable production systems.

Cas9-mediated gene-editing frequency in microalgae is doubled by harnessing the interaction between importin α and phytopathogenic NLSs

Pathogen-derived nuclear localization signals (NLSs) enable vigorous nuclear invasion in the host by the virulence proteins harboring them. Herein, inspired by the robust nuclear import mechanism, we show that NLSs originating from the plant infection-associated Agrobacterium proteins VirD2 and VirE2 can be incorporated into the Cas9 system as efficient nuclear delivery enhancers, thereby improving the low gene-editing frequency in a model microalga, Chlamydomonas reinhardtii, caused by poor nuclear localization of the bulky nuclease. Prior to evaluation of the NLSs, IPA1 (Cre04.g215850) was first defined in the alga as the nuclear import-related importin alpha (Impα) that serves as a counterpart adaptor protein of the NLSs, based on extensive in silico analyses considering the protein's sequence, tertiary folding behavior, and structural basis when interacting with a well-studied SV40TAg NLS. Through precursive affinity explorations, we reproducibly found that the NLSs mediated the binding between the Cas9 and Impα with nM affinities and visually confirmed that the fusion of the NLSs strictly localized the peptide-bearing cargoes in the microalgal nucleus without compensating for their cleavage ability. When employed in a real-world application, the VirD2 NLS increases the mutation frequency (~1.12 × 10-5) over 2.4-fold compared to an archetypal SV40TAg NLS (~0.46 × 10-5) when fused with Cas9. We demonstrate the cross-species versatility of the Impα-dependent strategy by successfully applying it to an industrial alga, Chlorella Sp. HS2. This work, focused on affinity augmentation, provides insights into increasing the frequency of gene editing, which can be advantageously used in programmable mutagenesis with broad applicability.

Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium

A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultivation parameters of Porphyridium. The BANE-XGBoost has the best prediction performance (train R2 > 0.96 and test R2 > 0.87). Based on Shapley Additive Explanation (SHAP) model, illumination intensity, culture time, and KH2PO4 are the most critical factors for Porphyridium growth. The combined facilitating roles of cultivation parameters are found using the SHAP value-based heat map and group. To reach high biomass and daily production rate concurrently, one-way and two-way partial dependent plots models find the optimal conditions. The top 2 critical parameters (illumination intensity and KH2PO4) were selected to verify using the graphical user interface website based on the optimized model and lab experiments, respectively. This study shows thegraph-learning-based model can improve prediction performance and optimize intricate low-carbon microalgal cultivation.

Strategies and tools to construct stable and efficient artificial coculture systems as biosynthetic platforms for biomass conversion

Inspired by the natural symbiotic relationships between diverse microbial members, researchers recently focused on modifying microbial chassis to create artificial coculture systems using synthetic biology tools. An increasing number of scientists are now exploring these systems as innovative biosynthetic platforms for biomass conversion. While significant advancements have been achieved, challenges remain in maintaining the stability and productivity of these systems. Sustaining an optimal population ratio over a long time period and balancing anabolism and catabolism during cultivation have proven difficult. Key issues, such as competitive or antagonistic relationships between microbial members, as well as metabolic imbalances and maladaptation, are critical factors affecting the stability and productivity of artificial coculture systems. In this article, we critically review current strategies and methods for improving the stability and productivity of these systems, with a focus on recent progress in biomass conversion. We also provide insights into future research directions, laying the groundwork for further development of artificial coculture biosynthetic platforms.

The current progress of tandem chemical and biological plastic upcycling

Each year, millions of tons of plastics are produced for use in such applications as packaging, construction, and textiles. While plastic is undeniably useful and convenient, its environmental fate and transport have raised growing concerns about waste and pollution. However, the ease and low cost of producing virgin plastic have so far made conventional plastic recycling economically unattractive. Common contaminants in plastic waste and shortcomings of the recycling processes themselves typically mean that recycled plastic products are of relatively low quality in some cases. The high cost and high energy requirements of typical recycling operations also reduce their economic benefits. In recent years, the bio-upcycling of chemically treated plastic waste has emerged as a promising alternative to conventional plastic recycling. Unlike recycling, bio-upcycling uses relatively mild process conditions to economically transform pretreated plastic waste into value-added products. In this review, we first provide a précis of the general methodology and limits of conventional plastic recycling. Then, we review recent advances in hybrid chemical/biological upcycling methods for different plastics, including polyethylene terephthalate, polyurethane, polyamide, polycarbonate, polyethylene, polypropylene, polystyrene, and polyvinyl chloride. For each kind of plastic, we summarize both the pretreatment methods for making the plastic bio-available and the microbial chassis for degrading or converting the treated plastic waste to value-added products. We also discuss both the limitations of upcycling processes for major plastics and their potential for bio-upcycling.

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.

Towards AI-designed genomes using a variational autoencoder

Genomes encode elaborate networks of genes whose products must seamlessly interact to support living organisms. Humans' capacity to understand these biological systems is limited by their sheer size and complexity. In this article, we develop a proof of concept framework for training a machine learning (ML) algorithm to model bacterial genome composition. To achieve this, we create simplified representations of genomes in the form of binary vectors that indicate the encoded genes, henceforth referred to as genome vectors. A denoising variational autoencoder was trained to accept corrupted genome vectors, in which most genes had been masked, and reconstruct the original. The resulting model, DeepGenomeVector, effectively captures complex dependencies in genomic networks, as evaluated by both qualitative and quantitative metrics. An in-depth functional analysis of a generated genome vector shows that its encoded pathways are interconnected, near complete, and ecologically cohesive. On the test set, where the model's ability to reconstruct uncorrupted genome vectors was evaluated, Area Under the Receiver Operating Curve (AUROC) and F1 scores of 0.98 and 0.83, respectively, support the model's strong performance. This article showcases the power of ML approaches for synthetic biology and highlights the possibility that artifical intelligence agents may one day be able to design genomes that animate carbon-based cells.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production

The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 μmol photons/m2/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 °C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.

Revisiting Solar Energy Flow in Nanomaterial-Microorganism Hybrid Systems

Nanomaterial-microorganism hybrid systems (NMHSs), integrating semiconductor nanomaterials with microorganisms, present a promising platform for broadband solar energy harvesting, high-efficiency carbon reduction, and sustainable chemical production. While studies underscore its potential in diverse solar-to-chemical energy conversions, prevailing NMHSs grapple with suboptimal energy conversion efficiency. Such limitations stem predominantly from an insufficient systematic exploration of the mechanisms dictating solar energy flow. This review provides a systematic overview of the notable advancements in this nascent field, with a particular focus on the discussion of three pivotal steps of energy flow: solar energy capture, cross-membrane energy transport, and energy conversion into chemicals. While key challenges faced in each stage are independently identified and discussed, viable solutions are correspondingly postulated. In view of the interplay of the three steps in affecting the overall efficiency of solar-to-chemical energy conversion, subsequent discussions thus take an integrative and systematic viewpoint to comprehend, analyze and improve the solar energy flow in the current NMHSs of different configurations, and highlighting the contemporary techniques that can be employed to investigate various aspects of energy flow within NMHSs. Finally, a concluding section summarizes opportunities for future research, providing a roadmap for the continued development and optimization of NMHSs.

Engineered Cyanobacteria-Based Living Materials for Bioremediation of Heavy Metals Both In Vitro and In Vivo

The pollution caused by heavy metals (HMs) represents a global concern due to their serious environmental threat. Photosynthetic cyanobacteria have a natural niche and the ability to remediate HMs such as cadmium. However, their practical application is hindered by a low tolerance to HMs and issues related to recycling. In response to these challenges, this study focuses on the development and evaluation of engineered cyanobacteria-based living materials for HMs bioremediation. Genes encoding phytochelatins (PCSs) and metallothioneins (MTs) were introduced into the model cyanobacterium Synechocystis sp. PCC 6803, creating PM/6803. The strain exhibited improved tolerance to multiple HMs and effectively removed a combination of Cd2+, Zn2+, and Cu2+. Using Cd2+ as a representative, PM/6803 achieved a bioremediation rate of approximately 21 μg of Cd2+/OD750 under the given test conditions. To facilitate its controllable application, PM/6803 was encapsulated using sodium alginate-based hydrogels (PM/6803@SA) to create "living materials" with different shapes. This system was feasible, biocompatible, and effective for removing Cd2+ under simulated conditions of zebrafish and mice models. Briefly, in vitro application of PM/6803@SA efficiently rescued zebrafish from polluted water containing Cd2+, while in vivo use of PM/6803@SA significantly decreased the Cd2+ content in mice bodies and restored their active behavior. The study offers feasible strategies for HMs bioremediation using the interesting biomaterials of engineered cyanobacteria both in vitro and in vivo.

Nutraceutical prospects of genetically engineered cyanobacteria- technological updates and significance

Genetically engineered cyanobacterial strains that have improved growth rate, biomass productivity, and metabolite productivity could be a better option for sustainable bio-metabolite production. The global demand for biobased metabolites with nutraceuticals and health benefits has increased due to their safety and plausible therapeutic and nutritional utility. Cyanobacteria are solar-powered green cellular factories that can be genetically tuned to produce metabolites with nutraceutical and pharmaceutical benefits. The present review discusses biotechnological endeavors for producing bioprospective compounds from genetically engineered cyanobacteria and discusses the challenges and troubleshooting faced during metabolite production. This review explores the cyanobacterial versatility, the use of engineered strains, and the techno-economic challenges associated with scaling up metabolite production from cyanobacteria. Challenges to produce cyanobacterial bioactive compounds with remarkable nutraceutical values have been discussed. Additionally, this review also summarises the challenges and future prospects of metabolite production from genetically engineered cyanobacteria as a sustainable approach.

Application of Cyanobacteria as Chassis Cells in Synthetic Biology

Synthetic biology is an exciting new area of research that combines science and engineering to design and build new biological functions and systems. Predictably, with the development of synthetic biology, more efficient and economical photosynthetic microalgae chassis will be successfully constructed, making it possible to break through laboratory research into large-scale industrial applications. The synthesis of a range of biochemicals has been demonstrated in cyanobacteria; however, low product titers are the biggest barrier to the commercialization of cyanobacterial biotechnology. This review summarizes the applied improvement strategies from the perspectives of cyanobacteria chassis cells and synthetic biology. The harvest advantages of cyanobacterial products and the latest progress in improving production strategies are discussed according to the product status. As cyanobacteria synthetic biology is still in its infancy, apart from the achievements made, the difficulties and challenges in the application and development of cyanobacteria genetic tool kits in biochemical synthesis, environmental monitoring, and remediation were assessed.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Engineering highly productive cyanobacteria towards carbon negative emissions technologies

Cyanobacteria are a diverse and ecologically important group of photosynthetic prokaryotes that contribute significantly to the global carbon cycle through the capture of CO2 as biomass. Cyanobacterial biotechnology could play a key role in a sustainable bioeconomy through negative emissions technologies (NETs), such as carbon sequestration or bioproduction. However, the primary issues of low productivities and high infrastructure costs currently limit the commercialisation of such applications. The isolation of several fast-growing strains and recent advancements in molecular biology tools now offer promising new avenues for improving yields, including metabolic engineering approaches guided by high-throughput screening and metabolic models. Furthermore, emerging research on engineering coculture communities could help to develop more robust culturing systems to support broader NET applications.

Breathing life into Mars: Terraforming and the pivotal role of algae in atmospheric genesis

The Martian environment, characterized by extreme aridity, frigid temperatures, and a lack of atmospheric oxygen, presents a formidable challenge for potential terraforming endeavors. This review article synthesizes current research on utilizing algae as biocatalysts in the proposed terraforming of Mars, assessing their capacity to facilitate Martian atmospheric conditions through photosynthetic bioengineering. We analyze the physiological and genetic traits of extremophile algae that equip them for survival in extreme habitats on Earth, which serve as analogs for Martian surface conditions. The potential for these organisms to mediate atmospheric change on Mars is evaluated, specifically their role in biogenic oxygen production and carbon dioxide sequestration. We discuss strategies for enhancing algal strains' resilience and metabolic efficiency, including genetic modification and the development of bioreactors for controlled growth in extraterrestrial environments. The integration of algal systems with existing mechanical and chemical terraforming proposals is also examined, proposing a synergistic approach for establishing a nascent Martian biosphere. Ethical and ecological considerations concerning introducing terrestrial life to extra-planetary bodies are critically appraised. This appraisal includes an examination of potential ecological feedback loops and inherent risks associated with biological terraforming. Biological terraforming is the theoretical process of deliberately altering a planet's atmosphere, temperature, and ecosystem to render it suitable for Earth-like life. The feasibility of a phased introduction of life, starting with microbial taxa and progressing to multicellular organisms, fosters a supportive atmosphere on Mars. By extending the frontier of biotechnological innovation into space, this work contributes to the foundational understanding necessary for one of humanity's most audacious goals-the terraforming of another planet.

Fast-growing cyanobacterial chassis for synthetic biology application

Carbon neutrality by 2050 has become one of the most urgent challenges the world faces today. To address the issue, it is necessary to develop and promote new technologies related with CO2 recycling. Cyanobacteria are the only prokaryotes performing oxygenic photosynthesis, capable of fixing CO2 into biomass under sunlight and serving as one of the most important primary producers on earth. Notably, recent progress on synthetic biology has led to utilizing model cyanobacteria such as Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942 as chassis for "light-driven autotrophic cell factories" to produce several dozens of biofuels and various fine chemicals directly from CO2. However, due to the slow growth rate and low biomass accumulation in the current chassis, the productivity for most products is still lower than the threshold necessary for large-scale commercial application, raising the importance of developing high-efficiency cyanobacterial chassis with fast growth and/or higher biomass accumulation capabilities. In this article, we critically reviewed recent progresses on identification, systems biology analysis, and engineering of fast-growing cyanobacterial chassis. Specifically, fast-growing cyanobacteria identified in recent years, such as S. elongatus UTEX 2973, S. elongatus PCC 11801, S. elongatus PCC 11802 and Synechococcus sp. PCC 11901 was comparatively analyzed. In addition, the progresses on their recent application in converting CO2 into chemicals, and genetic toolboxes developed for these new cyanobacterial chassis were discussed. Finally, the article provides insights into future challenges and perspectives on the synthetic biology application of cyanobacterial chassis.

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

Scale-up of microalgal systems for decarbonization and bioproducts: Challenges and opportunities

The threat of global climate change presents a significant challenge for humanity. Microalgae-based carbon capture and utilization (CCU) technology has emerged as a promising solution to this global issue. This review aims to comprehensively evaluate the current advancements in scale-up of microalgae cultivation and its applications, specifically focusing on decarbonization from flue gases, organic wastewater remediation, and biogas upgrading. The study identifies critical challenges that need to be addressed during the scale-up process and evaluates the economic viability of microalgal CCU within the carbon market. Additionally, it analyzes the commercial status of microalgae-derived products and highlights those with high market demand. This review serves as a crucial resource for researchers, industry professionals, and policymakers to develop and implement innovative approaches to enhance the efficiency of microalgae-based CO2 utilization while addressing the challenges associated with the scale-up of microalgae technologies.

Strategies to enhance biohydrogen production from microalgae: A comprehensive review

Microalgae represent a promising renewable feedstock for the sustainable production of biohydrogen. Their high growth rates and ability to fix carbon utilizing just sunlight, water, and nutrients make them well-suited for this application. Recent advancements have focused on improving microalgal hydrogen yields and cultivation methods. This review aims to summarize recent developments in microalgal cultivation techniques and genetic engineering strategies for enhanced biohydrogen production. Specific areas of focus include novel microalgal species selection, immobilization methods, integrated hybrid systems, and metabolic engineering. Studies related to microalgal strain selection, cultivation methods, metabolic engineering, and genetic manipulations were compiled and analyzed. Promising microalgal species with high hydrogen production capabilities such as Synechocystis sp., Anabaena variabilis, and Chlamydomonas reinhardtii have been identified. Immobilization techniques like encapsulation in alginate and integration with dark fermentation have led to improved hydrogen yields. Metabolic engineering through modulation of hydrogenase activity and photosynthetic pathways shows potential for enhanced biohydrogen productivity. Considerable progress has been made in developing microalgal systems for biohydrogen. However, challenges around process optimization and scale-up remain. Future work involving metabolic modeling, photobioreactor design, and genetic engineering of electron transfer pathways could help realize the full potential of this renewable technology.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

A precise microalgae farming for CO2 sequestration: A critical review and perspectives

Microalgae are great candidates for CO2 sequestration and sustainable production of food, feed, fuels and biochemicals. Light intensity, temperature, carbon supply, and cell physiological state are key factors of photosynthesis, and efficient phototrophic production of microalgal biomass occurs only when all these factors are in their optimal range simultaneously. However, this synergistic state is often not achievable due to the ever-changing environmental factors such as sunlight and temperature, which results in serious waste of sunlight energy and other resources, ultimately leading to high production costs. Most control strategies developed thus far in the bioengineering field actually aim to improve heterotrophic processes, but phototrophic processes face a completely different problem. Hence, an alternative control strategy needs to be developed, and precise microalgal cultivation is a promising strategy in which the production resources are precisely supplied according to the dynamic changes in key factors such as sunlight and temperature. In this work, the development and recent progress of precise microalgal phototrophic cultivation are reviewed. The key environmental and cultivation factors and their dynamic effects on microalgal cultivation are analyzed, including microalgal growth, cultivation costs and energy inputs. Future research for the development of more precise microalgae farming is discussed. This study provides new insight into developing cost-effective and efficient microalgae farming for CO2 sequestration.

Generative Artificial Intelligence GPT-4 Accelerates Knowledge Mining and Machine Learning for Synthetic Biology

Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published information related to microbial performance under complex strain engineering and bioreactor conditions. As a proof of concept, we proposed prompt engineering for a GPT-4 workflow pipeline to extract knowledge from 176 publications on two oleaginous yeasts (Yarrowia lipolytica and Rhodosporidium toruloides). After human intervention, the pipeline obtained a total of 2037 data instances. The structured data sets and feature selections enabled ML approaches (e.g., a random forest model) to predict Yarrowia fermentation titers with decent accuracy (R2 of 0.86 for unseen test data). Via transfer learning, the trained model could assess the production potential of the engineered nonconventional yeast, R. toruloides, for which there are fewer published reports. This work demonstrated the potential of generative artificial intelligence to streamline information extraction from research articles, thereby facilitating fermentation predictions and biomanufacturing development.

Cell surface composition, released polysaccharides, and ionic strength mediate fast sedimentation in the cyanobacterium Synechococcus elongatus PCC 7942

Cyanobacteria are photosynthetic prokaryotes of high ecological and biotechnological relevance that have been cultivated in laboratories around the world for more than 70 years. Prolonged laboratory culturing has led to multiple microevolutionary events and the appearance of a large number of 'domesticated' substrains among model cyanobacteria. Despite its widespread occurrence, strain domestication is still largely ignored. In this work we describe Synechococcus elongatus PCC 7942-KU, a novel domesticated substrain of the model cyanobacterium S. elongatus PCC 7942, which presents a fast-sedimenting phenotype. Under higher ionic strengths the sedimentation rate increased leading to complete sedimentation in just 12 h. Through whole genome sequencing and gene deletion, we demonstrated that the Group 3 alternative sigma factor F plays a key role in cell sedimentation. Further analysis showed that significant changes in cell surface structures and a three-fold increase in released polysaccharides lead to the appearance of a fast-sedimenting phenotype. This work sheds light on the determinants of the planktonic to benthic transitions and provides genetic targets to generate fast-sedimenting strains that could unlock cost-effective cyanobacterial harvesting at scale.

A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.

Genetic engineering for biohydrogen production from microalgae

The development of biohydrogen as an alternative energy source has had great economic and environmental benefits. Hydrogen production from microalgae is considered a clean and sustainable energy production method that can both alleviate fuel shortages and recycle waste. Although algal hydrogen production has low energy consumption and requires only simple pretreatment, it has not been commercialized because of low product yields. To increase microalgal biohydrogen production several technologies have been developed, although they struggle with the oxygen sensitivity of the hydrogenases responsible for hydrogen production and the complexity of the metabolic network. In this review, several genetic and metabolic engineering studies on enhancing microalgal biohydrogen production are discussed, and the economic feasibility and future direction of microalgal biohydrogen commercialization are also proposed.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents

A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models' performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040-4.5828)] and Cubist [3.1736 (1.1012-4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R² = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R² = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00-83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Biomass carbon mining to develop nature-inspired materials for a circular economy

A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature's efficiency and wasteless procedures are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials' mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material's development and process engineering.

Hydrogel-Encapsulated Engineered Microbial Consortium as a Photoautotrophic "Living Material" for Promoting Skin Wound Healing

Genetically modified engineered microorganisms have been encapsulated in hydrogels and used as "living materials" for the treatment of skin diseases. However, their applications are often limited by the epidermal dry, nutrient-poor environment and cannot maintain functions stably for an expected sufficient time. To solve this problem, a photoautotrophic "living material" containing an engineered microbial consortium was designed and fabricated. The engineered microbial consortium comprised Synechococcus elongatus PCC7942 for producing sucrose by photosynthesis and another heterotrophic engineered bacterium (Escherichia coli or Lactococcus lactis) that can utilize sucrose for the growth and secretion of functional biomolecules. These engineered microorganisms in the "living material" were proved to function stably for a longer time than only individual microbes. Subsequently, CXCL12-secreting engineered L. lactis was used to construct the "living material", and its effect on promoting wound healing was verified in a full-thickness rat-skin defect model. The wounds treated by our hydrogel-encapsulated engineered microbial consortium (HeEMC) healed faster, with a wound area ratio of only 13.2% at day 14, compared to the remaining 62.6, 51.4, and 40.8% of the control, PEGDA, and PEGDA/CS groups, respectively. In conclusion, we established an efficient living material HeEMC to offer promising applications in the treatment of skin diseases.

Developing Cyanobacterial Quorum Sensing Toolkits: Toward Interspecies Coordination in Mixed Autotroph/Heterotroph Communities

There has been substantial recent interest in the promise of sustainable, light-driven bioproduction using cyanobacteria, including developing efforts for microbial bioproduction using mixed autotroph/heterotroph communities, which could provide useful properties, such as division of metabolic labor. However, building stable mixed-species communities of sufficient productivity remains a challenge, partly due to the lack of strategies for synchronizing and coordinating biological activities across different species. To address this obstacle, we developed an inter-species communication system using quorum sensing (QS) modules derived from well-studied pathways in heterotrophic microbes. In the model cyanobacterium, Synechococcus elongatus PCC 7942 (S. elongatus), we designed, integrated, and characterized genetic circuits that detect acyl-homoserine lactones (AHLs), diffusible signals utilized in many QS pathways. We showed that these receiver modules sense exogenously supplied AHL molecules and activate gene expression in a dose-dependent manner. We characterized these AHL receiver circuits in parallel with Escherichia coli W (E. coli W) to dissect species-specific properties, finding broad agreement, albeit with increased basal expression in S. elongatus. Our engineered "sender" E. coli strains accumulated biologically synthesized AHLs within the supernatant and activated receiver strains similarly to exogenous AHL activation. Our results will bolster the design of sophisticated genetic circuits in cyanobacterial/heterotroph consortia and the engineering of QS-like behaviors across cyanobacterial populations.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

The composition of medium components is crucial for achieving the best performance of synthetic construction in genetically engineered cells. Which and how medium components determine the performance, e.g., productivity, remain poorly investigated. To address the questions, a comparative survey with two genetically engineered Escherichia coli strains was performed. As a case study, the strains carried the synthetic pathways for producing the aromatic compounds of 4-aminophenylalanine (4APhe) or tyrosine (Tyr), common in the upstream but differentiated in the downstream metabolism. Bacterial growth and compound production were examined in hundreds of medium combinations that comprised 48 pure chemicals. The resultant data sets linking the medium composition to bacterial growth and production were subjected to machine learning for improved production. Intriguingly, the primary medium components determining the production of 4PheA and Tyr were differentiated, which were the initial resource (glucose) of the synthetic pathway and the inducer (IPTG) of the synthetic construction, respectively. Fine-tuning of the primary component significantly increased the yields of 4APhe and Tyr, indicating that a single component could be crucial for the performance of synthetic construction. Transcriptome analysis observed the local and global changes in gene expression for improved production of 4APhe and Tyr, respectively, revealing divergent metabolic strategies for producing the foreign and native metabolites. The study demonstrated that ML-assisted medium optimization could provide a novel point of view on how to make the synthetic construction meet the designed working principle and achieve the expected biological function.

Carbon-negative synthetic biology: challenges and emerging trends of cyanobacterial technology

Global warming and climate instability have spurred interest in using renewable carbon resources for the sustainable production of chemicals. Cyanobacteria are ideal cellular factories for carbon-negative production of chemicals owing to their great potentials for directly utilizing light and CO₂ as sole energy and carbon sources, respectively. However, several challenges in adapting cyanobacterial technology to industry, such as low productivity, poor tolerance, and product harvesting difficulty, remain. Synthetic biology may finally address these challenges. Here, we summarize recent advances in the production of value-added chemicals using cyanobacterial cell factories, particularly in carbon-negative synthetic biology and emerging trends in cyanobacterial applications. We also propose several perspectives on the future development of cyanobacterial technology for commercialization.

Visualization of a Limonene Synthesis Metabolon Inside Living Bacteria by Hyperspectral SRS Microscopy

Monitoring biosynthesis activity at single-cell level is key to metabolic engineering but is still difficult to achieve in a label-free manner. Using hyperspectral stimulated Raman scattering imaging in the 670-900 cm-1 region, localized limonene synthesis are visualized inside engineered Escherichia coli. The colocalization of limonene and GFP-fused limonene synthase is confirmed by co-registered stimulated Raman scattering and two-photon fluorescence images. The finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands the knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research.