In silico strategies to couple production of bioethanol with growth in cyanobacteria

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Assessing and reducing phenotypic instability in cyanobacteria

Cyanobacteria have promising potential as sustainable cell factories. However, one challenge that is still largely unreported in scaling-up cyanobacteria bioproduction is phenotypic instability, where the emergence and selection of nonproducing cells leading to loss in production has longer evolutionary timescales to take place in industrial-scale bioreactors. Quantifying phenotypic instability early on in strain development allows researchers to make informed decisions on whether to proceed with scalable designs, or if present, devise countermeasures to reduce instability. One particularly effective strategy to mitigate instability is the use of genome-scale metabolic models to design growth-coupled production strains. In silico studies have predicted that creating certain cofactor imbalances or removing recycling reactions in cyanobacteria can be exploited to stably produce a wide variety of metabolites.

Metabolic Engineering Design Strategies for Increasing Carbon Fluxes Relevant for Biosynthesis in Cyanobacteria

Cyanobacteria are promising microbial cell factories for the direct production of biochemicals and biofuels from CO2. Through genetic and metabolic engineering, they can be modified to produce a variety of both natural and non-natural compounds. To enhance the yield of these products, various design strategies have been developed. In this chapter, strategies used to enhance metabolic fluxes towards common precursors used in biosynthesis, including pyruvate, acetyl-CoA, malonyl-CoA, TCA cycle intermediates, and aromatics, are discussed. Additionally, strategies related to cofactor availability and mixotrophic conditions for bioproduction are also summarize.

Cyanobacteria as a Promising Alternative for Sustainable Environment: Synthesis of Biofuel and Biodegradable Plastics

With the aim to alleviate the increasing plastic burden and carbon footprint on Earth, the role of certain microbes that are capable of capturing and sequestering excess carbon dioxide (CO2) generated by various anthropogenic means was studied. Cyanobacteria, which are photosynthetic prokaryotes, are promising alternative for carbon sequestration as well as biofuel and bioplastic production because of their minimal growth requirements, higher efficiency of photosynthesis and growth rates, presence of considerable amounts of lipids in thylakoid membranes, and cosmopolitan nature. These microbes could prove beneficial to future generations in achieving sustainable environmental goals. Their role in the production of polyhydroxyalkanoates (PHAs) as a source of intracellular energy and carbon sink is being utilized for bioplastic production. PHAs have emerged as well-suited alternatives for conventional plastics and are a parallel competitor to petrochemical-based plastics. Although a lot of studies have been conducted where plants and crops are used as sources of energy and bioplastics, cyanobacteria have been reported to have a more efficient photosynthetic process strongly responsible for increased production with limited land input along with an acceptable cost. The biodiesel production from cyanobacteria is an unconventional choice for a sustainable future as it curtails toxic sulfur release and checks the addition of aromatic hydrocarbons having efficient oxygen content, with promising combustion potential, thus making them a better choice. Here, we aim at reporting the application of cyanobacteria for biofuel production and their competent biotechnological potential, along with achievements and constraints in its pathway toward commercial benefits. This review article also highlights the role of various cyanobacterial species that are a source of green and clean energy along with their high potential in the production of biodegradable plastics.

In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems

The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.

Introduction of NADH-dependent nitrate assimilation in Synechococcus sp. PCC 7002 improves photosynthetic production of 2-methyl-1-butanol and isobutanol

Cyanobacteria hold promise for renewable chemical production due to their photosynthetic nature, but engineered strains frequently display poor production characteristics. These difficulties likely arise in part due to the distinctive photoautotrophic metabolism of cyanobacteria. In this work, we apply a genome-scale metabolic model of the cyanobacteria Synechococus sp. PCC 7002 to identify strain designs accounting for this unique metabolism that are predicted to improve the production of various biofuel alcohols (e.g. 2-methyl-1-butanol, isobutanol, and 1-butanol) synthesized via an engineered biosynthesis pathway. Using the model, we identify that the introduction of a large, non-native NADH-demand into PCC 7002's metabolic network is predicted to enhance production of these alcohols by promoting NADH-generating reactions upstream of the production pathways. To test this, we construct strains of PCC 7002 that utilize a heterologous, NADH-dependent nitrite reductase in place of the native, ferredoxin-dependent enzyme to create an NADH-demand in the cells when grown on nitrate-containing media. We find that photosynthetic production of both isobutanol and 2-methyl-1-butanol is significantly improved in the engineered strain background relative to that in a wild-type background. We additionally identify that the use of high-nutrient media leads to a substantial prolongment of the production curve in our alcohol production strains. The metabolic engineering strategy identified and tested in this work presents a novel approach to engineer cyanobacterial production strains that takes advantage of a unique aspect of their metabolism and serves as a basis on which to further develop strains with improved production of these alcohols and related products.

Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions

Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO₂ and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.

Combinatorial use of environmental stresses and genetic engineering to increase ethanol titres in cyanobacteria

Current industrial bioethanol production by yeast through fermentation generates carbon dioxide. Carbon neutral bioethanol production by cyanobacteria uses biological fixation (photosynthesis) of carbon dioxide or other waste inorganic carbon sources, whilst being sustainable and renewable. The first ethanologenic cyanobacterial process was developed over two decades ago using Synechococcus elongatus PCC 7942, by incorporating the recombinant pdc and adh genes from Zymomonas mobilis. Further engineering has increased bioethanol titres 24-fold, yet current levels are far below what is required for industrial application. At the heart of the problem is that the rate of carbon fixation cannot be drastically accelerated and carbon partitioning towards bioethanol production impacts on cell fitness. Key progress has been achieved by increasing the precursor pyruvate levels intracellularly, upregulating synthetic genes and knocking out pathways competing for pyruvate. Studies have shown that cyanobacteria accumulate high proportions of carbon reserves that are mobilised under specific environmental stresses or through pathway engineering to increase ethanol production. When used in conjunction with specific genetic knockouts, they supply significantly more carbon for ethanol production. This review will discuss the progress in generating ethanologenic cyanobacteria through chassis engineering, and exploring the impact of environmental stresses on increasing carbon flux towards ethanol production.

Approaches in the photosynthetic production of sustainable fuels by cyanobacteria using tools of synthetic biology

Cyanobacteria, photosynthetic prokaryotic microorganisms having a simple genetic composition are the prospective photoautotrophic cell factories for the production of a wide range of biofuel molecules. The simple genetic composition of cyanobacteria allows effortless genetic manipulation which leads to increased research endeavors from the synthetic biology approach. Various unicellular model cyanobacterial strains like Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942 have been successfully engineered for biofuels generation. Improved development of synthetic biology tools, genetic modification methods and advancement in transformation techniques to construct a strain that can contain multiple foreign genes in a single operon have vastly expanded the functions that can be used for engineering photosynthetic cyanobacteria for the generation of various biofuel molecules. In this review, recent advancements and approaches in synthetic biology tools used for cyanobacterial genome editing have been discussed. Apart from this, cyanobacterial productions of various fuel molecules like isoprene, limonene, α-farnesene, squalene, alkanes, butanol, and fatty acids, which can be a substitute for petroleum and fossil fuels in the future, have been elaborated.

Genetic, Genomics, and Responses to Stresses in Cyanobacteria: Biotechnological Implications

Cyanobacteria are widely-diverse, environmentally crucial photosynthetic prokaryotes of great interests for basic and applied science. Work to date has focused mostly on the three non-nitrogen fixing unicellular species Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002, which have been selected for their genetic and physiological interests summarized in this review. Extensive "omics" data sets have been generated, and genome-scale models (GSM) have been developed for the rational engineering of these cyanobacteria for biotechnological purposes. We presently discuss what should be done to improve our understanding of the genotype-phenotype relationships of these models and generate robust and predictive models of their metabolism. Furthermore, we also emphasize that because Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002 represent only a limited part of the wide biodiversity of cyanobacteria, other species distantly related to these three models, should be studied. Finally, we highlight the need to strengthen the communication between academic researchers, who know well cyanobacteria and can engineer them for biotechnological purposes, but have a limited access to large photobioreactors, and industrial partners who attempt to use natural or engineered cyanobacteria to produce interesting chemicals at reasonable costs, but may lack knowledge on cyanobacterial physiology and metabolism.

Cyanobacteria: Review of Current Potentials and Applications

Continual increases in the human population and growing concerns related to the energy crisis, food security, disease outbreaks, global warming, and other environmental issues require a sustainable solution from nature. One of the promising resources is cyanobacteria, also known as blue-green algae. They require simple ingredients to grow and possess a relatively simple genome. Cyanobacteria are known to produce a wide variety of bioactive compounds. In addition, cyanobacteria’s remarkable growth rate enables its potential use in a wide range of applications in the fields of bioenergy, biotechnology, natural products, medicine, agriculture, and the environment. In this review, we have summarized the potential applications of cyanobacteria in different areas of science and development, especially related to their use in producing biofuels and other valuable co-products. We have also discussed the challenges that hinder such development at an industrial level and ways to overcome such obstacles.