Pursuing the current trend, the "green-polymers", polyhydroxyalkanoates (PHAs) which are degradable and made from renewable sources have been a potential substitute for synthetic plastics. Due to the increasing concern towards escalating crude oil price, depleting petroleum resource and environmental damages done by plastics, PHAs have gained more and more attractions, both from industry and research. From the view point of Escherichia coli, a microorganism that used in the biopolymer large scale production, this paper describes the backgrounds of PHA and summarizes the current advances in PHA developments. In the short-chain-length (scl) PHAs section, the study of poly[(R)-3-hydroxybutyrate] [P(3HB)] as model polymer, ultra-high-molecular-weight P(3HB) which rarely discussed, and P(3HB-co-3HV), another commercialized PHA polymer are included. Other than that, this review also shed some light on the new members of PHA family, lactate-based PHAs and P(3HP) with topics such as block copolymers and invention of novel biopolymers. Flexibility of microorganisms in utilizing different carbon sources to accumulate medium-chain-length (mcl) PHAs and lastly, the promising scl-mcl-PHAs with interesting properties are also discussed.
Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
This chapter introduces different aspects of bioinformatics with a brief discussion in the systems biology context. Example applications in network pharmacology of traditional Chinese medicine, systems metabolic engineering, and plant genome-scale modelling are described. Lastly, this chapter concludes on how bioinformatics helps to integrate omics data derived from various studies described in previous chapters for a holistic understanding of secondary metabolite production in P. minus.
In the modern era of next-generation genomics and Fourth Industrial Revolution, there is a growing demand for translational research that brings about not only impactful research but also potential commercialisation of R- and D-based products. Advancement of metabolic engineering and synthetic biology has put forward a viable and innovative biotechnological platform for bioproduct development especially using microbial chassis. In this chapter, readers will be introduced on the concepts of metabolic engineering, synthetic biology and microbial chassis and the applications of these biological engineering (BioE) components in the advancement of industrial and agricultural biotechnology. Main strategies in employing BioE platform are discussed especially for waste bioconversion and value-added product development. More importantly, this chapter will also discuss current endeavours in integrating systems and synthetic biology for microbial production of natural products by introducing flavonoid biosynthesis genes of Polygonum minus, a medicinally important tropical plant in engineered yeast.
Polyhydroxyalkanoate (PHA) is a biogenic polymer that has the potential to substitute synthetic plastic in numerous applications. This is due to its unique attribute of being a biodegradable and biocompatible thermoplastic, achievable through microbial fermentation from a broad utilizable range of renewable resources. Among all the PHAs discovered, poly(3-hydroxybutyrate-co-4-hydroxybutyrate) [P(3HB-co-4HB)] stands out as a next generation healthcare biomaterial for having high biopharmaceutical and medical value since it is highly compatible to mammalian tissue. This review provides a critical assessment and complete overview of the development and trend of P(3HB-co-4HB) research over the last few decades, highlighting aspects from the microbial strain discovery to metabolic engineering and bioprocess cultivation strategies. The article also outlines the relevance of P(3HB-co-4HB) as a material for high value-added products in numerous healthcare-related applications.
Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.
Carotenoids are isoprenoid pigments synthesized exclusively by plants and microorganisms and play critical roles in light harvesting, photoprotection, attracting pollinators and phytohormone production. In recent years, carotenoids have been used for their health benefits due to their high antioxidant activity and are extensively utilized in food, pharmaceutical, and nutraceutical industries. Regulation of carotenoid biosynthesis occurs throughout the life cycle of plants, with vibrant changes in composition based on developmental needs and responses to external environmental stimuli. With advancements in metabolic engineering techniques, there has been tremendous progress in the production of industrially valuable secondary metabolites such as carotenoids. Application of metabolic engineering and synthetic biology has become essential for the successful and improved production of carotenoids. Synthetic biology is an emerging discipline; metabolic engineering approaches may provide insights into novel ideas for biosynthetic pathways. In this review, we discuss the current knowledge on carotenoid biosynthetic pathways and genetic engineering of carotenoids to improve their nutritional value. In addition, we investigated synthetic biological approaches for the production of carotenoids. Theoretical biology approaches that may aid in understanding the biological sciences are discussed in this review. A combination of theoretical knowledge and experimental strategies may improve the production of industrially relevant secondary metabolites.
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
Numerous plant secondary metabolites have remarkable impacts on both food supplements and pharmaceuticals for human health improvement. However, higher plants can only generate small amounts of these chemicals with specific temporal and spatial arrangements, which are unable to satisfy the expanding market demands. Cyanobacteria can directly utilize CO2, light energy, and inorganic nutrients to synthesize versatile plant-specific photosynthetic intermediates and organic compounds in large-scale photobioreactors with outstanding economic merit. Thus, they have been rapidly developed as a "green" chassis for the synthesis of bioproducts. Flavonoids, chemical compounds based on aromatic amino acids, are considered to be indispensable components in a variety of nutraceutical, pharmaceutical, and cosmetic applications. In contrast to heterotrophic metabolic engineering pioneers, such as yeast and Escherichia coli, information about the biosynthesis flavonoids and their derivatives is less comprehensive than that of their photosynthetic counterparts. Here, we review both benefits and challenges to promote cyanobacterial cell factories for flavonoid biosynthesis. With increasing concerns about global environmental issues and food security, we are confident that energy self-supporting cyanobacteria will attract increasing attention for the generation of different kinds of bioproducts. We hope that the work presented here will serve as an index and encourage more scientists to join in the relevant research area.
Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.
Many algae are rich sources of sulfated polysaccharides with biological activities. The physicochemical/rheological properties and biological activities of sulfated polysaccharides are affected by the pattern and number of sulfate moieties. Sulfation of carbohydrates is catalyzed by carbohydrate sulfotransferases (CHSTs) while modification of sulfate moieties on sulfated polysaccharides was presumably catalyzed by sulfatases including formylglycine-dependent sulfatases (FGly-SULFs). Post-translationally modification of Cys to FGly in FGly-SULFs by sulfatase modifiying factors (SUMFs) is necessary for the activity of this enzyme. The aims of this study are to mine for sequences encoding algal CHSTs, FGly-SULFs and putative SUMFs from the fully sequenced algal genomes and to infer their phylogenetic relationships to their well characterized counterparts from other organisms. Algal sequences encoding CHSTs, FGly-SULFs, SUMFs, and SUMF-like proteins were successfully identified from green and brown algae. However, red algal FGly-SULFs and SUMFs were not identified. In addition, a group of SUMF-like sequences with different gene structure and possibly different functions were identified for green, brown and red algae. The phylogeny of these putative genes contributes to the corpus of knowledge of an unexplored area. The analyses of these putative genes contribute toward future production of existing and new sulfated carbohydrate polymers through enzymatic synthesis and metabolic engineering.
Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time.
Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli).
The simultaneous production of hydrogen and ethanol by microorganisms from waste materials in a bioreactor system would establish cost-effective and time-saving biofuel production. This review aims to present the current status of fermentation processes producing hydrogen accompanied by ethanol as a co-product. We outlined the microbes used and their fundamental pathways for hydrogen and ethanol fermentation. Moreover, we discussed the exploitation of renewable and sustainable waste materials as promising feedstock and the limitations encountered. The low substrate bioconversion rate in hydrogen and ethanol co-production is regarded as the primary constraint towards the development of large scale applications. Thus, microbes with an enhanced capability have been generated via genetic manipulation to diminish the inefficiency of substrate consumption. In this review, other potential approaches to improve the performance of co-production through fermentation were also elaborated. This review will be a useful guide for the future development of hydrogen and ethanol co-production using waste materials.
Flavonoids are polyphenols that are important organic chemicals in plants. The health benefits of flavonoids that result in high commercial values make them attractive targets for large-scale production through bioengineering. Strategies such as engineering a flavonoid biosynthetic pathway in microbial hosts provide an alternative way to produce these beneficial compounds. Escherichia coli, Saccharomyces cerevisiae and Streptomyces sp. are among the expression systems used to produce recombinant products, as well as for the production of flavonoid compounds through various bioengineering approaches including clustered regularly interspaced short palindromic repeats (CRISPR)-based genome engineering and genetically encoded biosensors to detect flavonoid biosynthesis. In this study, we review the recent advances in engineering model microbial hosts as being the factory to produce targeted flavonoid compounds.
Lactic acid bacteria constitute a diverse group of industrially significant, safe microorganisms that are primarily used as starter cultures and probiotics, and are also being developed as production systems in industrial biotechnology for biocatalysis and transformation of renewable feedstocks to commodity- and high-value chemicals, and health products. Development of strains, which was initially based mainly on natural approaches, is also achieved by metabolic engineering that has been facilitated by the availability of genome sequences and genetic tools for transformation of some of the bacterial strains. The aim of this paper is to provide a brief overview of the potential of lactic acid bacteria as biological catalysts for production of different organic compounds for food and non-food sectors based on their diversity, metabolic- and stress tolerance features, as well as the use of genetic/metabolic engineering tools for enhancing their capabilities.
Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.
Systems metabolic engineering and in silico analyses are necessary to study gene knockout candidate for enhanced succinic acid production by Escherichia coli. Metabolically engineered E. coli has been reported to produce succinate from glucose and glycerol. However, investigation on in silico deletion of ptsG/b1101 gene in E. coli from glycerol using minimization of metabolic adjustment algorithm with the OptFlux software platform has not yet been elucidated. Herein we report what is to our knowledge the first direct predicted increase in succinate production following in silico deletion of the ptsG gene in E. coli GEM from glycerol with the OptFlux software platform. The result indicates that the deletion of this gene in E. coli GEM predicts increased succinate production that is 20% higher than the wild-type control model. Hence, the mutant model maintained a growth rate that is 77% of the wild-type parent model. It was established that knocking out of the ptsG/b1101 gene in E. coli using glucose as substrate enhanced succinate production, but the exact mechanism of this effect is still obscure. This study informs other studies that the deletion of ptsG/b1101 gene in E. coli GEM predicted increased succinate production, enabling a model-driven experimental inquiry and/or novel biological discovery on the underground metabolic role of this gene in E. coli central metabolism in relation to increasing succinate production when glycerol is the substrate.
Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes.
Flavonoid is an industrially-important compound due to its high pharmaceutical and cosmeceutical values. However,
conventional methods in extracting and synthesizing flavonoids are costly, laborious and not sustainable due to small
amount of natural flavonoids, large amounts of chemicals and space used. Biotechnological production of flavonoids
represents a viable and sustainable route especially through the use of metabolic engineering strategies in microbial
production hosts. In this review, we will highlight recent strategies for the improving the production of flavonoids
using synthetic biology approaches in particular the innovative strategies of genetically-encoded biosensors for in
vivo metabolite analysis and high-throughput screening methods using fluorescence-activated cell sorting (FACS).
Implementation of transcription factor based-biosensor for microbial flavonoid production and integration of systems
and synthetic biology approaches for natural product development will also be discussed.