Genome-scale metabolic modeling of Ruminiclostridium cellulolyticum: a microbial cell factory for valorization of lignocellulosic biomass

Biotechnology is currently evolving through the era of big data, thanks to advances in the high-throughput technologies for rapid and inexpensive genome sequencing and other genome-wide studies [1]. With the daunting amount of data, it has been possible to put them together into a coherently organized biological network that provides counterintuitive insights on biological systems [2]. Among such biological networks, a genome-scale metabolic network model is expected to play an increasingly important role in the biopharmaceutical industry [3]. Before enumerating their specific strengths, it is important to note that principles underlying genome-scale metabolic network models are consistent with the holistic perspective of systems biology, the aim of which is to unveil hidden factors causing diseases and to find relevant treatment strategies [4]. Despite the importance of metabolism in a biological system, studies on diseases in relation to metabolism were far fewer in number than those performed on signaling and transcriptional regulatory networks [5]. However, metabolism, highly linked with observable phenotypes, is a biological network that is more comprehensively characterized when compared with the other two types of networks [6]. Metabolism is, therefore, amenable to large-scale mathematical modeling and simulation. It is with this motivation that the genome-scale metabolic simulation deserves more attention in drug discovery campaigns and optimization of a host strain for the production of biopharmaceuticals. Reconstruction and application of genome-scale metabolic network models have been forged as a major research strategy of systems biology. Over the last decade, genome-scale metabolic models have been built for almost all biologically important organisms across the domains of archea, bacteria and eukaryotes [3]. They range from simple micro organisms such as Escherichia coli [7] and Saccharomyces cerevisiae [8] to higher organisms including Chinese hamster ovary (CHO) cells [9,10] and a generic human cell [11,12]. It should be noted that all these organisms that have been subjected to metabolic modeling are important cellular hosts for biopharmaceutical production or medically meaningful organisms that need to be cured (e.g., specific cancer cells) or destroyed (e.g., pathogens). A recent notable development of importance in the genomescale metabolic modeling would be the newly updated human metabolic network Recon 2 [12]. Recon 2 is a result of efforts from a group of researchers, going over a vast amount of literature and biochemical data and reconciling conflicting information. Scope of the hitherto reconstructed genome-scale metabolic models manifest high expectations for their potential contributions to biopharmaceutical industry. Genome-scale metabolic network models are not just a simple pileup of biochemical reactions, but allow mathematical simulation under precisely defined conditions of constraints [13]. Once the experimentally Applications of genome-scale metabolic network models in the biopharmaceutical industry

Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

From pollutants to products: Microbial cell factories driving sustainable biomanufacturing and environmental conservation

Author for correspondence: Institut de Biotecnologia i de Biomedicina, Universitat Autonoma de Barcelona, Bellaterra, 08193 Barcelona, Spain and Department de Genetica i de Microbiologia, Universitat Autonoma de Barcelona, Bellaterra, 08193 Barcelona, Spain and CIBER en Bioingenieria, Biomateriales y Nanomedicina, Bellaterra, 08193 Barcelona, Spain antoni.villaverde@uab.cat Microbial biofabrication for nanomedicine: biomaterials, nanoparticles and beyond

Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

Metabolic modeling to understand and redesign microbial systems

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Lignin biodegradation is an attractive approach for producing value-added products. These valuable products are produced by the processing and refining of lignocellulosic residues. A set of ligninolytic enzymes including lignin peroxidase (LiP), manganese-dependent peroxidase (MnP), and laccase (Lac) were individually produced from Ganoderma lucidum, Trametes versicolor, and Pleurotus ostreatus. Solid state fermentation under pre-optimized culture conditions with varying ratios of enzymes were used for the delignification of lignocellulosic biomass residues. The fungal enzymes were purified in four steps including ammonium sulfate precipitation, dialysis, ion exchange chromatography, and gel filtration chromatography. The purified enzymes were subsequently used in varying ratios (with each containing 200 U/mL) for the delignification of wheat straw, sugarcane bagasse, and rice straw. The consortium of enzymes caused the removal of 58.5%, 46%, and 52% of the lignin from the wheat straw, sugarcane bagasse, and rice straw, respectively, at LiP: MnP: Lac ratios of 1:2:2, 1:1:2, and 2:1:2. The best delignification was observed in wheat straw (58.5%), exposing 76.54% cellulose content. The results suggested that the ligninolytic enzymes are effective catalysts for the selective partial delignification of lignocellulosic biomass residues. After delignification these lignocellulosic residues could be utilized as cost-effective substrates for the production of enzymes, biofuels, and other industrially significant products.

3-hydroxypropanoic acid (3-HP) is a valuable platform chemical with a high demand in the global market. 3-HP can be produced from various renewable resources. It is used as a precursor in industrial production of a number of chemicals, such as acrylic acid and its many derivatives. In its polymerized form, 3-HP can be used in bioplastic production. Several microbes naturally possess the biosynthetic pathways for production of 3-HP, and a number of these pathways have been introduced in some widely used cell factories, such as Escherichia coli and Saccharomyces cerevisiae. Latest advances in the field of metabolic engineering and synthetic biology have led to more efficient methods for bio-production of 3-HP. These include new approaches for introducing heterologous pathways, precise control of gene expression, rational enzyme engineering, redirecting the carbon flux based on in silico predictions using genome scale metabolic models, as well as optimizing fermentation conditions. Despite the fact that the production of 3-HP has been extensively explored in established industrially relevant cell factories, the current production processes have not yet reached the levels required for industrial exploitation. In this review, we explore the state of the art in 3-HP bio-production, comparing the yields and titers achieved in different microbial cell factories and we discuss possible methodologies that could make the final step toward industrially relevant cell factories.

Genome-scale metabolic modelling common cofactors metabolism in microorganisms

Modeling of metabolism at the genome-scale has proved to be an efficient method for explaining thephenotypic traits observed in living organisms. Further, it can be used as a means of predicting the effect of genetic modifications for example,development of microbial cell factories. With the increasing amount of genome sequencing data available, there exists a need to accurately and efficiently generate such genome-scale metabolic models (GEMs) of nonmodel organisms, for which data is sparse. In this study, we present an automatic reconstruction approach applied to 24 Penicillium species, which have potential for production of pharmaceutical secondary metabolites or use in the manufacturing of food products, such as cheeses. The models were based on the MetaCyc database and a previously published Penicillium GEM and gave rise to comprehensive genome-scale metabolic descriptions. The models proved that while central carbon metabolism is highly conserved, secondary metabolic pathways represent the main diversity amongspecies. The automatic reconstruction approach presented in this study can be applied to generate GEMs of other understudied organisms, and the developed GEMs are a useful resource for the study of Penicillium metabolism, for example, for the scope of developing novel cell factories.

13C metabolic flux analysis at a genome-scale

Systems metabolic engineering, industrial biotechnology and microbial cell factories

As a food-safe microorganism, Saccharomyces cerevisiae is widely studied in metabolic engineering and synthetic biology, and can be used as a cell factory to produce natural compounds. Flavonoids are valuable natural products with multiple biological activities such as estrogen, antioxidant, antibacterial and anticancer activities, and are widely used in food, medicine and other fields. However, the development and utilization of flavonoids is limited by problems such as low concentration and long cycle in obtaining them from plants. With the development of metabolic engineering technology and synthetic biology, the synthesis of flavonoids through microbial cell factories has good prospects. Based on microbial synthesis of flavonoids, this paper comprehensively reviews the research progress of some flavonoids synthesized in S. cerevisiae, summarizes and prospects the current difficulties and challenges as well as future research directions.

Valorization of dairy wastes into wonder products by the novel use of microbial cell factories