Reconstruction and analyses of genome-scale metabolic network yield a highly efficient PHA production.

In pursuit of reliable and efficient industrial microbes, this study integrates cutting-edge systems biology tools with TD01, a robust halophilic bacterium. We generated the complete and annotated circular genome sequence for this model organism, constructed and meticulously curated a genome-scale metabolic network, achieving striking 86.32% agreement with Biolog Phenotype Microarray data and visualize the network via an interactive Electron/Thrift server architecture.

Transcriptome analysis of Aureobasidium pullulans BL06 and identification of key factors affecting pullulan production.

Pullulan, a versatile water-soluble polysaccharide, is widely used across various industries. To minimize byproduct interference, Aureobasidium pullulans BL06ΔPMAs was engineered, resulting in a higher yield and a lower molecular weight (MW) than the parent strain A. pullulans BL06.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature () of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined data and the insufficient accuracy of existing computational methods in predicting , there is an urgent need for a computational approach to predict the values of enzymes accurately.

AutoESDCas: A Web-Based Tool for the Whole-Workflow Editing Sequence Design for Microbial Genome Editing Based on the CRISPR/Cas System.

Genome editing is the basis for the modification of engineered microbes. In the process of genome editing, the design of editing sequences, such as primers and sgRNA, is very important for the accurate positioning of editing sites and efficient sequence editing. The whole process of genome editing involves multiple rounds and types of editing sequence design, while the development of related whole-workflow design tools for high-throughput experimental requirements lags.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in .

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels.

Regiodivergent biosynthesis of bridged bicyclononanes.

Medicinal compounds from plants include bicyclo[3.3.1]nonane derivatives, the majority of which are polycyclic polyprenylated acylphloroglucinols (PPAPs).

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Background: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production.

Omics data analysis reveals the system-level constraint on cellular amino acid composition.

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species.

Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium .

Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the genus, along with the process of energy production, remain areas that necessitate further research and elucidation.

Elucidating the impact of cultivation on metabolism through combined modeling and multiomics analysis.

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of cultivation on the tobacco metabolic network.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of A1501.

A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects.

Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in .

Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments.

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions.

Abnormal prefrontal functional network in adult obstructive sleep apnea: A resting-state fNIRS study.

To assess prefrontal brain network abnormality in adults with obstructive sleep apnea (OSA), resting-state functional near infrared spectroscopy (rs-fNIRS) was used to evaluate 52 subjects, including 27 with OSA and 25 healthy controls (HC). The study found that patients with OSA had a decreased connection edge number, particularly in the connection between the right medial frontal cortex (MFG-R) and other right-hemisphere regions. Graph-based analysis also revealed that patients with OSA had a lower global efficiency, local efficiency, and clustering coefficient than the HC group.

[Digital cell models and their applications: a review].

Various omics technologies are changing Biology into a data-driven science subject. Development of data-driven digital cell models is key for understanding system level organization and evolution principles of life, as well as for predicting cellular function under various environmental/genetic perturbations and subsequently for the design of artificial life. Consequently, the construction, analysis and design of digital cell models have become one of the core supporting technologies in synthetic biology.

[Artificial bioconversion of carbon dioxide].

Utilization of carbon dioxide (CO) is a huge challenge for global sustainable development. Biological carbon fixation occurs in nature, but the low energy efficiency and slow speed hamper its commercialization. Physical-chemical carbon fixation is efficient, but relies on high energy consumption and often generates unwanted by-products.

Independent component analysis of Corynebacterium glutamicum transcriptomes reveals its transcriptional regulatory network.

Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C.

prediction of mutation sites for anthranilate synthase from to deregulate tryptophan feedback inhibition.

Allosteric feedback inhibition of the committed step in amino acid biosynthetic pathways is a major concern for production of amino acids at industrial scale. Anthranilate synthase (AS) catalyzes the first reaction of tryptophan biosynthetic pathway found in microorganisms and is feedback inhibited by its own product i.e.

Protein engineering and iterative multimodule optimization for vitamin B production in Escherichia coli.

Vitamin B is an essential nutrient with extensive applications in the medicine, food, animal feed, and cosmetics industries. Pyridoxine (PN), the most common commercial form of vitamin B, is currently chemically synthesized using expensive and toxic chemicals. However, the low catalytic efficiencies of natural enzymes and the tight regulation of the metabolic pathway have hindered PN production by the microbial fermentation process.

Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method.

Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology.