Systematic design and evaluation of artificial CO assimilation pathways.

With the advancement of industry and bio-agriculture, the effective management of CO has emerged as a critical challenge for humanity. This study systematically explores multiple CO assimilation pathways using the comb-FBA algorithm, aiming to identify efficient artificial carbon fixation pathways. By extracting 49 CO and HCO involved reactions and combining them with 6,529 reactions from MetaCyc, we constructed the computational set for analysis.

Improving metabolic engineering design with enzyme-thermo optimization.

Metabolic target and strategy design play a critical role in enhancing the DBTL (Design-Build-Test-Learn) cycle in metabolic engineering. Classical stoichiometric algorithms such as OptForce and FSEOF narrow the experimental search space but fail to account for thermodynamic feasibility and enzyme-usage costs, leaving a space for their predictive performance. In this study, we introduce ET-OptME, a framework integrating two algorithms that systematically incorporate enzyme efficiency and thermodynamic feasibility constraints into genome-scale metabolic models.

MTD: A cloud-based omics database and interactive platform for .

Nowadays, biological databases are playing an increasingly critical role in biological research. is an excellent thermophilic fungal chassis for industrial enzyme production and plant biomass-based chemical synthesis. The lack of a dedicated public database has made access to and reanalysis of data difficult.

[Databases, knowledge bases, and large models for biomanufacturing].

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO emissions, lowering energy consumption, and cutting costs.

Large language model for knowledge synthesis and AI-enhanced biomanufacturing.

Large language models (LLMs) are transforming synthetic biology (SynBio) education and research. In this review we cover the advancements and potential impacts of LLMs in biomanufacturing. First, we summarize recent developments and compare the capabilities of US and Chinese language models in addressing fundamental SynBio questions.

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.

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.

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.

[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.

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.

Insight into the phylogeny and metabolic divergence of species (, , and ) at the genome level.

Background: Species of the genus are economically important and widely used in the production of food colorants and monacolin K. However, they have also been known to produce the mycotoxin citrinin. Currently, taxonomic knowledge of this species at the genome level is insufficient.

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework.

Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved.

CAVE: a cloud-based platform for analysis and visualization of metabolic pathways.

Flux balance analysis (FBA) is an important method for calculating optimal pathways to produce industrially important chemicals in genome-scale metabolic models (GEMs). However, for biologists, the requirement of coding skills poses a significant obstacle to using FBA for pathway analysis and engineering target identification. Additionally, a time-consuming manual drawing process is often needed to illustrate the mass flow in an FBA-calculated pathway, making it challenging to detect errors or discover interesting metabolic features.

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Based on the ECMpy Workflow.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism prior to this study.

Artificial Diploid Escherichia coli by a CRISPR Chromosome-Doubling Technique.

Synthetic biology has been represented by the creation of artificial life forms at the genomic scale. In this work, a CRISPR-based chromosome-doubling technique is designed to first construct an artificial diploid Escherichia coli cell. The stable single-cell diploid E.

Reliability and validity of Foot Posture Index (FPI-6) for evaluating foot posture in participants with low back pain.

Previous studies have demonstrated that Foot Posture Index (FPI-6) is a valid and moderately reliable tool to evaluate foot posture. However, data about reliability and validity of FPI-6 in the assessment of foot posture in people with low back pain (LBP) is lacking. To investigate reliability and validity of FPI-6 in the assessment of foot posture in people with LBP.

Acupuncture for low back and/or pelvic pain during pregnancy: a systematic review and meta-analysis of randomised controlled trials.

Objective: Acupuncture is emerging as a potential therapy for relieving pain, but the effectiveness of acupuncture for relieving low back and/or pelvic pain (LBPP) during the pregnancy remains controversial. This meta-analysis aims to investigate the effects of acupuncture on pain, functional status and quality of life for women with LBPP pain during the pregnancy.

Design: Systematic review and meta-analysis.

A CRISPR-based chromosomal-separation technique for Escherichia coli.

Background: Natural life systems can be significantly modified at the genomic scale by human intervention, demonstrating the great innovation capacity of genome engineering. Large epi-chromosomal DNA structures were established in Escherichia coli cells, but some of these methods were inconvenient, using heterologous systems, or relied on engineered E. coli strains.

Construction and Analysis of an Enzyme-Constrained Metabolic Model of .

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets.