The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) is a crucial cofactor in metabolic networks. The efficient regeneration of NADPH is one of the limiting factors for productivity in biotransformation processes. To date, many metabolic engineering tools and static regulation strategies have been developed to regulate NADPH regeneration. However, traditional static regulation methods often lead to the NADPH/NADP⁺ imbalance, causing disruptions in cell growth and production. These methods also fail to provide real-time monitoring of intracellular NADP(H) or NADPH/NADP⁺ levels. In recent years, various biosensors have been developed for the detection, monitoring, and dynamic regulate of the intracellular NADP(H) levels or the NADPH/NADP⁺ balance. These NADPH-related biosensors are mainly used in the cofactor engineering of bacteria, yeast, and mammalian cells. This review analyzes and summarizes the NADPH metabolic regulation strategies from both static and dynamic perspectives, highlighting current challenges and potential solutions, and discusses future directions for the advanced regulation of the NADPH/NADP⁺ balance.

2-Oxobutyrate (2-OBA), as a toxic metabolic intermediate, generally arrests the cell growth of most microorganisms and blocks the biosynthesis of target metabolites. In this study, we demonstrated that using the acetate bypass to replace the pyruvate dehydrogenase complex (PDHc) in Escherichia coli could recharge the intracellular acetyl-CoA pool to alleviate the metabolic toxicity of 2-OBA. Furthermore, based on the crystal structure of pyruvate oxidase (PoxB), two candidate residues in the substrate-binding pocket of PoxB were predicted by computational simulation. Site-directed saturation mutagenesis was performed to attenuate 2-OBA-binding affinity, and one of the variants, PoxB^F112W, exhibited a 20-fold activity ratio of pyruvate/2-OBA in substrate selectivity. PoxB^F112W was employed to remodel the acetate bypass in E. coli, resulting in l-threonine (a precursor of 2-OBA) biosynthesis with minimal inhibition from 2-OBA. After metabolic detoxification of 2-OBA, the supplies of intracellular acetyl-CoA and NADPH (nicotinamide adenine dinucleotide phosphate) used for l-threonine biosynthesis were restored. Therefore, 2-OBA is the substitute for pyruvate to engage in enzymatic reactions and disturbs pyruvate metabolism. Our study makes a straightforward explanation of the 2-OBA toxicity mechanism and gives an effective approach for its metabolic detoxification.

Electrostatic fields are crucial for achieving the highly efficient filtration of airborne pollutants. However, the dissipation of static charges over time, especially under humid conditions, limits their practical application. In this study, we present a self-charging air filter (SAF) powered by a triboelectric nanogenerator (TENG). This SAF is integrated into a commercial mask, termed SAFM, which can effectively capture and degrade airborne pollutants without requiring an external power source. By leveraging the triboelectric effect during breathing, the TENG within the SAFM continuously replenishes static charges, maintaining the triboelectric field. The system employs a cellulose aerogel/Ti₃C₂T_x composite as the electron donor and an esterified cellulose-based electrospun nanofiber as the electron acceptor. Remarkably, the triboelectric field significantly enhances filtration performance, with the SAF achieving up to 95.7% filtration efficiency for particulate matter as small as 0.3 μm. This work underscores the potential of TENG-powered triboelectric fields in the development of multifunctional, human-machine interactive facemasks.