Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes

Transcriptional regulator-based biosensors for biomanufacturing in Corynebacterium glutamicum

Intracellular biosensors based on transcriptional regulators have become essential instruments in biomanufacturing, extensively employed for the semi-quantitative assessment of intracellular metabolites, high-throughput screening of production strains, and the directed evolution of enzymes. Corynebacterium glutamicum serves as an industrial chassis for the production of amino acids and a variety of high-value-added chemicals. This paper discusses the varieties and modes of action of transcriptional regulators employed in the construction of intracellular biosensors in C. glutamicum. It also reviews the design principles and progress in the application of transcriptional regulator-based biosensors. Furthermore, measures designed to improve the efficacy of these biosensors are delineated. The challenges and future prospects of biosensors based on transcriptional regulators in practical applications are analyzed. This review seeks to offer theoretical direction for the systematic design and development of transcriptional regulator-based biosensors and to aid researchers in enhancing the growth and productivity of microbial production strains.

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

A review on machine learning approaches for microalgae cultivation systems

Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.