Transcription factor (TF)-based biosensors have been widely applied in metabolic engineering, synthetic biology, metabolites monitoring, etc. These biosensors are praised for the high orthogonality, modularity, and operability. However, most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications. Herein, we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence. This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data. In comparison with conventional tools, computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors. Thus, these technologies will greatly promote the development of novel biosensors for applications.
Biosensing Techniques/methods* ; Transcription Factors/metabolism* ; Artificial Intelligence ; Protein Engineering/methods* ; Computer Simulation ; Synthetic Biology ; Machine Learning

As an efficient and promising protein engineering strategy, directed evolution includes the construction of mutant libraries and screening of desirable mutants. A rapid and high-throughput screening method has played a critical role in the successful application of directed evolution strategy. We reviewed several high-throughput screening tools which have great potential to be applied in directed evolution. The development of powerful high-throughput screening tools will make great contributions to the advancement of protein engineering.
Directed Molecular Evolution ; methods ; High-Throughput Screening Assays ; methods ; Mutagenesis, Site-Directed ; methods ; Mutant Proteins ; genetics ; Protein Engineering ; methods

Lignocellulose is the most abundant natural biomass. Bioconversion of lignocelluloses becomes a bottleneck for biorefinery, because of its complex structures and heterogeneous composition. Besides screening or engineering approach for single free enzymes with improved properties, an alternative approach is to study synergistic pattern with hydrolysis systems or mimic natural cellulosome for better performance in cellulolytic substrate degradation. Besides, bacterial co-cultures provide another synergistic cellulolytic system. Engineered strains with modified metabolic network could facilitate consolidated bioprocess by increasing yields as well as reducing costs.
Bacteria ; genetics ; metabolism ; Biomass ; Cellulase ; genetics ; metabolism ; Cellulosomes ; genetics ; metabolism ; Enzymes ; metabolism ; Fermentation ; Lignin ; metabolism ; Metabolic Networks and Pathways