Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

Lu, Minrui; Sha, Yuanyuan; Kumar, Vinod; Xu, Zhaoxian; Zhai, Rui; Jin, Mingjie

doi:10.1016/j.biotechadv.2024.108339

Biotechnology Advances

2024

DOI: 10.1016/j.biotechadv.2024.108339

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Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

Minrui Lu,

Yuanyuan Sha,

Vinod Kumar

et al.

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Cited by 2 publications

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Engineered transcription factor-binding diversed functional nucleic acid-based synthetic biosensor

Liu,

Zhou,

et al. 2024

Biotechnology Advances

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Engineered transcription factor-binding diversed functional nucleic acid-based synthetic biosensor

Liu,

Zhou,

et al. 2024

Biotechnology Advances

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AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

Ding,

Yuan,

et al. 2024

Molecules

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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.

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Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

Cited by 2 publications

References 113 publications

Engineered transcription factor-binding diversed functional nucleic acid-based synthetic biosensor

Engineered transcription factor-binding diversed functional nucleic acid-based synthetic biosensor

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

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