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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology.

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Engineered biosensors in living cells offer powerful tools for environmental monitoring and biobased production. Advances in high-throughput assays, machine learning, and genetic engineering are rapidly expanding their capabilities, though complex sensing and real-world deployment still present challenges.

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Area of Science:

  • Synthetic biology
  • Biosensor development
  • Genetic engineering

Background:

  • Living cells possess inherent sensing capabilities that can be repurposed for engineered biosensors.
  • Synthetic biology leverages these cellular systems for applications in environmental monitoring and biobased production.
  • Genetic sensors that modulate gene expression are a key focus in this field.

Purpose of the Study:

  • To provide a comprehensive review of current biosensor technologies and their development methodologies.
  • To highlight advancements in creating genetic sensors for diverse targets.
  • To identify challenges and future directions in biosensor design and deployment.

Main Methods:

  • Review of existing literature on biosensor development, scale-up, and deployment.
  • Focus on genetic sensors with gene expression outputs.
  • Integration of high-throughput assays, evolutionary approaches, bioinformatics, and machine learning.

Main Results:

  • Pipelines are emerging to create genetic sensors for a wide range of molecules (small molecules, proteins, nucleic acids).
  • High-throughput experimental assays, evolutionary methods, bioinformatics, and machine learning accelerate sensor development.
  • Complex sensing tasks and reliable real-world deployment remain significant challenges.

Conclusions:

  • Further advances in modifying nonmodel organisms are crucial for expanding biosensor applications.
  • Integrating control engineering principles, such as feedback, is essential for overcoming deployment hurdles.
  • Realizing the full potential of engineered biosensors requires addressing challenges in complex sensing and real-world integration.