Genome-scale transcriptional dynamics and environmental biosensing

Area of Science:

• Molecular biology
• Systems biology
• Bioengineering

Background:

• Cells utilize complex regulatory feedback for environmental response.
• High-throughput temporal data collection and analysis are crucial for understanding cellular dynamics.
• Existing technologies require advancement for real-time, large-scale transcriptional monitoring.

Purpose of the Study:

• To develop a novel microfluidic platform for monitoring temporal gene expression.
• To integrate deep neural networks (DNN) and explainable artificial intelligence (XAI) for analyzing transcriptional data.
• To demonstrate the platform's capability as a field-deployable real-time biosensor.

Main Methods:

• Development of the Dynomics microfluidic platform to monitor over 2,000 gene promoters.
• Application of DNN and XAI algorithms to analyze genome-scale transcriptional data.
• Testing the platform's biosensing capabilities using *Escherichia coli* promoter profiles.

Main Results:

• The Dynomics platform successfully monitored temporal gene expression from over 2,000 promoters.
• Machine learning algorithms identified patterns and key genes in transcriptional data.
• The platform accurately predicted heavy metal presence in environmental samples as a real-time biosensor.

Conclusions:

• The Dynomics platform offers a powerful tool for high-throughput temporal gene expression analysis.
• Integration with AI enables deep insights into cellular regulatory networks.
• The platform demonstrates significant potential for environmental monitoring and biosensing applications.