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Nitrogen is a very important element for life because it is a major constituent of proteins and nucleic acids. It is a macronutrient, and in nature, it is recycled from organic compounds and stored in the form of  ammonia, ammonium ions, nitrate, nitrite, or  nitrogen gas by many metabolic processes. Many of these metabolic processes are carried out only by prokaryotes.
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Related Experiment Video

Updated: May 23, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Generative deep learning model assisted multi-objective optimization for wastewater nitrogen to protein conversion by

Pengfei Hou1, Duofei Hu1, Shiqi Liu1

  • 1School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.

Bioresource Technology
|May 21, 2025
PubMed
Summary

This study uses generative deep learning to optimize photosynthetic bacteria for wastewater nitrogen treatment and protein recovery. Near-infrared light significantly boosts nitrogen-to-protein conversion, enhancing resource recovery.

Keywords:
Generative learningMicrobial proteinMulti-objective frameworkPhotosynthetic microorganismsWastewater valorization

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Area of Science:

  • Environmental Microbiology
  • Biotechnology
  • Artificial Intelligence

Background:

  • Photosynthetic bacteria (PSB) are explored for wastewater nitrogen treatment and valorization.
  • Balancing nitrogen removal and resource recovery in PSB remains a challenge.

Purpose of the Study:

  • To optimize nitrogen removal, protein concentration, and nitrogen-to-protein conversion in PSB using generative deep learning.
  • To identify key factors influencing these processes and achieve multi-objective optimization.

Main Methods:

  • Generative deep learning (Variational Auto-Encoders) for data augmentation.
  • Elastic Neural Network (ENN) for model fitting.
  • SHapley Additive exPlanations (SHAP) for factor identification.
  • Multi-objective optimization to identify Pareto front solutions.
  • Validation experiments using Near Infrared (NIR) light.

Main Results:

  • Generative models enhanced datasets, improving ENN model fitting.
  • Key factors identified: carbon/nitrogen sources, light type, NLR, COD, and HRT.
  • Multi-objective optimization yielded eight Pareto front solutions.
  • Near Infrared (NIR) light significantly improved nitrogen-to-protein conversion (up to 44%) and related gene expression.

Conclusions:

  • Generative AI frameworks offer innovative solutions for multi-objective optimization in wastewater nitrogen valorization, especially with limited data.
  • NIR light is a promising factor for enhancing nitrogen-to-protein conversion efficiency in PSB systems.