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Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering.

Somtirtha Roy1,2, Tijana Radivojevic1,2,3, Mark Forrer2,3,4

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|February 26, 2021
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Summary
This summary is machine-generated.

Computational tools now enable prediction of bioengineered organism behavior, improving isoprenol production by 23%. This advance in synthetic biology leverages multiomics data for better strain design and outcomes.

Keywords:
biofuelsflux analysismachine learningmetabolic engineeringmultiomics analysissynthetic biology

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Area of Science:

  • Synthetic biology
  • Bioengineering
  • Computational biology

Background:

  • Biology has evolved into a design science with advanced cellular modification tools and large multimodal datasets.
  • Synthetic biology's potential is limited by the inability to accurately predict biological system behavior.
  • Predictive modeling is crucial for advancing bioengineering and renewable bioproduct development.

Purpose of the Study:

  • To present computational tools for storing, visualizing, and utilizing multiomics data to predict bioengineering outcomes.
  • To demonstrate a method for improving isoprenol production in engineered strains using data-driven predictions.
  • To provide a tutorial for leveraging computational tools in synthetic biology.

Main Methods:

  • Development and application of a computational framework for multiomics data management.
  • Utilization of online repositories for storing and accessing strain information and multiomics data.
  • Training machine learning algorithms on multiomics data to predict strain performance.
  • Generation of synthetic multiomics data using a novel library for tool validation.

Main Results:

  • Successfully uploaded, visualized, and outputted multiomics data and strain information.
  • Trained machine learning models that accurately predicted improvements in isoprenol production.
  • Achieved a 23% predicted increase in isoprenol production through data-informed strain design recommendations.
  • Validated the computational tools using credible synthetic multiomics data.

Conclusions:

  • Computational tools can effectively predict biological system behavior, overcoming a key limitation in synthetic biology.
  • Leveraging multiomics data and machine learning significantly enhances the design and optimization of bioengineered strains.
  • The presented framework and tutorial facilitate improved production of bioproducts through advanced bioengineering.