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Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...
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Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Machine learning applications in systems metabolic engineering.

Gi Bae Kim1, Won Jun Kim1, Hyun Uk Kim2

  • 1Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea.

Current Opinion in Biotechnology
|October 4, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning accelerates systems metabolic engineering for sustainable chemical production by optimizing microbial strains. This approach leverages big bio data for improved strain selection, pathway reconstruction, and fermentation processes.

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Area of Science:

  • Biotechnology and Synthetic Biology
  • Computational Biology and Bioinformatics

Background:

  • Systems metabolic engineering enables the development of microbial strains for sustainable chemical and material production.
  • The increasing volume of biological big data, including omics data, is driving the integration of machine learning (ML) into this field.

Purpose of the Study:

  • To review the recent applications of machine learning techniques across key stages of systems metabolic engineering.
  • To provide insights into the future prospects of ML in advancing sustainable bioproduction.

Main Methods:

  • Literature review of recent studies applying ML to systems metabolic engineering.
  • Analysis of ML contributions in host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation.

Main Results:

  • ML is actively used in various stages of systems metabolic engineering, enhancing efficiency and performance.
  • The integration of ML is crucial for handling and interpreting large-scale biological data.

Conclusions:

  • Machine learning is becoming indispensable in systems metabolic engineering due to the growing volume of bio big data.
  • Future applications of ML hold significant promise for further advancements in sustainable bioproduction through optimized microbial systems.