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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Published on: February 8, 2017

Probabilistic pathway construction.

Mona Yousofshahi1, Kyongbum Lee, Soha Hassoun

  • 1Department of Computer Science, Tufts University, Medford, MA 02155, USA.

Metabolic Engineering
|February 5, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic algorithm for designing metabolic pathways. The uniformly random selection scheme efficiently identifies high-yield synthesis routes for valuable compounds, outperforming exhaustive searches.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

  • Metabolic Engineering
  • Synthetic Biology
  • Biotechnology

Background:

  • Metabolic engineering employs novel synthesis pathways in genetically modified hosts for metabolite overproduction.
  • Developing efficient algorithms is crucial for identifying viable pathways compatible with cell growth.

Purpose of the Study:

  • To present a novel pathway construction algorithm using probabilistic reaction selection.
  • To identify viable synthesis pathways for overproducing commercially useful metabolites.

Main Methods:

  • Investigated probabilistic selection of reactions (high/low connectivity, uniformly random).
  • Developed a pathway construction algorithm compatible with balanced cell growth.
  • Compared probabilistic approach to exhaustive search for pathway enumeration.

Main Results:

  • The uniformly random selection scheme yielded the highest average maximum yield across diverse metabolites.
  • The probabilistic algorithm achieved comparable yield distributions to exhaustive search but in significantly less time.
  • Identified pathways confirmed in literature as viable, high-yield synthesis routes.

Conclusions:

  • Probabilistic pathway construction is an efficient strategy for metabolic engineering.
  • The developed algorithm facilitates the design of novel, non-native synthesis routes.
  • This approach accelerates the discovery of efficient bioproduction pathways.