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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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OptPipe - a pipeline for optimizing metabolic engineering targets.

András Hartmann1, Ana Vila-Santa1, Nicolai Kallscheuer2

  • 1IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal.

BMC Systems Biology
|December 23, 2017
PubMed
Summary

OptPipe optimizes metabolic engineering by combining multiple algorithms for robust predictions. This consensus approach successfully increased malonyl-CoA production in Corynebacterium glutamicum, validated by experiments.

Keywords:
Metabolic engineeringMetabolic networksOptimizationRank productSoftware

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Area of Science:

  • Metabolic Engineering
  • Computational Biology
  • Synthetic Biology

Background:

  • Metabolic engineering aims to optimize cellular functions for specific production goals.
  • Developing robust computational tools is crucial for guiding experimental efforts in metabolic engineering.
  • Existing optimization methods may yield diverse solutions, necessitating a consensus approach.

Purpose of the Study:

  • To introduce OptPipe, a novel pipeline for optimizing metabolic engineering targets using a consensus approach.
  • To enhance the reliability of metabolic engineering predictions by integrating multiple optimization algorithms.
  • To provide a framework for ranking and selecting optimal genetic modifications.

Main Methods:

  • OptPipe integrates solutions from distinct optimization algorithms.
  • A consensus approach is employed to generate robust hypotheses for metabolic engineering.
  • Rank product tests, corrected for multiple comparisons, are used for ranking solutions based on objectives like biomass and target production.

Main Results:

  • OptPipe was applied to a genome-scale model of Corynebacterium glutamicum.
  • The pipeline successfully predicted strategies to maximize malonyl-CoA production.
  • Experimental validation confirmed increased malonyl-CoA levels upon implementing a predicted gene deletion (ΔsdhCAB).

Conclusions:

  • OptPipe provides a reliable method for combining and ranking optimization solutions in metabolic engineering.
  • The pipeline aids in identifying effective gene knockout strategies for enhanced production.
  • The implementation is freely available, facilitating its adoption in research.
  • Experimental validation supports the in silico predictions generated by OptPipe.