Enhancing in silico strain design predictions through next generation metabolic modeling approaches

Adil Alsiyabi, Niaz Bahar Chowdhury, Dianna Long, Rajib Saha

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Original languageEnglish (US)
Article number107806
JournalBiotechnology Advances
Volume54
DOIs
StatePublished - Jan 1 2022

Keywords

  • Kinetic modeling
  • Metabolic engineering
  • Metabolic modeling
  • Resource allocation
  • Strain design
  • Thermodynamic analysis

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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