A review of computational approaches for in silico metabolic engineering for microbial fuel production

Weng H. Chan, Mohd S. Mohamad, Safaai Deris, Rosli M. Illias

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


High energy consumption nowadays alongside with concerns on the environment had caused rising demand for synthetic alternative fuels. These include biofuels that can be produced from a variety of engineered microbes such as Escherichia coli. In the metabolic engineering field, this is done by genetically modifying the target microbes to obtain optimal production of a particular biochemical. Conventional metabolic engineering approaches often intuitive, but with advancements in modern biology, vast amount of informative data generated from time to time to describe the metabolism system of the microbes more thoroughly. Discoveries from interpreting these available data using computational approaches are highly beneficial to metabolic engineers, especially professionals working in the in silico metabolic engineering field. Within the past decade, many computational approaches and routines have been proposed and developed in providing a platform to discover rational strategies to aid biologists in engineering the metabolic network. Here, efforts to find the optimal butanol production route in E. coli as well as several optimization algorithms currently available for finding optimal solution to enhance biochemical production in designated target microbe are discussed. This review aims to show different optimization algorithms developed for in silico metabolic engineering and their applications in microbial fuel production.

Original languageEnglish
Pages (from-to)253-258
Number of pages6
JournalCurrent Bioinformatics
Issue number2
Publication statusPublished - 2013
Externally publishedYes


  • Biofuels
  • Computational biology
  • In silico metabolic engineering
  • Metabolic networks
  • Microbial strain improvement
  • Optimization
  • System biology

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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