Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

An overview of pathway prediction tools for synthetic design of microbial chemical factories

Bioinformatics Research Group (BIRG), Department of Biosciences and Health Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor. Malaysia

The increasing need for the bio-based industrial production of compounds via microbial cell factories leads to a demand for computational pathway prediction tools. A variety of algorithms have been developed that can be used to identify possible metabolic pathways and their corresponding enzymatic parts. These prediction tools play a central role in metabolic pathway design and microbial chassis selection for industrial chemical production. Here, we briefly discuss how the development of some key computational tools, which are currently available for pathway construction, could facilitate the synthetic redesign of microbial chassis. Special emphasis is given to the characteristics and drawback(s) of some of the computational tools used in pathway prediction, and a generalized workflow for the design of microbial chemical factories is provided. Perspectives, challenges and future trends are briefly highlighted.
  Article Metrics

Keywords metabolic pathways; computational tools; synthetic design; microbial chemical factories

Citation: Bashir Sajo Mienda, Mohd Shahir Shamsir. An overview of pathway prediction tools for synthetic design of microbial chemical factories. AIMS Bioengineering, 2015, 2(1): 1-14. doi: 10.3934/bioeng.2015.1.1


  • 1. Chou CH, Chang WC, Chiu CM, et al.( 2009) FMM: a web server for metabolic pathway reconstruction and comparative analysis. Nucleic Acids Res 37: W129–34.
  • 2. Hatzimanikatis V, Li C, Ionita JA, et al. (2005) Exploring the diversity of complex metabolic networks. Bioinformatics 21: 1603–1609.
  • 3. Henry CS, Broadbelt LJ, Hatzimanikatis V (2010) Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol Bioeng 106: 462–473.
  • 4. Rodrigo G, Carrera J, Prather KJ, et al. (2008) DESHARKY: Automatic design of metabolic pathways for optimal cell growth. Bioinformatics 24: 2554–2556.
  • 5. Carbonell P, Planson AG, Fichera D, et al. (2011) A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst Biol 5: 122.    
  • 6. Carbonell P, Planson AG, Faulon JL (2013) Retrosynthetic design of heterologous pathways, in Methods Mol Biol. Springer Science+Business Media, LLC. 149–173.
  • 7. Cho A, Yun H, Park JH, et al. (2010) Prediction of novel synthetic pathways for the production of desired chemicals. BMC Syst Biol 4: 35.    
  • 8. Chatsurachai S, Furusawa C, Shimizu H (2012) An in silico platform for the design of heterologous pathways in nonnative metabolite production. BMC Bioinformatics 13: 93.    
  • 9. Kanehisa M, Goto S, Sato Y, et al. (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40: D109–114.
  • 10. Medema MH, van Raaphorst R, Takano E, et al. (2012) Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol 10: 191–202.
  • 11. Khosla C, Keasling JD (2003) Metabolic Engineering for drug discovery and development. Nat Rev Drug Discov 2: 1019–1025.
  • 12. Mienda BS, Shamsir MS (2013) Thermotolerant micro-organisms in Consolidated Bioprocessing for ethanol production: A review. Res Biotechnol 4: 1–6.
  • 13. Schomburg I, Chang A, Placzek S, et al. (2013) BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res 41: D764–772.
  • 14. Schellenberger J, Park JO, Conrad TM, et al. (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11: 213.    
  • 15. Le Novere N, Bornstein B, Broicher A, et al. (2006) BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34: D689–691.
  • 16. Rocha I, Maia P, Evangelista P, et al. (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4: 45.    
  • 17. Hoops S, Sahle S, Gauges R, et al. (2006) COPASI--a COmplex PAthway SImulator. Bioinformatics 22: 3067–74.
  • 18. Schaber J (2012) Easy parameter identifiability analysis with COPASI. Biosystems 110: 183–5.
  • 19. Planson AG, Carbonell P, Paillard E, et al. (2012) Compound toxicity screening and structure-activity relationship modeling in Escherichia coli. Biotechnol Bioeng 109: 846–850.
  • 20. Fehér T, Planson AG, Carbonell P, et al. (2014) Validation of RetroPath, a computer-aided design tool for metabolic pathway engineering. Biotechnol J 9: 1446–1457.
  • 21. Planson AG, Carbonell P, Grigoras I, et al. (2012) A retrosynthetic biology approach to therapeutics: from conception to delivery. Curr Opin Biotechnol 23: 948–956.
  • 22. Fernandez-Castane A, Feher T, Carbonell P, et al. (2014) Computer-aided design for metabolic engineering. J Biotechnol 192: 302–313.
  • 23. Atsumi S, Hanai T, Liao JC (2008) Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451: 86–89.
  • 24. Reed JL, Vo TD, Schilling CH, et al. (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4: R54.    
  • 25. Shinfuku Y, Sorpitiporn N, Sono M, et al. (2009) Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum. Microb Cell Fact 8: 43.    
  • 26. Mo ML, Palsson BO, Herrgard MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3: 37.    
  • 27. Becker SA, Feist AM, Mo ML, et al. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2: 727–738.
  • 28. Schellenberger J, Que R, Fleming RM, et al. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6: 1290–1307.
  • 29. Trinh CT, Wlaschin A, Srienc F (2009) Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl Microbiol Biotechnol 81: 813–826.
  • 30. Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: A computational framework for redesign of microbial production systems. Genome Res 14: 2367–2376.
  • 31. Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657.
  • 32. Mienda BS, Shamsir MS, Shehu I, et al. (2014) In Silico Metabolic Engineering Interventions of Escherichia coli for Enhanced Ethanol Production, Based on Gene Knockout Simulation. IIOAB J 5: 16–23.
  • 33. Mienda BS, Shamsir MS (2014) In silico Gene knockout metabolic interventions in Escherichia coli for Enhanced Ethanol production on Glycerol. Res J Pharm Biol Chem Sci 5: 964–974.
  • 34. Mienda BS, Shamsir MS, Salleh FM (2014) In silico metabolic engineering prediction of Escherichia coli genome model for production of D-lactic acid from glycerol using the OptFlux software platform. Int J Computational Bioinformatics In Silico Modeling 3: 460–465.
  • 35. Dhamankar H, Prather KL (2011) Microbial chemical factories: recent advances in pathway engineering for synthesis of value added chemicals. Curr Opin Struct Biol 21: 488–494.


This article has been cited by

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, Bashir Sajo Mienda, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved