Sofia Ferreira

Background

The growing global demand for new energy sources combined with environmental concerns had motivated the search for alternative fuels, produced from renewable raw materials. In this sense, n-butanol (also designed as 1-butanol) is considered the next generation of biofuels due to its superior fuel characteristics when compared with ethanol, including higher energy density, lower hygroscopicity and volatility. Besides its application as fuel, n-butanol has an important role in the manufacturing of pharmaceuticals, polymers, herbicide esters and butyl xanthate, and is also used as solvent for paints, coatings, natural resins, gums, synthetic resins, dyes, and alkaloids (Y. S. Jang et al. 2012) (Dürre 2008).

n-Butanol is naturally produced by solventogenic bacteria through Acetone-Butanol-Ethanol (ABE) fermentation, usually with low productivities (Tashiro et al. 2013) (Dürre 2008). Thus, most of n-butanol is currently chemical synthesised via petrochemical routes and its price is extremely sensitive to crude oil’s price, becoming imperative to seek for alternative ways to produce it. One possible approach is to express novel biosynthetic pathways in more user-friendly hosts as Escherichia coli or Saccharomyces cerevisiae (Atsumi, Hanai, et al. 2008). In this sense, this work aims at evaluating and implementing in vivo novel pathways to produce n-butanol. These heterologous pathways, previously generated using a (hyper)graph-based algorithm (Liu et al. 2014), will be evaluated according to diverse criteria such as size of the solution, yield and novelty. Then, the pathways identified as the most promising ones will be implemented in E. coli. Finally, advanced techniques will be used to characterize and further improve the developed strains, including metabolomics and directed evolution.

Objectives

  1. Compare in silico the alternative routes suggested by the algorithms in terms of yields and other criteria, select the most promising ones and analyze the literature to evaluate the respective novelty;
  2. Simulate and optimize n-butanol production in E. coli via the selected pathways using OptFlux, discovering mutants with increased production of n-butanol;
  3. Engineer E. coli to produce n-butanol trough the pathway(s) selected using molecular biology techniques;
  4. Characterize the engineered strains using 13C-labeled substrates;
  5. Enhance n-butanol tolerance in the selected microorganism trough adaptive evolution techniques;
  6. Identify the mutations acquired during the evolution process and implement them into the ancestral strain;

The work developed at TU Delft comprises the strain characterization using 13C Metabolic Flux Analysis (13C-MFA)
Using 13C labelled substrates and a mathematical model of the central carbon metabolism, it is possible to quantify the internal fluxes in a microorganism. A 13C-MFA study involves diverse steps: the culture of the cells into a 13C-labeled substrate; cell disruption and sample preparation; identification of the isotopic enrichment by Gas Chromatography-Mass Spectrometry (GC-MS); and, finally, data interpretation through mathematical and a statistical analysis (Wahl et al. 2008). In this task, the most promising strains will be analyzed and eventual mismatches between in silico predictions and in vivo phenotypes will be scrutinized, which could be the basis for less productive strains, and eventually additional steps in the metabolic engineering cycle will be performed.


  • Atsumi, S., Hanai, T. & Liao, J.C., 2008. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature, 451(7174), pp.86–9. Available at: www.ncbi.nlm.nih.gov/pubmed/18172501 [Accessed July 9, 2014].
  • Dürre, P., 2008. Fermentative butanol production: bulk chemical and biofuel. Annals of the New York Academy of Sciences, 1125, pp.353–62. Available at: www.ncbi.nlm.nih.gov/pubmed/18378605 [Accessed November 11, 2014].
  • Jang, Y.-S. et al., 2012. Bio-based production of C2-C6 platform chemicals. Biotechnology and bioengineering, 109(10), pp.2437–59. Available at: www.ncbi.nlm.nih.gov/pubmed/22766912 [Accessed July 14, 2014].
  • Liu, F. et al., 2014. Development and application of efficient pathway enumeration algorithms for metabolic engineering applications. Computer Methods and Programs in Biomedicine. Available at: linkinghub.elsevier.com/retrieve/pii/S0169260714003897 [Accessed December 12, 2014].
  • Tashiro, Y. et al., 2013. Recent advances and future prospects for increased butanol production by acetone-butanol-ethanol fermentation. Engineering in Life Sciences, 13(5), pp.432–445. Available at: doi.wiley.com/10.1002/elsc.201200128 [Accessed November 13, 2014].
  • Wahl, S.A., Nöh, K. & Wiechert, W., 2008. 13C labeling experiments at metabolic nonstationary conditions: an exploratory study. BMC bioinformatics, 9, p.152. Available at: www.pubmedcentral.nih.gov/articlerender.fcgi [Accessed January 29, 2015].