fevereiro 2015 vol. 1 num. 2 - XX Congresso Brasileiro de Engenharia Química

Artigo - Open Access.

Idioma principal

Optimization of Clostridium acetobutylicum metabolism for biobutanol production using in silico tools.



Alternative biofuels to petroleum-derived compounds are in high demand nowadays. Ethanol has played an important role in the picture as the major biofuel currently produced [1], [2]. However, advances in technology and the current search for alternatives biofuels, brought butanol into the scenario. The characteristics of this primary alcohol make it an interesting alternative to ethanol as it can directly replace gasoline without the need of engine adaptation. Moreover, it is an excellent diluent and solvent for manufacturing intermediates of chemical industry augmenting the potential of this compound. With the most recent Genome-scale Model (GEM) of C. acetobutylicum available, new in silico strategies can be further explored to improve butanol production. By modeling clostridial metabolism, new insights on the acidogenic-solventogenic metabolism are debated in this work. 1. INTRODUCTION Research on biofuels has been growing over the years due to the pressure caused by the cost and availability of fossil-derived fuels along with an environmental conscience related to an increasing biomass waste produced from agricultural activities [3]. Sustainable technologies for alternative fuel production are on demand in the globalized world we live in. Butanol has been regarded as an interesting alternative because it can be directly used in the existing engines without need for adaptation, it has a higher energy density and better performance than ethanol [4]. Additionally, butanol and its derivatives can also be used for surface coating, plasticizers, diluent and solvent for antibiotics and hormones, confirming the versatility and market interest of this compound [5]. Clostridium genera are of particular interest in this field since some species are natural producers of butanol, such as Clostridium acetobutylicum and Clostridium beijerinckii with butanol titers as high as 18g/L [3]. Área temática: Processos Biotecnológicos 1Figure 1. Central metabolism of C. acetobutylicum for acids and solvent production. The numbering from (1) to (3) are flux ratios defined later in the results section. C. acetobutylicum is a spore forming, gram positive, anaerobic bacterium. Solventogenic organisms such as C. acetobutylicum can utilize a wide variety of substrates for growth with concomitant production of solvents. These organisms normally carry a biphasic fermentation, the first phase being known as acidogenesis and the second as solventogenesis (Figure 1). Acidogenic phase is characterized by the degradation of sugars via glycolysis coupled to acid production. The major end products are acetate and butyrate along with production of ethanol, hydrogen and CO2. Due to acid production, the pH drops, which triggers the Clostridium cells to switch its metabolism into a solventogenic phase which is characterized by the re-consumption of the acids and further conversion into solvents, namely butanol, acetone and ethanol. With the complete sequenced genome of C. acetobutylicum, in silico genome-scale models (GEM) have been reconstructed [6]–[8] in order to better understand its metabolism and accurately predict the in vivo phenotypes using mathematical tools. This model was analyzed and compared with the published data using Optflux software which is a computational tool developed to perform in silico simulations, manipulate and analyze GEM in an user friendly interface [9]. This software gives the researcher the flexibility required to perform different tasks such as to perform simulations and/or metabolic engineering optimizations. The purpose of this work was to gather the in silico knowledge so far obtained from different Área temática: Processos Biotecnológicos 2studies related to C. acetobutylicum and use mathematical approaches to explore the metabolism of this organism in search for new insights and strategies to enhance butanol production.



DOI: 10.5151/chemeng-cobeq2014-1178-20642-176045

Referências bibliográficas
  • [1] S. Prasad, A. Singh, and H. C. Joshi, “Ethanol as an alternative fuel from agricultural, industrial and urban residues,” Resour. Conserv. Recycl., vol. 50, no. 1, pp. 1–39, Mar. 2007.
  • [2] M. O. S. Dias, A. V. Ensinas, S. a. Nebra, R. Maciel Filho, C. E. V. Rossell, and M. R. W. Maciel, “Production of bioethanol and other bio-based materials from sugarcane bagasse: Integration to conventional bioethanol production process,” Chem. Eng. Res. Des., vol. 87, no. 9, pp. 1206–1216, Sep. 2009.
  • [3] Y.-S. Jang, A. Malaviya, C. Cho, J. Lee, and S. Y. Lee, “Butanol production from renewable biomass by clostridia.,” Bioresour. Technol., vol. 123, pp. 653–63, Nov. 2012.
  • [4] Área temática: Processos Biotecnológicos 7 E. M. Green, “Fermentative production of butanol--the industrial perspective.,” Curr. Opin. Biotechnol., vol. 22, no. 3, pp. 337–43, Jun. 2011.
  • [5] S. Y. Lee, J. H. Park, S. H. Jang, L. K. Nielsen, J. Kim, and K. S. Jung, “Fermentative butanol production by Clostridia.,” Biotechnol. Bioeng., vol. 101, no. 2, pp. 209–28, Oct. 2008.
  • [6] R. S. Senger and E. T. Papoutsakis, “Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.,” Biotechnol. Bioeng., vol. 101, no. 5, pp. 1036–52, Dec. 2008.
  • [7] J. Lee, H. Yun, A. M. Feist, B. Ø. Palsson, and S. Y. Lee, “Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network.,” Appl. Microbiol. Biotechnol., vol. 80, no. 5, pp. 849–62, Oct. 2008.
  • [8] M. J. McAnulty, J. Y. Yen, B. G. Freedman, and R. S. Senger, “Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico.,” BMC Syst. Biol., vol. 6, no. 1, p. 42, Jan. 2012.
  • [9] I. Rocha, P. Maia, P. Evangelista, P. Vilaça, S. Soares, J. P. Pinto, J. Nielsen, K. R. Patil, E. C. Ferreira, and M. Rocha, “OptFlux: an open-source software platform for in silico metabolic engineering.,” BMC Syst. Biol., vol. 4, no. 1, p. 45, Jan. 2010.
  • [10] M. Rocha, P. Maia, R. Mendes, J. P. Pinto, E. C. Ferreira, J. Nielsen, K. R. Patil, and I. Rocha, “Natural computation meta-heuristics for the in silico optimization of microbial strains.,” BMC Bioinformatics, vol. 9, p. 499, Jan. 2008.
  • [11] R. P. Desai, L. K. Nielsen, and E. T. Papoutsakis, “Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints,” J. Biotechnol., vol. 71, pp. 191–205, 1999.
  • [12] W. Andersch, H. Bahl, and G. Gottschalk, “Level of Enzymes Involved in Acetate , Butyrate , Acetone and Butanol Formation by Clostridium acetobutylicum,” Appl. Microbiol. Biotechnol., vol. 3, no. 18, pp. 327–332, 1983.
Como citar:

PORTELA, C.; FREITAS, S.; ROCHA, I.; "Optimization of Clostridium acetobutylicum metabolism for biobutanol production using in silico tools.", p. 1615-1622 . In: Anais do XX Congresso Brasileiro de Engenharia Química - COBEQ 2014 [= Blucher Chemical Engineering Proceedings, v.1, n.2]. São Paulo: Blucher, 2015.
ISSN 2359-1757, DOI 10.5151/chemeng-cobeq2014-1178-20642-176045

últimos 30 dias | último ano | desde a publicação