부탄올을 생산하는 발효반응기에서는 아세톤, 부탄올 그리고 에탄올을 주로 생산하는 Clostridium acetobutylicum이 사용된다. 본 연구에서는 이 미생물을 이용한 발효공정의 개발을 위하여, Clostridium acetobutylicum ATCC824의 대사망의 동적 모델이 제안되었다. 많은 효소기반의 대사반응들로 구성된 대사망의 복잡성과 대사반응속도식의 비선형적 특성 때문에, 유전 알고리듬과 Levenberg-Marquardt 알고리듬이 결합된 효율적인 최적화 기법을 이용하여 회분식 발효반응기의 실험 결과값으로 58개의 반응속도상수들을 결정하였다. 그리고 이 반응속도상수 결정의 정확도를 제고하기 위하여, 유전자 조작을 통해 특정 대사경로를 차단한 미생물을 이용했을 때의 실험과 초기 글루코스의 농도를 다르게 한 실험들을 수행하여 개발된 대사망의 동적모델을 분석하였다. 결과적으로, 본 연구를 통해서 개발된 대사망 모델의 정확도를 확인하였고, 이를 활용하여 발효반응공정의 생산성 향상을 위한 적절한 클로스트리듐의 개발과 발효반응기의 최적화를 위한 연구에 기여할 수 있을 것으로 기대된다.

To produce biobutanol, fermentation processes using clostridia that mainly produce acetone, butanol and ethanol are used. In this work, a dynamic model describing the metabolic reactions in an acetone-butanol-ethanol (ABE)-producing clostridium, Clostridium acetobutylicum ATCC824, was proposed. To estimate the 58 kinetic parameters of the metabolic network model with experimental data obtained from a batch fermentor, we used an efficient optimization method combining a genetic algorithm and the Levenberg-Marquardt method because of the complexity of the metabolism of the clostridium. For the verification of the determined parameters, the developed metabolic model was evaluated by experiments where genetically modified clostridium was used and the initial concentration of glucose was changed. Consequently, we found that the developed kinetic model for the metabolic network was considered to describe the dynamic metabolic state of the clostridium sufficiently. Thus, this dynamic model for the metabolic reactions will contribute to designing the clostridium as well as the fermentor for higher productivity.

Clostridium Acetobutylycum Metabolic Reactions Parameter Estimation

Mun LT, Ishizaki A, Yoshino S, Furukawa K, Biotechnol. Lett., 17(6), 649 (1995)
Lopez-Contreras AM, Claassen PAM, Mooibroek H, De Vos WM, Butanol and Ethanol. Appl. Microbiol. Biotechnol., 54, 162 (2000)
Madihah MS, Ariff AB, Khalil MS, Suraini AA, Karim MIA, Folia Microbiol., 46, 197 (2001)
Qureshi N, Meagher MM, Huang J, Hutkins RW, J. Membr. Sci., 187(1-2), 93 (2001)
Stephanopoulos G, Vallino JJ, Science., 252, 1675 (1991)
Shinto H, Tashiro Y, Yamashita M, Kobayashi G, Sekiguchi T, Hanai T, Kuriya Y, Okamoto M, Sonomoto K, J. Biotechnol., 131, 45 (2007)
Shinto H, Tashiro Y, Kobayashi G, Sekiguchi T, Hanai T, Kuriya Y, Okamoto M, Sonomoto K, Process Biochem., 43, 1452 (2008)
Durre P, Ann. NY Acad. Sci., 22, 337 (2008)
Green EM, Curr. Opin. Biotechnol., 22, 337 (2011)
Jones DT, Woods DR, Microbiological Reviews., 50, 484 (1986)
Milne CB, Eddy JA, Raju R, Ardekani S, Kim PJ, Senger RS, Jin YS, Blaschek HP, Price ND, BMC Systems Biology., 5, 130 (2011)
Heap JT, Pennington OJ, Cartman ST, Carter GP, Minton NP, Journal of Microbial Methods., 70, 452 (2007)
Desai RP, Harris LM, Welker NE, Papoutsaki ET, Metabolic Engineering., 26, 1206 (1999)
Lee J, Yun H, Feist AM, Palsson BO, Lee SY, Appl. Microbiol. Biotechnol., 80(5), 849 (2008)
Borden JR, Papoutsakis ET, Appl. Environ. Microbiol., 73, 3061 (2007)
Lee J, Jang Y, Choi SJ, Im JA, Song H, Cho JH, Seung DY, Papoutsakis ET, Bennette GN, Lee SY, Appl. Environ. Microbiol., 78, 1416 (2012)
Burdette D, Zeikus JG, Biochemistry Journal., 302, 163 (1994)
Palosaari NR, Rogers P, J. Bacteriol., 170, 2971 (1988)
Lutke-Eversloh T, Bahl H, “Metabolic Engineering of Clostridium Acetobutylicum: Recent Advances to Improve Butanol Production," Current Opinion in Biotechnology (2011)
Kim W, Yun C, Kim Y, Park J, Park S, Jung KT, Lee YH, Kim SH, Ind. Eng. Chem. Res., 50(3), 1187 (2011)
Goldberg DE, “Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edition, Addison-Wesley Professional,” Reading, Massachusetts, U.S. (1989)
Holland JH, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan, U.S. (1975)