Constraint-based genome-scale metabolic modeling of Clostridium acetobutylicum behavior in an immobilized column

被引:7
|
作者
Wallenius, Janne
Viikila, Matti
Survase, Shrikant
Ojamo, Heikki
Eerikainen, Tero
机构
[1] Aalto University, School of Chemical Technology, Department of Biotechnology and Chemical Technology, P.O. Box 6100
关键词
Genome-scale metabolic modeling; Flux balance analysis; Clostridia; Continuous fermentation; Cell immobilization; ACETONE-BUTANOL-ETHANOL; QUANTITATIVE PREDICTION; CELLULAR-METABOLISM;
D O I
10.1016/j.biortech.2013.05.085
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
In this study a step-wise optimization procedure was developed to predict solvent production using continuous ABE fermentation with immobilized cells. The modeling approach presented here utilizes previously published constraint-based metabolic model for Clostridium acetobutylicum without direct flux constraints. A recently developed flux ratio constraint method was adopted for the model. An experimental data set consisting of 25 experiments using different sugar mixtures as substrates and differing dilution rates was simulated successfully with the modeling approach. Converted to end product concentrations the mean absolute error for acetone was 0.31 g/l, for butanol 0.49 g/l, and for ethanol 0.17 g/l. The modeling approach was validated with another data set from similar experimental setup. The model errors for the validation data set was 0.24 g/l, 0.60 g/l, and 0.17 g/l for acetone, butanol, and ethanol, respectively. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:603 / 610
页数:8
相关论文
共 50 条
  • [41] Genome-scale modeling forBacillus coagulansto understand the metabolic characteristics
    Chen, Yu
    Sun, Yan
    Liu, Zhihao
    Dong, Fengqing
    Li, Yuanyuan
    Wang, Yonghong
    BIOTECHNOLOGY AND BIOENGINEERING, 2020, 117 (11) : 3545 - 3558
  • [42] Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks
    Mhamdi, Hanen
    Bourdon, Jeremie
    Larhlimi, Abdelhalim
    Elloumi, Mourad
    INFORMATICS-BASEL, 2020, 7 (01):
  • [43] Flux sampling in genome-scale metabolic modeling of microbial communities
    Gelbach, Patrick E.
    Cetin, Handan
    Finley, Stacey D.
    BMC BIOINFORMATICS, 2024, 25 (01)
  • [44] Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis
    Wendering, Philipp
    Nikoloski, Zoran
    MSYSTEMS, 2022, 7 (01)
  • [45] Enhancing Microbiome Research through Genome-Scale Metabolic Modeling
    Ankrah, Nana Y. D.
    Bernstein, David B.
    Biggs, Matthew
    Carey, Maureen
    Engevik, Melinda
    Garcia-Jimenez, Beatriz
    Lakshmanan, Meiyappan
    Pacheco, Alan R.
    Sulheim, Snorre
    Medlock, Gregory L.
    MSYSTEMS, 2021, 6 (06)
  • [46] A review of genome-scale metabolic flux modeling of anaerobiosis in biotechnology
    Senger, Ryan S.
    Yen, Jiun Y.
    Fong, Stephen S.
    CURRENT OPINION IN CHEMICAL ENGINEERING, 2014, 6 : 33 - 42
  • [47] Flux sampling in genome-scale metabolic modeling of microbial communities
    Patrick E. Gelbach
    Handan Cetin
    Stacey D. Finley
    BMC Bioinformatics, 25
  • [48] Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri
    Feist, Adam M.
    Scholten, Johannes C. M.
    Palsson, Bernhard O.
    Brockman, Fred J.
    Ideker, Trey
    MOLECULAR SYSTEMS BIOLOGY, 2006, 2 (1) : 2006.0004
  • [49] Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris
    Saitua, Francisco
    Torres, Paulina
    Ricardo Perez-Correa, Jose
    Agosin, Eduardo
    BMC SYSTEMS BIOLOGY, 2017, 11
  • [50] Genome-Scale Metabolic Modeling and Its Application to Microbial Communities
    Reed, Jennifer L.
    CHEMISTRY OF MICROBIOMES, 2017, : 85 - 91