Fed‐batch optimization of α‐amylase and protease‐producing <i>Bacillus subtilis</i> using Markov chain methods

Skolpap, Wanwisa; Scharer, Jeno M.; Douglas, Peter; Moo‐Young, Murray

doi:10.1002/bit.20079

Cited by 22 publications

(41 citation statements)

References 25 publications

(23 reference statements)

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“…For example, physiological state variables were estimated for feeding strategy optimization in a fed-batch Bacillus subtilis fermentation. 34 More recently, MCMC was used for Bayesian parameter estimation in Escherichia coli fermentation 35 and to estimate parameters associated with glucose metabolism in humans. 36 With the advances in solving the nonlinear differential equations numerically, it may be feasible to estimate parameters of a kinetic model directly from a fed-batch mammalian cell culture process using the MCMC method.…”

Section: Introductionmentioning

confidence: 99%

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Xing

Bishop

Leister

et al. 2009

Biotechnology Progress

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Markov chain Monte Carlo (MCMC) method was applied to model kinetics of a fed-batch Chinese hamster ovary cell culture process in 5,000-L bioreactors. The kinetic model consists of six differential equations, which describe dynamics of viable cell density and concentrations of glucose, glutamine, ammonia, lactate, and the antibody fusion protein B1 (B1). The kinetic model has 18 parameters, six of which were calculated from the cell culture data, whereas the other 12 were estimated from a training data set that comprised of seven cell culture runs using a MCMC method. The model was confirmed in two validation data sets that represented a perturbation of the cell culture condition. The agreement between the predicted and measured values of both validation data sets may indicate high reliability of the model estimates. The kinetic model uniquely incorporated the ammonia removal and the exponential function of B1 protein concentration. The model indicated that ammonia and lactate play critical roles in cell growth and that low concentrations of glucose (0.17 mM) and glutamine (0.09 mM) in the cell culture medium may help reduce ammonia and lactate production. The model demonstrated that 83% of the glucose consumed was used for cell maintenance during the late phase of the cell cultures, whereas the maintenance coefficient for glutamine was negligible. Finally, the kinetic model suggests that it is critical for B1 production to sustain a high number of viable cells. The MCMC methodology may be a useful tool for modeling kinetics of a fed-batch mammalian cell culture process.

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Section: Introductionmentioning

confidence: 99%

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Xing

Bishop

Leister

et al. 2009

Biotechnology Progress

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“…Substrates included starch, citrate, NH 3 and amino acids supplied in the medium. Measured products included biomass, extracellular protein products (a-amylase and protease), by-products such as acetate, lactate, formate, ethanol, and 2, 3-butanediol, and some intracellular metabolites (TCA acids, ethanol, and acidic by-products) [9].…”

Section: Metabolic Flux Modelmentioning

confidence: 99%

“…The metabolite distribution space was sampled using Monte Carlo parameter sampling method [9]. The sampled values were then employed as input parameters for metabolic flux estimation using the proposed algorithm.…”

Section: Metabolic Flux Modelmentioning

confidence: 99%

Parametric analysis of metabolic fluxes of α-amylase and protease-producing Bacillus subtilis

Skolpap

Nuchprayoon

Scharer

et al. 2007

Bioprocess Biosyst Eng

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Parametric analysis was applied for a metabolic flux model for the fed-batch culture of Bacillus subtilis producing recombinant alpha-amylase and protease. The metabolic flux model was formulated as a linear programming problem consisting of 49 reactions (decision variables) and 50 metabolites (equality constraints). This study was aimed to determine the response of the metabolic fluxes and objective function value of minimizing the difference between ATP consumption and ATP production (ATP balance). With regard to intracellular metabolite accumulation, the objective function value was least sensitive to variation in succinate and most sensitive to variation in malate. Amongst the variations in the accumulation rates of extracellular metabolites, the objective function value was least sensitive to variation in glutamate and most sensitive to variation in starch hydrolysis and triglyceride synthesis. A 10% variation in metabolite accumulation rates caused a maximum of 13.8% variation (standard error = 3.8%) in the objective function value.

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“…Due to its ability to secrete large amounts of proteins directly into culture broth, B. subtilis is used via fed-batch cultivation to produce various heterologous proteins like a-amylases [3,4], luciferase [5] or other products like vitamins [6]. Using fed-batch strategy, high cell concentrations can be attained and same time avoid both substrate and metabolite (acetic acid) inhibition [7].…”

Section: Introductionmentioning

confidence: 99%

Control of α-amylase production by Bacillus subtilis

Lyubenova

Ignatova

Salonen

et al. 2010

Bioprocess Biosyst Eng

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This study proposes two adaptive control algorithms for the fed-batch production of α-amylase. The first one uses online information from hardware measuring glucose. Online information of both biomass and glucose concentrations measured with different frequency is used in the second algorithm. Hardware measuring variables are inputs for software sensors of glucose concentration and (specific) glucose consumption rate. Either of the algorithms do not require any kinetic coefficients. This is a benefit, because the kinetic coefficients can vary during cultivation and between cultivations, leading to low process reproducibility and the non-stationary state of the bioprocess. The results of simulation investigations show good performance of the proposed control schemes.

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Fed‐batch optimization of α‐amylase and protease‐producing Bacillus subtilis using Markov chain methods

Cited by 22 publications

References 25 publications

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Parametric analysis of metabolic fluxes of α-amylase and protease-producing Bacillus subtilis

Control of α-amylase production by Bacillus subtilis

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