Abstract
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600 nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600 nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties.
Highlights
Intracellular production of recombinant proteins in Escherichia coli has been firmly established a method of choice due to its faster growth, well-characterized genetics, higher protein expression and yield (Huang et al, 2000)
We have reported modeling and optimization of continuous bead milling process for efficient bacterial cell lysis for the production of cholesterol oxidase (COD) using response surface methodology (RSM) (Haque et al, 2016)
The present study proves the significance of ANNGA in large-scale bacterial cell lysis and product recovery (COD in this case) in optimization of bead milling process for the very first time
Summary
Intracellular production of recombinant proteins in Escherichia coli has been firmly established a method of choice due to its faster growth, well-characterized genetics, higher protein expression and yield (Huang et al, 2000). The commonly used cell lysis methods including chemical, physical, or enzymatic (Hughes et al, 1971; Fish and Lilly, 1984; Morein et al, 1994; Byreddy et al, 2015) prove inefficient when attempted at larger scale. Mechanical methods such as high-pressure homogenization, bead milling are milder, result in lower protein degradation, non-selective, have comparable efficiency and scalable to pilot and process scale (Middleberg, 1995). Cell lysis optimizations need to focus on shortening the run time and minimize the amount of beads loaded, without compromising or even increasing the productivity of the process
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