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Background: Demand for high-throughput bioprocessing has dramatically increased especially in the biopharmaceutical industry because the technologies are of vital importance to process optimization and media development. This can be efficiently boosted by using microtiter plate (MTP) cultivation setup embedded into an automated liquid-handling system. The objective of this study was to establish an automated microscale method for upstream and downstream bioprocessing of α-IFN2b production by recombinant Escherichia coli. The extraction performance of α-IFN2b by osmotic shock using two different systems, automated microscale platform and manual extraction in MTP was compared., Results: The amount of α-IFN2b extracted using automated microscale platform (49.2 μg/L) was comparable to manual osmotic shock method (48.8 μg/L), but the standard deviation was 2 times lower as compared to manual osmotic shock method. Fermentation parameters in MTP involving inoculum size, agitation speed, working volume and induction profiling revealed that the fermentation conditions for the highest production of α-IFN2b (85.5 μg/L) was attained at inoculum size of 8%, working volume of 40% and agitation speed of 1000 rpm with induction at 4 h after the inoculation., Conclusion: Although the findings at MTP scale did not show perfect scalable results as compared to shake flask culture, but microscale technique development would serve as a convenient and low-cost solution in process optimization for recombinant protein.

Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R² and adjusted R² values for the model. Although the R (2) value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R², adjusted R², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.

Induction strategies for the periplasmic production of recombinant human IFN-alpha2b (interferon-alpha2b) by recombinant Escherichia coli Rosetta-gami 2(DE3) were optimized in shake-flask cultures using response surface methodology based on the central composite design. The factors included in the present study were induction point, which related to the attenuance of the cell culture, IPTG (isopropyl beta-D-thiogalactoside) concentration and induction temperature. Second-order polynomial models were used to correlate the abovementioned factors to soluble periplasmic IFN-alpha2b formation and percentage of soluble IFN-alpha2b translocated to the periplasmic space of E. coli. The models were found to be significant and subsequently validated. The proposed induction strategies consisted of induction at an attenuance of 4 (measured as D600), IPTG concentration of 0.05 mM and temperature of 25 degrees C. The optimized induction strategy reduced inclusion-body formation as evidenced by electron microscopy and yielded 323.8 ng/ml of IFN-alpha2b in the periplasmic space with translocation of 74% of the total soluble product. In comparison with the non-optimized condition, soluble periplasmic production and the percentage of soluble IFN-alpha2b translocated to the periplasmic space obtained in optimized induction strategies were increased by approx. 20-fold and 1.4-fold respectively.