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AbstractFor 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 Model (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 coupled with 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.

AIM: Association studies of serotonin transporter gene SLC6A4 I/S polymorphism and irritable bowel syndrome (IBS) have shown inconsistent and contradictory results among different populations. In the present study, meta-analysis was performed to evaluate the association between SLC6A4 I/S polymorphism and IBS susceptibility. METHODOLOGY: Systemic assessment was performed for the published studies based on the association of SLC6A4 I/S polymorphism and IBS risk from PubMed (Medline), EMBASE search. A meta-analysis was done to appraise the said association. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for allele contrast, homozygous, heterozygous, dominant and recessive genetic model. RESULTS: A total of twelve studies comprising 2068 IBS cases and 2076 controls were included in this meta-analysis. Overall, no significant results were obtained for S allele carrier (S vs. I: p=0.488; OR=1.073, 95% CI=0.879 to 1.311) Co-dominant (SS vs. II; p=0.587; OR=1.112, 95% CI=0.758 to 1.631), (IS vs. II; p=0.361; OR=0.878, 95% CI=0.665 to 1.160). Similarly, dominant (SS+IS vs. II: p=0.853; OR=0.974, 95% CI=0.736 to 1.288) and recessive (SS vs. II+IS: p=0.267; OR=1.172, 95% CI=0.886 to 1.522) genetic models did not demonstrate risk. In the subgroup population based analysis, reduced risks were found in American (IS vs. II: p=0.009; OR=0.685, 95% CI=0.516 to 0.908) and Asian (SS+IS vs. II; p=0.001; OR=0.116, 95% CI=0.068 to 0.197) population. However, no risk was observed in European population. CONCLUSIONS: This investigation clearly demonstrates that SLC6A4 (Ins/Del) polymorphism is associated with reduced risk of IBS in American and Asian population. However, future well-designed studies with stratified case control and biological characterization will be needed to validate this finding.