Monika Másilková, František Vorel
Alcohol and addictive substances in deceased active participants of traffic accidents
Soudni lekarstvi
maximizing recombinant protein yield necessitates optimizing production medium done using variety methods including conventional onefactoratatime approach recent statistical mathematical methods artificial neural network ann genetic algorithm etc every approach advantages disadvantages yet even technique flaws nevertheless used get best results one categorical variable four numerical parameters including postinduction time inducer concentration postinduction temperature preinduction cell density optimized using 232 experimental assays central composite design direct indirect effects factors yield antiepithelial cell adhesion molecule extracellular domain fragment antibody examined using statistical methods analysis variance results indicate response surface methodology rsm model effective predicting amount produced singlechain fragment variable pvalue 00001 r2 0905 ann modeling evaluation using normalized root mean square error nrmse r2 values shows good fit r2 0942 accurate predictions nrmse 0145 analysis error parameters r2 dataset contained 30 data points randomly selected complete dataset showed ann model higher r2 value 0968 compared rsm model 0932 furthermore ann model demonstrated stronger predictive ability lower nrmse 0048 vs 0064 induction cell density 07 isopropyl d1thiogalactopyranoside concentration 06 mm 32 h 30c bw25113 ideal culture condition leading protein yield 25951 mgl optimum conditions output values predicted ann model 25983 mgl line experimental data 25951 mgl rsm 27613 mgl expected value outcome demonstrated ann model outperforms rsm terms prediction accuracy