Results of lipase production by a soil microorganism, expressed in terms of lipolytic activities of the culture were modeled and optimized using artificial neural network (ANN) and genetic algorithm (GA) techniques, respectively. ANN model, developed based on back propagation algorithm, were highly accurate in predicting the system with coefficient of determination (R2) value being close to 0.99. Optimization using GA, based on the ANN model developed, resulted in the following values of the media constituents: 9.991 ml/l oil, 0.100 g/l MgSO4 and 0.009 g/l FeSO4. And a maximum value of 7.69 U/ml of lipolytic activity at 72 h of culture was obtained using the ANN-GA method, which was found to be 8.8% higher than the maximum values predicted by a statistical regression-based optimization technique-response surface methodology.
Soil contaminated with vegetable cooking oil was used in the isolation of a lipase-producing microorganism. The effectiveness of two different statistical design techniques in the screening and optimization of media constituents for enhancing the lipolytic activity of the soil microorganism was determined. The media constituents for lipase production by the isolated soil microorganism were screened using a Plackett-Burman design. Oil, magnesium sulfate, and ferrous sulfate were found to influence lipolytic activity at 24 and 72 h of culture with very high confidence levels. Whereas oil and ferrous sulfate showed a positive effect, magnesium sulfate indicated a negative effect on the lipolytic activity. A central composite design (CCD) followed by response surface methodology was used in optimizing these media constituents for enhancing the lipolytic activity. The regression model obtained for 72 h of lipolytic activity was found to be the best fit, with R 2=0.97, compared with the other model. An optimum combination at 9.3 mL/L of oil, 0.311 g/L of magnesium sulfate, and 0.007 g/L of ferrous sulfate in the media gave a maximum measured lipolytic activity of 7.1 U/mL at 72 h of culture. This increase in lipolytic activity was found to be 10.25% higher than the maximum experimentally observed value in the CCD.
Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.
A double-hybridization approach was developed for the enzyme-free detection of specific mRNA of a housekeeping gene. Targeted mRNA was immobilized by hybridization to complementary DNA capture probes spotted onto a microarray. A second hybridization step of Cy5-conjugated label DNA to another section of the mRNA enabled specific labeling of the target. Thus, enzymatic artifacts could be avoided by omitting transcription and amplification steps. This manuscript describes the development of capture probe molecules used in the transcription- and amplification-free analysis of RPLP0 mRNA in isolated total RNA. An increase in specific signal was found with increasing length of the target-specific section of capture probes. Unspecific signal comprising spot autofluorescence and unspecific label binding did not correlate with the capture length. An additional spacer between the specific part of the capture probe and the substrate attachment site increased the signal significantly only on a short capture probe of approximately 30 nt length.
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