Integration of Modeling in the Optimization
of Experimental Parameters in Microfluidics
and Capillary Electrophoresis

For several years we have focused and collaborated in applying chemometrics in the optimization of experimental parameters in CE and microfluidics. The heterogeneity of complex chemical and biological systems raises fundamental difficulties with regard to the ability of investigators to separate and detect analytes of interest, and to correlate variables and predict patterns within samples and differing sample matrices. Fortunately, the recent development of novel learning algorithms, hybrid computational techniques, and gains in raw computing power and speed, has raised several fundamental modeling and simulation research lines to account for these difficulties. We recently implemented the use of hybrid neural network methodology in optimizing fluorescence upon binding a receptor to a ligand on a microchip. Previous work has applied experimental design-based techniques in flow-injection, affinity capillary electrophoresis (ACE) and electrophoretically mediated microanalysis (EMMA).

References

  1. "Implementation of Chemometric Methodology in Affinity Capillary Electrophoresis (ACE): Predictive Investigation of Protein-Ligand Binding", Hanrahan, G.; Montes, R.; Pao, A.; Johnson, A.; Gomez, F. A. Electrophoresis, 2007, 28, 2853-2860.
  2. "Response Surface Examination of the Relationship Between Experimental Conditions and Product Distribution in Electrophoretically Mediated Microanalysis (EMMA)," Montes, R.; Gomez, F. A.; Hanrahan, G., Electrophoresis, 2008, 29, 375-380.
  3. "Chemometric Experimental Design-Based Optimization Techniques in Capillary Electrophoresis: A Critical Review of Modern Applications", Hanrahan, G.; Montes, R.; Gomez, F. A., Anal. Bioanal. Chem. 2008, 390, 169-179.
  4. "Chemometrical Experimental Design-Based Optimization Studies in Capillary Electrophoresis Applications", Montes, R.; Dahdouh, F.; Riveros, T. A.; Hanrahan, G.; Gomez, F. A. LCGC, 2008, 26, 712-721.
  5. "Use of Chemometric Methodology in Optimizing Conditions for Competitive Binding Partial Filling Affinity Capillary Electrophoresis (PFACE)", Montes, R.; Hanrahan, G.; Gomez, F. A., Electrophoresis, 2008, 29, 3325-3332.
  6. "Chemometrical Examination of Active Parameters and Interactions in Flow Injection-Capillary Electrophoresis (FI-CE)," Dahdouh, F. T.; Clarke, K.; Salgado, M.; Hanrahan, G.; Gomez, F. A. Electrophoresis, 2008, 29, 3779-3785.
  7. "Application of Artificial Neural Networks in the Prediction of Product Distribution in Electrophoretically Mediated Microanalysis (EMMA)", Riveros, T. A.; Porcasi,L.; Muliadi, S.; Hanrahan, G.; Gomez, F. A. Electrophoresis 2009, 30, 2385-2389.
  8. "On-Capillary Derivatization Using a Hybrid Artificial Neural Network-Genetic Algorithm Approach", Riveros, T. A.; Hanrahan, G.; Muliadi, S.; Arceo, J.; Gomez, F. A. Analyst, 2009, 134, 2067-2070.
  9. "Implementation of a Genetically Tuned Neural Platform in Optimizing Fluorescence from Receptor-Ligand Binding Interactions on Microchips", Alvarado, J.; Hanrahan, G.; Nguyen, H. T. H.; Gomez, F. A. Electrophoresis, 2012, 33, 2711-2717.