Anand Ramakrishnan

An evaluation of hybridization kinetics in biosensors using a single‐fractal analysis

The diffusion‐limited hybridization kinetics of analyte in solution to receptor immobilized on a biosensor or immunosensor surface is analysed within a fractal framework. The data may be analysed by a single‐fractal analysis. This was indicated by the regression analysis provided by Sigmaplot [Sigmaplot Users Manual (1993) Jandel Scientific, San Rafael, CA]. It is of interest to note that the binding‐rate coefficient and the fractal dimension both exhibit changes in the same and in the opposite directions for the single example presented in each case. The binding‐rate coefficient(s) expressions developed as a function of the analyte (DNA) concentration in solution and the fractal dimension are of particular value, since they provide a means to better control biosensor or immunosensor performance and provide physical insights into the hybridization process.

A kinetic study of analyte–receptor binding and dissociation for biosensor applications: a fractal analysis for cholera toxin and peptide–protein interactions

Abstract A fractal analysis is presented for (a) analyte–receptor binding and (b) binding and dissociation kinetics for biosensor applications. Examples analyzed include the binding of different concentrations of cholera toxin (CT) in solution to fluorophore-labeled ganglioside GM1 immobilized on a biomimetic membrane surface [16] , and tyrosine kinase lck SH2 domain in solution to a phosphorylated peptide immobilized on a new cuvette-based surface plasmon resonance (SPR) instrument [14] . A single and a dual-fractal analysis is required to describe the binding kinetics for CT. The dual-fractal analysis represents a change in the binding mechanism as the reaction progresses on the surface. A single and a dual-fractal analysis is required to describe the binding kinetics for tyrosine kinase lck SH2 domain in solution to the phosphorylated peptide. In this case, the dissociation kinetics may be described by a single-fractal analysis. Relationships are presented for the dissociation rate coefficient as a function of (a) the fractal dimension, D fd or the degree of heterogeneity that exists on the surface, and (b) as a function of the phosphotyrosine peptide, EPQY∗EEIPIYL concentration. When analyte–receptor dissociation is involved, an increase in the heterogeneity on the surface (increase in D fd ) leads to an increase in the dissociation rate coefficient.

Analyte-receptor binding and dissociation kinetics for biosensor applications: a fractal analysis

A fractal analysis of confirmative nature only is presented for analyte-receptor binding and dissociation kinetics for biosensor applications. Data taken from the literature may be modeled, in the case of binding using a single-fractal analysis or a dual-fractal analysis. The dual-fractal analysis represents a change in the binding mechanism as the reaction progresses on the surface. Relationships are presented for the binding and dissociation rate coefficients as a function of their corresponding fractal dimension, Df or the degree of heterogeneity that exists on the surface. When analyte-receptor binding or dissociation is involved, an increase in the heterogeneity on the surface (increase in Df) leads to an increase in the binding and in the dissociation rate coefficient. It is suggested that an increase in the degree of heterogeneity on the surface leads to an increase in the turbulence on the surface owing to the irregularities on the surface. This turbulence promotes mixing, minimizes diffusional limitations, and leads subsequently to an increase in the binding and in the dissociation rate coefficient (Martin S.J., Granstaff, V.E., Frye, G.C., Anal. Chem., 65, (1991) 2910). The binding and the dissociation rate coefficient are rather sensitive to the degree of heterogeneity, Df,bind and Df,diss respectively, that exists on the biosensor surface. For example, the order of dependence on Df,bind is 19.2 for the binding rate coefficient, kbind for the binding of 0.03-1.0 microM SH-2Ld in solution to 2C TCR immobilized on a surface plasmon resonance (SPR) biosensor (Corr, M., Salnetz, A.E., Boyd, L.F., Jelonek, M.T., Khilko, S., Al-Ramadi, B.K., Kim, Y.S., Maher, S.E., Bothwell, A.L.M., Margulies, D.H., Science, 265, (1994) 946). The order of dependence on Df,diss is -6.22 for the dissociation rate coefficient, kdiss for the dissociation of 250-1000 nM Sophora japonica agglutinin (SJA)-lactose complex from the SPR surface. In general, the technique is applicable to other reactions occurring on different types of surfaces, such as cell-surface reactions.

A fractal analysis of analyte-estrogen receptor binding and dissociation kinetics using biosensors: environmental and biomedical effects.

A fractal analysis is used to model the binding and dissociation kinetics between analytes in solution and estrogen receptors (ERs) immobilized on a sensor chip of a surface plasmon resonance (SPR) biosensor. The influence of different ligands is also analyzed. A better understanding of the kinetics provides physical insights into the interactions, and suggests means by which appropriate interactions (to promote correct signaling) and inappropriate interactions such as with xenoestrogens (to minimize inappropriate and deleterious to health signaling) may be better controlled. The fractal approach is applied to analyte-ER interaction data available in the literature. The units for the different parameters (rate coefficients and affinities) in fractal-type kinetics are different from those obtained in classical kinetics. Numerical values obtained for the binding and the dissociation rate coefficients are linked to the degree of roughness or heterogeneity (fractal dimension, D(f)) present on the biosensor chip surface. In general, the binding and the dissociation rate coefficients are very sensitive to the degree of heterogeneity on the surface. A single-fractal analysis is adequate in some cases. In others (that exhibit complexities in the binding or the dissociation curves) a dual-fractal analysis is required to obtain a better fit. This has biomedical and environmental implications in that the dissociation (and the binding) rate coefficient may be used to alleviate (deleterious effects) or enhance (beneficial effects) by selective modulation of the surface. The affinity values obtained in the analysis are consistent with the numbers required to (a). promote signaling between the correct analyte and the estrogen receptor, and (b). minimize the signaling between xenoestrogens and the estrogen receptor.

Analysis of analyte–receptor binding kinetics for biosensor applications: an overview of the influence of the fractal dimension on the surface on the binding rate coefficient

An overview of fractal analysis is presented for analyte–receptor binding kinetics for different types of biosensor application. Data taken from the literature can be modelled by using (1) a single‐fractal analysis, (2) a single‐ and a dual‐fractal analysis, and (3) a dual‐fractal analysis. Cases (2) and (3) represent a change in the binding mechanism as the reaction progresses on the surface. Predictive relationships developed for the binding rate coefficient as a function of the analyte concentration are of particular value because they provide a means by which the binding rate coefficients can be manipulated. Relationships are presented for the binding rate coefficients as a function of the fractal dimension, Df, or the degree of heterogeneity that exists on the surface. The binding rate coefficient is rather sensitive to the degree of heterogeneity, Df, that exists on the biosensor surface. For the examples analysed, the order of dependence of the binding rate coefficient on Df ranges from 1.4770 (k1), for the binding of intercalators and metabolites in solution to DNA immobilized at a positively charged surface, to 4.9434 for the binding of 5 nM nucleotide+GroEL in solution to GroES immobilized on a Ni2+‐nitriloacetic acid sensor chip [Nieba, Nieba‐Axmann, Persson, Hamalainen, Edebratt, Hansson, Lidholm, Magnusson, Karlsson and Pluckhun (1997) Anal. Biochem. 252, 217–228]. GroEl and GroES are two proteins (chaperones) which facilitate protein folding in the cell in an ATP‐dependent manner [Hemmingson, Woolford, van der Vies, Tilly, Dennis, Georgopoulos, Henfrix and Ellis (1988) Nature (London) 333, 330–334]. The overview provides an overall analysis of the reaction parameters of importance observed and how they are influenced in antigen–antibody‐binding kinetics for different biosensor applications. The predictive relationships presented provide further physical insights into the binding reactions on the surface, and should assist in enhancing biosensor performance. In general, the technique and the overview presented are applicable for the most part to other reactions occurring on different types of surface, for example cell‐surface reactions.

A kinetic study of analyte-receptor binding and dissociation for surface plasmon resonance biosensors applications

A fractal analysis, which takes into account the effect of surface heterogeneity brought about by ligand immobilization on the reaction kinetics in surface plasmon resonance (SPR) biosensors, is presented. The binding and dissociation of estrogen receptors (ERs), ERa and ER/spl alpha/ and ER/spl beta/, in solution to different ligands immobilized on the SPR biosensor is analyzed within the fractal framework. The heterogeneity on the biosensor surface is made quantitative by using a single number, the fractal dimension D/sub f/. The analysis provides physical insights into the binding of these receptors to different ligands and compounds, particularly the endocrine disrupting compounds (EDCs). These EDCs have deleterious effects on humans and on wildlife. Single- and dual-fractal models were employed to fit the ER-binding data obtained from the literature. Values of the binding and dissociation rate coefficient and fractal dimensions were obtained from a regression analysis provided by Corel Quattro Pro, 8.0. Values for the affinity K/sub D/(=k/sub d//k/sub a/) were also calculated. This provides us with some extra flexibility in designing biomolecular assays. The analysis should provide further information on the mode of action and interaction of EDCs with the ERs. This would help in the design of agents and modulators against these EDCs.

A kinetic study of analyte–receptor binding and dissociation for biosensor applications: a fractal analysis for two different DNA systems

A fractal analysis of DNA binding and dissociation kinetics on biosensor surfaces is presented. The fractal approach provides an attractive, convenient method to model the kinetic data taking into account the effects of surface heterogeneity brought about by ligand immobilization. The fractal technique can be used in conjunction or as an alternate approach to conventional modeling techniques, such as the Langmuir model, saturation model, etc. Examples analyzed include a DNA molecular beacon biosensor and a plasmid DNA-(cationic polymer) interaction biosensor. The molecular beacon example provides some insights into the nature of the surface and how it influences the binding rate coefficients. The DNA-cationic polymer interaction example provides some quantitative results on the binding and dissociation rate coefficients. Data taken from the literature may be modeled, in the case of binding, using a single-fractal analysis or a dual-fractal analysis. The dual-fractal analysis results indicate a change in the binding mechanism as the reaction progresses on the surface. A single-fractal analysis is adequate to model the dissociation kinetics in the example presented. Relationships are presented for the binding rate coefficients as a function of their corresponding fractal dimension, D(f), which is an indication of the degree of heterogeneity that exists on the surface. When analyte-receptor binding is involved, an increase in the heterogeneity of the surface (increase in D(f)) leads to an increase in the binding rate coefficient.

A kinetic study of analyte-receptor binding and dissociation for surface plasmon resonance biosensor applications

A fractal analysis which takes into account the effect of surface heterogeneity brought about by ligand immobilization on the reaction kinetics is presented. The binding and dissociation of estrogen receptors ER/spl alpha/ and ER/spl beta/ to different ligands is analyzed within the fractal framework. The heterogeneity on the biosensor surface is made quantitative by using a single number, the fractal dimension, D/sub f/. The analysis provides physical insights into the binding of these receptors to different ligands and compounds, particularly the EDCs (endocrine disrupting compounds), which can have deleterious affects on humans and wildlife. Single- and dual-fractal models were employed to fit the ER binding data obtained from literature. Values of the binding and dissociation rate coefficient and fractal dimensions were obtained from a regression analysis provided by Corel Quattro Pro 8.0 (1997). In some cases both a single- and dual-fractal model was required to completely and adequately describe the kinetics involved. Values for the affinity, K/sub D/ (=k/sub d//k/sub a/) were also calculated. This provides us with some extra flexibility in designing biomolecular assays.A fractal analysis, which takes into account the effect of surface heterogeneity brought about by ligand immobilization on the reaction kinetics in surface plasmon resonance (SPR) biosensors, is presented. The binding and dissociation of estrogen receptors (ERs), ERa and ER/spl alpha/ and ER/spl beta/, in solution to different ligands immobilized on the SPR biosensor is analyzed within the fractal framework. The heterogeneity on the biosensor surface is made quantitative by using a single number, the fractal dimension D/sub f/. The analysis provides physical insights into the binding of these receptors to different ligands and compounds, particularly the endocrine disrupting compounds (EDCs). These EDCs have deleterious effects on humans and on wildlife. Single- and dual-fractal models were employed to fit the ER-binding data obtained from the literature. Values of the binding and dissociation rate coefficient and fractal dimensions were obtained from a regression analysis provided by Corel Quattro Pro, 8.0. Values for the affinity K/sub D/(=k/sub d//k/sub a/) were also calculated. This provides us with some extra flexibility in designing biomolecular assays. The analysis should provide further information on the mode of action and interaction of EDCs with the ERs. This would help in the design of agents and modulators against these EDCs.

Economics of bioseparation processes

Publisher Summary This chapter discusses the economics of bioseparation processes. Valuable products are being produced increasingly by biotechnological methods. One needs to separate the biological macromolecule of interest; and herein lies a very significant cost of the entire process. It is indicated that the purification and recovery costs may be as high as 80% of the total manufacturing costs. These costs may be higher if ultrahigh purity of DNA-involved products are manufactured. Recognize that during processing one may have to purify products at 99.9% levels with virtual complete removal of DNA, viruses, and endotoxins. The key to cutting production costs is to emphasize improvements in downstream processing. Traditionally all the steps occurring in the fermentor that result in the production of the desired biological macromolecule can be considered as upstream processes. All the other processes occuring after the fermentation and which result in the separation, purification, concentration, and conversion of the biomolecule to a form suitable for its intended final use can be classified under downstream processes. Thus, it is helpful to better analyze and understand the different facets involved during downstream processing. Better physical insights are required and are continuously being obtained in downstream processes, and these will eventually lead to a more efficient and economical process.

Biosensors: An Emerging Tool in Bioanalytical Research — A Kinetic Analysis of the Analyte-Receptor Interactions in Biosensors

A promising area in the investigation of biomolecular interactions is the development of biosensors. These biosensors are finding applications, as an important bioanalytical tools in the areas of biotechnology, physics, chemistry, medicine, aviation, oceanography, and environmental control. These sensors or biosensors may be utilized to monitor the analyte-receptor reactions in real time [1]; furthermore, some techniques like the surface plasmon resonance (SPR) biosensor do not require radiolabeling or biochemical tagging [2], are reusable, have a flexible experimental design, provide a rapid and automated analysis, and have a completely integrated system. Besides, the SPR in combination with mass spectrometry (MS) exhibits the potential to provide a proteomic analysis [3].