Antigen-antibody binding kinetics for biosensor applications

The diffusion-limited binding kinetics of antigen (or antibody) in solution to antibody (or antigen) immobilized on a biosensor surface is analyzed within a fractal framework. The fit obtained by a dual-fractal analysis is compared with that obtained from a single-fractal analysis. In some cases, the dual-fractal analysis provides an improved fit when compared with a single-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot (San Rafael, CA). These examples are presented. It is of interest to note that the state of disorder (or the fractal dimension) and the binding rate coefficient both increase (or decrease, a single example is presented for this case) as the reaction progresses on the biosensor surface. For example, for the binding of monoclonal antibody MAb 49 in solution to surface-immobilized antigen, a 90.4% increase in the fractal dimension (D_f1 toD _f2 ) from 1.327 to 2.527 leads to an increase in the binding rate coefficient (k₁ to k₂) by a factor of 9.4 from 11.74 to 110.3. The different examples analyzed and presented together provide a means by which the antigen-antibody reactions may be better controlled by noting the magnitude of the changes in the fractal dimension and in the binding rate coefficient as the reaction progresses on the biosensor surface.     

Analyte-receptor binding kinetics for different types of biosensors

A fractal analysis is presented for analyte-receptor binding kinetics for different types of biosensor applications. Data taken from the literature may be modeled using a single-fractal analysis, a single- and a dual-fractal analysis, or a dual-fractal analysis. The latter two methods 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 since they provide a means by which the binding rate coefficients may be manipulated. Relationships are presented for the binding rate coefficients as a function of the fractal dimension D _f or the degree of heterogeneity that exists on the surface. When analyte-receptor binding is involved, an increase in the heterogeneity on the surface (increase in D _f ) leads to an increase in the binding 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 rate coefficient. The binding rate coefficient is rather sensitive to the degree of heterogeneity, D _f , that exists on the biosensor surface. For example, the order of dependence on D _f1 is 7.25 for the binding rate coefficient k ₁ for the binding of a Fab fragment of an antiparaquat monoclonal antibody in solution to an antigen in the form of a paraquat analog immobilized on a sensor surface. The predictive relationships presented for the binding rate coefficient and the fractal dimension as a function of the analyte concentration in solution provide further physical insights into the binding reactions on the surface, and should assist in enhancing biosensor performance. In general, the technique is applicable to other reactions occurring on different types of surfaces, such as cell-surface reactions.     

A Single and a Dual-Fractal Analysis of Analyte-Receptor Binding Kinetics for Surface Plasmon Resonance Biosensor Applications

The diffusion-limited binding kinetics of analyte in solution to either a receptor immobilized on a surface or to a receptorless surface is analyzed within a fractal framework for a surface plasmon resonance biosensor. The data is adequately described by a single- or a dual-fractal analysis. Initially, the data was modeled by a single-fractal analysis. If an inadequate fit was obtained then a dual-fractal analysis was utilized. The regression analysis provided by Sigmaplot (32) was used to determine if a single fractal analysis is sufficient or if a dual-fractal analysis is required. In general, it is of interest to note that the binding rate coefficient and the fractal dimension exhibit changes in the same direction (except for a single example) for the analyte-receptor systems analyzed. Binding rate coefficient expressions as a function of the fractal dimension developed for the analyte-receptor binding systems indicate, in general, the high sensitivity of the binding rate coefficient on the fractal dimension when both a single- and a dual-fractal analysis is used. For example, for a single-fractal analysis and for the binding of human endothelin-1 (ET-1) antibody in solution to ET-115-21.BSA immobilized on a surface plasmon resonance (SPR) surface (33), the order of dependence of the binding rate coefficient, k, on the fractal dimension, Df, is 6.4405. Similarly, for a dual-fractal analysis and for the binding of 10(-6) to 10(-4) M bSA in solution to a receptorless surface (direct binding to SPR surface) (41) the order of dependence of k1 and k2 on Df1 and Df2 were -2.356 and 6.241, respectively. Binding rate coefficient expressions are also developed as a function of the analyte concentration in solution. The binding rate coefficient expressions developed as a function of the fractal dimension(s) are of particular value since they provide a means to better control SPR biosensor performance by linking it to the degree of heterogeneity that exists on the SPR biosensor surface. Copyright 1999 Academic Press.     

Antigen-antibody binding kinetics for biosensors

The diffusion-limited binding kinetics of antigen in solution to antibody immobilized on a biosensor surface is analyzed within a fractal framework. Changes in the fractal dimension, D_f observed are in the same and in the reverse directions as the forward binding rate coefficientk. For example, an increase in the concentration of the isoenzyme human creatine kinase isoenzyme MB form (CK-MB) (antigen) solution from 0.1 to 50 ng/mL and bound to anti-CK-MB antibody immobilized on fused silica fiber rods leads to increases in the fractal dimension D_f from 0.294 to 0.5080, and in the forward binding rate coefficientk from 0.1194 to 9.716, respectively. The error in the fractal dimension D_f decreases with an increase in the CK-MB isoenzyme concentration in solution. An increase in the concentration of human chorionic gonadotrophin (hCG) in solution from 4000 to 6000 mIU/mL hCG and bound to anti-hCG antibody immobilized on a fluorescence capillary fill device leads to a decrease in the fractal dimension D_f from 2.6806 to 2.6164, and to an increase in the forward binding rate coefficientk from 3.571 to 4.033, respectively. The different examples analyzed and presented together indicate one means by which the forward binding rate coefficientk may be controlled, that is by changing the fractal dimension or the ‘disorder’ on the surface. The analysis should assist in helping to improve the stability, the sensitivity, and the response time of biosensors.     

A Fractal Analysis Approach for the Evaluation of Hybridization Kinetics in Biosensors

The diffusion-limited hybridization kinetics of analyte in solution to a receptor immobilized on a biosensor or immunosensor surface is analyzed within a fractal framework. The data may be analyzed by a single- or a dual-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot (Sigmaplot, Scientific Graphing Software, User's Manual, Jandel Scientific, CA, 1993). It is of interest to note that the binding rate coefficient and the fractal dimension both exhibit changes, in general, in the same direction for both the single-fractal and the dual-fractal analysis examples presented. The binding rate coefficient expression developed as a function of the analyte concentration in solution and the fractal dimension is of particular value since it provides a means to better control biosensor or immunosensor performance. Copyright 2001 Academic Press.     

An Evaluation of Cellular Analyte-Receptor Binding Kinetics Utilizing Biosensors: A Fractal Analysis

A fractal analysis is presented for cellular analyte-receptor binding kinetics utilizing biosensors. Data taken from the literature can be modeled by using (a) a single-fractal analysis and (b) a single- and a dual-fractal analysis. Case (b) represents a change in the binding mechanism as the reaction progresses on the biosensor surface. Relationships are presented for the binding rate coefficient(s) as a function of the fractal dimension for the single-fractal analysis examples. In general, the binding rate coefficient is rather sensitive to the degree of heterogeneity that exists on the biosensor surface. For example, for the binding of mutagenized and back-mutagenized forms of peptide E1037 in solution to salivary agglutinin immobilized on a sensor chip, the order of dependence of the binding rate coefficient, k, on the fractal dimension, D(f), is 13.2. It is of interest to note that examples are presented where the binding coefficient (k) exhibits an increase as the fractal dimension (D(f)) or the degree of heterogeneity increases on the surface. The predictive relationships presented provide further physical insights into the binding reactions occurring on the surface. These should assist us in understanding the cellular binding reaction occurring on surfaces, even though the analysis presented is for the cases where the cellular "receptor" is actually immobilized on a biosensor or other surface. The analysis suggests possible modulations of cell surfaces in desired directions to help manipulate the binding rate coefficients (or affinities). In general, the technique presented is applicable for the most part to other reactions occurring on different types of biosensors or other surfaces. Copyright 2000 Academic Press.     

A Predictive Approach Using Fractal Analysis for Analyte-Receptor Binding and Dissociation Kinetics for Surface Plasmon Resonance Biosensor Applications

A predictive approach using fractal analysis 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. A single-fractal analysis is adequate to model the dissociation kinetics in the examples presented. Predictive relationships developed for the binding and the affinity (k(diss)/k(bind)) as a function of the analyte concentration are of particular value since they provide a means by which the binding and the affinity rate coefficients may be manipulated. Relationships are also presented for the binding and the dissociation rate coefficients and for the affinity as a function of their corresponding fractal dimension, D(f), 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 D(f)) 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. The binding and the dissociation rate coefficients are rather sensitive to the degree of heterogeneity, D(f,bind) (or D(f1)) and D(f,diss), respectively, that exists on the biosensor surface. For example, the order of dependence on D(f,bind) (or D(f1)) and D(f2) is 6.69 and 6.96 for k(bind,1) (or k(1)) and k(2), respectively, for the binding of 0.085 to 0.339 μM Fab fragment 48G7(L)48G7(H) in solution to p-nitrophenyl phosphonate (PNP) transition state analogue immobilized on a surface plasmon resonance (SPR) biosensor. The order of dependence on D(f,diss) (or D(f,d)) is 3.26 for the dissociation rate coefficient, k(diss), for the dissociation of the 48G7(L)48G7(H)-PNP complex from the SPR surface to the solution. The predictive relationships presented for the binding and the affinity as a function of the analyte concentration in solution provide further physical insights into the reactions on the surface and should assist in enhancing SPR biosensor performance. In general, the technique is applicable to other reactions occurring on different types of biosensor surfaces and other surfaces such as cell-surface reactions. Copyright 2000 Academic Press.     

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

A fractal analysis is used to model the binding and dissociation kinetics between analytes in solution and estrogen receptors (ER) immobilized on a sensor chip of a surface plasmon resonance (SPR) biosensor. Both cases are analyzed: unliganded as well as liganded. 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 signaling and signaling deleterious to health) may be better controlled. The fractal approach is applied to analyte-ER interaction data available in the literature. 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. For example, the binding rate coefficient, k, exhibits a 4.60 order of dependence on the fractal dimension, D(f), for the binding of unliganded and liganded VDR mixed with GST-RXR in solution to Spp-1 VDRE (1,25-dihydroxyvitamin D(3) receptor element) DNA immobilized on a sensor chip surface (Cheskis and Freedman, Biochemistry 35 (1996) 3300-3318). 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. A predictive relationship is also presented for the ratio K(A)(=k/k(d)) as a function of the ratio of the fractal dimensions (D(f)/D(fd)). This has biomedical and environmental implications in that the dissociation and binding rate coefficients may be used to alleviate deleterious effects or enhance beneficial effects by selective modulation of the surface. The K(A) exhibits a 112-order dependence on the ratio of the fractal dimensions for the ligand effects on VDR-RXR interaction with specific DNA.     

Surface Roughness Characterization of Poly(methylmethacrylate) Films with Immobilized Eu(III) β-Diketonates by Fractal Analysis

The structural complexity of the 3-D surface of poly(methylmethacrylate) films with immobilized europium β-diketonates was studied by atomic force microscopy and fractal analysis. Fractal analysis of surface roughness revealed that the 3-D surface has fractal geometry at the nanometer scale. Poly(methylmethacrylate) (PMMA) as immobilization matrix is dense and uniform, and a tendency for formation of chain structures was observed. Fractal analysis can quantify key elements of 3-D surface roughness such as the fractal dimensions D _f determined by the morphological envelopes method of the Eu(DBM)₃ and Eu(DBM)₃ · dpp nanostructures, which are not taken into account by traditional surface statistical parameters.     

Antigen-antibody diffusion-limited binding kinetics for biosensors

A fractal analysis is made for antigen-antibody binding kinetics for different biosensor applications available in the literature. Both types of examples are considered wherein: (1) the antigen is in solution and the antibody is immobilized on the fiberoptic surface, and (2) the antibody is in solution and the antigen is immobilized on the fiberoptic surface. For example, when the antibody is immobilized on the surface, an increase in the antigenClostridium botulinum toxin A concentration in solution leads to (1) a decrease in the fractal dimension value or state of disorder, and (2) a higher rate constant for binding on the fiberoptic surface. An analysis of the effect of the influence of different parameters on the fractal dimension values for a particular example, such as varying treatments or incubation procedures, helps provide insights into the conformational states and reactions occurring on the fiberoptic surface. The analysis of the different examples taken together provides novel physical insights into the state of “disorder” and reactions occurring on the surface. Such types of analysis should help contribute toward manipulating the reactions occurring on the fiberoptic surfaces in desired directions.     

Growth process for fractal polymer aggregates formed by perfluorooctyltrimethoxysilane. Time-resolved small-angle X-ray scattering study

The acid-catalyzed condensation reaction of perfluorooctyltrimethoxysilane (PFOS) and n-octyltrimethoxysilane (OTMS) in ethanol has been followed by time-resolved synchrotron radiation small-angle X-ray scattering (SAXS) on a short time scale. SAXS curves for PFOS and OTMS have been interpreted as arising from mass fractals with D _f=2 (PFOS) and D _f=1.7 (OTMS). The time dependence of the apparent radius of gyration, obtained from the Guinier plots, showed that the growth of fractal precursors occurs in a two-step process within 2 h for PFOS and within 1.5 h for OTMS, in which small clusters involving monomers, dimers and trimers are formed in the initial step and formation of larger clusters occurs in the second step. Furthermore, it has been suggested that the hydrophobicity and lipophobicity of the bulky alkyl groups may also contribute to the formation of these giant aggregates. Received: 13 July 1999/Accepted in revised form: 6 October 1999     

Design variations of a polymer–enzyme composite biosensor for glucose: Enhanced analyte sensitivity without increased oxygen dependence

The glucose sensitivity and oxygen dependence of a variety of implantable biosensors based on glucose oxidase (GOx), incorporating an electrosynthesized poly-o-phenylenediamine (PPD) permselective barrier on 125-μm diameter Pt disks (Pt_D) and cylinders (Pt_C, 1-mm length), were measured and compared. Full glucose calibrations and experimental monitoring of solution oxygen concentration allowed us to determine apparent Michaelis–Menten parameters for glucose and oxygen. In the linear region of glucose response, the most sensitive biosensor design studied was Pt_D/PPD/GOx (enzyme deposited over polymer) that was 20 times more sensitive than the more widely used Pt_C/GOx/PPD (enzyme immobilized before polymer deposition) configuration. The oxygen dependence, quantified as K_M(O₂), of both active and less active designs was surprisingly similar, a finding that could be rationalized in terms of an increase in K_M(G) with increased enzyme loading. The Pt_D/PPD/GOx design will now enable us to explore glucose concentration dynamics in smaller and layered brain regions with good sensitivity and minimal interference from fluctuations in tissue pO₂.     

An optical biosensor for kinetic analysis of soluble Interleukin-1 receptor I binding to immobilized Interleukin-1alpha

We report here the development of an optical biosensor based on the resonant mirror for kinetic analysis of soluble Interleukin-1 receptor I (sIL-1R I) in solution binding to immobilized Interleukin-1α (IL-1α). IL-1α was immobilized through its surface amine groups via amide bonds with the carboxyl groups of the carboxymethyl dextran (CMD) on cuvette surface. The interaction of sIL-1R I and IL-1α was monitored in real time. Evaluation of the binding curves allowed the analysis of the binding kinetics. The linear range of sIL-1R I in solution was over a range of 100-1600 nM (R = 0.9962). Equilibrium dissociation constant (K_D) was derived by Scatchard plot analysis for sIL-1R I binding to immobilized IL-1α. For this assay, the K_D was 2.6 × 10⁻⁶ M. The CMD cuvette modified by IL-1α was successfully regenerated using 10 mM HCl, and the same sensing surface was used repeatedly for the interaction analysis.     

Fractal dimension and phase transition of graphene oxide (GO) doped polyacrylamide

Graphene Oxide (GO)- Polyacrylamide composites prepared between 5 and 50 μl GO were performed by Fluorescence Spectroscopy. The phase transition performed on the composites was measured by calculating the critical exponents, β and γ, respectively. In addition, fractal analysis of the composites was calculated by a fluorescence intensity of 427 nm. The geometrical distribution of GO in the composites was calculated based on the power law exponent values using scaling models. While the gelation proceeded GO plates first organized themselves into a 3D percolation cluster with the fractal dimension (D_f) of the composite, D_f = 2.63, then After it goes to diffusion limited clusters with D_f = 1.4, its dimension lines up to a Von Koch curve with a random interval of D_f = 1.14.     

Size exclusion chromatography on porous fractals

Summary Some porous packings used in chromatography have been claimed to be fractals with a scale of sizes a<l<L, where a is a molecular size and L is the size of the largest pores. For a fractal porous packing, the excluded volume for molecules in solution in the vicinity of the packing surface is directly related to D_f, the fractal dimension of the pore surface (2<D_f<3). Since retention in size exclusion chromatography is itself directly related to this excluded volume, the fractal nature of the packing provides a model of retention in this technique. According to this model there is a linear relationship between log R_s and log(1-K_d), where R_s is the hydrodynamic radius of the solute macromolecules and K_d the distribution coefficient. The fractal dimension is derived from the slope of this plot. Size exclusion chromatographic retention data have been analyzed according to the model. It is found that some HPLC packings are fractals with fractal dimensions ranging from about 2.15 to 2.6, depending on the material. Such a large range of D_f values indicates large variations in the selectivities and domains of applications of the different packings. For some classical gel filtration chromatographic gels, the fractal retention model does not seem to apply.Presented at the 17th International Symposium on Chromatography, September 25–30, 1988, Vienna, Austria.     

Effect of microabsorption heterogeneity in the X-ray fluorescence analysis of ultrafine particles

The dependence of the intensity (I _i ) of X-ray fluorescence on the size (D) of finely ground particles was studied for saturated and unsaturated samples. It was found that even at the wet grinding (addition of ethanol) of powders with D < 10 μm, the aggregation and covering of larger grains (α) with smaller grains of different compositions occur, which changes the character of the dependence I _i = f(D), particularly, if fluorescence is emitted by the grains α. The nature of the observed effects is proved by the results of granulometric analysis and by electron probe X-ray analysis. In the transition from saturated to unsaturated samples, the discussed effects are enhanced.     

Direct Electrochemistry of Hemoglobin Immobilized on Carbon‐Coated Iron Nanoparticles for Amperometric Detection of Hydrogen Peroxide

A novel method for preparation of hydrogen peroxide biosensor was presented based on immobilization of hemoglobin (Hb) on carbon‐coated iron nanoparticles (CIN). CIN was firstly dispersed in a chitosan solution and cast onto a glassy carbon electrode to form a CIN/chitosan composite film modified electrode. Hb was then immobilized onto the composite film with the cross‐linking of glutaraldehyde. The immobilized Hb displayed a pair of stable and quasireversible redox peaks and excellent electrocatalytic reduction of hydrogen peroxide (H₂O₂), which leading to an unmediated biosensor for H₂O₂. The electrocatalytic response exhibited a linear dependence on H₂O₂ concentration in a wide range from 3.1 μM to 4.0 mM with a detection limit of 1.2 μM (S/N=3). The designed biosensor exhibited acceptable stability, long‐term life and good reproducibility.     

Fractal Dimensions of Coals and Cokes

Using adsorption data, we get formulas for the calculation of fractal dimensions: log[A_CO2(DP)/A_N2(BET)] = −5.3984(2 −D₁)/2 and log[A_CO2(BET)/A_N2(BET)] = −4.9569(2 −D₂)/2. The fractal dimensions (D) of 27 coals and 2 cokes have been obtained. TheDof coals decreased with the increase of f_aand reached a maximum at H/C equal to 0.66 (orC_dafabout 86%). The fractal dimension is relative to ash and volatiles of coal:D= 2.2237 + 0.6249V_daf+ 0.8863A_d. The relationship betweenDof coal coke and its conversions (X) obeys the following equation:D = aexp(−bX) +c.