Sensitivity, Specificity, and the Hybridization Isotherms of DNA Chips

Halperin, A.; Buhot, Arnaud; Zhulina, Ekaterina B.

doi:10.1016/s0006-3495(04)74150-8

Cited by 132 publications

(195 citation statements)

References 36 publications

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“…A quantity related to ⌸ also arises in the hybridization theory of Halperin, Buhot and Zhulina (HBZ) (32), where it delineates between different predictions for hybridization isotherms. The HBZ parameter is ⌸ HBZ ϭ r D 2 /H⌳ where r D is the Debye screening length in solution, H is the thickness of the probe layer, and ⌳ ϳ1/ is the Gouy-Chapman length which acts as a metric of the immobilized areal density of charge .…”

Section: Hybridization At Low Conversion: Electrostatics and The Onsementioning

confidence: 99%

“…These trends were reproduced in a second set of experiments using potassium instead of sodium as the counterion (SI). Attempts to fit theoretical hybridization isotherms incorporating repulsive site interactions (7,32,35,36) to the data were able to approximately account for the SH regime but not for the ionic strength-dependent PL plateau.…”

Section: Hybridization At Low Conversion: Electrostatics and The Onsementioning

confidence: 99%

See 1 more Smart Citation

DNA surface hybridization regimes

Gong

Levicky²

2008

Proc. Natl. Acad. Sci. U.S.A.

218

282

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Surface hybridization reactions, in which sequence-specific recognition occurs between immobilized and solution nucleic acids, are routinely carried out to quantify and interpret genomic information. Although hybridization is fairly well understood in bulk solution, the greater complexity of an interfacial environment presents new challenges to a fundamental understanding, and hence application, of these assays. At a surface, molecular interactions are amplified by the two-dimensional nature of the immobilized layer, which focuses the nucleic acid charge and concentration to levels not encountered in solution, and which impacts the hybridization behavior in unique ways. This study finds that, at low ionic strengths, an electrostatic balance between the concentration of immobilized oligonucleotide charge and solution ionic strength governs the onset of hybridization. As ionic strength increases, the importance of electrostatics diminishes and the hybridization behavior becomes more complex. Suppression of hybridization affinity constants relative to solution values, and their weakened dependence on the concentration of DNA counterions, indicate that the immobilized strands form complexes that compete with hybridization to analyte strands. Moreover, an unusual regime is observed in which the surface coverage of immobilized oligonucleotides does not significantly influence the hybridization behavior, despite physical closeness and hence compulsory interactions between sites. These results are interpreted and summarized in a diagram of hybridization regimes that maps specific behaviors to experimental ranges of ionic strength and probe coverage.biosensor ͉ ferrocene ͉ ionic strength ͉ microarray ͉ probe coverage S olid-phase or ''surface'' hybridization is the foundation of modern microarray and biosensing technologies widely used in applied genomics for genotyping, drug discovery, gene expression profiling, and related applications based on measurement of genomic information (1, 2). These tools function through detection of interactions between nucleic acids immobilized on a solid support, or ''probes,'' with analyte ''target'' nucleic acids present in solution. Binding, or hybridization, between probes and targets to form an immobilized duplex takes place based on the degree of complementarity between the probe and target base sequences. The tremendous growth in applications of surface hybridization has been mirrorred by increased emphasis on experimental and fundamental aspects of the assays that directly impact measurement and interpretation of data (3, 4).As summarized in several recent reviews (5-8), physical studies under simplified experimental conditions have begun to unravel the rich phenomenology that occurs in diagnostic assays. Applications typically operate away from equilibrium and are faced with a highly diverse pool of target sequences competing for the probe sites. A variety of aids to enhance hybridization performance is used, including surfactants and blocking agents to control nonspecific adsorp...

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Section: Hybridization At Low Conversion: Electrostatics and The Onsementioning

confidence: 99%

Section: Hybridization At Low Conversion: Electrostatics and The Onsementioning

confidence: 99%

DNA surface hybridization regimes

Gong

Levicky²

2008

Proc. Natl. Acad. Sci. U.S.A.

218

282

View full text Add to dashboard Cite

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“…In the context of a mean-field approximation Vainrub and Pettit, 27 Halperin Buhot and Zhulina 29 derived the following equilibrium model θ eq c(1 − θ eq ) = e −∆G/RT −Γ(1+θ eq )/RT (11) where, as in the main text, θ eq is the fraction of hybridized probes, c the target concentration, ∆G the hybridization free energy between a single probe and a single target. The term Γ(1 + θ eq )/RT accounts for the electrostatic repulsion, where Γ is a constant independant of the sequence.…”

Section: Appendix 1: the Effect Of Salt Concentrationmentioning

confidence: 99%

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

et al. 2011

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It has recently been shown that in some DNA microarrays the time needed to reach thermal equilibrium may largely exceed the typical experimental time, which is about 15h in standard protocols (Hooyberghs et al. Phys. Rev. E 81, 012901 (2010)). In this paper we discuss how this breakdown of thermodynamic equilibrium could be detected in microarray experiments without resorting to real time hybridization data, which are difficult to implement in standard experimental conditions. The method is based on the analysis of the distribution of fluorescence intensities I from different spots for probes carrying base mismatches. In thermal equilibrium and at sufficiently low concentrations, log I is expected to be linearly related to the hybridization free energy ∆G with a slope equal to 1/RT exp , where T exp is the experimental temperature and R is the gas constant. The breakdown of equilibrium results in the deviation from this law. A model for hybridization kinetics explaining the observed experimental behavior is discussed, the so-called 3-state model. It predicts that deviations from equilibrium yield a proportionality of log I to ∆G/RT eff . Here, T eff is an "effective" temperature, higher than the experimental one. This behavior is indeed observed in some experiments on Agilent arrays. 16,20 We analyze experimental data from two other microarray platforms and discuss, on the basis of the results, the attainment of equilibrium in these cases. Interestingly, the same 3-state model predicts a (dynamical) saturation of the signal at values below the expected one at equilibrium.

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“…where ∆G has units of J/mole and is the change in the sum of all of the contributions to the free energy of the surface of the cantilever, Γ(t) is the surface concentration of the species of interest (typically in molecules per cm 2 ) on the surface of the cantilever [14,15]and M A is Avogadro's number. Sulfur-terminated molecules bind preferentially to the Au-coated side of the cantilever.…”

Section: Microcantilever Sensorsmentioning

confidence: 99%

“…y t with known inputs, { } ( ) u t ,and parameters, { } θ , specified by the approximate Gauss-Markov model of (14), FIND the best (minimum error variance) estimate of the displacement and surface concentrations, , respectively.…”

Section: A Design For Cantilever Arraysmentioning

confidence: 99%

Model-based Processing of Micro-cantilever Sensor Arrays

Tringe¹,

Clague²,

Candy³

et al. 2004

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Abstract-We have developed a model-based processor (MBP) for a microcantilever-array sensor to detect target species in solution. We perform a proof-of-concept experiment, fit model parameters to the measured data and use them to develop a Gauss-Markov simulation. We then investigate two cases of interest, averaged deflection data and multi-channel data. For this evaluation we extract model parameters via a model-based estimation, perform a Gauss-Markov simulation, design the optimal MBP and apply it to measured experimental data. The performance of the MBP in the multi-channel case is evaluated by comparison to a "smoother" (averager) typically used for microcantilever signal analysis. It is shown that the MBP not only provides a significant gain (~ 80dB) in signal-to-noise ratio (SNR), but also consistently outperforms the smoother by 40-60 dB. Finally, we apply the processor to the smoothed experimental data and demonstrate its capability for chemical detection. The MBP performs quite well, apart from a correctable systematic bias error.

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Sensitivity, Specificity, and the Hybridization Isotherms of DNA Chips

Cited by 132 publications

References 36 publications

DNA surface hybridization regimes

DNA surface hybridization regimes

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

Model-based Processing of Micro-cantilever Sensor Arrays

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