ABSTRACT
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 15 h in standard protocols (Hooyberghs et al. Phys. Rev. E2010, 81, 012901). 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 ΔG 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 ΔG/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 [Hooyberghs et al. Phys. Rev. E2010, 81, 012901 and Hooyberghs et al. Nucleic Acids Res. 2009, 37, e53]. 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.
Subject(s)
DNA/chemistry , Oligonucleotide Array Sequence Analysis/methods , Models, Chemical , ThermodynamicsABSTRACT
DNA microarrays are devices that are able, in principle, to detect and quantify the presence of specific nucleic acid sequences in complex biological mixtures. The measurement consists in detecting fluorescence signals from several spots on the microarray surface onto which different probe sequences are grafted. One of the problems of the data analysis is that the signal contains a noisy background component due to nonspecific binding. We present a physical model for background estimation in Affymetrix Genechips. It combines two different approaches. The first is based on the sequence composition, specifically its sequence-dependent hybridization affinity. The second is based on the strong correlation of intensities from locations which are the physical neighbors of a specific spot on the chip. Both effects are incorporated in a background estimator which contains 24 free parameters, fixed by minimization on a training data set. In all data analyzed the sequence-specific parameters, obtained by minimization, are found to strongly correlate with empirically determined stacking free energies for RNA-DNA hybridization in solution. Moreover, there is an overall agreement with experimental background data and we show that the physics-based model that we propose performs on average better than purely statistical approaches for background calculations. The model thus provides an interesting alternative method for background subtraction schemes in Affymetrix Genechips.
Subject(s)
Oligonucleotide Array Sequence Analysis/instrumentation , Oligonucleotide Array Sequence Analysis/methods , Algorithms , Artifacts , Biophysics/methods , DNA/chemistry , Equipment Design , Genome, Human , Humans , Models, Statistical , Models, Theoretical , Nucleic Acid Hybridization , RNA/chemistry , Reproducibility of Results , Signal Processing, Computer-Assisted , ThermodynamicsABSTRACT
We combine a density functional theory (DFT) treatment of capillary evaporation in a cylindrical pore with the morphometric approach in order to study the formation and breaking of bubbles in a hydrophobically lined part of a cone. The morphometric approach, in which the grand potential of a system is described in four geometrical terms with corresponding thermodynamical coefficients, allows extrapolation or scaling from macroscopic system sizes to nanoscales. Since only a small number of fluid particles are involved in bubble formation, it is a pseudo phase transition, and the system is subjected to fluctuations between states with and without a bubble. Fluctuations are not included in a DFT treatment, which makes it possible to explore both states of the system in great detail, in contrast to computer simulations, in which averages might be obscured by fluctuations.
