Title: Competitive Hybridization Model 


Authors: Vera Cherepinsky, Ghazala Hashmi, Michael Seul and Bud Mishra 

Microarray technology, in its simplest form, allows one to gather abundance
data for target DNA molecules, associated with genomes or gene-expressions,
and relies on hybridizing the target to many short probe oligonucleotides
arrayed on a surface. While for such multiplexed reactions conditions are
optimized to make the most of each individual probe-target interaction,
subsequent analysis of these experiments is based on the implicit assumption
that a given experiment gives the same result regardless of whether it was
conducted in isolation or in parallel with many others. It has been
discussed in the literature that this assumption is frequently false, and
its validity depends on the types of probes and their interactions with each
other. We present a detailed physical model of hybridization as a means of
understanding probe interactions in a multiplexed reaction. The model is
formulated as a system of ordinary di.erential equations (ODE.s) describing
kinetic mass action and conservation-of-mass equations completing the

We examine pair-wise probe interactions in detail and present a model of .competition. between the probes for the target.especially, when target is in short supply. These e.ects are shown to be predictable from the a.nity constants for each of the four probe sequences involved, namely, the match and mismatch for both probes. These a.nity constants are calculated from the thermodynamic parameters such as the free energy of hybridization, which are in turn computed according to the nearest neighbor (NN) model for each probe and target sequence.

Simulations based on the competitive hybridization model explain the observed variability in the signal of a given probe when measured in parallel with di.erent groupings of other probes or individually. The results of the simulations are used for experiment design and pooling strategies, based on which probes have been shown to have a strong e.ect on each other.s signal in the in silico experiment. These results are aimed at better design of multiplexed reactions on arrays used in genotyping (e.g., HLA typing, SNP or CNV detection, etc.) and mutation analysis (e.g., cystic .brosis, cancer, autism, etc.).