The analysis of real-time biomolecular interactions (observation is performed as the biological interaction occurs)
provides information on the formation of target/probe complexes, particularly on their dynamic behaviours.
Namely, it allows the determination of the affinity constant, a static value that characterizes the interaction
properties, using two dynamic values, the association and dissociation constants. Such dynamic behaviour can
be assessed either with surface plasmon resonance (SPR) or
uorescence-based biosensors. The challenging issue
is the automatic extraction and analysis of the interaction signal for each spotted probe on the biosensor in a highthroughput
framework (hundreds of probes). This paper addresses such issue and develops a uniffied approach
for analyzing the image data provided by the above-mentioned technologies. A mathematical modelling of the
image data allowed building-up a virtual biosensor able to simulate biologic experiences related to various possible
parameters (level of signal and noise, presence of artefacts, surface functionalization, spotting heterogeneity).
Based on such simulation, a generic and automated approach combining 3D mathematical morphology and
spatio-temporal classiffication is proposed for detecting the interacting probes, segmenting the regions of effective
signal, and characterizing the associated affinity constants. The developed method has been assessed both
qualitatively and quantitatively on simulated and experimental datasets and showed accurate results (maximum
error of 7% for the most difficult cases in terms of noise and surface functionalization).