Introduction : Mass spectrometry approaches are very attractive to detect
protein panels in a sensitive and high speed way. MS can be coupled to many
proteomic separation techniques. However, controlling technological variability
on these analytical chains is a critical point. Adequate information processing
is mandatory for data analysis to take into account the complexity of the
analysed mixture, to improve the measurement reliability and to make the
technology user friendly. Therefore we develop a hierarchical parametric
probabilistic model of the LC-MS analytical chain including the technological
variability. We introduce a Bayesian reconstruction methodology to recover the
protein biomarkers content in a robust way. We will focus on the digestion step
since it brings a major contribution to technological variability. Method : In
this communication, we introduce a hierarchical model of the LC-MS analytical
chain. Such a chain is a cascade of molecular events depicted by a graph
structure, each node being associated to a molecular state such as protein,
peptide and ion and each branch to a molecular processing such as digestion,
ionisation and LC-MS separation. This molecular graph defines a hierarchical
mixture model. We extend the Bayesian statistical framework we have introduced
previously [1] to this hierarchical description. As an example, we will
consider the digestion step. We describe the digestion process on a pair of
peptides within the targeted protein as a Bernoulli random process associated
with a cleavage probability controlled by the digestion kinetic law.Comment: pr\'esentation orale; 59th American Society for Mass Spectrometry
Conference, Dallas : France (2011