INFERENCE OF PROTEIN-PROTEIN INTERACTION NETWORKS FROM LIQUID-CHROMATOGRAPHY MASS-SPECTROMETRY DATA BY APPROXIMATE BAYESIAN COMPUTATION-SEQUENTIAL MONTE CARLO SAMPLING

We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of LiquidChromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation - Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements corresponding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately.