Modelling dynamic of cellular signaling using a probabilistic approach

CREATED: 200809290200 Speaker: David Hsu ** Introduction

• static: elements and interactions
• dynamics: bistability, ultrasentivity, oscillations
• traditional approach: ODE
• ODE’s are not sufficient ** no explicit representaiton of pathway structure ** false sense of high accuracy ** How to create models automatically
• structure modeling
• parameter modeling ** local descent ** simulated annealing ** evolutionary algorithms ** Modelling with Factor Graphs
• variable node corresponds to parameter
• factor node corresponds to ODE
• variable node has an associated probability distribution
• factor node has an associated joint probability distribution ** simulate the underlying ODE ** fit results and determine error measure ** convert error to probability
• encode probability distribution over the parameter values
• exploits independence assumptions suggested by pathway structure ** Using the Factor Graph model
• determine MAP parameter estimate using belief propagation (e.g. max-product algorithm)
• converges to MAP if pathway does not have loops, gives good results in practice even if there are loops ** Application
• model composition ** model components of a model using factor graphs ** combine component by sitching together factor graphs
• data integration ** each data set results in a new instance of the same factor graph ** integrate data by sitching together the instances ** apply belief propagation across instances