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Motivation: Modeling biological signaling networks using ordinarydifferential equations (ODEs) has proven to be a powerful technique forgenerating insight into cellular dynamics, but it typically requires estimatingrate parameters based on experimentally observed concentrations. Newmeasurement methods can measure concentrations for all molecular species in apathway, which creates a new opportunity to decompose the optimization of rateparameters. Results: In contrast with conventional methods that minimize thedisagreement between simulated and observed concentrations, the BPPE methodfits a spline curve through the observed concentration points, and then matchesthe derivatives of the spline-curve to the production and consumption of eachspecies. Whereas traditional methods follow the ODEs exactly and then attemptto match the data, BPPE follows the data exactly and then attempts to match theODEs. The new objective function is an extreme decomposition of the problembecause each factor in the function is enforcing the equality of one ODE at onetimeslice. A "loopy belief propagation" algorithm solves this factorizedapproximation of the parameter estimation problem providing systematic coverageof the search space and unique asymptotic behavior; the run time is polynomialin the number of molecules and timepoints, but exponential in the degree of thebiochemical network. The implementation is a global-local hybrid optimization,and we compare with the performance of local, global, and hybrid methods. BPPEis demonstrated for a novel model of Akt activation dynamics includingredox-mediated inactivation of PTEN. Availability: Software and supplementaryinformation are available at http://webbppe.nus.edu.sg:8080/opal2/WebBPPE .Contact: LisaTK@nus.edu.sg . Keywords: probabilistic graphical models,physico-chemical modeling, systems biology, signal transduction.

Estimating Reaction Rate Parameters in Cell Signaling Pathways Using Extreme Decomposition and Belief Propagation Tailored for Data-Rich Cases