Estimating Propensity Parameters using Google PageRank and Genetic Algorithms

TL;DR

This paper introduces a novel method combining Google PageRank and genetic algorithms to estimate propensity parameters in stochastic discrete dynamical systems, enhancing model accuracy for molecular interaction networks.

Contribution

It presents a new approach for estimating propensity parameters in SDDS using ergodicity induced by PageRank and genetic algorithms, with efficient approximation techniques.

Findings

Method successfully estimates propensity parameters in SDDS.

Approach guarantees ergodicity and stationary distribution.

Code implementation available online.

Abstract

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS) is a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html.