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Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms.

David Murrugarra¹, Jacob Miller¹, Alex N Mueller¹

¹Department of Mathematics, University of Kentucky Lexington, KY, USA.

Frontiers in Neuroscience

|November 29, 2016

Summary

This summary is machine-generated.

This study introduces a novel method for estimating propensity parameters in Stochastic Discrete Dynamical Systems (SDDS). The approach uses Google PageRank and genetic algorithms to ensure system ergodicity and accurately determine parameters for molecular network models.

Keywords:

Boolean networks Google PageRank Markov chains genetic algorithms propensity parameters stationary distribution stochastic systems

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Area of Science:

Computational biology
Systems biology
Network modeling

Background:

Stochastic Boolean networks are key computational models for molecular interactions.
Standard synchronous and asynchronous updates offer deterministic or stochastic dynamics.
Stochastic Discrete Dynamical Systems (SDDS) introduce propensity parameters for nuanced modeling.

Purpose of the Study:

To present a method for estimating propensity parameters in SDDS.
To address the complexity of parameter estimation in SDDS.
To enhance the simulation capabilities of SDDS models.

Main Methods:

Employing the Google PageRank approach to add noise and ensure system ergodicity.
Utilizing a genetic algorithm for efficient propensity parameter estimation.
Developing approximation techniques to optimize search algorithm efficiency.

Main Results:

Successfully developed and tested a method for propensity parameter estimation in SDDS.
Demonstrated the effectiveness of the Google PageRank and genetic algorithm approach.
Provided accessible Matlab/Octave code for algorithm testing.

Conclusions:

The proposed method effectively estimates propensity parameters for SDDS.
The approach enhances the practical application of SDDS in molecular network analysis.
This work facilitates more accurate and efficient computational modeling of biological systems.