Paper Info
Title
A Double Swarm Methodology For Parameter Estimation In Oscillating Gene Regulatory Networks
Abstract
systems are mathematical models based on the power-law formalism, which are widely employed for the investigation of Gene Regulatory Networks (GRNs). Because of their complex dynamics - characterized by multi-modality and non-linearity - the parameterization of S-systems is far from straight-forward, demanding global optimization techniques. The problem of parameter estimation of S-systems is further complicated when the desired dynamics is characterized by oscillations. In this work, we describe a novel methodology based on Particle Swarm Optimization for the automatic parameterization of oscillating S-systems. In this methodology, two swarms perform independent optimizations, and cooperate by periodically exchanging the best particles. The two swarms exploit two different fitness functions: a traditional point-to-point distance, and a spectra-based fitness function. We show that this cooperative approach allows the double swarm to outperform the common methodology, based on a single swarm exploiting a single fitness function. We demonstrate the effectiveness of our method using a GRN of five genes, performing tests of increasing complexity, up to the simultaneous inference of 17 parameters.
Year
Venue
Keywords
2015
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Synthetic Biology, Gene Regulation, Parameter Estimation, Particle Swarm Optimization, Fast Fourier Transform
Field
DocType
Citations 
Particle swarm optimization,Mathematical optimization,Complex dynamics,Swarm behaviour,Global optimization,Computer science,Multi-swarm optimization,Fitness function,Artificial intelligence,Estimation theory,Mathematical model,Machine learning
Conference
1
PageRank 
References 
Authors
0.37
11
2
Name
Order
Citations
PageRank
Marco S. Nobile114323.69
Hitoshi Iba21541138.51