Modeling Transcriptional Regulation in Chondrogenesis Using Particle Swarm Optimization

Abstract

Chondrogenesis is a complex developmental process involving many transcription factors. Using mRNA expression data and regulatory DNA sequences, we formulated a quantitative model to predict a set of transcription-factor binding motifs (TFBMs) as a combinatorial problem. To solve such a problem, an efficient computational algorithm should be employed. In the current study, particle swarm optimization was applied. Swarm intelligence is an artificial intelligence approach that mimics a behavior of swarm-forming agents. Such systems are made up with a population of individuals that interact locally and globally. Here, a group of TFBMs was predicted using 200 artificial bees and the results were compared to biologically known binding motifs.