The inverse problem is one of the most pressing challenges in Systems Biology today. The identification/reconstruction of gene regulatory networks from a variety of gene expression data sources is a complex task plagued, among others, by the curse of dimensionality, i.e. the striking insufficiency of observations in relation to the number of measured genes.
This talk will introduce a model-based approach to reverse-engineering gene networks from gene expression time series, using a framework of swarm intelligence techniques, namely Ant Colony and Particle Swarm Optimization. Results on artificial and real-world gene networks will also be presented and discussed. The problem domain and methods used will be introduced so that no prior subject knowledge is required.