Evolution of Stochastic Gene Transcription Networks
Research mentors: Ivan Erill (Biological Sciences), Muruhan Rathinam (Mathematics/Statistics)
Transcriptional regulation is a fundamental step in the gene expression cascade. Gene transcription is regulated by the coordinated activity of transcription factors, which often organize themselves into small networks, or motifs, capable of generating complex temporal patterns of gene expression. Transcription is a noisy process and capturing the full repertoire of expression profiles produced by different network motifs requires hybrid stochastic and deterministic simulation methods. Experimental data and genomic surveys suggest that living beings tend to exploit shallow network motifs with few components. Here we propose to investigate the evolutionary rationale for this design strategy by evolving arbitrarily large networks in the context of a genetic programming framework based on a hybrid transcription simulator.