Evolution of Regulatory Sequence Motifs

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Transcription factors are proteins that regulate the expression of genes by binding to DNA at specific locations. In spite of years of research, little is known about the evolutionary constraints faced by transcription factors and their binding sites in the genome. As a result, it is difficult to improve on the decades-old models of transcription factor binding that are still in use today. The advent of high-throughput technologies, the widespread use of comparative genomics techniques and the rapid emergence of synthetic biology both facilitate and make more pressing the need for understanding how these genetic elements evolve and interact, since this will enable the development of models capable of establishing a much needed link between the encoding of transcription on DNA sequence and its effects on the regulatory networks it spawns. This project aims at validating and exploiting a robust computational framework for the co-evolution of transcription factors and their binding motifs in a realistic genome environment. The research will capitalize on the integration of a genetic algorithm backbone with innovative modeling of transcription factors in order to analyze how transcription factors and their binding motifs evolve when confronted with specific genomic selective pressures, regulatory needs and genome sizes. Most importantly, this research introduces a reverse engineering approach aimed at infering basic principles on the behavior of these genetic elements. The project will systematically analyze and quantify for the first time key constraints in the evolution of binding sites and thus has the potential to reshape the way we think about and model these genomic elements. Specific emphasis will be made in compiling published data to validate the predictions made by the system and in testing the efficacy of inferred model constraints at improving computational discovery of transcription factor binding sites.Broader ImpactsThis project will advance our understanding of transcriptional regulation and its evolution, leading to improved models of transcription factor binding motifs. It will also substantiate a progressive movement towards in-silico simulations in biology, and to the analysis of theoretical models using a bottom-up approach. The research will provide a scalable tool for the exploration of transcriptional evolution, as well as an up-to-date curated database of transcription factor binding sites. Both items will be made freely available to the public, facilitating the development and benchmarking of new transcription factor models. Training and involvement of undergraduates in research is an essential component of this project and will be centralized on the adoption of a peer-mentored system for database curation. The project also defines participative methods for integrating research results into courses taught by the PI and others through the use of evolutionary videos, and the use of these elements in specific events targeting K-12 students and in popularizing evolutionary theory. The PI will exploit the leverage provided by UMBC programs to mentor and involve minority students in research, with particular emphasis on the growing Hispanic community. The pro-active outreach and dissemination components of the project will also capitalize on previous efforts to exploit evolutionary simulations in order to foster an informed debate on evolutionary questions across a broad range of audiences.