CAREER: Machine Learning Approaches to Understanding Molecular Mechanisms Underlying Convergent Evolution of Vocal Learning Behavior

The ability to perform a variety of complex behaviors, like human speech, is encoded in the billions of nucleotides that make up the genome of an organism. Although speech itself is uniquely human, vocal learning, the ability to modify vocal output as a result of experience, has evolved independently in multiple mammals and birds, including songbirds, parrots, hummingbirds, bats, and whales, as well as humans, uniquely among great apes.

During the evolution of each of these species, genome sequence mutations over millions of years have led to differences in the molecular properties of cell types within their brains, allowing for their vocalizations to be learned. This project leverages that diversity across species to take a comparative genomic approach to understanding how vocal learning evolved: what features do the genomes of vocal learning species have in common relative to species without this ability?

To answer that question, this research will develop artificial intelligence methods to look for common patterns of gene activity across dozens of brain cell types and common genome sequence patterns across hundreds of mammals. In addition to linking vocal learning behavior to specific cell types and genome sequence mutations, the artificial intelligence methods that will be developed have the potential to be applied to study the evolution of additional behaviors and other traits that vary across species with available genomes.

To help facilitate the adoption of these methods, as well as other new artificial intelligence approaches, this project seeks to train the next generation of interdisciplinary scientists, who are experts in artificial intelligence, evolutionary biology, and neuroscience. Undergraduate neuroscience and biology majors will get the opportunity to participate in the research by applying the computational techniques to other behaviors.

More broadly, a video and guided tutorials that explain the research and how to conduct it will be made publicly available.

Vocal learning demonstrates striking similarities across several of the lineages at multiple levels including the behavior itself, neural circuit features, and even shared gene expression patterns. Despite this wealth of information, there is still no solid connection between genotype and phenotype. This project seeks to make that connection by linking vocal learning-associated gene expression patterns to specific neural cell types that form the neural circuits for the production of vocalization and by linking them to variation in genome sequence at candidate regulatory elements.

To uncover the cell type-specific gene expression features associated with vocal learning behavior, a nested tree probabilistic graphical model-- a machine learning approach that simultaneously models hierarchies of cell types and species -- will be applied. Then, to trace the evolution of regulatory elements associated with those gene expression patterns, convolutional neural network models that predict differences in open chromatin from differences in genome sequence will be applied.

As a result of these analyses, this project will produce hypotheses for how differences in genome sequence between vocal learners and non-learners lead to differences in cell type-specific gene expression and open chromatin. Beyond the contributions to the study of vocal learning, the tools developed here fill a growing need for methods to study the evolution of cell types and regulatory elements across a rapidly increasing set of genomes. The results of the project will be found at http://www.pfenninglab.org/project/vocal-2/.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.