Description:

Sissi Chen, Carrasco Research Group

“The Root of Black Hole Evaporation”

Einstein’s general relativity predicts that black holes can only absorb matter and radiation. However, when quantum effects near the event horizon are included, particles are created and carry energy away from the black hole. This process leads to the emission of thermal radiation, known as Hawking radiation, and ultimately causes the black hole to evaporate over time. Using the double copy formalism, we show that for a non-Abelian Yang–Mills theory with gauge group SU(N) in the large-N limit, the radiation spectrum of the gauge field is thermal in its color-charge eigenvalue. This demonstrates that the apparent energy thermality of gravity is the direct dual of charge thermality in its underlying non-Abelian gauge theory, and that color charge screening is at the root of black hole evaporation.

Buduka Ogonor, Motter Research Group

“The Strategy of the Genes: Finding Gene Combinations Underlying Complex Phenotypes”

Most human traits are complex, that is, they emerge from the interactions among multiple genes. The sheer number of possible gene set-to-phenotype mappings makes it challenging to identify the gene sets underlying complex traits using statistical approaches like genome and transcriptome-wide association studies. These techniques are further limited by the fact that they do not account for how molecular level changes propagate through the intracellular networks that govern cell behavior. 

After a brief discussion of Boolean network modeling and the Waddington epigenetic landscape framework to motivate studying cell phenotype via gene regulatory network dynamics, we present an approach that nominates sets of genes underlying complex phenotypes by finding combinations of gene perturbations whose transcriptional responses cause phenotype change. Our approach leverages publicly available transcriptional data, and takes into account the network-structured nature of responses to experimental gene perturbations. Along the way, we implement a generative machine learning approach to further resolve the relationship between transcriptional state and phenotype. Our implementation of this approach includes a variational autoencoder trained on human transcriptional data, which is incorporated into an optimization framework. By considering several complex traits, we show that the approach identifies causal genes that cannot be detected by the primary existing techniques. We suggest that the approach be used to design tailored experiments to identify multi-genic drug targets to address complex diseases.