Ph.D. Candidate: Alexander Wu

Advisor: Prof. Bonnie Berger (MATH, EECS)

Title: Towards causality in gene regulatory network inference

Abstract: 

Understanding the coordination of biomolecules that underlies gene regulation is key to gaining

mechanistic insights into cellular functions, phenotypes, and diseases. Advances in single-cell

technologies promise to unveil mechanisms of gene regulation at unprecedented resolution by

enabling measurements of genomic and/or epigenetic features for individual cells. However,

unlocking insights from single-cell data requires algorithmic innovations.

This thesis introduces a series of methods for uncovering gene regulatory relationships

underlying cellular identity and function from single-cell data. Firstly, we present a framework for

enhancing the detection of statistical associations in small sample size settings for gene

regulatory network inference. We then describe the use of single-cell genetic perturbation

screens for determining the causal roles of critical regulatory complexes, focusing specifically on

its applications for revealing mechanistic insights about the mammalian SWI/SNF family of

chromatin remodeling complexes.

To bridge the gap between methods that identify statistical associations from observational data

and those that infer causal relationships using interventions, we also introduce a new category

of techniques that extends the econometric concept of Granger causality to complex

graph-based dynamical systems, such as those found in single-cell trajectories. In particular, we

describe a graph neural network-based generalization of Granger causality for single-cell

multimodal data that enables the detection of noncoding genomic loci implicated in the

regulation of specific genes. We then demonstrate how we use this approach to link genetic

variants to gene dysregulation in disease, focusing on its applications to schizophrenia etiology.

Lastly, we present an extension of this graph-based Granger causal framework that leverages

RNA velocity dynamics for causal gene regulatory network inference and enables inquiries into

the role of temporal control in gene regulatory function and disease.