Guang Yao, Ph.D. - US grants

Gene regulation plays a fundamental role in cellular activities and functions, such as growth, division, and responses to environmental stimuli. The regulatory interactions among genes and their expression products (RNAs and proteins) intertwine into complex and dynamic gene regulatory networks (GRNs) in cells. Recent technical breakthrough has enabled large-scale experimental studies of GRNs. A central question in GRN analysis is to elucidate network topologies and dynamics that give rise to biological properties at study. However, the magnitude and complexity of these network data pose serious challenges in extracting useful information from within. This project aims to develop statistical and computational tools to reveal underlying structure, dynamics, and functionality of GRNs. New statistical theory and inference methods will be developed to tackle theoretical and computational challenges in modeling and analyzing large-scale GRNs. Results from this research will establish a novel framework to dissect dynamical and complex biological networks, and particularly a GRN that regulates cell proliferation in our case study.

Traditional statistical analysis of GRNs typically assumes that interactions between network nodes can be described by linear functions or low-order polynomials. However, biological processes are usually complex and molecular interactions between network nodes may not be accurately described by simple functions. The main goal of this project is to develop novel and flexible statistical approaches to dissect and reconstruct GRNs by learning nonlinear interactions from time-course experimental data, with either continuous- or discrete-valued gene expression. Specifically, we will develop new modeling and analysis approaches to study GRNs using semiparametric ordinary differential equations (ODEs), and will develop state of art computational tools to characterize the structures and dynamics of GRNs, to help scientists address crucial cellular systems regulated by GRNs. The project has two parts. The first part focuses on Methods and Theory, consisting of three aims: (1) to develop new and automated statistical procedures for studying local patterns and dynamic structures in large and complex GRNs; (2) to establish valid statistical inferences on topological features and regulatory interactions of GRNs; and (3) to develop efficient computational algorithms and software for analyzing large-scale GRNs. Developed methods from this research will provide valuable tools for modeling the topologies and dynamics of GRNs using ODEs. In the second part, we will focus on real data applications. Specifically, we will apply newly developed tools in the first part to analyze a retinoblastoma (Rb)-E2F gene network, which plays a key role in controlling cell proliferation and the gene regulation within which is frequently disrupted in human diseases such as cancer and aging.

Cells sharing the same genome exhibit different phenotypes. For the development and health of the body, cells in various tissues need to faithfully maintain their functional properties and meanwhile make necessary changes responding to environmental cues. That is, cells need to be phenotypically stable and plastic. Cells achieve this demand through proper regulation of gene expression, transcriptionally and epigenetically. Gene regulation at the transcriptional level via transcription factors has been well studied. Gene regulation at the epigenetic level via chromatin modifications has been extensively researched recently. Yet, the quantitative nature of epigenetic regulation remains largely elusive, and little is known about the coupled effects of epigenetic and transcriptional regulations. This project will develop a mathematical framework for epigenetic regulation as well as coupled epigenetic and transcriptional regulations. By combining modeling and quantitative experimental measurements, this research will address the fundamental question of cell phenotype stability and plasticity. This collaborative project will provide unique opportunities for graduate and undergraduate students to work at the interface of mathematical, physical, and life sciences. Through a summer internship program, high school students will have opportunities to experience integrated modeling/experimental research, which will encourage them to explore their interests in pursuing cross-disciplinary research careers in the future.

Rapidly accumulating evidence has revealed the critical role of epigenetic regulation of gene expression. Epigenetic modifications refer to stable and inheritable changes of gene expression caused by non-genetic chromatin modifications. However, the quantitative properties of epigenetic regulation, and the coupled effects of epigenetic and transcriptional regulation on gene expression, are poorly understood. In this project, the investigators will first develop a theoretical framework describing epigenetic and transcriptional regulations of gene expression, and analyze how epigenetic dynamics and gene transcription are coupled to control stable and plastic gene activities. Investigators will then focus on a case study, the coupled epigenetic/transcriptional regulation of the Rb-E2F pathway that controls the transition between two distinct cell fates, cellular quiescence and proliferation. Guided by modeling analysis, investigators will experimentally determine the highly needed but unresolved function form for the coupling between epigenetic modification and gene transcription. By integrating approaches across statistical and chemical physics, nonlinear dynamics, and experimental biology, this project will advance our understanding of gene expression stability and flexibility via coupled transcriptional and epigenetic mechanisms, and correspondingly, of cell fate maintenance and transition between quiescence and proliferation.