CAREER: Inference of Gene Regulatory Networks and Cell Dynamics That Control Stem Cell Fate

Stem cells are responsible for producing and maintaining organs throughout our lifetime. Stem cells differentiate into mature cells through the process of stem cell fate decision-making: in most cases, we cannot yet explain or predict this process. This project will develop mathematical models to understand how stem cells make fate decisions.

The models will describe the dynamics of stem cells as well as the gene dynamics occurring inside cells and the higher-level coordination that occurs between the many cells of a tissue. The PI will take the novel approach of using publicly available genomics data to inform models of two organs: the blood (hematopoietic) and kidney epithelial systems.

The research outcome will be an ability to predict and explain stem cell fate decisions: knowledge that will bring us one step closer to being able to build new organs. The educational objective of this project is to train a new generation of scientists to be equally skilled in biological and quantitative thinking. This will be achieved by developing new curricula for undergraduate, middle/high, and elementary level students, closely coupled to the research goals of the project.

This project will develop models to explain how stem cell differentiation is coordinated and maintained in complex signaling environments. Stem cell differentiation is controlled by cell-internal gene regulatory networks as well as signals from the microenvironment and cell- and tissue-scale effects. Predictive models of stem cell differentiation must take into account the dynamics occurring on all of these biological scales.

Stem cell states (both homeostatic and perturbed) in two systems will be studied: hematopoiesis and kidney epithelia. For each system, publicly available single-cell genomics data will be leveraged to infer regulatory networks across transcriptional-to-cellular scales. This will be achieved via three scientific objectives: 1) Develop Bayesian inference methods to learn single-cell regulatory networks; 2) Investigate stem cell lineage dynamics via dynamical systems modeling and parameter inference; 3) Predict tissue-scale responses to stimuli through multiscale modeling.

The resulting models will give fundamental new insight into how stem cells function, and may reveal shared regulatory logic of signaling pathway motifs across different organs. The educational goal of this project is to train a new generation of scientists to be simultaneously literate in mathematical and life sciences. This will be achieved in collaboration with the USC Joint Educational Project via two educational objectives: 1) Develop a curriculum for local elementary schools to diversify participation in and spark enthusiasm for mathematics and biology; 2) Develop a quantitative biology undergraduate course with a service-learning component in middle/high schools.

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.