The specific goal of this proposal is to construct a data-driven mathematical model of human embryonic stem cells so that we can make predictions about how best to optimize self-renewal and differentiation. Long-term self-renewal is a property shared only by stem cells. Self-renewal is the ability to grow robustly without differentiating or mutating. Human embryonic stem cell (hES cell) lines differ dramatically in their potential for self-renewal. We will compare federally-approved hES cell lines that have poor self-renewal with newer, non-approved lines that are robust. The data from these comparisons will be entered into our mathematical model of hES cells and used to make predictions about the genetic differences between approved and non-approved lines that explain the discrepancies in their performance, and to predict changes in the growth conditions that improve self-renewal for any given hES cell line. We will compare undifferentiated, self-renewing hES cells to their progeny during and after the process of differentiation. The data from these comparisons will be entered into our mathematical model of hES cells and used to make predictions about genetic differences or conditions of growth that enable the early and terminal stages of differentiation. We will examine in detail the proteins that mediate metabolism in hES cells and predict which proteins and their substrates within the cells or in the growth media determine self-renewal and differentiation. As part of this detailed examination we will isolate the metabolic engines of hES cells, mitochondria and peroxisomes, and compare the changes in proteins and metabolites that occur in them during self-renewal or differentiation. The product of this project will be a computer model that any investigator can download and use to: guide their creation of hES cell lines; develop hES cell growth conditions; or produce differentiated tissues from hES cells for regenerative medicine.
California will benefit from funding this grant because it will accelerate the progress of many, if not all, research projects on hES cells. Our data-driven model of hES cells could be the basis for new biotechnology companies or it could be broadly licensed to enable all companies to accelerate their development of cell-based therapies.