A critical event in mammalian embryo development is construction of embryonic versus extra embryonic structures. Initially, a single cell divides multiple times to generate an aggregate of 10-20 similar cells. Soon after, the progeny of these precursor cells differentiate and self-organize to form other structures. The remarkable feature of this process is its high level of reproducibility despite a number of sources of stochastic variation. In recent years, experimental interrogation has suggested multiple theories for what might be responsible for this process. It is still unclear, however, what drives organization, and in particular, what is responsible for its robustness. This project takes a joint experimental and modeling approach to address this issue. On the modeling side, a novel computational modeling platform will be developed that incorporates known and hypothesized physical and genetic processes to test their influence individually and jointly on development. On the experimental side, imaging and image analysis will be performed on mouse embryos to inform this model and test its predictions. This framework will be initially developed in relation to mouse development and later extended to determine how known differences between early mouse and human cells influence organization, which evidence suggests is less robust in developing human embryos.

This project will shed new light into how mammalian pre-implantation forms from a single cell. The research will also establish new principles for how the interplay between physical and biochemical regulatory processes leads to development of tissues an organisms of exquisite complexity. A multi-scale and stochastic computational framework to study both mouse and human early embryo development will be developed. In addition, this project will develop new training activities involving graduate, undergraduate, and high school students with an emphasis on promoting quantitative thinking and developing modeling and simulation skills. This training will equip students with computational and system-level approaches needed to investigate complex systems. The activities will take advantage of the strengths of the undergraduate and graduate training programs in computational systems biology at the University of California, Irvine and the strong interdisciplinary training programs and ties with minority serving institutions at Vanderbilt University. These programs will be cross-pollinated to promote diversity at all educational levels.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Junping Wang
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University of California Irvine
United States
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