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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01n296x253p
Title: Computational biology of embryogenesis: strain maps and cell fates
Authors: Denberg, David William
Advisors: Shvartsman, Stanislav Y
Contributors: Quantitative Computational Biology Department
Keywords: Biophysical Model
Cell Specification
Computational Model
Drosophila
Embryogenesis
Subjects: Developmental biology
Cellular biology
Issue Date: 2024
Publisher: Princeton, NJ : Princeton University
Abstract: Gastrulation and early cell fate decisions in embryonic development are critical processes that shape the formation of multi-layered embryos and distinct germ layers, ultimately giving rise to various tissues and organs. Research across species has illuminated the mechanisms of gastrulation, highlighting localized epithelial deformations and the importance of quantifying strain tensors to understand the dynamics at the embryo scale. These tensors describe the differences in cell configurations over time, enabling insights into morphogenetic movements. In a complementary study of the preimplantation mouse embryo, the segregation of epiblast and primitive endoderm cell types has emerged as a model for understanding the balance between predetermined and stochastic developmental patterns. Using quantitative live imaging of tagged transcription factors, an initial symmetry breaking event linked to the dynamics of prior cell fate decisions was identified. Notably, epiblast precursor cells, influenced by SOX2 expression, initiate FGF4 signaling that drives differentiation toward the primitive endoderm fate. This differentiation rate, however, is modulated by the stochastic expression of NANOG in individual cells. Together, these studies provide a unified perspective on the interplay of mechanical dynamics and molecular features during early embryonic development, emphasizing the significance of both deterministic and stochastic processes in shaping developmental trajectories.
URI: http://arks.princeton.edu/ark:/88435/dsp01n296x253p
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Quantitative Computational Biology

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