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Cell-based computational model of early ovarian development in mice†
Author(s) -
Hannah M. Wear,
Annika Eriksson,
Humphrey HungChang Yao,
Karen H. Watanabe
Publication year - 2017
Publication title -
biology of reproduction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.366
H-Index - 180
eISSN - 1529-7268
pISSN - 0006-3363
DOI - 10.1093/biolre/iox089
Subject(s) - biology , oocyte , microbiology and biotechnology , mitosis , ovary , germ cell , ovarian cortex , follicle , ovarian follicle , medicine , embryo , endocrinology , genetics , gene , cryopreservation
Despite its importance to reproduction, certain mechanisms of early ovarian development remain a mystery. To improve our understanding, we constructed the first cell-based computational model of ovarian development in mice that is divided into two phases: Phase I spans embryonic day 5.5 (E5.5) to E12.5; and Phase II spans E12.5 to postnatal day 2. We used the model to investigate four mechanisms: in Phase I, (i) whether primordial germ cells (PGCs) undergo mitosis during migration; and (ii) if the mechanism for secretion of KIT ligand from the hindgut resembles inductive cell-cell signaling or is secreted in a static manner; and in Phase II, (iii) that changes in cellular adhesion produce germ cell nest breakdown; and (iv) whether localization of primordial follicles in the cortex of the ovary is due to proliferation of granulosa cells. We found that the combination of the first three hypotheses produced results that aligned with experimental images and PGC abundance data. Results from the fourth hypothesis did not match experimental images, which suggests that more detailed processes are involved in follicle localization. Phase I and Phase II of the model reproduce experimentally observed cell counts and morphology well. A sensitivity analysis identified contact energies, mitotic rates, KIT chemotaxis strength, and diffusion rate in Phase I and oocyte death rate in Phase II as parameters with the greatest impact on model predictions. The results demonstrate that the computational model can be used to understand unknown mechanisms, generate new hypotheses, and serve as an educational tool.

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