Computer simulations of the mouse spermatogenic cycle

Debjit Ray; Philip B Pitts; Cathryn A Hogarth; Leanne S Whitmore; Michael D Griswold; Ping Ye

doi:10.1242/bio.20149068

Computer simulations of the mouse spermatogenic cycle

Biol Open. 2014 Dec 12;4(1):1-12. doi: 10.1242/bio.20149068.

Authors

Debjit Ray¹, Philip B Pitts², Cathryn A Hogarth³, Leanne S Whitmore³, Michael D Griswold³, Ping Ye⁴

Affiliations

¹ School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA.
² School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.
³ School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA.
⁴ School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA pye@wsu.edu.

Abstract

The spermatogenic cycle describes the periodic development of germ cells in the testicular tissue. The temporal-spatial dynamics of the cycle highlight the unique, complex, and interdependent interaction between germ and somatic cells, and are the key to continual sperm production. Although understanding the spermatogenic cycle has important clinical relevance for male fertility and contraception, there are a number of experimental obstacles. For example, the lengthy process cannot be visualized through dynamic imaging, and the precise action of germ cells that leads to the emergence of testicular morphology remains uncharacterized. Here, we report an agent-based model that simulates the mouse spermatogenic cycle on a cross-section of the seminiferous tubule over a time scale of hours to years, while considering feedback regulation, mitotic and meiotic division, differentiation, apoptosis, and movement. The computer model is able to elaborate the germ cell dynamics in a time-lapse movie format, allowing us to trace individual cells as they change state and location. More importantly, the model provides mechanistic understanding of the fundamentals of male fertility, namely how testicular morphology and sperm production are achieved. By manipulating cellular behaviors either individually or collectively in silico, the model predicts causal events for the altered arrangement of germ cells upon genetic or environmental perturbations. This in silico platform can serve as an interactive tool to perform long-term simulation and to identify optimal approaches for infertility treatment and contraceptive development.

Keywords: Agent-based model; Germ cell; Mouse; Simulation; Spermatogenesis; Spermatogenic cycle.

Abstract

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