Inferring simple but precise quantitative models of human oocyte and early embryo development

Author:

Leahy Brian D.12ORCID,Racowsky Catherine34,Needleman Daniel125

Affiliation:

1. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

2. SEAS, Harvard University, Cambridge, MA, USA

3. Brigham Women’s Hospital, Boston, MA, USA

4. Harvard Medical School, Boston, MA, USA

5. Center for Computational Biology, Flatiron Institute, New York, NY, USA

Abstract

Macroscopic, phenomenological models are useful as concise framings of our understandings in fields from statistical physics to finance to biology. Constructing a phenomenological model for development would provide a framework for understanding the complicated, regulatory nature of oogenesis and embryogenesis. Here, we use a data-driven approach to infer quantitative, precise models of human oocyte maturation and pre-implantation embryo development, by analysing clinical in-vitro fertilization (IVF) data on 7399 IVF cycles resulting in 57 827 embryos. Surprisingly, we find that both oocyte maturation and early embryo development are quantitatively described by simple models with minimal interactions. This simplicity suggests that oogenesis and embryogenesis are composed of modular processes that are relatively siloed from one another. In particular, our analysis provides strong evidence that (i) pre-antral follicles produce anti-Müllerian hormone independently of effects from other follicles, (ii) oocytes mature to metaphase-II independently of the woman’s age, her BMI and other factors, (iii) early embryo development is memoryless for the variables assessed here, in that the probability of an embryo transitioning from its current developmental stage to the next is independent of its previous stage. Our results both provide insight into the fundamentals of oogenesis and embryogenesis and have implications for the clinical IVF.

Funder

National Institutes of Health

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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