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Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights.

Benjamin M Brisard¹, Kylie D Cashwell¹, Stephanie M Stewart¹

¹Department of Biology, East Carolina University, Greenville NC.

Biorxiv : the Preprint Server for Biology

|July 1, 2024

Summary

Mammalian sperm undergo selection during fertilization. This study develops computational models to quantify sperm selection, crucial for improving assisted reproductive technologies (ART) and understanding fertilization dynamics.

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Last Updated: Jul 2, 2026

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Area of Science:

Reproductive Biology
Computational Biology
Biophysics

Background:

Mammalian fertilization involves significant sperm selection, reducing millions of sperm to a few.
Classical sperm competition theory doesn't fully address purifying selection on individual sperm traits during fertilization.
Assisted reproductive technologies (ART), like intracytoplasmic sperm injection (ICSI), rely on sperm selection, risking the use of low-fitness sperm.

Purpose of the Study:

To develop a unifying quantitative framework for sperm selection.
To reframe sperm function as a collective diffusive search process.
To explore causal dynamics constraining sperm fitness during fertilization.

Main Methods:

Development of multi-scale computational models.
Reframing sperm physiology as a collective diffusive search.
Assessing sperm selection under varying microenvironmental pressures.

Main Results:

Introduction of a probabilistic measure of sperm fitness.
Development of an information-theoretic measure for the magnitude of sperm selection.
Quantification of selection dynamics based on sperm motility patterns.

Conclusions:

The study provides a novel theoretical framework for understanding sperm selection.
The developed measures offer quantitative tools for assessing sperm quality in ART.
Understanding these dynamics is vital for optimizing fertilization success in assisted reproduction.