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

Abstract

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from [~]106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or stabilizing selection acting on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because low fitness sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm fitness during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm fitness as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns. Author SummaryFertilization in mammals requires many orders of magnitude more sperm than eggs to be fertilized. This study models collective sperm movement as a diffusive search process, combining experiments and simulations to explore how heterogeneity in motility patterns and microenvironmental complexity shape fertilization success. We introduce simple metrics to quantify sperm fitness and the magnitude of selection pressure imposed by the microenvironment, revealing that sperm phenotype distributions interact with environmental constraints to determine the range of sperm traits that ultimately support successful egg contact. These insights improve our understanding of sperm population dynamics and offer practical tools for optimizing assisted reproductive technologies in clinical and agricultural settings.