Our group's mission is to understand how the constitutive metabolic regimes that underlie cellular organization inform the core principles of biological self-organization. We are currently focused on the events that precede fertilization of an egg by sperm, modeling the forces and uncertainties that shape sperm search and selection. Broader impacts of our research are aimed at improving family planning technologies and agriculture.
In mammals, sperm outnumber eggs by many millions to one. Sperm are capable of finding and fertilizing an egg in a range of microenvironments. They are able to do this despite not sharing pairwise information the way somatic cells do through cell-to-cell networks (e.g., neurons in the brain). We are investigating how sperm collectively compute via parallel execution of stochastic searches to better understand the fundamental principles that enable complex tissues to form and maintain stability in the face of environmental uncertainty.
Sperm are much more challenging to image than adherent somatic cells. Computer aided sperm motility analysis has not changed much since its advent in the 1980's. Rudimentary acquisition and analysis methods have limited the understanding of how sperm collectively search for an egg. Our collaborative efforts with Dr. David Hart at ECU are focused on building AI methods for tracking sperm over physiologically relevant timescales at the high cell densities characteristic of undiluted semen samples.
Sperm must move in a way that increases the probability of egg contact using their flagella. Sperm don't actually 'swim', but instead do something more like crawling. This is because inertia plays essentially no role in movement at small scales. Through our collaborations with Dr. Martin Bier at ECU, we are working to understand how sperm flagellar beat frequency varies over time to produce long term phase transitions in trajectories that enable sperm to optimize search patterns in response to external chemical cues from the environment.
Paper coming soon in Entropy!
Quantitative prediction of fertilizing potential requires information about the distribution of sperm phenotypes, the nature of the reproductive microenvironment, and the conditional relationships between phenotypes and fertility success. In collaboration with Dr. Paul Vos at ECU we are using time-lapse spectral flow cytometry, statistics, and differential equations to model the cell population scale dynamics of sperm acrosome reaction kinetics and cell regulatory pathways during in vitro capacitation.
Read our Paper on Sperm Redox Transitions
Paper on Subpopulation Dynamics Coming Soon!
ECU Biology and the Eastern Region Pharma Training Center in Greenville, NC
We are located in Greenville, North Carolina (The Greatest City on Earth)
East Carolina University, Dept. of Biology
Life Sciences and Biotechnology Building (Office: 2414)
Email: schmidtc18@ecu.edu