A bio-math approach to elucidate stem cell dynamics in Ceratopteris gametophytes - The development of multicellular organisms raises a key question: how are cell behaviors dynamically
controlled to produce tissue and shape? Whether in plants, animals, or humans, misregulated cell division
and growth can lead to developmental defects, i.e., organ dysfunction, reduced reproduction, and disease.
While some regulatory pathways have been uncovered in different species, the quantitative prediction of
cell behavior at single-cell resolution in multicellular organisms—though critical—is incomplete. This project
will address this gap by combining experiments, predictive modeling, and data-driven methods in
Ceratopteris (fern) gametophytes. Ceratopteris gametophytes comprise a single layer of cells that grows
from 1 to nearly 500 cells in 12 days, transforming from round to heart-shaped and developing pluripotent
meristems (stem cells). Ceratopteris gametophytes are highly suited for an interdisciplinary approach to
understanding cell dynamics. They allow for non-invasive time-lapse confocal imaging to trace every cell
and are efficient for perturbations. With the long-term goal of determining stem cell behavior using
Ceratopteris gametophytes as a model, this 3-year project will uncover the cellular and molecular
interactions at work during tissue growth and meristem notch formation, and develop a data-driven
approach to associate vertex models with efficient particle models. This project has 3 aims: Aim 1 will
develop the first mathematical models of cell behavior in Ceratopteris gametophytes and establish a cycle
of testing empirical hypotheses in silico and model-generated predictions in vivo. It will uncover how
spatiotemporal differences in cell division and growth drive meristem formation and organ morphology
through mechanical perturbation experiments, quantitative time-lapse imaging, and vertex modeling. Aim 2
will elucidate the signaling molecules behind these spatiotemporal differences through a close coupling of
new chemical and genetic perturbation experiments and hybrid modeling. Aim 3 will tailor a recent method
for learning equations to account for changes in cell number, multi-scale dynamics, and the connected
structure of cells in plant tissue. This research will use this approach to associate vertex models with
simplified—more efficient—particle models and discover equations of motion directly from in vivo nucleus
trajectories. Together this work will shed light on how cell division, growth, and differentiation are controlled
to maintain meristems and define gametophyte shape, and it will increase the applicability of data-driven
methods to collective cell dynamics more broadly.
