1. Simulation of Early Mouse Ovarian Development
Using a Cellular Potts Model
A Development Framework
Hannah Wear1
, Annika Eriksson2
, and Karen Watanabe1
1
Institute of Environmental Health; 2
Department of Medical Informatics & Clinical Epidemiology
Oregon Health & Science University
Research Objective
In pursuit of humane, efficient alternatives to traditional toxicity
testing, develop a computational model of ovarian development
in mouse demonstrating spatial, temporal, and cellular interac-
tions defined by primary literature sources.
To predict adverse effects we first need to understand and simulate normal
reproductive system development and function. Our research focuses on in
silico simulation of normal early ovarian development in mouse, one of the
most common animals used in toxicity testing.
Approach
Cellular Potts Model
• Multi-scale, multi-cell Monte Carlo method [1]
• Implemented using CompuCell3D software [3]
• Includes adhesion, growth, apoptosis, mitosis, secretion, and migration,
and other normal cellular behaviors
• Effective energy function determines the probability of cell modification
E = EAdhesion + EVolume + EChemotaxis (1)
• Net adhesion or repulsion between each pair of neighboring cell mem-
branes, a function of the binding energy (J) and the area of contact (K)
between two cells (a) and (b).
EAdhesion =
All Cells
a
All Cells
b
Ja,b × Ka,b (2)
• Deviations of the actual volume (v) and surface area (s) from the target
volume (vT ) and surface area (sT ) as cells divide and grow, where λv and
λs represent the corresponding elasticity of volume and surface
EVolume =
All Cells
λv(v − vT )2
+ λs(s − sT )2
(3)
• The effect of chemotaxis is expressed as a function of local concentration,
C, of a particular species of signaling molecule and µ, the chemical poten-
tial determined by a set of reaction-diffusion equations
EChemotaxis = µC (4)
Parameter Tuning
1. Estimate
• Target volume and surface area of each cell type
• Volume and surface elasticity of each cell type
• Relative cell-cell binding energies
• Levels of secretion and diffusion of ligands
• Chemotactic response of cell types to chemical fields
2. Run simulation
3. Compare behavior of simulation to experimental data
4. Adjust and repeat
Literature Review
Figure 1: Published graphics used for simulation
setup. [2] Stained mouse embryo from embryonic day
7 used for Part One (left). Whole-mount mouse ovary
on embryonic day 12 used for Part Two (right).
Results
Figure 2: Part One (left) simulates migration of primordial germ cells into the
gonadal ridge, and Part Two (right) simulates proliferation of germ cells in the
gonadal ridge and development of primordial germ nests and follicles.
Figure 3: Primordial germ cells migrate in response to receptor-ligand inter-
actions, originating from the gonadal ridge (left) and the hindgut basal epithe-
lial cells (right). Concentration gradients represent SDF1 and KIT secretions,
respectively.
Scan the QR code to watch the simulation video.
Broader Impacts
• Predictive modeling efforts for toxicity testing
• Identify biological perturbations that lead to adverse development
• Tool to explore how changes in parameter values affect development
• Provides a framework for modeling whole organs
Future Directions
• Incorporation of additional molecular signaling pathways
• Expanding the model to later stages of ovarian development
• Expanding the model to other species (e.g. rhesus monkey)
Funded in part by the Alternatives Research and Development Foundation
[1] Nan Chen, James A Glazier, Jes´us A Izaguirre, and Mark S Alber. A parallel implementation of the cellular potts model for simulation of cell-based morphogenesis. Computer physics
communications, 176(11-12):670–681, 06 2007.
[2] MALKA Ginsburg, MH Snow, and ANNE McLAREN. Primordial germ cells in the mouse embryo during gastrulation. Development, 110(2):521–528, 1990.
[3] Maciej H Swat, Gilberto L Thomas, Julio M Belmonte, Abbas Shirinifard, Dimitrij Hmeljak, and James A Glazier. Multi-scale modeling of tissues using compucell3d. Methods in cell biology,
110:325–366, 2012.