際際滷

際際滷Share a Scribd company logo

latex-portrait-poster

A
Annika Eriksson

The document describes a computational model of early mouse ovarian development using a cellular Potts model. The model simulates the migration of primordial germ cells into the gonadal ridge and their proliferation into germ cell nests and follicles. Key parameters like cell adhesion, growth, apoptosis and signaling molecule concentrations are modeled based on literature. The model is tuned by comparing simulations to experimental data. The goals are to understand normal development, identify biological perturbations, and provide a framework for toxicity testing alternatives.

1 of 1
Download to read offline
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, ef鍖cient alternatives to traditional toxicity testing, develop a computational model of ovarian development in mouse demonstrating spatial, temporal, and cellular interac- tions de鍖ned by primary literature sources. To predict adverse effects we 鍖rst 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 modi鍖cation 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 了v and 了s represent the corresponding elasticity of volume and surface EVolume = All Cells 了v(v vT )2 + 了s(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 袖, the chemical poten- tial determined by a set of reaction-diffusion equations EChemotaxis = 袖C (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 鍖elds 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卒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):670681, 06 2007. [2] MALKA Ginsburg, MH Snow, and ANNE McLAREN. Primordial germ cells in the mouse embryo during gastrulation. Development, 110(2):521528, 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, 110:325366, 2012.

More Related Content

latex-portrait-poster

  • 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, ef鍖cient alternatives to traditional toxicity testing, develop a computational model of ovarian development in mouse demonstrating spatial, temporal, and cellular interac- tions de鍖ned by primary literature sources. To predict adverse effects we 鍖rst 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 modi鍖cation 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 了v and 了s represent the corresponding elasticity of volume and surface EVolume = All Cells 了v(v vT )2 + 了s(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 袖, the chemical poten- tial determined by a set of reaction-diffusion equations EChemotaxis = 袖C (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 鍖elds 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卒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):670681, 06 2007. [2] MALKA Ginsburg, MH Snow, and ANNE McLAREN. Primordial germ cells in the mouse embryo during gastrulation. Development, 110(2):521528, 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, 110:325366, 2012.