1. Biology 624 - Developmental Genetics Lecture #6 – Modeling in Development

2. Why Developmental Biology Needs Models 1.understand how mechanisms at one level of scale (ie cell-level) interact to produce higher level phenomena (ie tissue-level) 2.provides testable hypotheses for experimentation 3.this is the time to enhance the use of this approach in developmental biology 4. you don’t need to be a mathematician to do modeling

3. Cell Behavior to Tissue Integration Robertson et al, 2007

4. Provides Testable Hypotheses - Predictions Thorne et al, 2007

5. What is a Computational Model? 1. Uses experimental data computers can understand and assumptions of scientists to predict outcomes 2. Concept of “simulations” – run data through time and/or space to produce outcomes 3. Toggle from simulation outcomes to experimental outcomes 4. Do NOT make bad data turn into good data – experiments important 5. Help scientists better understand processes by emphasis on modularity, randomness, non-randomness, feedback loops, etc.

6. Modeling Diffusion in Fly Embryos Tomlin and Axelrod, 2007

7. Diffusion of Morphogens in Fields with Receptors Lander et al, 2002

8. Modeling Fly Stripes Tomlin and Axelrod, 2007 Von Dassow et al, 2000 Wg = green En = red

9. Modeling Intercalation during Frog Gastrulation Longo et al, 2004 Stain = Fibronectin

10. Cell Behavior is Governed by Rules in the Simulation Longo et al, 2004

11. Time Sequence of BCR Thinning Longo et al, 2004

12. Model Predicts Lateral Movement of Implanted Cells Longo et al, 2004

13. Modeling Approaches: Top-Down --aims to reveal overarching control mechanisms --high-level attributes, ie “these cells die” --governing rule set are potential relationships loosely derived from qualitative experiments --GOAL: deduce a minimal rule set to reveal systems level controls

14. Modeling Approaches: Bottom-Up --explicitly accounts for fine processes --assembles these processes to predict higher level processes --rules derived from quantitative empirical data --GOAL: deduce emergent phenomena at a higher level from interactions at level below

15. Model Types - Continuum --based on kinetic parameters --uses partial differential equations a lot --models environmental changes precisely --does NOT model spatial heterogeneity well --not very intuitive

16. Model Types – Agent Based Models --agents (ie cells) behave according to rules --allow for spatial heterogeneity --allow for random or stochastic response --does NOT account for precise concentrations,etc --intuitive

17. Model Types – Combinations +

18. How Models Work Thorne et al, 2007