1 Toward a Multiscale Model of Cell Migration Michelle Wynn Indiana University Spring 2008 School of Informatics

2 Why is Cell Migration Important? Embryo Development Congenital Defects Wound/Tissue Repair Immune System Tumor Formation Cancer Vital to life but can also make us very sick (video: -- made in 1952!)

3 Neural Crest Cells migrate from the neural tube and form vital structures in vivo video of NCCs as they migrate to the site of spinal ganglia development (video: Kasemeier-Kulesa et al, 2005, image: Brain Region

4 (image: from Rupp and Kulesa, 2007 – experiments performed on chick embryos) Objective: why do NCCs form chains during migration? Hypothesis: contact between protrusions plays an important role in guiding cells toward other cells, in the direction of migration (but may not be unique mechanism) Contact Guidance

5 Testing the Contact Hypothesis Use chick as animal model to study NCC migration. Analyze live video microscopy data of developing chick embryos and derive possible model parameters from video data and published literature. Computationally model via a rules based simulation to test mechanism of cell contact. (image: Teddy and Kulesa, 2004 – experiments performed on chick embryos) Want to determine the physical mechanism before modeling the molecular and gene level components Identify those parameters which create the most stable chains

6 Stable chain must have a net positive displacement toward target, over time

7 How I built the model framework DESIGN PATTERNS! Built extensible, non-specific and reusable simulation framework with O.O. design patterns (Strategy, Observer, Factory, Memento, others). Fully configurable at runtime with dynamic class loading (limited compile time dependency) Can easily change out various strategies (update, rules, output, etc) Suite of over 20 tests are run whenever model is altered to make sure model is not broken! Algorithm/Strategy Factory Grid Update Algorithm Neighborhood Algorithm Rule Algorithm Output Algorithm Proliferation Algorithm Parameter Factory Probability Distributions Discrete Values Boolean Values Can, for example, chose to change output strategy (file or graphic viewer), or change grid update strategy all at run time.

8 Contact Guidance Computational Model Rules Follower cells may be directionally polarized and have up to two protrusions Leader cells are not polarized and can have many protrusions Cells protrude and retract their extensions randomly (via an internal clock) Leader cells move according to a probability distribution (assumed to be biased toward target) Follower cells move randomly, unless in contact, then preferential movement toward a contacted cell is assumed Follower cells randomly change polarization direction when not in contact (via an internal clock)

9 Use Two Main Types of Parameters Protrusion Parameters protrusion count protrusion length protrusion / retraction clock Directionality Parameters Polarization Directionality Bias (chemotaxis?) direction change clock Likelihood to move in direction of contact Movement Likelihood to retract when in contact

10 Contact Guidance Preliminary Results Current results indicate that single most important parameter is relative bias of the leader cell to move toward target. Suggests chemotaxis plays a role. Computationally intensive – still running parameter analysis

11 Current Model Limitations Not considering the importance of synchronization of internal clocks Molecular and gene expression parameters not yet included Contacts between cells do not stretch if one cell moves away (probably not biologically realistic) Not incorporating other plausible mechanistic hypothesis

12 Alternate Hypothesis: Path of Least Resistance Red leader cells form clear channel which the blue follower cells seem to follow but they do not keep up with the red cells. Red leader cells are biased to move toward target. Blue followers are not directionally biased. Follower cells move randomly but prefer to move to an “open” site over a “closed” one. TODO: combine this model with contact guidance model

13 Acknowledgements Thank you to Professor Santiago Schnell Professor Gregory Rawlins Schnell Systems Biology Research Group Bioinformatics Group at SOI Linda Hostetter Linda Roos

14 Questions?