Modeling the Adaptive Venation Network of <emp>Physarum polycephalum</emp>

Hannah Blumberg

Physarum polycephalum is an organism that one cannot help but find interesting. This single-celled amoeboid is able to self-organize and self-optimize without the help of any sort of central nervous system. It can find the shortest path connecting any number of food sources, solve mazes created by physical barriers, and create paths that avoid light. I was introduced to this organism by my mentor, a member of the Laboratory of Mathematical Physics at The Rockefeller University in Manhattan, New York. The general theme that connected the research within this laboratory was optimized networks; researchers worked on everything from the venation in plant leaves to the structure of rat brains. There was no work being done with Physarum polycephalum at the time, but my mentor cited its venation patterns as examples of optimized network . . . I began to wonder if I could take these [existing] mathematical models a step further by creating a computer program that could model the organism’s behavior continuously rather than discretely. This would not only provide useful insight into the optimization process itself, but would also be an educational exercise in creating a dynamic simulation from a static model . . . Perhaps the most valuable lesson I learned from conducting research throughout high school is the importance of stupidity. This phrase, borrowed from the title of an essay by Martin Schwartz, means that before you can focus on discovering, you have to free yourself from the burden of knowing. You will almost inevitably encounter roadblocks throughout the research process, and it is important not to let the feeling of stupidity discourage you from continuing. The truth is comforting: when one is conducting research, he or she is not expected to have the answers. We enter the unknown in the pursuit of knowledge, and what we discover brings us closer to understanding.