The growth of plants is an application that lends itself well to mathematical modeling using discrete steps. An interesting implementation of such models is to represent the structure of plants as a string of symbols and to use string rewriting rules to define the growth of every plant part.
In this project, I developed a growth model for Floerkea Proserpinacoides, a small annual plant commonly found on forest floors. This plant exhibits a relatively simple structure and an interaction with its environment limited to a few parameters, while showing interesting nondeterministic behavior that strongly influence the evolutionary strength of the species. Using field data collected in a previous botanical study of the plant, it was possible to calibrate the computational model and to validate its results against the experimental results.
Custom software was written to provide a precise level of control over the data structures and the algorithms involved in the processing of the rewriting rules. In order to bypass the exponential complexity of keeping track of every possible instance of a plant generated by nondeterministic rules, a statistical sampling approach based on random choices made it possible to gather information on the whole population while keeping the time and space complexity within computable values for more complex models.