Browsing by Author "Hovhannisyan, Karen"

Microbial growth dynamics play an important role in virtually any ecosystem. To know the underlying laws of growth would help in understanding how bacteria interact with each other and their environment. In this thesis we try to automate the process of scientific discovery of said dynamics, via symbolic regression. It has historically been implemented with genetic algorithms, and although many of the new implementations have different approaches, we stick with a highly optimized genetic-programming based package. Whatever the approach, the purpose of symbolic regression is to search for a mathematical expression that explains a response variable. We test the highly interpretable machine learning method on several datasets, each generated to mimic certain patterns of growth. Our findings confirm its ability to reverse-engineer theory from data. Even when the generating equations contain the latent nutrient variable, whose dynamics are not observable through the raw data, symbolic regression is able to find an analytically correct reparametrization and exact solution. In this thesis we discuss these results and give an overview of symbolic regression and its applications.