A First Glance on Coevolution of Boolean Networks to Simulate the Development of Interacting Systems in Molecular Biology

Biological systems must adapt to a variety of changing circumstances over the course of lifetime. They therefore rely on complex and robust mechanisms. This includes the exchange of information between different subsystems, which can influence each other’s development. In this chapter, we use mathematical models of biological systems, called Boolean networks, to develop a framework for analyzing how interacting systems change over time to better understand them. Keeping track of the changes, the systems experience aims to study the underlying mechanisms and potential high-impact changes which may lead to effects such as the development of diseases. Therefore, we have developed a coevolutionary algorithm, extending the evolutionary CANTATA approach, with which a system consisting of several components can evolve toward a desired behavior. In this extension, multiple populations of different models co-evolve by influencing each other via a mutual exchange of information. Additionally, we extended the original algorithm to track every change that the networks undergo over time. This feature allows us to examine the trajectory of evolution for essential changes. In an initial proof of concept, we were able to demonstrate the basic validity as well as the impact of different sharing strategies of our coevolutionary algorithm.