Biological systems exhibit remarkable complexity, with modern multicellular organisms showcasing intricate behaviors and intelligence. However, this complexity is relatively recent. For the vast majority of natural history, only single-celled organisms existed, and it was within these seemingly simple systems that evolution produced the functions and behaviors fundamental to life as we know it. Many of these functions are regulatory and can be viewed as control systems. Understanding their evolution in single cells is key to explaining how these simple organisms gave rise to more integrated life forms. To address this, we introduce Elfa, a digital evolution platform that emulates the genome and regulatory systems of prokaryotic cells while abstracting complex cellular phenotypes. Designed both as a model for studying cellular evolution and as a tool for solving practical problems, Elfa provides a biologically inspired framework for exploring evolutionary dynamics. In our initial experiments, simulating photosynthetic bacteria in variable light environments, we show that regulation evolves as expected in response to environmental variation, but the rate of fixation of regulation increases with the combination of different sources of environmental variation. Most promising, though, is the degree of evolvability demonstrated by our model in this challenging multi-objective optimization problem, where the traits under evolution are highly epistatic and the dynamics to be regulated are noisy and mostly non-linear. These findings demonstrate that Elfa is an effective model for studying biological evolution and for exploring how environmental factors drive the evolution of regulatory mechanisms in early cells.Elfa