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In this position paper, I argue that a fruitful, and as yet largely unexplored, avenue for artificial life research lies in modelling organisms (specifically, phenotypes) and environment as a single dynamical system. From this perspective, the origin and evolution of life is the progressive control of the dynamical system at a local level by constraints which are represented on an organism's genome. Such an approach shifts the focus of artificial life models away from the design of individuals, towards the interaction of an individual with its dynamic environment. It also blurs the boundary between organism and environment; the most important modelling distinction is no longer between an organism's body and its external environment, but rather between the genome (which is treated as an essentially symbolic structure) and phenotype-plus-environment combined. An evolutionary cellular automata system, called EvoCA, is introduced as a tool to explore these ideas. To demonstrate how this approach differs from traditional studies, two example applications of EvoCA are presented. One concerns sensor and effector evolution; the other concerns the origin of life, and in particular the evolution of genome-regulated self-stabilising dynamics. Advantages of the new approach are swmnarised, and sorne potential criticisms are considered. The paper concludes with a discussion of some implications of this shift in perspective.