The biggest open problems in the life sciences concern the algorithms by which competent subunits (cells) could cooperate to form large-scale structures with new, system-level properties. In synthetic bioengineering, multiple cells of diverse origin can be included in chimeric constructs. To facilitate progress in this field, we sought an understanding of multi-scale decision-making by diverse subunits beyond those observed in frozen accidents of biological phylogeny: abstract models of life-as-it-can-be. Neural Cellular Automata (NCA) are a very good inspiration for understanding current and possible living organisms: researchers managed to create NCA that are able to converge to any morphology. In order to simulate a more dynamic situation, we took the NCA model and generalized it to consider multiple NCA rules. We then used this generalized model to change the behavior of a NCA by injecting other types of cells (adversaries) and letting them take over the entire organism to solve a different task. Next we demonstrate that it is possible to stop aging in an existing NCA by injecting adversaries that follow a different rule. Finally, we quantify a distance between NCAs and develop a procedure that allows us to find adversaries close to the original cells.