In this study we develop, using basic object-oriented paradigms, and in collaboration with biologists, a comprehensive model of landscapes and ecosystems dynamics based on bioinspiration principles. Faced with the issue of taking into consideration a variety of elements, processes, interactions, contexts, and scales simultaneously effective, we iteratively develop this model using successive aggregation of new components based on specific case studies. These were then generalized and consolidated to form a coherent platform. To address robustness, the model was continually reworked in search of the closest resemblance to the concrete workings of Nature. We have arrived at a general architecture built from the bottom up that is both generic and as parsimonious as possible. The model emerging from this compilation is a shared class tree with three primary categories of variability: (i) cognitive living agents, (ii) containers of agents that can be nested at various functional scales, and (iii) particular genomes that instantiate attributes for each type of agent. The results of the iterative strategy to modeling synthetic ecology are discussed, as well as the suitability of object-oriented paradigms (composition, aggregation, inheritance, generalization…) for achieving the goal of bioinspired modeling.