In this work we present IHDNs: an original model of computation for the simulation of interacting, dynamic, multi-scale systems. We show that a novel message passing mechanism that operates across layers of abstraction in hierarchical dynamic networks is effective in expressing the complex dependencies of living systems. Using a conventional computational model of cell evolution in cancerous tumour growth for comparison, we demonstrate the validity of IHDNs in emulating the behaviour of life-like systems, as well as the additional capabilities in enabling Neo4j Cypher patternmatching queries, demonstrated here in the analysis of evolutionary cell heritage.

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