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The unraveling of balanced complexes in metabolic networks

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks. Highlights Balanced complexes are ubiquitous in real-world networks, and facilitate insights in steady state flux phenotypes in large-scale metabolic networks. Novel factorizations are proposed that explain the formation of balanced complexes and enable their categorization. The results also provide a computationally-efficient tool for the identification of balanced complexes in large-scale networks. Examination of metabolic network models across all kingdoms of life shows that all categories naturally arise in this type of networks.