Constrained reduction mapping for a class of network models of genomic regulation

Constructing network models of genomic regulation from data can help to better understand the manner in which genes interact in an integrative and holistic way within a given genome. One of the major impediments for the practical application of such models is their structural and computational complexity. Thus, it is sometimes necessary to construct computationally tractable sub-networks while still carrying sufficient structure for the application at hand. Hence, there is a need for size reducing mappings. This paper focuses on constrained reduction mappings for a particular class of network models that are inferred from non-temporal data. The constraints arise naturally from the structural and dynamical properties of the considered models.