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Despite the deluge of bioinformatics data, the extraction of information with respect to complex diseases remains an open challenge. The development of efficient tools allowing the re-engineering of functional biological networks will therefore be crucial for the future of the pharmaceutical and biotech industry. In this paper we present a method for efficient re-engineering of meso-scale network topologies for biomedical systems from stationary data. We show that the meso-scale topology is related to functional structures of the input-output data of the entire system, which can be unravelled from high throughput screening experiments, without information with respect to intermediate variables. Analysis of the functional structure of the data provides a complementary approach to established network reengineering methods based on combinatorial optimization. A combination of both approaches will help to overcome the drawbacks of the established network reengineering algorithms.

Efficient reengineering of meso-scale topologies for functional networks in biomedical applications