Network optimal retrieval of sparse perturbations for steady-state control

Prioritizing targeted perturbation experiments remains a central challenge in systems biology, where experimental constraints limit network manipulation. We introduce NORSP (Network Optimal Retrieval of Sparse Perturbations). This novel computational framework integrates network propagation with supervised subset selection to identify minimal perturbation sets that can shift a system from its initial to a desired steady state. NORSP leverages a sensitivity matrix derived solely from network topology, enabling control prediction without requiring full knowledge of system dynamics. Applicable to undirected, directed, and signed networks, NORSP accommodates a broad range of biological models and experimental scenarios. We validate its effectiveness using YBX1 knockdown transcriptomics data and 61 curated metabolic networks from the BioModels repository, demonstrating NORSP's robustness, scalability, and experimental relevance. Even under constraints that obscure true perturbations, the algorithm reliably infers alternative targets that achieve comparable control. Control is confirmed both in graphical approximations and through full dynamical model simulations. Overall, NORSP provides a practical and generalizable solution for steady-state control in complex biological systems, laying the foundation for multi-omics hypothesis generation and systems-level experimental design.