title: A computational framework for the topological analysis and targeted disruption of signal transduction networks.
abstract: In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.
authors.affiliation: Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Madhukar S
authors.initials: MS
authors.lastname: Dasika
authors.forename: Anthony
authors.initials: A
authors.lastname: Burgard
authors.forename: Costas D
authors.initials: CD
authors.lastname: Maranas
mesh_terms: D000818:Animals; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003198:Computer Simulation; D020868:Gene Silencing; D018390:Gene Targeting / Q000379:methods*; D006801:Humans; D008297:Male; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000378:metabolism*; D015398:Signal Transduction; D014407:Tumor Cells, Cultured
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins