ArticleBiophysical journal2006
A computational framework for the topological analysis and targeted disruption of signal transduction networks.
Article in Biophysical journal, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed, 52 citations in OpenAlex.
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- Diverse array-designed modes of combination therapies in Fangjiomics.Acta pharmacologica Sinica · 2015Review
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- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- A flood-based information flow analysis and network minimization method for gene regulatory networks.BMC bioinformatics · 2013Article
- Optimality and thermodynamics determine the evolution of transcriptional regulatory networks.Molecular bioSystems · 2012Article
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- Drug repositioning using disease associated biological processes and network analysis of drug targets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- Towards genome-scale signalling network reconstructions.Nature reviews. Genetics · 2010Review
- Network analyses in systems pharmacology.Bioinformatics (Oxford, England) · 2009Review
- New connections, new components, real dynamics.Science signaling · 2009Article
- Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity.Nature genetics · 2009Article
- Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.PLoS computational biology · 2009Article
- Identification of potential pathway mediation targets in Toll-like receptor signaling.PLoS computational biology · 2009Article
- Systems pharmacology and genome medicine: a future perspective.Genome medicine · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.