title: Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.
abstract: Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.
authors.affiliation: National Technical University of Athens, Athens, Greece.
authors.forename: Ioannis N
authors.initials: IN
authors.lastname: Melas
authors.forename: Regina
authors.initials: R
authors.lastname: Samaga
authors.forename: Leonidas G
authors.initials: LG
authors.lastname: Alexopoulos
authors.forename: Steffen
authors.initials: S
authors.lastname: Klamt
mesh_terms: D003196:Computer Graphics; D008954:Models, Biological; D015398:Signal Transduction
references.citation: Mol Cell Proteomics. 2010 Sep;9(9):1849-65
references.pmid: 20460255
references.citation: Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32
references.pmid: 15608231
references.citation: PLoS Comput Biol. 2011 Mar;7(3):e1001099
references.pmid: 21408212
references.citation: Genome Res. 2003 Nov;13(11):2435-43
references.pmid: 14597655
references.citation: Mol Biosyst. 2012 Apr;8(5):1571-84
references.pmid: 22446821
references.citation: Syst Biol (Stevenage). 2006 Jul;153(4):201-11
references.pmid: 16986622
references.citation: PLoS Comput Biol. 2009 Dec;5(12):e1000591
references.pmid: 19997482
references.citation: PLoS Comput Biol. 2009 Aug;5(8):e1000438
references.pmid: 19662154
references.citation: BMC Bioinformatics. 2006 Feb 07;7:56
references.pmid: 16464248
references.citation: PLoS Comput Biol. 2009 Apr;5(4):e1000340
references.pmid: 19343194
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):27-30
references.pmid: 10592173
references.citation: BMC Syst Biol. 2007 Jan 08;1:2
references.pmid: 17408509
references.citation: Bioinformatics. 2007 Dec 1;23(23):3209-16
references.pmid: 18042557
references.citation: BMC Genomics. 2009 May 26;10:244
references.pmid: 19470162
references.citation: Bioinformatics. 2012 Nov 1;28(21):2811-8
references.pmid: 22923292
references.citation: Nat Biotechnol. 2002 Apr;20(4):370-5
references.pmid: 11923843
references.citation: Biosystems. 2006 May;84(2):153-74
references.pmid: 16556482
references.citation: IEEE Trans Biomed Eng. 2011 Dec;58(12):3518-21
references.pmid: 21803675
references.citation: Mol Syst Biol. 2009;5:331
references.pmid: 19953085
references.citation: Sci Signal. 2011 Feb 15;4(160):pe10
references.pmid: 21325202
references.citation: Phys Biol. 2012 Oct;9(5):055001
references.pmid: 23011283
references.citation: BMC Syst Biol. 2007 Feb 15;1:13
references.pmid: 17408503
references.citation: IET Syst Biol. 2007 Nov;1(6):342-52
references.pmid: 18203580
references.citation: Science. 2005 Apr 22;308(5721):523-9
references.pmid: 15845847
references.citation: Nucleic Acids Res. 2011 Jan;39(Database issue):D685-90
references.pmid: 21071392
references.citation: Biochemistry. 2010 Apr 20;49(15):3216-24
references.pmid: 20225868