CytoASP: a Cytoscape app for qualitative consistency reasoning, prediction and repair in biological networks

BMC Systems Biology

Sophia Tsoka¹  Niels Grabe²  Jekaterina Sereshti²  Amélie Barozet¹  Aristotelis Kittas¹ 

[1] Department of Informatics, King’s College London, Strand, London WC2R 2LS, UK;Department of Medical Oncology, NCT, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg 69120, Germany

关键词: Qualitative modelling;    BioASP;    Regulatory networks;    Biological networks;   

Others  :  1230658
DOI  :  10.1186/s12918-015-0179-6

 received in 2015-01-23, accepted in 2015-06-11,  发布年份 2015

【 摘 要 】

Background

Qualitative reasoning frameworks, such as the Sign Consistency Model (SCM), enable modelling regulatory networks to check whether observed behaviour can be explained or if unobserved behaviour can be predicted. The BioASP software collection offers ideal tools for such analyses. Additionally, the Cytoscape platform can offer extensive functionality and visualisation capabilities. However, specialist programming knowledge is required to use BioASP and no methods exist to integrate both of these software platforms effectively.

Results

We report the implementation of CytoASP, an app that allows the use of BioASP for influence graph consistency checking, prediction and repair operations through Cytoscape. While offering inherent benefits over traditional approaches using BioASP, it provides additional advantages such as customised visualisation of predictions and repairs, as well as the ability to analyse multiple networks in parallel, exploiting multi-core architecture. We demonstrate its usage in a case study of a yeast genetic network, and highlight its capabilities in reasoning over regulatory networks.

Conclusion

We have presented a user-friendly Cytoscape app for the analysis of regulatory networks using BioASP. It allows easy integration of qualitative modelling, combining the functionality of BioASP with the visualisation and processing capability in Cytoscape, and thereby greatly simplifying qualitative network modelling, promoting its use in relevant projects.

【 授权许可】

   
2015 Kittas et al.

【 图 表 】

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【 参考文献 】

• [1]Samaga R, Klamt S: Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Commun Sign 2013, 11(1):43.
• [2]Siegel A, Radulescu O, Le Borgne M, et al.: Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks. Bio Syst 2006, 84(2):153-74.
• [3]Gebser M, König A, Schaub T, et al.: The BioASP library: ASP solutions for systems biology. [http://goo.gl/Yb3k7D] webcite2010 22nd IEEE Int. Conf. on Tools with Art. Intel. (ICTAI) IEEE, Arras; 2010. Available at: http://goo.gl/Yb3k7D
• [4]Baral C: Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press, New York, NY, USA; 2003.
• [5]Brewka G, Eiter T, Truszczyński M: Answer set programming at a glance. Commun ACM 2011, 54(12):92-103.
• [6]Shannon P, Markiel A, Ozier O, et al.: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13(11):2498-504.
• [7]Gebser M, Schaub T, Thiele S, et al.: Detecting inconsistencies in large biological networks with answer set programming. In Logic Programming. Lect. Notes in Comp. Sci.. Springer, Berlin; 2008.
• [8]Guelzim N, Bottani S, Bourgine P, et al.: Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet 2002, 31(1):60-3.
• [9]Sudarsanam P, Iyer VR, Brown PO, Winston F: Whole-genome expression analysis of snf/swi mutants of saccharomyces cerevisiae. Proc Nat Acad Sci 2000, 97(7):3364-369.
• [10]García-Arranz M, Maldonado AM, Mazón MJ, Portillo F: Transcriptional control of yeast plasma membrane h(+)-ATPase by glucose. cloning and characterization of a new gene involved in this regulation. J Biol Chemi 1994, 269(27):18076-82.
• [11]Sasaki H, Uemura H: Influence of low glycolytic activities ingcr1 andgcr2 mutants on the expression of other metabolic pathway genes inSaccharomyces cerevisiae. Yeast 2005, 22(2):111-27.
• [12]Thomas D, Barbey R, Surdin-Kerjan Y: Gene-enzyme relationship in the sulfate assimilation pathway of saccharomyces cerevisiae. study of the 3’-phosphoadenylylsulfate reductase structural gene. J Biol Chem 1990, 265(26):15518-24.
• [13]Baumbach J, Apeltsin L: Linking Cytoscape and the corynebacterial reference database CoryneRegNet. BMC Genomics 2008, 9(1):184.
• [14]Melas IN, Samaga R, Alexopoulos LG, Klamt S: Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs. PLoS Comput Biol 2013, 9(9):1003204.
• [15]Guziolowski C, Bourd’e A, Moreews F, et al.: BioQuali cytoscape plugin: analysing the global consistency of regulatory networks. BMC Genomics 2009, 10(1):244.
• [16]Gebser M, Guziolowski C, Ivanchev M, et al.Repair and prediction (under inconsistency) in large biological networks with answer set programming. In: Twelfth International Conference on the Principles of Knowledge Representation and Reasoning 2010.. http://aaai.org/ocs/index.php/KR/KR2010/paper/view/1334 webcite