Jimena: efficient computing and system state identification for genetic regulatory networks

BMC Bioinformatics

Stefan Karl¹  Thomas Dandekar¹ 

[1] Department of Bioinformatics, University of Würzburg, Am Hubland, Würzburg, Germany

关键词: Boolean tree;    Binary decision diagram;    Stable state;    Interpolation;    Genetic regulatory network;    Boolean function;   

Others  :  1087730
DOI  :  10.1186/1471-2105-14-306

 received in 2013-04-06, accepted in 2013-10-03,  发布年份 2013

【 摘 要 】

Background

Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed.

Results

(i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones).

Conclusions

Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

【 授权许可】

   
2013 Karl and Dandekar; licensee BioMed Central Ltd.

【 图 表 】

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