CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.02 - March-April

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Issue No.02 - March-April (2013 vol.10)

pp: 310-322

Thomas A. Henzinger , Inst. of Sci. & Technol. Austria (IST Austria), Klosterneuburg, Austria

Maria Mateescu , Inst. of Sci. & Technol. Austria (IST Austria), Klosterneuburg, Austria

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.91

ABSTRACT

We introduce propagation models (PMs), a formalism able to express several kinds of equations that describe the behavior of biochemical reaction networks. Furthermore, we introduce the propagation abstract data type (PADT), which separates concerns regarding different numerical algorithms for the transient analysis of biochemical reaction networks from concerns regarding their implementation, thus allowing for portable and efficient solutions. The state of a propagation abstract data type is given by a vector that assigns mass values to a set of nodes, and its next operator propagates mass values through this set of nodes. We propose an approximate implementation of the next operator, based on threshold abstraction, which propagates only “significant” mass values and thus achieves a compromise between efficiency and accuracy. Finally, we give three use cases for propagation models: the chemical master equation (CME), the reaction rate equation (RRE), and a hybrid method that combines these two equations. These three applications use propagation models in order to propagate probabilities and/or expected values and variances of the model's variables.

INDEX TERMS

Mathematical model, Computational modeling, Biological system modeling, Equations, Vectors, Abstracts, Numerical models,formal methods, Mathematical model, Computational modeling, Biological system modeling, Equations, Vectors, Abstracts, Numerical models, biochemical reaction networks, Chemical master equation, propagation models, abstract data type

CITATION

Thomas A. Henzinger, Maria Mateescu, "The Propagation Approach for Computing Biochemical Reaction Networks",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.10, no. 2, pp. 310-322, March-April 2013, doi:10.1109/TCBB.2012.91REFERENCES