MORE: Mixed Optimization for Reverse Engineering—An Application to Modeling Biological Networks Response via Sparse Systems of Nonlinear Differential Equations
Issue No. 05 - Sept.-Oct. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.56
Francesco Sambo , Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Marco A. Montes de Oca , Dept. of Math. Sci., Univ. of Delaware, Newark, DE, USA
Barbara Di Camillo , Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Gianna Toffolo , Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Thomas Stutzle , IRIDIA-CoDE, Univ. Libre de Bruxelles, Brussels, Belgium
Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.
time series, bioinformatics, nonlinear differential equations, optimisation, reverse engineering, bioinformatics, MORE, reverse engineering mixed optimization algorithm, biological network modeling, sparse systems, nonlinear differential equations, time series data, biological network topology, enforce system sparsity, real-world networks, mixed discrete-continuous optimization approach, Optimization, Algorithm design and analysis, Proteins, Mathematical model, Reverse engineering, Biological information theory, sparse systems of differential equations., Reverse engineering, mixed optimization, biological networks
G. Toffolo, B. Di Camillo, M. A. de Oca, F. Sambo and T. Stutzle, "MORE: Mixed Optimization for Reverse Engineering—An Application to Modeling Biological Networks Response via Sparse Systems of Nonlinear Differential Equations," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1459-1471, 2012.