PREMER: Parallel Reverse Engineering of Biological Networks with Information Theory

  • Alejandro F. Villaverde
  • Kolja Becker
  • Julio R. Banga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9859)

Abstract

A common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms – which are crucial for discarding spurious interactions and determining causal relationships – to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://sites.google.com/site/premertoolbox/) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial software.

Keywords

Network inference Information theory Parallel computing 

References

  1. 1.
    Bonneau, R., Reiss, D.J., Shannon, P., Facciotti, M., Hood, L., Baliga, N.S., Thorsson, V.: The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7(5), R36 (2006)CrossRefGoogle Scholar
  2. 2.
    Cellucci, C., Albano, A., Rapp, P.: Statistical validation of mutual information calculations: comparison of alternative numerical algorithms. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 71(6), 066208 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)CrossRefGoogle Scholar
  4. 4.
    Folch-Fortuny, A., Villaverde, A.F., Ferrer, A., Banga, J.R.: Enabling network inference methods to handle missing data and outliers. BMC Bioinform. 16(1), 283 (2015)CrossRefGoogle Scholar
  5. 5.
    Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Saeys, Y., Geurts, P.: Inferring regulatory networks from expression data using tree-based methods. PLOS ONE 5(9), e12776 (2010)CrossRefGoogle Scholar
  6. 6.
    Jang, I., Margolin, A., Califano, A.: hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. Interface Focus 3(4), 20130011 (2013)CrossRefGoogle Scholar
  7. 7.
    Le Novère, N.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16, 146–158 (2015)CrossRefGoogle Scholar
  8. 8.
    Meyer, P., Lafitte, F., Bontempi, G.: minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinform. 9(1), 461 (2008)CrossRefGoogle Scholar
  9. 9.
    Samoilov, M., Arkin, A., Ross, J.: On the deduction of chemical reaction pathways from measurements of time series of concentrations. Chaos 11(1), 108–114 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Schaffter, T., Marbach, D., Floreano, D.: GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16), 2263–2270 (2011)CrossRefGoogle Scholar
  11. 11.
    Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85(2), 461 (2000)CrossRefGoogle Scholar
  12. 12.
    Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Villaverde, A.F., Ross, J., Morán, F., Banga, J.R.: MIDER: network inference with mutual information distance and entropy reduction. PLOS ONE 9(5), e96732 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alejandro F. Villaverde
    • 1
    • 2
    • 4
  • Kolja Becker
    • 3
  • Julio R. Banga
    • 4
  1. 1.Department of Systems and Control EngineeringUniversity of VigoGaliciaSpain
  2. 2.Centre for Biological EngineeringUniversity of MinhoBragaPortugal
  3. 3.Modelling of Biological NetworksInstitute of Molecular Biology GGmbHMainzGermany
  4. 4.Bioprocess Engineering GroupInstituto de Investigacións Mariñas (IIM-CSIC)Vigo, GaliciaSpain

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