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Beyond Element-Wise Interactions: Identifying Complex Interactions in Biological Processes

Author

Listed:
  • Christophe Ladroue
  • Shuixia Guo
  • Keith Kendrick
  • Jianfeng Feng

Abstract

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem.

Suggested Citation

  • Christophe Ladroue & Shuixia Guo & Keith Kendrick & Jianfeng Feng, 2009. "Beyond Element-Wise Interactions: Identifying Complex Interactions in Biological Processes," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-14, September.
  • Handle: RePEc:plo:pone00:0006899
    DOI: 10.1371/journal.pone.0006899
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    References listed on IDEAS

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    1. Shuixia Guo & Jianhua Wu & Mingzhou Ding & Jianfeng Feng, 2008. "Uncovering Interactions in the Frequency Domain," PLOS Computational Biology, Public Library of Science, vol. 4(5), pages 1-10, May.
    2. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Cleiton Guollo Taufemback, 2023. "Non‐parametric short‐ and long‐run Granger causality testing in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 69-92, January.
    2. repec:jss:jstsof:44:i13 is not listed on IDEAS
    3. Pradhan, Ashis Kumar & Mishra, Bibhuti Ranjan & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2020. "Macroeconomic factors and frequency domain causality between Gold and Silver returns in India," Resources Policy, Elsevier, vol. 68(C).
    4. Tian Ge & Keith M Kendrick & Jianfeng Feng, 2009. "A Novel Extended Granger Causal Model Approach Demonstrates Brain Hemispheric Differences during Face Recognition Learning," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-13, November.
    5. Federico Malizia & Alessandra Corso & Lucia Valentina Gambuzza & Giovanni Russo & Vito Latora & Mattia Frasca, 2024. "Reconstructing higher-order interactions in coupled dynamical systems," Nature Communications, Nature, vol. 15(1), pages 1-8, December.

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