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Probabilistic Computational Causal Discovery for Systems Biology

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Uncertainty in Biology

Part of the book series: Studies in Mechanobiology, Tissue Engineering and Biomaterials ((SMTEB,volume 17))

Abstract

Discovering the causal mechanisms of biological systems is necessary to design new drugs and therapies. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations and causal models under certain conditions with a limited set of interventions/manipulations. This chapter reviews the basic concepts and principles of CD, the nature of the assumptions to enable it, potential pitfalls in its application, and recent advances and directions. Importantly, several success stories in molecular and systems biology are discussed in detail.

Vincenzo Lagani, Sofia Triantafillou and Gordon Ball have contributed equally to this work.

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Notes

  1. 1.

    Direct causation is defined in the context of all other modeled variables, i.e., a causal relation mediated by none of the observed variables.

  2. 2.

    Linkage disequilibrium, pleiotropic effects and other factors can invalidate the Mendelian Randomization approach; these issues are better explained later in the text.

  3. 3.

    Statistical algorithms for identifying and quantifying mediation effects were known even earlier [58, 97]. However, these algorithms usually assume some particular (linear) distributional model and “fell short of providing a general, causally defensible measure of mediation” [80].

  4. 4.

    Notably, the “Causal Equivalence Theorem” is identical to the LCD procedure presented in [18].

  5. 5.

    Explaining the details of the do-calculus is beyond the scope of this chapter. Interested readers can refer to Pearl’s original publication.

  6. 6.

    An implementation of the IDA algorithm is available in the R package pcalg [46].

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Acknowledgments

This work was partially funded by STATegra EU FP7 project, No 306000 and EPILOGEAS GSRT ARISTEIA II project, No 3446.

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Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology - Hellas, N. Plastira 100, Vassilika Vouton, GR-700 13, Heraklion, Crete, Greece

    Vincenzo Lagani, Sofia Triantafillou & Ioannis Tsamardinos

  2. Unit of Computational Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, L8:05, SE-171 76, Stockholm, Sweden

    Sofia Triantafillou

  3. Computer Science Department, University of Crete, Voutes Campus, GR-700 13, Heraklion, Crete, Greece

    Gordon Ball & Jesper Tegnér

Authors
  1. Vincenzo Lagani
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  2. Sofia Triantafillou
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  3. Gordon Ball
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  4. Jesper Tegnér
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  5. Ioannis Tsamardinos
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Corresponding author

Correspondence to Ioannis Tsamardinos .

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Editors and Affiliations

  1. Biomechanics Research Unit, University of Liège, Liège 1, Belgium

    Liesbet Geris

  2. Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden

    David Gomez-Cabrero

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The authors declare that they have no conflict of interest.

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© 2016 Springer International Publishing Switzerland

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Lagani, V., Triantafillou, S., Ball, G., Tegnér, J., Tsamardinos, I. (2016). Probabilistic Computational Causal Discovery for Systems Biology. In: Geris, L., Gomez-Cabrero, D. (eds) Uncertainty in Biology. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-21296-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-21296-8_3

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  • Online ISBN: 978-3-319-21296-8

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