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Using deep learning to model the hierarchical structure and function of a cell

Nature Methods volume 15, pages 290–298 (2018)Cite this article

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Abstract

Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) that couple the model's inner workings to those of real systems. Here we develop DCell, a VNN embedded in the hierarchical structure of 2,526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in silico investigations of the molecular mechanisms underlying genotype–phenotype associations. These mechanisms can be validated, and many are unexpected; some are governed by Boolean logic. Cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. DCell provides a foundation for decoding the genetics of disease, drug resistance and synthetic life.

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Figure 1: Modeling system structure and function with visible learning.
Figure 2: Prediction of cell viability and genetic interaction phenotypes.
Figure 3: Interpretation of genotype–phenotype associations.
Figure 4: Identification of subsystems important for cell growth.
Figure 5: Analysis of subsystem functional logic.
Figure 6: Analysis of a new DNA repair subsystem.

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Acknowledgements

We gratefully acknowledge support for this work provided by grants from the National Institutes of Health to T.I. (TR002026, GM103504, CA209891, ES014811). We also wish to thank T. Sejnowski and M. Kramer for very helpful comments during development of this work.

Author information

Author notes

  1. Jianzhu Ma, Michael Ku Yu and Samson Fong: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine, University of California San Diego, La Jolla, California, USA

    Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak & Trey Ideker

  2. Program in Bioinformatics, University of California San Diego, La Jolla, California, USA

    Michael Ku Yu & Trey Ideker

  3. Department of Bioengineering, University of California San Diego, La Jolla, California, USA

    Samson Fong & Trey Ideker

  4. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Roded Sharan

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Contributions

J.M., M.K.Y., S.F., R.S. and T.I. designed the study and developed the conceptual ideas. J.M. implemented the main algorithm. M.K.Y. collected all the input sources. J.M. and S.F. implemented all other computational methods and conducted analysis. J.M., M.K.Y., S.F. and T.I. wrote the manuscript with suggestions from the other authors. J.M., M.K.Y., S.F., K.O., E.S. and B.D. designed and developed the server.

Corresponding author

Correspondence to Trey Ideker.

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Competing interests

T.I. is co-founder of Data4Cure, Inc. and has an equity interest. T.I. has an equity interest in Ideaya BioSciences, Inc. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

Integrated supplementary information

Supplementary Figure 1 Precision-recall curves for classification of negative genetic interactions.

Performance of DCell is compared to the same methods as in Fig. 2c. Genetic interactions with scores ≤ -0.08 are labeled as negative.

Supplementary Figure 2 CliXO top subsystem states for translation of genotype to growth.

a, Ranking of all CliXO subsystems by their importance in determining genetic interactions (RLIPP score, see Methods). Inset: ten highest-scoring subsystems. b-j, Two-dimensional state maps of informative subsystems from (a), in which each subsystem’s set of neuron states is reduced to the first two Principal Components (PCs). Each point represents the subsystem state induced by a genotype, with point color indicating the corresponding growth phenotype (genetic interaction score).

Supplementary Figure 3 Calculating relative local improvement in predictive power (RLIPP).

a, Two L2-regularized linear regression models are fit to predict phenotype using either the neurons of a parent subsystem (bottom) or the neurons of that subsystem’s children (top). b-c, Measured versus predicted phenotype (genetic interactions) for the children-based model (b) or the parent-based model (c). The example values are for the “DNA repair” subsystem. d, The RLIPP score is calculated from the Spearman correlation of both models.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3

Life Sciences Reporting Summary

Supplementary Table 1

RLIPP scores for subsystems in the Gene Ontology andCliXO

Supplementary Table 2

Boolean logic approximating the states of subsystems in the Gene Ontology and CliXO

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Ma, J., Yu, M., Fong, S. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15, 290–298 (2018). https://doi.org/10.1038/nmeth.4627

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