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Using the hierarchy of biological ontologies to identify mechanisms in flat networks

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Abstract

Systems biology has provided new resources for discovering and reasoning about mechanisms. In addition to generating databases of large bodies of data, systems biologists have introduced platforms such as Cytoscape to represent protein–protein interactions, gene interactions, and other data in networks. Networks are inherently flat structures. One can identify clusters of highly connected nodes, but network representations do not represent these clusters as at a higher level than their constituents. Mechanisms, however, are hierarchically organized: they can be decomposed into their parts and their activities can be decomposed into component operations. A potent bridge between flat networks and hierarchical mechanisms is provided by biological ontologies, both those curated by hand such as Gene Ontology (GO) and those extracted directly from databases such as Network Extracted Ontology (NeXO). I examine several examples in which by applying ontologies to networks, systems biologists generate new hypotheses about mechanisms and characterize these novel strategies for developing mechanistic explanations.

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Fig. 1

From Constanzo et al. (2010), Figure 1, reprinted with permission from AAAS. (Color figure online)

Fig. 2

From Constanzo et al. (2010), Figure 2, reprinted with permission from AAAS. (Color figure online)

Fig. 3

Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics from Ashburner et al. (2000). (Color figure online)

Fig. 4

Reprinted by permission from Macmillan Publishers Ltd: Nature Biotechnology, from Dutkowski et al. (2013), Figure 1. (Color figure online)

Fig. 5

Reprinted by permission from Macmillan Publishers Ltd: Nature Biotechnology, from Dutkowski et al. (2013), Figure 2. (Color figure online)

Fig. 6

Reprinted by permission from Macmillan Publishers Ltd: Nature Biotechnology, from Dutkowski et al. (2013), Figure 4b. (Color figure online)

Fig. 7

Reprinted from Kramer et al. (2017), Figure 2, with permission from Elsevier. (Color figure online)

Fig. 8

From Kramer (2016), Chapter 4, Figure 6. (Color figure online)

Fig. 9

Reprinted from Kramer et al. (2017), Figure 7, with permission from Elsevier. (Color figure online)

Fig. 10

Reprinted from Yu et al. (2016), Figure 1, with permission from Elsevier. (Color figure online)

Fig. 11

Reprinted from Yu et al. (2016), Figure 4, with permission from Elsevier. (Color figure online)

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Notes

  1. Over the past 15 years numerous large databases based on a variety of interactions between genes or between molecules in cells have been made publically available. A regularly updated compilation of molecular biology databases is maintained at https://www.oxfordjournals.org/our_journals/nar/database/c/. It currently includes 1685 databases. Starting with supplementary issues in April 1991 and May 1992 and a regular issue in July, 1993, the journal Nucleic Acids Research has regularly reviewed databases. Beginning in 1996, the journal identified its first issue of each year as the database issue.

  2. MGI was itself the product of integrating two mouse databases. In 2000 the Arabidopsis Information Resource (TAIR) and the Caenorhabditis elegans group joined GO.

  3. “These particular classifications were chosen because they represent information sets that are common to all living forms and are basic to our annotation of information about genes and gene products” (Ashburner et al. 2001).

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Acknowledgements

I thank Michael Kramer for sharing his dissertation (Kramer 2016), including what became Kramer et al. (2017), with me. I also thank Trey Ideker, who invited me to attend his lab meetings at which some of the work discussed here and much related research was presented and discussed.

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  1. Department of Philosophy, University of California, San Diego, San Diego, CA, USA

    William Bechtel

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Correspondence to William Bechtel.

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Bechtel, W. Using the hierarchy of biological ontologies to identify mechanisms in flat networks. Biol Philos 32, 627–649 (2017). https://doi.org/10.1007/s10539-017-9579-x

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