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Yan KK, Wang D, Sethi A, et al. Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs. Cell Syst. 2016;2(3):147-157doi: 10.1016/j.cels.2016.02.014.
Yan, K. K., Wang, D., Sethi, A., Muir, P., Kitchen, R., Cheng, C., & Gerstein, M. (2016). Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs. Cell systems, 2(3), 147-157. https://doi.org/10.1016/j.cels.2016.02.014
Yan, Koon-Kiu, et al. "Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs." Cell systems vol. 2,3 (2016): 147-157. doi: https://doi.org/10.1016/j.cels.2016.02.014
Yan KK, Wang D, Sethi A, Muir P, Kitchen R, Cheng C, Gerstein M. Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs. Cell Syst. 2016 Mar 23;2(3):147-157. doi: 10.1016/j.cels.2016.02.014. PMID: 27047991; PMCID: PMC4817108.
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Cell Syst. 2016 Mar 23;2(3):147-157. doi: 10.1016/j.cels.2016.02.014.

Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs.

Cell systems

Koon-Kiu Yan, Daifeng Wang, Anurag Sethi, Paul Muir, Robert Kitchen, Chao Cheng, Mark Gerstein

Affiliations

  1. Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520.
  2. Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT 06520.
  3. Department of Genetics, Dartmouth School of Medicine, Hanover NH 03755.
  4. Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520; Department of Computer Science, Yale University, New Haven, CT 06520.

PMID: 27047991 PMCID: PMC4817108 DOI: 10.1016/j.cels.2016.02.014

Abstract

Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable 'hairballs'. We argue that one way of untangling these hairballs is through cross-disciplinary network comparison-leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases where the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant day-to-day experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems.

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