Discriminating direct and indirect connectivities in biological networks

Taek Kang, Richard Moore, Yi Li, Eduardo Sontag, Leonidas Bleris

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks.

Original languageEnglish (US)
Pages (from-to)12893-12898
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number41
DOIs
StatePublished - Oct 13 2015

ASJC Scopus subject areas

  • General

Keywords

  • Direct and indirect connectivities
  • Human cells
  • Nonparametric resampling
  • Reverse engineering
  • Synthetic biology

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