Discriminating direct and indirect connectivities in biological networks
Abstract
We used a combination of computational and theoretical approaches coupled to synthetic biology experimentation in mammalian cells to study direct and indirect connectivities in biological networks. After subjecting benchmark circuits to a range of perturbations, we recovered the edge weights using nonparametric single-cell data resampling coupled with modular response analysis. We discovered that inferred weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is topology dependent. Incorporating this insight in the analysis of high-throughput experiments may provide a sought-after solution to a longstanding reverse engineering problem.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 29, 2015
- Source ID
- 10.1073/pnas.1507168112
Entities
People
- Eduardo D. Sontag
- Leonidas Bleris
- Richard Moore
- Taek Kang
- Yi Li
Organizations
- Air Force Office of Scientific Research
- National Institutes of Health
- National Science Foundation
- University of Texas at Dallas