TY - GEN
T1 - Discriminating direct and indirect connectivities in biological networks
AU - Kang, Taek
AU - Moore, Richard
AU - Li, Yi
AU - Sontag, Eduardo
AU - Bleris, Leonidas
N1 - Funding Information:
This work was funded by the US National Institutes of Health Grants GM098984, GM096271, CA17001801, National Science Foundation Grant CBNET-1105524, and the University of Texas at Dallas. E.S. partially supported by Air Force Office of Scientific Research Grant FA9550-14-1-0060.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Direct and indirect connectivities
KW - Human cells
KW - Nonparametric resampling
KW - Reverse engineering
KW - Synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=84970004042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84970004042&partnerID=8YFLogxK
U2 - 10.73/pnas.1507168112/-/DCSupplemental
DO - 10.73/pnas.1507168112/-/DCSupplemental
M3 - Conference contribution
AN - SCOPUS:84970004042
T3 - Food, Pharmaceutical and Bioengineering Division 2015 - Core Programming Area at the 2015 AIChE Meeting
SP - 754
EP - 759
BT - Food, Pharmaceutical and Bioengineering Division 2015 - Core Programming Area at the 2015 AIChE Meeting
PB - AIChE
T2 - Food, Pharmaceutical and Bioengineering Division 2015 - Core Programming Area at the 2015 AIChE Meeting
Y2 - 8 November 2015 through 13 November 2015
ER -