Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics.
Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene signaling. Analysis of each network topology results in predictions about changes that occur in network components that can be experimentally tested to give insights into which, if either, network underlies ethylene responses.
Department of Chemical and Biomolecular Engineering, University of Tennessee Knoxville, TN, USA.
Aaron M
AM
Prescott
Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee Knoxville, TN, USA.
Forest W
FW
McCollough
Department of Chemical and Biomolecular Engineering, University of Tennessee Knoxville, TN, USA.
Bryan L
BL
Eldreth
Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee Knoxville, TN, USA.
Brad M
BM
Binder
Department of Chemical and Biomolecular Engineering, University of TennesseeKnoxville, TN, USA; National Institute for Mathematical and Biological Synthesis, University of TennesseeKnoxville, TN, USA.
Steven M
SM
Abel
computational modeling; ethylene; evolutionary algorithm; network topologies; signal transduction