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. However, the dynamical...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2016-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.01308/full |
| _version_ | 1817997036073517056 |
|---|---|
| author | Aaron M. Prescott Forest W. McCollough Bryan L. Eldreth Brad M Binder Steven M Abel Steven M Abel |
| author_facet | Aaron M. Prescott Forest W. McCollough Bryan L. Eldreth Brad M Binder Steven M Abel Steven M Abel |
| author_sort | Aaron M. Prescott |
| collection | DOAJ |
| description | 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. However, 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. |
| first_indexed | 2024-04-14T02:31:05Z |
| format | Article |
| id | doaj.art-6f3ec94fd9a2493cb4f1ed8eb92a40ce |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-14T02:31:05Z |
| publishDate | 2016-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-6f3ec94fd9a2493cb4f1ed8eb92a40ce2022-12-22T02:17:40ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2016-08-01710.3389/fpls.2016.01308216158Analysis of Network Topologies Underlying Ethylene Growth Response KineticsAaron M. Prescott0Forest W. McCollough1Bryan L. Eldreth2Brad M Binder3Steven M Abel4Steven M Abel5University of TennesseeUniversity of TennesseeUniversity of TennesseeUniversity of TennesseeUniversity of TennesseeUniversity of TennesseeMost 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. However, 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.http://journal.frontiersin.org/Journal/10.3389/fpls.2016.01308/fullSignal Transductioncomputational modelingethyleneevolutionary algorithmnetwork topologies |
| spellingShingle | Aaron M. Prescott Forest W. McCollough Bryan L. Eldreth Brad M Binder Steven M Abel Steven M Abel Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics Frontiers in Plant Science Signal Transduction computational modeling ethylene evolutionary algorithm network topologies |
| title | Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics |
| title_full | Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics |
| title_fullStr | Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics |
| title_full_unstemmed | Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics |
| title_short | Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics |
| title_sort | analysis of network topologies underlying ethylene growth response kinetics |
| topic | Signal Transduction computational modeling ethylene evolutionary algorithm network topologies |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.01308/full |
| work_keys_str_mv | AT aaronmprescott analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics AT forestwmccollough analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics AT bryanleldreth analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics AT bradmbinder analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics AT stevenmabel analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics AT stevenmabel analysisofnetworktopologiesunderlyingethylenegrowthresponsekinetics |