vernoux1

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000351) Biomodels notes: Figure 3 of the supplementary material of the reference article has been reproduced here. Time evolution of all the variables in the model are plotted, under the influence of a step input of auxin level (auxin=5, when time>1000; 0.11, otherwise). pi_A is varied between 0 and 2 by steps of 0.1. The model was integrated simulated using Copasi v4.7 (Build 34). Data were obtained from Copasi and plotted using Gnuplot. JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. No additional changes were made.

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The auxin signalling network translates dynamic input into robust patterning at the shoot apex.

  • Teva Vernoux
  • Géraldine Brunoud
  • Etienne Farcot
  • Valérie Morin
  • Hilde Van den Daele
  • Jonathan Legrand
  • Marina Oliva
  • Pradeep Das
  • Antoine Larrieu
  • Darren Wells
  • Yann Guédon
  • Lynne Armitage
  • Franck Picard
  • Soazig Guyomarc'h
  • Coralie Cellier
  • Geraint Parry
  • Rachil Koumproglou
  • John H Doonan
  • Mark Estelle
  • Christophe Godin
  • Stefan Kepinski
  • Malcolm Bennett
  • Lieven De Veylder
  • Jan Traas
Mol. Syst. Biol. 2011; 7 : 508
Abstract
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Unit definitions have no effect on the numerical analysis of the model. It remains the responsibility of the modeler to ensure the internal numerical consistency of the model. If units are provided, however, the consistency of the model units will be checked.

Name Definition
Id Name Spatial dimensions Size
compartment_0000001 c_1 3.0 1.0
Id Name Initial quantity Compartment Fixed
A ARF 10.0 compartment_0000001 (c_1)
D_IA Aux/IAA:ARF 10.0 compartment_0000001 (c_1)
D_II Aux/IAA:Aux/IAA 10.0 compartment_0000001 (c_1)
I Aux/IAA 10.0 compartment_0000001 (c_1)
R mRNA 1.0 compartment_0000001 (c_1)
aux auxin 0.11 compartment_0000001 (c_1)

Initial assignments are expressions that are evaluated at time=0. It is not recommended to create initial assignments for all model entities. Restrict the use of initial assignments to cases where a value is expressed in terms of values or sizes of other model entities. Note that it is not permitted to have both an initial assignment and an assignment rule for a single model entity.

Definition
Id Name Objective coefficient Reaction Equation and Kinetic Law Flux bounds
reac_DIA reac_DIA A + I = D_IA

k_IA * I * A - kprime_IA * D_IA
reac_DIAxA reac_DIAxA D_IA > A

gamma_I * d_I * K_aux * aux / (K_aux * aux + 1) * D_IA
reac_DII reac_DII I + I = D_II

k_II * I * I - kprime_II * D_II
reac_DIIxI reac_DIIxI D_II > I

gamma_I * d_I * K_aux * aux / (K_aux * aux + 1) * D_II
reac_degrA reac_degrA A > ∅

d_A * A
reac_degrDIA reac_degrDIA D_IA > ∅

d_IA * D_IA
reac_degrDII reac_degrDII D_II > ∅

d_II * D_II
reac_degrI reac_degrI I > ∅

gamma_I * d_I * K_aux * aux / (K_aux * aux + 1) * I
reac_degrR reac_degrR R > ∅

d_r * R
reac_prodA reac_prodA ∅ > A

pi_A
reac_prodI reac_prodI ∅ > I

pi_I * R
reac_prodR reac_prodR ∅ > R

(1 + f_c / B_d * A * (1 + w_A * f_A * A / B_d)) / (1 + A / B_d * (1 + w_A * A / B_d) + w_I * A * I / (K_IA * B_d) + w_D * D_IA / B_d + k_Am)

Global parameters

Id Value
B_d 100.0
K_IA 10.0
K_II 10.0
K_aux 1.0
aux_basal 0.11
d_A 0.003
d_I 0.05
d_IA 0.003
d_II 0.003
d_r 0.007
f_A 10.0
f_c 10.0
gamma_I 10.0
k_Am 10.0
k_IA 1.0
k_II 1.0
kprime_IA 10.0
kprime_II 10.0
pi_A 1.0
pi_I 1.0
w_A 10.0
w_D 10.0
w_I 10.0

Local parameters

Id Value Reaction

Assignment rules

Definition
aux_basal = 1.0 / (K_aux * (gamma_I - 1.0))
kprime_II = K_II * k_II
kprime_IA = K_IA * k_IA
aux = piecewise(5.0, gt(time, 1000.0), 0.0)

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments