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ratushny5

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ratushny5

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000421) Biomodels notes: The plot corresponding to "ASSURE11" in Figure 2f of the reference publication has been reproduced here. The data for the plot was obtained by simulating the model using Copasi v4.8 (Build 35) 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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Manuscript information

Asymmetric positive feedback loops reliably control biological responses.

  • Alexander V Ratushny
  • Ramsey A Saleem
  • Katherine Sitko
  • Stephen A Ramsey
  • John D Aitchison
Mol. Syst. Biol. 2012; 8 : 577
Abstract
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.

Unit definitions 0

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

Compartments 1

Id Name Spatial dimensions Size
univ — 3.0 1.0

Species 2

Id Name Initial quantity Compartment Fixed
P1 — 0.0 univ ✘
Target — 0.0 univ ✘

Initial assignments 0

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
Reactions 4
Id Name Objective coefficient Reaction Equation and Kinetic Law Flux bounds
___r1 — ∅ > P1

ks * (k0 + pow(dsp1p2kd / ka, h)) / (1 + pow(dsp1p2kd / ka, h))
___r2 — P1 > ∅

__RATE__ * P1
___r3 — ∅ > Target

ks * (k0 + pow(dsp1p2kd / ka, h)) / (1 + pow(dsp1p2kd / ka, h))
___r4 — Target > ∅

__RATE__ * Target

Parameters 2   11

Global parameters

Id Value
Kd 0.00001
Ksp 0.001
P2 40.0
dsp1ksp 0.0
dsp1p2kd 0.0
h 2.0
k0 0.1
ka 40.0
ks 10.0
ku 0.1
s 1000.0

Local parameters

Id Value Reaction
__RATE__ 0.1 ___r2
__RATE__ 0.1 ___r4

Rules 0   0   2

Assignment rules

Definition
dsp1p2kd = Kd / 2.0 * (1.0 + (dsp1ksp + P2) / Kd - pow(pow(1.0 + (dsp1ksp + P2) / Kd, 2.0) - 4.0 * dsp1ksp * P2 / pow(Kd, 2.0), 0.5))
dsp1ksp = Ksp / 2.0 * (1.0 + (s + P1 * univ) / Ksp - pow(pow(1.0 + (s + P1 * univ) / Ksp, 2.0) - 4.0 * s * P1 * univ / pow(Ksp, 2.0), 0.5))

Rate rules

Definition

Algebraic rules

Definition

Events 0

Trigger Assignments