palini1

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000325) Biomodels notes: Ligand concentration response curve as in figure 1B of the publication. All calculation were performed using Copasi v4.6.33. Two logarithmic parameter scans over the the given ligand range with steady state determination were combined in the graph. For the low activity state, the systems initial conditions were taken from the steady state values at L = 0.001, for the high activity from L = 0.2. 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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Synthetic conversion of a graded receptor signal into a tunable, reversible switch.

  • Santhosh Palani
  • Casim A Sarkar
Mol. Syst. Biol. 2011; 7 : 480
Abstract
The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network 'switches' have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (n(H)>20), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties. Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways.

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
cell 3.0 1.0
Id Name Initial quantity Compartment Fixed
A 0.0 cell
C 0.0 cell
I 1.0 cell
L 0.1 cell
R 1.0 cell
X 0.0 cell

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
A_degradation A > ∅

cell * kdegA * A
C_I_binding C + I = X

cell * (k1 * C * I - k2 * X)
C_degradation C > ∅

cell * kdegC * C
I_activation X > C + A

cell * k3 * X
I_degradation I > ∅

cell * kdegI * I
I_expression ∅ > I

cell * (BI + TFs * A / (KD + A))
R_L_binding R + L = C

cell * (kon * L * R - koff * C)
R_degradation R > ∅

cell * kdegR * R
R_expression ∅ > R

cell * (BR + Rs * A / (KD + A))
X_degradation X > ∅

cell * kdegX * X

Global parameters

Id Value
BI 0.005
BR 0.005
KD 200.0
Rs 3.0
TFs 3.0
k1 1.0
k2 5.0
k3 45.0
kdegA 0.005
kdegC 0.01
kdegI 0.005
kdegR 0.005
kdegX 0.005
koff 0.05
kon 0.001

Local parameters

Id Value Reaction

Assignment rules

Definition

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments