gorlich1

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000192) Biomodels notes: This model represents a concentration gradient of RanGTP across the nuclear envelope. This gradient is generated by distribution of regulators of RanGTPase. We have taken a log linear plot of graphs generated by GENESIS and compared with the experimental graphs. The model is simulated and integrated using SBML OdeSolver with options (-n --printstep 500). Figure 4 (Simulated time course for generation of the RanGTP gradient) of the original paper (Gorlich et al., 2003) is reproduced in here. JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. No additional changes were made.

None

None

None

None

None

None

Characterization of Ran-driven cargo transport and the RanGTPase system by kinetic measurements and computer simulation.

  • Dirk Görlich
  • Michael J Seewald
  • Katharina Ribbeck
EMBO J. 2003; 22 (5): 1088-1100
Abstract
Here, we analyse the RanGTPase system and its coupling to receptor-mediated nuclear transport. Our simulations predict nuclear RanGTP levels in HeLa cells to be very sensitive towards the cellular energy charge and to exceed the cytoplasmic concentration approximately 1000-fold. The steepness of the RanGTP gradient appears limited by both the cytoplasmic RanGAP concentration and the imperfect retention of nuclear RanGTP by nuclear pore complexes (NPCs), but not by the nucleotide exchange activity of RCC1. Neither RanBP1 nor the NPC localization of RanGAP has a significant direct impact on the RanGTP gradient. NTF2-mediated import of Ran appears to be the bottleneck for maximal capacity of Ran-driven nuclear transport. We show that unidirectional nuclear transport can be faithfully simulated without the assumption of a vectorial NPC passage; transport receptors only need to reversibly cross NPCs and switch their affinity for cargo in response to the RanGTP gradient. A significant RanGTP gradient after nuclear envelope (NE) breakdown can apparently exist only in large cytoplasm. This indicates that RanGTP gradients can provide positional information for mitotic spindle and NE assembly in early embryonic cells, but hardly any in small somatic cells.

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
1e-06 mole
1e-06 mole second^(-1.0) litre^(-1.0)
1000000.0 litre mole^(-1.0) second^(-1.0)
1.0 second^(-1.0)
1e-06 mole litre^(-1.0)
Id Name Spatial dimensions Size
cytoplasm 3.0 0.000000000018
nucleus 3.0 0.000000000012
Id Name Initial quantity Compartment Fixed
GDP 1.6 nucleus
GTP 500.0 nucleus
RCC1 0.7 nucleus
RCC1_Ran 0.0 nucleus
RCC1_RanGDP 0.0 nucleus
RCC1_RanGTP 0.0 nucleus
RanBP1 2.0 cytoplasm
RanGAP 0.7 cytoplasm
RanGDP_cy 5.0 cytoplasm
RanGDP_nuc 0.0 nucleus
RanGTP_RanBP1 0.0 cytoplasm
RanGTP_cy 0.0 cytoplasm
RanGTP_nuc 0.0 nucleus

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
Cytoplasmic_transfer RanGTP_nuc = RanGTP_cy

kpermRanGTP * nucleus * (RanGTP_nuc - RanGTP_cy)
GDP_dissociation RCC1_RanGDP = RCC1_Ran + GDP

nucleus * (r2 * RCC1_RanGDP - r7 * RCC1_Ran * GDP)
GTP_binding RCC1_Ran + GTP = RCC1_RanGTP

nucleus * (r3 * RCC1_Ran * GTP - r6 * RCC1_RanGTP)
Nucleoplasmic_transfer RanGDP_nuc = RanGDP_cy

kpermRanGDP * nucleus * (RanGDP_nuc - RanGDP_cy)
RCC1_binding RanGDP_nuc + RCC1 = RCC1_RanGDP

nucleus * (r1 * RanGDP_nuc * RCC1 - r8 * RCC1_RanGDP)
RanBP1_RanGDP RanBP1_RanGDP RanGTP_RanBP1 = RanGDP_cy + RanBP1

cytoplasm * kcat * RanGTP_RanBP1 * RanGAP / (RanGTP_RanBP1 + Km)
RanGAP_RanGDP RanGAP_RanGDP RanGTP_cy = RanGDP_cy

cytoplasm * kcat_GAP * RanGTP_cy * RanGAP / (Km_GAP + RanGTP_cy)
RanGTP_binding RanGTP_cy + RanBP1 = RanGTP_RanBP1

(kon * RanGTP_cy * RanBP1 - koff * RanGTP_RanBP1) * cytoplasm
RanGTP_release RCC1_RanGTP = RanGTP_nuc + RCC1

nucleus * (r4 * RCC1_RanGTP - r5 * RanGTP_nuc * RCC1)

Global parameters

Id Value

Local parameters

Id Value Reaction
r1 74.0 pmicroMpsec RCC1_binding
r8 55.0 psec RCC1_binding
r2 21.0 psec GDP_dissociation
r7 11.0 pmicroMpsec GDP_dissociation
r3 0.6 pmicroMpsec GTP_binding
r6 19.0 psec GTP_binding
r4 55.0 psec RanGTP_release
r5 100.0 pmicroMpsec RanGTP_release
kpermRanGTP 0.03 psec Cytoplasmic_transfer
kpermRanGDP 0.12 psec Nucleoplasmic_transfer
kon 0.3 pmicroMpsec RanGTP_binding
koff 0.0004 psec RanGTP_binding
kcat 10.8 psec RanBP1_RanGDP (RanBP1_RanGDP)
Km 0.1 microM RanBP1_RanGDP (RanBP1_RanGDP)
kcat_GAP 10.6 psec RanGAP_RanGDP (RanGAP_RanGDP)
Km_GAP 0.7 microM RanGAP_RanGDP (RanGAP_RanGDP)

Assignment rules

Definition

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments