marwan1

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000037). Biomodels notes: "Figure 5B reproduced in COPASI 4 build 19. Although the peak is a little bit higher, the bistable switch is exact, wether in intensity or dynamics." 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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Abstract
Mutants of Physarum polycephalum can be complemented by fusion of plasmodial cells followed by cytoplasmic mixing. Complementation between strains carrying different mutational defects in the sporulation control network may depend on the signaling state of the network components. We have previously suggested that time-resolved somatic complementation (TRSC) analysis with such mutants may be used to probe network architecture and dynamics. By computer simulation it is now shown how and under which conditions the regulatory hierarchy of genes can be determined experimentally. A kinetic model of the sporulation control network is developed, which is then used to demonstrate how the mechanisms of TRSC can be understood and simulated at the kinetic level. On the basis of theoretical considerations, experimental parameters that determine whether functional complementation of two mutations will occur are identified. It is also shown how gene dosage-effect relationships can be employed for network analysis. The theoretical framework provided may be used to systematically analyze network structure and dynamics through time-resolved somatic complementation studies. The conclusions drawn are of general relevance in that they do not depend on the validity of the model from which they were derived.

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
3600.0 second
Id Name Spatial dimensions Size
compartment 3.0 1.0
Id Name Initial quantity Compartment Fixed
Gluc 0.0 compartment
Pfr 10.0 compartment
Pi 0.0 compartment
Pr 0.0 compartment
S 0.0 compartment
V 30.0 compartment
Xa 0.0 compartment
Xi 6.0 compartment
Ya 0.9 compartment
Yi 0.0 compartment
preS 0.0 compartment
prepreS 200.0 compartment

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
Glucose_sensor_inactivation Ya + Gluc > Yi

kG * Ya * Gluc * compartment
Photoreceptor_activation Pfr > Pr

compartment * Pfr * IfrSfrPfr
Photoreceptor_decay Pr > Pi

compartment * kd * Pr
Photoreceptor_inactivation Pr > Pfr

IrSrPr * Pr * compartment
S_degradation S > ∅

kd_s * S * compartment
S_formation ∅ > S

compartment * (alpha1 / (1 + pow(V, 3)))
S_generation preS > S

preS * ky * Ya * compartment
Transducer_activation Xi > Xa

Xi * kia * Pr * compartment
Transducer_inactivation Xa > Xi

kai * Xa * compartment
V_degradation V > ∅

compartment * V * kd_v
V_formation ∅ > V

compartment * (alpha2 / (1 + pow(S, 3)))
preS_formation prepreS > preS

prepreS * kx * Xa * compartment

Global parameters

Id Value

Local parameters

Id Value Reaction
kd_v 1.0 V_degradation
kd 0.1 Photoreceptor_decay
IfrSfrPfr 0.1 Photoreceptor_activation
IrSrPr 0.0 Photoreceptor_inactivation
kia 0.1 Transducer_activation
kai 0.8 Transducer_inactivation
kx 0.2 preS_formation
ky 1.0 S_generation
kG 0.1 Glucose_sensor_inactivation
alpha1 30.0 S_formation
alpha2 50.0 V_formation
kd_s 1.0 S_degradation

Assignment rules

Definition

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments