/*
 * Kinetic Model for the Sensory Control of Sporulation in Physarum
 * polycephalum
 * 
 * Model Status
 * 
 * This is the original unchecked version of the model imported
 * from the previous CellML model repository, 24-Jan-2006.
 * 
 * Model Structure
 * 
 * Complementation is the production of a wildtype phenotype when
 * two recessive mutant alleles are combined in the same genotype.
 * In fungi complementation can arise when two mutant cells form
 * to produce a heterokaryon. In such heterokaryons two nuclear
 * populations mix, providing the biochemical basis for the complementation
 * effect.
 * 
 * In the Marwan 2003 publication described here, the author uses
 * the photosensory control of sporulation in the protist Physarum
 * polycephalum as an example of how complementation in a heterokaryon
 * can create a wildtype phenotype. Marwan also investigates how
 * such a process can depend on the time-dependent physiological
 * state adopted after pathway activation by one of the mutant
 * fusion partners.
 * 
 * The kinetic model of this process can be seen in below. Two
 * inputs, light of the far-red wavelength and glucose, control
 * the devlopmental switch network through formation of the sporulation
 * signal S. Photoreceptor and glucose sensor dependent pathways
 * are integrated by a mechanism which behaves like a logic AND
 * gate. Transcription of gene S is repressed by the product of
 * gene V, while the transcription of gene V is reiprocally repressed
 * by the product of gene S.
 * 
 * The model has been described here in CellML (the raw CellML
 * description of the Marwan 2003 model can be downloaded in various
 * formats as described in ).
 * 
 * The complete original paper reference is cited below:
 * 
 * Theory of Time-Resolved Somatic Complementation and Its Use
 * to Explore the Sporulation Control Network in Physarum polycephalum
 * , Wolfgang Marwan, 2003, Genetics, 164, 105-115. PubMed ID:
 * 12750324
 * 
 * reaction diagram
 * 
 * [[Image file: marwan_2003.png]]
 * 
 * Schematic diagram of the kinetic model for the sensory control
 * of sporulation in Physarum polycephalum.
 */

import nsrunit;
// Warning: unit conversion turned off due to unit errors in 4 equation(s)
unit conversion off;
//Warning:  unit hour_ renamed from hour, as the latter is predefined in JSim with different fundamental units.
unit hour_=3.6E6 meter^(-3)*second^1;
// unit micromolar predefined
unit flux=2.7777778E-10 second^(-1)*mole^1;
unit first_order_rate_constant=2.7777778E-7 meter^3*second^(-1);
unit second_order_rate_constant=2.7777778E-4 meter^6*second^(-1)*mole^(-1);

math main {
	realDomain time hour_;
	time.min=0;
	extern time.max;
	extern time.delta;
	real Pfr(time) micromolar;
	when(time=time.min) Pfr=10.0;
	real Pr(time) micromolar;
	when(time=time.min) Pr=0.0;
	real Ifr_sigma_fr_phi_fr dimensionless;
	Ifr_sigma_fr_phi_fr=0.1;
	real Ir_sigma_r_phi_r dimensionless;
	Ir_sigma_r_phi_r=0.0;
	real kd first_order_rate_constant;
	kd=0.1;
	real Xi(time) micromolar;
	when(time=time.min) Xi=6.0;
	real Xa(time) micromolar;
	when(time=time.min) Xa=0.0;
	real kia second_order_rate_constant;
	kia=0.1;
	real kai first_order_rate_constant;
	kai=0.8;
	real prepreS(time) micromolar;
	when(time=time.min) prepreS=200.0;
	real kx second_order_rate_constant;
	kx=0.2;
	real preS(time) micromolar;
	when(time=time.min) preS=0.0;
	real Ya(time) micromolar;
	when(time=time.min) Ya=0.9;
	real ky second_order_rate_constant;
	ky=1.0;
	real S(time) micromolar;
	when(time=time.min) S=0.0;
	real alpha1 micromolar;
	alpha1=30.0;
	real V(time) micromolar;
	when(time=time.min) V=50.0;
	real alpha2 micromolar;
	alpha2=50.0;
	real kG second_order_rate_constant;
	kG=0.1;
	real glucose micromolar;
	glucose=1.0;

	// <component name="environment">

	// <component name="Pfr">
	Pfr:time=(Ir_sigma_r_phi_r*Pr-Ifr_sigma_fr_phi_fr*Pfr);

	// <component name="Pr">
	Pr:time=(Ifr_sigma_fr_phi_fr*Pr-(Ir_sigma_r_phi_r*Pr+kd*Pr));

	// <component name="Xi">
	Xi:time=(kai*Xa-kia*Pr*Xi);

	// <component name="Xa">
	Xa:time=(kia*Pr*Xi-kai*Xa);

	// <component name="prepreS">
	prepreS:time=((-1)*(kx*Xa*prepreS));

	// <component name="preS">
	preS:time=(kx*Xa*prepreS-ky*Ya*preS);

	// <component name="S">
	S:time=(ky*Ya*preS+alpha1/(1+V^3)-S);

	// <component name="V">
	V:time=(alpha2/(1+S^3)-V);

	// <component name="Ya">
	Ya:time=((-1)*(kG*glucose*Ya));

	// <component name="model_parameters">
}