/// <summary>
/// Updates the compartment volume.
/// </summary>
/// <param name="volume">Volume in nl</param>
public void UpdateCompartmentVolume(double volume)
{
compartment.setInitialValue(volume);
CCopasiObject obj = compartment.getInitialValueReference();
changedObjects.Add(obj);
}
static void Main()
{
Debug.Assert(CCopasiRootContainer.getRoot() != null);
// create a new datamodel
CCopasiDataModel dataModel = CCopasiRootContainer.addDatamodel();
Debug.Assert(CCopasiRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
model.setVolumeUnit(CUnit.fl);
model.setTimeUnit(CUnit.s);
model.setQuantityUnit(CUnit.fMol);
CModelValue fixedModelValue = model.createModelValue("F");
Debug.Assert(fixedModelValue != null);
fixedModelValue.setStatus(CModelEntity.FIXED);
fixedModelValue.setInitialValue(3.0);
CModelValue variableModelValue = model.createModelValue("V");
Debug.Assert(variableModelValue != null);
variableModelValue.setStatus(CModelEntity.ASSIGNMENT);
// we create a very simple assignment that is easy on the optimization
// a parabole with the minimum at x=6 should do just fine
string s = fixedModelValue.getValueReference().getCN().getString();
s = "(<" + s + "> - 6.0)^2";
variableModelValue.setExpression(s);
// now we compile the model and tell COPASI which values have changed so
// that COPASI can update the values that depend on those
model.compileIfNecessary();
ObjectStdVector changedObjects = new ObjectStdVector();
changedObjects.Add(fixedModelValue.getInitialValueReference());
changedObjects.Add(variableModelValue.getInitialValueReference());
model.updateInitialValues(changedObjects);
// now we set up the optimization
// we want to do an optimization for the time course
// so we have to set up the time course task first
CTrajectoryTask timeCourseTask = (CTrajectoryTask)dataModel.getTask("Time-Course");
Debug.Assert(timeCourseTask != null);
// since for this example it really doesn't matter how long we run the time course
// we run for 1 second and calculate 10 steps
// run a deterministic time course
timeCourseTask.setMethodType(CTaskEnum.deterministic);
// pass a pointer of the model to the problem
timeCourseTask.getProblem().setModel(dataModel.getModel());
// get the problem for the task to set some parameters
CTrajectoryProblem problem = (CTrajectoryProblem)timeCourseTask.getProblem();
Debug.Assert(problem != null);
// simulate 10 steps
problem.setStepNumber(10);
// start at time 0
dataModel.getModel().setInitialTime(0.0);
// simulate a duration of 1 time units
problem.setDuration(1);
// tell the problem to actually generate time series data
problem.setTimeSeriesRequested(true);
// get the optimization task
COptTask optTask = (COptTask)dataModel.getTask("Optimization");
Debug.Assert(optTask != null);
// we want to use Levenberg-Marquardt as the optimization method
optTask.setMethodType(CTaskEnum.LevenbergMarquardt);
// next we need to set subtask type on the problem
COptProblem optProblem = (COptProblem)optTask.getProblem();
Debug.Assert(optProblem != null);
optProblem.setSubtaskType(CTaskEnum.timeCourse);
// we create the objective function
// we want to minimize the value of the variable model value at the end of
// the simulation
// the objective function is normally minimized
string objectiveFunction = variableModelValue.getObject(new CCopasiObjectName("Reference=Value")).getCN().getString();
// we need to put the angled brackets around the common name of the object
objectiveFunction = "<" + objectiveFunction + ">";
// now we set the objective function in the problem
optProblem.setObjectiveFunction(objectiveFunction);
// now we create the optimization items
// i.e. the model elements that have to be changed during the optimization
// in order to get to the optimal solution
COptItem optItem = optProblem.addOptItem(new CCopasiObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN()));
// we want to change the fixed model value from -100 to +100 with a start
// value of 50
optItem.setStartValue(50.0);
optItem.setLowerBound(new CCopasiObjectName("-100"));
optItem.setUpperBound(new CCopasiObjectName("100"));
// now we set some parameters on the method
// these parameters are specific to the method type we set above
// (in this case Levenberg-Marquardt)
COptMethod optMethod = (COptMethod)optTask.getMethod();
Debug.Assert(optMethod != null);
// now we set some method parameters for the optimization method
// iteration limit
CCopasiParameter parameter = optMethod.getParameter("Iteration Limit");
Debug.Assert(parameter != null);
parameter.setIntValue(2000);
// tolerance
parameter = optMethod.getParameter("Tolerance");
Debug.Assert(parameter != null);
parameter.setDblValue(1.0e-5);
// create a report with the correct filename and all the species against
// time.
CReportDefinitionVector reports = dataModel.getReportDefinitionList();
// create a new report definition object
CReportDefinition report = reports.createReportDefinition("Report", "Output for optimization");
// set the task type for the report definition to timecourse
report.setTaskType(CTaskEnum.optimization);
// we don't want a table
report.setIsTable(false);
// the entries in the output should be seperated by a ", "
report.setSeparator(new CCopasiReportSeparator(", "));
// we need a handle to the header and the body
// the header will display the ids of the metabolites and "time" for
// the first column
// the body will contain the actual timecourse data
ReportItemVector header = report.getHeaderAddr();
ReportItemVector body = report.getBodyAddr();
// in the report header we write two strings and a separator
header.Add(new CRegisteredObjectName(new CCopasiStaticString("best value of objective function").getCN().getString()));
header.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
header.Add(new CRegisteredObjectName(new CCopasiStaticString("initial value of F").getCN().getString()));
// in the report body we write the best value of the objective function and
// the initial value of the fixed parameter separated by a komma
body.Add(new CRegisteredObjectName(optProblem.getObject(new CCopasiObjectName("Reference=Best Value")).getCN().getString()));
body.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
body.Add(new CRegisteredObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN().getString()));
// set the report for the task
optTask.getReport().setReportDefinition(report);
// set the output filename
optTask.getReport().setTarget("example5.txt");
// don't append output if the file exists, but overwrite the file
optTask.getReport().setAppend(false);
bool result = false;
try
{
result = optTask.processWithOutputFlags(true, (int)CCopasiTask.ONLY_TIME_SERIES);
}
catch (System.ApplicationException e)
{
System.Console.Error.WriteLine("ERROR: " + e.Message);
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
if (!result)
{
System.Console.Error.WriteLine("Running the optimization failed.");
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
// now we check if the optimization actually got the correct result
// the best value it should have is 0 and the best parameter value for
// that result should be 6 for the initial value of the fixed parameter
double bestValue = optProblem.getSolutionValue();
Debug.Assert(System.Math.Abs(bestValue) < 1e-3);
// we should only have one solution variable since we only have one
// optimization item
Debug.Assert(optProblem.getSolutionVariables().size() == 1);
double solution = optProblem.getSolutionVariables().get(0);
Debug.Assert(System.Math.Abs((solution - 6.0) / 6.0) < 1e-3);
}
static void Main()
{
Debug.Assert(CCopasiRootContainer.getRoot() != null);
// create a new datamodel
CCopasiDataModel dataModel = CCopasiRootContainer.addDatamodel();
Debug.Assert(CCopasiRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
// set the units for the model
// we want seconds as the time unit
// microliter as the volume units
// and nanomole as the substance units
model.setTimeUnit(CUnit.s);
model.setVolumeUnit(CUnit.microl);
model.setQuantityUnit(CUnit.nMol);
// we have to keep a set of all the initial values that are changed during
// the model building process
// They are needed after the model has been built to make sure all initial
// values are set to the correct initial value
ObjectStdVector changedObjects=new ObjectStdVector();
// create a compartment with the name cell and an initial volume of 5.0
// microliter
CCompartment compartment = model.createCompartment("cell", 5.0);
CCopasiObject obj = compartment.getValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(compartment != null);
Debug.Assert(model.getCompartments().size() == 1);
// create a new metabolite with the name S and an inital
// concentration of 10 nanomol
// the metabolite belongs to the compartment we created and is is to be
// fixed
CMetab S = model.createMetabolite("S", compartment.getObjectName(), 10.0, CMetab.FIXED);
obj = S.getInitialConcentrationReference();
Debug.Assert((obj != null));
changedObjects.Add(obj);
Debug.Assert((compartment != null));
Debug.Assert(S != null);
Debug.Assert(model.getMetabolites().size() == 1);
// create a second metabolite called P with an initial
// concentration of 0. This metabolite is to be changed by reactions
CMetab P = model.createMetabolite("P", compartment.getObjectName(), 0.0, CMetab.REACTIONS);
Debug.Assert(P != null);
obj = P.getInitialConcentrationReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 2);
// now we create a reaction
CReaction reaction = model.createReaction("reaction");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 1);
// reaction converts S to P
// we can set these on the chemical equation of the reaction
CChemEq chemEq = reaction.getChemEq();
// S is a substrate with stoichiometry 1
chemEq.addMetabolite(S.getKey(), 1.0, CChemEq.SUBSTRATE);
// P is a product with stoichiometry 1
chemEq.addMetabolite(P.getKey(), 1.0, CChemEq.PRODUCT);
Debug.Assert(chemEq.getSubstrates().size() == 1);
Debug.Assert(chemEq.getProducts().size() == 1);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
CModelValue MV = model.createModelValue("K", 42.0);
// set the status to FIXED
MV.setStatus(CModelValue.FIXED);
Debug.Assert(MV != null);
obj = MV.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getModelValues().size() == 1);
// now we ned to set a kinetic law on the reaction
// for this we create a user defined function
CFunctionDB funDB = CCopasiRootContainer.getFunctionList();
Debug.Assert(funDB != null);
CFunction function = (CFunction)funDB.createFunction("My Rate Law",CEvaluationTree.UserDefined);
CFunction rateLaw = (CFunction)funDB.findFunction("My Rate Law");
Debug.Assert(rateLaw != null);
// now we create the formula for the function and set it on the function
string formula = "(1-0.4/(EXPONENTIALE^(temp-37)))*0.00001448471257*1.4^(temp-37)*substrate";
bool result = function.setInfix(formula);
Debug.Assert(result == true);
// make the function irreversible
function.setReversible(COPASI.TriFalse);
// the formula string should have been parsed now
// and COPASI should have determined that the formula string contained 2 parameters (temp and substrate)
CFunctionParameters variables = function.getVariables();
// per default the usage of those parameters will be set to VARIABLE
uint index = function.getVariableIndex("temp");
CFunctionParameter param = variables.getParameter(index);
Debug.Assert(param.getUsage() == CFunctionParameter.VARIABLE);
// This is correct for temp, but substrate should get the usage SUBSTRATE in order
// for us to use the function with the reaction created above
// So we need to set the usage for "substrate" manually
index = function.getVariableIndex("substrate");
param = variables.getParameter(index);
param.setUsage(CFunctionParameter.SUBSTRATE);
// set the rate law for the reaction
reaction.setFunction(function);
Debug.Assert(reaction.getFunction() != null);
// COPASI also needs to know what object it has to assocuiate with the individual function parameters
// In our case we need to tell COPASI that substrate is to be replaced by the substrate of the reaction
// and temp is to be replaced by the global parameter K
reaction.setParameterMapping("substrate", S.getKey());
reaction.setParameterMapping("temp", MV.getKey());
// finally compile the model
// compile needs to be done before updating all initial values for
// the model with the refresh sequence
model.compileIfNecessary();
// now that we are done building the model, we have to make sure all
// initial values are updated according to their dependencies
model.updateInitialValues(changedObjects);
// save the model to a COPASI file
// we save to a file named example1.cps
// and we want to overwrite any existing file with the same name
// Default tasks are automatically generated and will always appear in cps
// file unless they are explicitley deleted before saving.
dataModel.saveModel("example7.cps", true);
// export the model to an SBML file
// we save to a file named example1.xml, we want to overwrite any
// existing file with the same name and we want SBML L2V3
try
{
dataModel.exportSBML("example7.xml", true, 2, 3);
}
catch
{
System.Console.Error.WriteLine("Error. Exporting the model to SBML failed.");
}
}
static void Main()
{
Debug.Assert(CCopasiRootContainer.getRoot() != null);
// create a new datamodel
CCopasiDataModel dataModel = CCopasiRootContainer.addDatamodel();
Debug.Assert(CCopasiRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
model.setVolumeUnit(CUnit.fl);
model.setTimeUnit(CUnit.s);
model.setQuantityUnit(CUnit.fMol);
CModelValue fixedModelValue=model.createModelValue("F");
Debug.Assert(fixedModelValue != null);
fixedModelValue.setStatus(CModelEntity.FIXED);
fixedModelValue.setInitialValue(3.0);
CModelValue variableModelValue=model.createModelValue("V");
Debug.Assert(variableModelValue != null);
variableModelValue.setStatus(CModelEntity.ASSIGNMENT);
// we create a very simple assignment that is easy on the optimization
// a parabole with the minimum at x=6 should do just fine
string s=fixedModelValue.getValueReference().getCN().getString();
s="(<"+s+"> - 6.0)^2";
variableModelValue.setExpression(s);
// now we compile the model and tell COPASI which values have changed so
// that COPASI can update the values that depend on those
model.compileIfNecessary();
ObjectStdVector changedObjects=new ObjectStdVector();
changedObjects.Add(fixedModelValue.getInitialValueReference());
changedObjects.Add(variableModelValue.getInitialValueReference());
model.updateInitialValues(changedObjects);
// now we set up the optimization
// we want to do an optimization for the time course
// so we have to set up the time course task first
CTrajectoryTask timeCourseTask = (CTrajectoryTask)dataModel.getTask("Time-Course");
Debug.Assert(timeCourseTask != null);
// since for this example it really doesn't matter how long we run the time course
// we run for 1 second and calculate 10 steps
// run a deterministic time course
timeCourseTask.setMethodType(CTaskEnum.deterministic);
// pass a pointer of the model to the problem
timeCourseTask.getProblem().setModel(dataModel.getModel());
// get the problem for the task to set some parameters
CTrajectoryProblem problem = (CTrajectoryProblem)timeCourseTask.getProblem();
Debug.Assert(problem != null);
// simulate 10 steps
problem.setStepNumber(10);
// start at time 0
dataModel.getModel().setInitialTime(0.0);
// simulate a duration of 1 time units
problem.setDuration(1);
// tell the problem to actually generate time series data
problem.setTimeSeriesRequested(true);
// get the optimization task
COptTask optTask=(COptTask)dataModel.getTask("Optimization");
Debug.Assert(optTask != null);
// we want to use Levenberg-Marquardt as the optimization method
optTask.setMethodType(CTaskEnum.LevenbergMarquardt);
// next we need to set subtask type on the problem
COptProblem optProblem=(COptProblem)optTask.getProblem();
Debug.Assert(optProblem != null);
optProblem.setSubtaskType(CTaskEnum.timeCourse);
// we create the objective function
// we want to minimize the value of the variable model value at the end of
// the simulation
// the objective function is normally minimized
string objectiveFunction=variableModelValue.getObject(new CCopasiObjectName("Reference=Value")).getCN().getString();
// we need to put the angled brackets around the common name of the object
objectiveFunction="<"+objectiveFunction+">";
// now we set the objective function in the problem
optProblem.setObjectiveFunction(objectiveFunction);
// now we create the optimization items
// i.e. the model elements that have to be changed during the optimization
// in order to get to the optimal solution
COptItem optItem=optProblem.addOptItem(new CCopasiObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN()));
// we want to change the fixed model value from -100 to +100 with a start
// value of 50
optItem.setStartValue(50.0);
optItem.setLowerBound(new CCopasiObjectName("-100"));
optItem.setUpperBound(new CCopasiObjectName("100"));
// now we set some parameters on the method
// these parameters are specific to the method type we set above
// (in this case Levenberg-Marquardt)
COptMethod optMethod=(COptMethod)optTask.getMethod();
Debug.Assert(optMethod != null);
// now we set some method parameters for the optimization method
// iteration limit
CCopasiParameter parameter=optMethod.getParameter("Iteration Limit");
Debug.Assert(parameter != null);
parameter.setIntValue(2000);
// tolerance
parameter=optMethod.getParameter("Tolerance");
Debug.Assert(parameter != null);
parameter.setDblValue(1.0e-5);
// create a report with the correct filename and all the species against
// time.
CReportDefinitionVector reports = dataModel.getReportDefinitionList();
// create a new report definition object
CReportDefinition report = reports.createReportDefinition("Report", "Output for optimization");
// set the task type for the report definition to timecourse
report.setTaskType(CTaskEnum.optimization);
// we don't want a table
report.setIsTable(false);
// the entries in the output should be seperated by a ", "
report.setSeparator(new CCopasiReportSeparator(", "));
// we need a handle to the header and the body
// the header will display the ids of the metabolites and "time" for
// the first column
// the body will contain the actual timecourse data
ReportItemVector header = report.getHeaderAddr();
ReportItemVector body = report.getBodyAddr();
// in the report header we write two strings and a separator
header.Add(new CRegisteredObjectName(new CCopasiStaticString("best value of objective function").getCN().getString()));
header.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
header.Add(new CRegisteredObjectName(new CCopasiStaticString("initial value of F").getCN().getString()));
// in the report body we write the best value of the objective function and
// the initial value of the fixed parameter separated by a komma
body.Add(new CRegisteredObjectName(optProblem.getObject(new CCopasiObjectName("Reference=Best Value")).getCN().getString()));
body.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
body.Add(new CRegisteredObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN().getString()));
// set the report for the task
optTask.getReport().setReportDefinition(report);
// set the output filename
optTask.getReport().setTarget("example5.txt");
// don't append output if the file exists, but overwrite the file
optTask.getReport().setAppend(false);
bool result=false;
try
{
result=optTask.processWithOutputFlags(true, (int)CCopasiTask.ONLY_TIME_SERIES);
}
catch(System.ApplicationException e)
{
System.Console.Error.WriteLine("ERROR: "+e.Message);
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
if(!result)
{
System.Console.Error.WriteLine("Running the optimization failed.");
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
// now we check if the optimization actually got the correct result
// the best value it should have is 0 and the best parameter value for
// that result should be 6 for the initial value of the fixed parameter
double bestValue=optProblem.getSolutionValue();
Debug.Assert(System.Math.Abs(bestValue) < 1e-3);
// we should only have one solution variable since we only have one
// optimization item
Debug.Assert(optProblem.getSolutionVariables().size() == 1);
double solution=optProblem.getSolutionVariables().get(0);
Debug.Assert(System.Math.Abs((solution-6.0)/6.0) < 1e-3);
}
static void Main()
{
Debug.Assert(CRootContainer.getRoot() != null);
// create a new datamodel
CDataModel dataModel = CRootContainer.addDatamodel();
Debug.Assert(CRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
// set the units for the model
// we want seconds as the time unit
// microliter as the volume units
// and nanomole as the substance units
model.setTimeUnit(CUnit.s);
model.setVolumeUnit(CUnit.microl);
model.setQuantityUnit(CUnit.nMol);
// we have to keep a set of all the initial values that are changed during
// the model building process
// They are needed after the model has been built to make sure all initial
// values are set to the correct initial value
ObjectStdVector changedObjects = new ObjectStdVector();
// create a compartment with the name cell and an initial volume of 5.0
// microliter
CCompartment compartment = model.createCompartment("cell", 5.0);
CDataObject obj = compartment.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(compartment != null);
Debug.Assert(model.getCompartments().size() == 1);
// create a new metabolite with the name glucose and an inital
// concentration of 10 nanomol
// the metabolite belongs to the compartment we created and is is to be
// fixed
CMetab glucose = model.createMetabolite("glucose", compartment.getObjectName(), 10.0, CModelEntity.Status_FIXED);
obj = glucose.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(glucose != null);
Debug.Assert(model.getMetabolites().size() == 1);
// create a second metabolite called glucose-6-phosphate with an initial
// concentration of 0. This metabolite is to be changed by reactions
CMetab g6p = model.createMetabolite("glucose-6-phosphate", compartment.getObjectName(), 0.0, CModelEntity.Status_REACTIONS);
Debug.Assert(g6p != null);
obj = g6p.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 2);
// another metabolite for ATP, also fixed
CMetab atp = model.createMetabolite("ATP", compartment.getObjectName(), 10.0, CModelEntity.Status_FIXED);
Debug.Assert(atp != null);
obj = atp.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 3);
// and one for ADP
CMetab adp = model.createMetabolite("ADP", compartment.getObjectName(), 0.0, CModelEntity.Status_REACTIONS);
Debug.Assert(adp != null);
obj = adp.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 4);
// now we create a reaction
CReaction reaction = model.createReaction("hexokinase");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 1);
// hexokinase converts glucose and ATP to glucose-6-phosphate and ADP
// we can set these on the chemical equation of the reaction
CChemEq chemEq = reaction.getChemEq();
// glucose is a substrate with stoichiometry 1
chemEq.addMetabolite(glucose.getKey(), 1.0, CChemEq.SUBSTRATE);
// ATP is a substrate with stoichiometry 1
chemEq.addMetabolite(atp.getKey(), 1.0, CChemEq.SUBSTRATE);
// glucose-6-phosphate is a product with stoichiometry 1
chemEq.addMetabolite(g6p.getKey(), 1.0, CChemEq.PRODUCT);
// ADP is a product with stoichiometry 1
chemEq.addMetabolite(adp.getKey(), 1.0, CChemEq.PRODUCT);
Debug.Assert(chemEq.getSubstrates().size() == 2);
Debug.Assert(chemEq.getProducts().size() == 2);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
// now we ned to set a kinetic law on the reaction
// maybe constant flux would be OK
// we need to get the function from the function database
CFunctionDB funDB = CRootContainer.getFunctionList();
Debug.Assert(funDB != null);
// it should be in the list of suitable functions
// lets get all suitable functions for an irreversible reaction with 2 substrates
// and 2 products
CFunctionStdVector suitableFunctions = funDB.suitableFunctions(2, 2, COPASI.TriFalse);
Debug.Assert((suitableFunctions.Count > 0));
int i, iMax = (int)suitableFunctions.Count;
for (i = 0; i < iMax; ++i)
{
// we just assume that the only suitable function with Constant in
// it's name is the one we want
if (suitableFunctions[i].getObjectName().IndexOf("Constant") != -1)
{
break;
}
}
if (i != iMax)
{
// we set the function
// the method should be smart enough to associate the reaction entities
// with the correct function parameters
reaction.setFunction(suitableFunctions[i]);
Debug.Assert(reaction.getFunction() != null);
// constant flux has only one function parameter
Debug.Assert(reaction.getFunctionParameters().size() == 1);
// so there should be only one entry in the parameter mapping as well
Debug.Assert(reaction.getParameterCNs().Count == 1);
CCopasiParameterGroup parameterGroup = reaction.getParameters();
Debug.Assert(parameterGroup.size() == 1);
CCopasiParameter parameter = parameterGroup.getParameter(0);
// make sure the parameter is a local parameter
Debug.Assert(reaction.isLocalParameter(parameter.getObjectName()));
// now we set the value of the parameter to 0.5
Debug.Assert(parameter.getType() == CCopasiParameter.Type_DOUBLE);
parameter.setDblValue(0.5);
obj = parameter.getValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
}
else
{
System.Console.Error.WriteLine("Error. Could not find a kinetic law that conatins the term \"Constant\".");
System.Environment.Exit(1);
}
// now we also create a separate reaction for the backwards reaction and
// set the kinetic law to irreversible mass action
// now we create a reaction
reaction = model.createReaction("hexokinase-backwards");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 2);
chemEq = reaction.getChemEq();
// glucose is a product with stoichiometry 1
chemEq.addMetabolite(glucose.getKey(), 1.0, CChemEq.PRODUCT);
// ATP is a product with stoichiometry 1
chemEq.addMetabolite(atp.getKey(), 1.0, CChemEq.PRODUCT);
// glucose-6-phosphate is a substrate with stoichiometry 1
chemEq.addMetabolite(g6p.getKey(), 1.0, CChemEq.SUBSTRATE);
// ADP is a substrate with stoichiometry 1
chemEq.addMetabolite(adp.getKey(), 1.0, CChemEq.SUBSTRATE);
Debug.Assert(chemEq.getSubstrates().size() == 2);
Debug.Assert(chemEq.getProducts().size() == 2);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
// now we ned to set a kinetic law on the reaction
CFunction massAction = (CFunction)funDB.findFunction("Mass action (irreversible)");
Debug.Assert(massAction != null);
// we set the function
// the method should be smart enough to associate the reaction entities
// with the correct function parameters
reaction.setFunction(massAction);
Debug.Assert(reaction.getFunction() != null);
Debug.Assert(reaction.getFunctionParameters().size() == 2);
// so there should be two entries in the parameter mapping as well
Debug.Assert(reaction.getParameterCNs().Count == 2);
// mass action is a special case since the parameter mappings for the
// substrates (and products) are in a vector
// Let us create a global parameter that is determined by an assignment
// and that is used as the rate constant of the mass action kinetics
// it gets the name rateConstant and an initial value of 1.56
CModelValue modelValue = model.createModelValue("rateConstant", 1.56);
Debug.Assert(modelValue != null);
obj = modelValue.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getModelValues().size() == 1);
// set the status to assignment
modelValue.setStatus(CModelEntity.Status_ASSIGNMENT);
// the assignment does not have to make sense
modelValue.setExpression("1.0 / 4.0 + 2.0");
// now we have to adjust the parameter mapping in the reaction so
// that the kinetic law uses the global parameter we just created instead
// of the local one that is created by default
// The first parameter is the one for the rate constant, so we point it to
// the key of out model value
reaction.setParameterObject(0, modelValue);
// now we have to set the parameter mapping for the substrates
reaction.addParameterObject("substrate", g6p);
reaction.addParameterObject("substrate", adp);
// finally compile the model
// compile needs to be done before updating all initial values for
// the model with the refresh sequence
model.compileIfNecessary();
// now that we are done building the model, we have to make sure all
// initial values are updated according to their dependencies
model.updateInitialValues(changedObjects);
// save the model to a COPASI file
// we save to a file named example1.cps
// and we want to overwrite any existing file with the same name
// Default tasks are automatically generated and will always appear in cps
// file unless they are explicitley deleted before saving.
dataModel.saveModel("example1.cps", true);
// export the model to an SBML file
// we save to a file named example1.xml, we want to overwrite any
// existing file with the same name and we want SBML L2V3
try
{
dataModel.exportSBML("example1.xml", true, 2, 3);
}
catch
{
System.Console.Error.WriteLine("Error. Exporting the model to SBML failed.");
}
}
static void Main()
{
Debug.Assert(CRootContainer.getRoot() != null);
// create a new datamodel
CDataModel dataModel = CRootContainer.addDatamodel();
Debug.Assert(CRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
// set the units for the model
// we want seconds as the time unit
// microliter as the volume units
// and nanomole as the substance units
model.setTimeUnit(CUnit.s);
model.setVolumeUnit(CUnit.microl);
model.setQuantityUnit(CUnit.nMol);
// we have to keep a set of all the initial values that are changed during
// the model building process
// They are needed after the model has been built to make sure all initial
// values are set to the correct initial value
ObjectStdVector changedObjects = new ObjectStdVector();
// create a compartment with the name cell and an initial volume of 5.0
// microliter
CCompartment compartment = model.createCompartment("cell", 5.0);
CDataObject obj = compartment.getValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(compartment != null);
Debug.Assert(model.getCompartments().size() == 1);
// create a new metabolite with the name S and an inital
// concentration of 10 nanomol
// the metabolite belongs to the compartment we created and is is to be
// fixed
CMetab S = model.createMetabolite("S", compartment.getObjectName(), 10.0, CModelEntity.Status_FIXED);
obj = S.getInitialConcentrationReference();
Debug.Assert((obj != null));
changedObjects.Add(obj);
Debug.Assert((compartment != null));
Debug.Assert(S != null);
Debug.Assert(model.getMetabolites().size() == 1);
// create a second metabolite called P with an initial
// concentration of 0. This metabolite is to be changed by reactions
CMetab P = model.createMetabolite("P", compartment.getObjectName(), 0.0, CModelEntity.Status_REACTIONS);
Debug.Assert(P != null);
obj = P.getInitialConcentrationReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 2);
// now we create a reaction
CReaction reaction = model.createReaction("reaction");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 1);
// reaction converts S to P
// we can set these on the chemical equation of the reaction
CChemEq chemEq = reaction.getChemEq();
// S is a substrate with stoichiometry 1
chemEq.addMetabolite(S.getKey(), 1.0, CChemEq.SUBSTRATE);
// P is a product with stoichiometry 1
chemEq.addMetabolite(P.getKey(), 1.0, CChemEq.PRODUCT);
Debug.Assert(chemEq.getSubstrates().size() == 1);
Debug.Assert(chemEq.getProducts().size() == 1);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
CModelValue MV = model.createModelValue("K", 42.0);
// set the status to FIXED
MV.setStatus(CModelEntity.Status_FIXED);
Debug.Assert(MV != null);
obj = MV.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getModelValues().size() == 1);
// now we ned to set a kinetic law on the reaction
// for this we create a user defined function
CFunctionDB funDB = CRootContainer.getFunctionList();
Debug.Assert(funDB != null);
CFunction function = (CFunction)funDB.createFunction("My Rate Law", CEvaluationTree.UserDefined);
CFunction rateLaw = (CFunction)funDB.findFunction("My Rate Law");
Debug.Assert(rateLaw != null);
// now we create the formula for the function and set it on the function
string formula = "(1-0.4/(EXPONENTIALE^(temp-37)))*0.00001448471257*1.4^(temp-37)*substrate";
var result = function.setInfix(formula);
Debug.Assert(result.isSuccess());
// make the function irreversible
function.setReversible(COPASI.TriFalse);
// the formula string should have been parsed now
// and COPASI should have determined that the formula string contained 2 parameters (temp and substrate)
CFunctionParameters variables = function.getVariables();
// per default the usage of those parameters will be set to VARIABLE
uint index = function.getVariableIndex("temp");
CFunctionParameter param = variables.getParameter(index);
Debug.Assert(param.getUsage() == CFunctionParameter.VARIABLE);
// This is correct for temp, but substrate should get the usage SUBSTRATE in order
// for us to use the function with the reaction created above
// So we need to set the usage for "substrate" manually
index = function.getVariableIndex("substrate");
param = variables.getParameter(index);
param.setUsage(CFunctionParameter.SUBSTRATE);
// set the rate law for the reaction
reaction.setFunction(function);
Debug.Assert(reaction.getFunction() != null);
// COPASI also needs to know what object it has to assocuiate with the individual function parameters
// In our case we need to tell COPASI that substrate is to be replaced by the substrate of the reaction
// and temp is to be replaced by the global parameter K
reaction.setParameterMapping("substrate", S.getKey());
reaction.setParameterMapping("temp", MV.getKey());
// finally compile the model
// compile needs to be done before updating all initial values for
// the model with the refresh sequence
model.compileIfNecessary();
// now that we are done building the model, we have to make sure all
// initial values are updated according to their dependencies
model.updateInitialValues(changedObjects);
// save the model to a COPASI file
// we save to a file named example1.cps
// and we want to overwrite any existing file with the same name
// Default tasks are automatically generated and will always appear in cps
// file unless they are explicitley deleted before saving.
dataModel.saveModel("example7.cps", true);
// export the model to an SBML file
// we save to a file named example1.xml, we want to overwrite any
// existing file with the same name and we want SBML L2V3
try
{
dataModel.exportSBML("example7.xml", true, 2, 3);
}
catch
{
System.Console.Error.WriteLine("Error. Exporting the model to SBML failed.");
}
}
static void Main()
{
Debug.Assert(CCopasiRootContainer.getRoot() != null);
// create a new datamodel
CCopasiDataModel dataModel = CCopasiRootContainer.addDatamodel();
Debug.Assert(CCopasiRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
// set the units for the model
// we want seconds as the time unit
// microliter as the volume units
// and nanomole as the substance units
model.setTimeUnit(CModel.s);
model.setVolumeUnit(CModel.microl);
model.setQuantityUnit(CModel.nMol);
// we have to keep a set of all the initial values that are changed during
// the model building process
// They are needed after the model has been built to make sure all initial
// values are set to the correct initial value
ObjectStdVector changedObjects=new ObjectStdVector();
// create a compartment with the name cell and an initial volume of 5.0
// microliter
CCompartment compartment = model.createCompartment("cell", 5.0);
CCopasiObject obj= compartment.getInitialValueReference();
Debug.Assert(obj!= null);
changedObjects.Add(obj);
Debug.Assert(compartment != null);
Debug.Assert(model.getCompartments().size() == 1);
// create a new metabolite with the name glucose and an inital
// concentration of 10 nanomol
// the metabolite belongs to the compartment we created and is is to be
// fixed
CMetab glucose = model.createMetabolite("glucose", compartment.getObjectName(), 10.0, CMetab.FIXED);
obj = glucose.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(glucose != null);
Debug.Assert(model.getMetabolites().size() == 1);
// create a second metabolite called glucose-6-phosphate with an initial
// concentration of 0. This metabolite is to be changed by reactions
CMetab g6p = model.createMetabolite("glucose-6-phosphate", compartment.getObjectName(), 0.0, CMetab.REACTIONS);
Debug.Assert(g6p != null);
obj = g6p.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 2);
// another metabolite for ATP, also fixed
CMetab atp = model.createMetabolite("ATP", compartment.getObjectName(), 10.0, CMetab.FIXED);
Debug.Assert(atp != null);
obj = atp.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 3);
// and one for ADP
CMetab adp = model.createMetabolite("ADP", compartment.getObjectName(), 0.0, CMetab.REACTIONS);
Debug.Assert(adp != null);
obj = adp.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getMetabolites().size() == 4);
// now we create a reaction
CReaction reaction = model.createReaction("hexokinase");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 1);
// hexokinase converts glucose and ATP to glucose-6-phosphate and ADP
// we can set these on the chemical equation of the reaction
CChemEq chemEq = reaction.getChemEq();
// glucose is a substrate with stoichiometry 1
chemEq.addMetabolite(glucose.getKey(), 1.0, CChemEq.SUBSTRATE);
// ATP is a substrate with stoichiometry 1
chemEq.addMetabolite(atp.getKey(), 1.0, CChemEq.SUBSTRATE);
// glucose-6-phosphate is a product with stoichiometry 1
chemEq.addMetabolite(g6p.getKey(), 1.0, CChemEq.PRODUCT);
// ADP is a product with stoichiometry 1
chemEq.addMetabolite(adp.getKey(), 1.0, CChemEq.PRODUCT);
Debug.Assert(chemEq.getSubstrates().size() == 2);
Debug.Assert(chemEq.getProducts().size() == 2);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
// now we ned to set a kinetic law on the reaction
// maybe constant flux would be OK
// we need to get the function from the function database
CFunctionDB funDB = CCopasiRootContainer.getFunctionList();
Debug.Assert(funDB != null);
// it should be in the list of suitable functions
// lets get all suitable functions for an irreversible reaction with 2 substrates
// and 2 products
CFunctionStdVector suitableFunctions = funDB.suitableFunctions(2, 2, COPASI.TriFalse);
Debug.Assert((suitableFunctions.Count > 0));
int i,iMax=(int)suitableFunctions.Count;
for (i=0;i<iMax;++i)
{
// we just assume that the only suitable function with Constant in
// it's name is the one we want
if (suitableFunctions[i].getObjectName().IndexOf("Constant") != -1)
{
break;
}
}
if (i != iMax)
{
// we set the function
// the method should be smart enough to associate the reaction entities
// with the correct function parameters
reaction.setFunction(suitableFunctions[i]);
Debug.Assert(reaction.getFunction() != null);
// constant flux has only one function parameter
Debug.Assert(reaction.getFunctionParameters().size() == 1);
// so there should be only one entry in the parameter mapping as well
Debug.Assert(reaction.getParameterMappings().Count == 1);
CCopasiParameterGroup parameterGroup = reaction.getParameters();
Debug.Assert(parameterGroup.size() == 1);
CCopasiParameter parameter = parameterGroup.getParameter(0);
// make sure the parameter is a local parameter
Debug.Assert(reaction.isLocalParameter(parameter.getObjectName()));
// now we set the value of the parameter to 0.5
Debug.Assert(parameter.getType() == CCopasiParameter.DOUBLE);
parameter.setDblValue(0.5);
obj = parameter.getValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
}
else
{
System.Console.Error.WriteLine("Error. Could not find a kinetic law that conatins the term \"Constant\".");
System.Environment.Exit(1);
}
// now we also create a separate reaction for the backwards reaction and
// set the kinetic law to irreversible mass action
// now we create a reaction
reaction = model.createReaction("hexokinase-backwards");
Debug.Assert(reaction != null);
Debug.Assert(model.getReactions().size() == 2);
chemEq = reaction.getChemEq();
// glucose is a product with stoichiometry 1
chemEq.addMetabolite(glucose.getKey(), 1.0, CChemEq.PRODUCT);
// ATP is a product with stoichiometry 1
chemEq.addMetabolite(atp.getKey(), 1.0, CChemEq.PRODUCT);
// glucose-6-phosphate is a substrate with stoichiometry 1
chemEq.addMetabolite(g6p.getKey(), 1.0, CChemEq.SUBSTRATE);
// ADP is a substrate with stoichiometry 1
chemEq.addMetabolite(adp.getKey(), 1.0, CChemEq.SUBSTRATE);
Debug.Assert(chemEq.getSubstrates().size() == 2);
Debug.Assert(chemEq.getProducts().size() == 2);
// this reaction is to be irreversible
reaction.setReversible(false);
Debug.Assert(reaction.isReversible() == false);
// now we ned to set a kinetic law on the reaction
CFunction massAction = (CFunction)funDB.findFunction("Mass action (irreversible)");
Debug.Assert(massAction != null);
// we set the function
// the method should be smart enough to associate the reaction entities
// with the correct function parameters
reaction.setFunction(massAction);
Debug.Assert(reaction.getFunction() != null);
Debug.Assert(reaction.getFunctionParameters().size() == 2);
// so there should be two entries in the parameter mapping as well
Debug.Assert(reaction.getParameterMappings().Count == 2);
// mass action is a special case since the parameter mappings for the
// substrates (and products) are in a vector
// Let us create a global parameter that is determined by an assignment
// and that is used as the rate constant of the mass action kinetics
// it gets the name rateConstant and an initial value of 1.56
CModelValue modelValue = model.createModelValue("rateConstant", 1.56);
Debug.Assert(modelValue != null);
obj = modelValue.getInitialValueReference();
Debug.Assert(obj != null);
changedObjects.Add(obj);
Debug.Assert(model.getModelValues().size() == 1);
// set the status to assignment
modelValue.setStatus(CModelValue.ASSIGNMENT);
// the assignment does not have to make sense
modelValue.setExpression("1.0 / 4.0 + 2.0");
// now we have to adjust the parameter mapping in the reaction so
// that the kinetic law uses the global parameter we just created instead
// of the local one that is created by default
// The first parameter is the one for the rate constant, so we point it to
// the key of out model value
reaction.setParameterMapping(0, modelValue.getKey());
// now we have to set the parameter mapping for the substrates
reaction.addParameterMapping("substrate", g6p.getKey());
reaction.addParameterMapping("substrate", adp.getKey());
// finally compile the model
// compile needs to be done before updating all initial values for
// the model with the refresh sequence
model.compileIfNecessary();
// now that we are done building the model, we have to make sure all
// initial values are updated according to their dependencies
model.updateInitialValues(changedObjects);
// save the model to a COPASI file
// we save to a file named example1.cps
// and we want to overwrite any existing file with the same name
// Default tasks are automatically generated and will always appear in cps
// file unless they are explicitley deleted before saving.
dataModel.saveModel("example1.cps", true);
// export the model to an SBML file
// we save to a file named example1.xml, we want to overwrite any
// existing file with the same name and we want SBML L2V3
try
{
dataModel.exportSBML("example1.xml", true, 2, 3);
}
catch
{
System.Console.Error.WriteLine("Error. Exporting the model to SBML failed.");
}
}