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Moving inference.metabolic to probs repository.

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Marcel Kronfeld
2008-08-04 15:22:59 +00:00
parent 85e0b5674b
commit 8ec49d3551
17 changed files with 0 additions and 5742 deletions
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303
src/eva2/server/go/problems/inference/metabolic/AbstractCurveFittingProblem.java
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package eva2.server.go.problems.inference.metabolic;
import java.io.Serializable;
import java.util.LinkedList;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.ESIndividualDoubleData;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.InterfaceHasInitRange;
import eva2.tools.Mathematics;
import eva2.tools.des.RKSolver;
/**
* This class provides a collection of methods to compare two curves one against
* the other and to optimize the parameters of a model to be fitted as close as
* possible to given measurements.
*
* @author Andreas Dräger
* @date Sept. 18 2006
*/
public abstract class AbstractCurveFittingProblem extends
AbstractOptimizationProblem implements InterfaceHasInitRange,
Serializable {
protected AbstractEAIndividual best = null;
protected LinkedList<String> metabolites;
protected TimeSeries timeSeries;
protected double xOffSet, noise, yOffSet;
protected int m_ProblemDimension;
protected double t0, t1;
protected RKSolver solver;
protected boolean monteCarlo = false;
protected double[][] initRange;
public AbstractCurveFittingProblem() {
m_Template = new ESIndividualDoubleData();
}
public void setIndividualTemplate(AbstractEAIndividual indy) {
m_Template = indy;
}
/**
* This method allows you to swich to an initialization that is completely
* by chance instead of a certain more sophisticated initialization
* procedure.
*
* @param mc
* If true, a Monte Carlo initialization will be performed. Else
* another possibly more sophisticated procedure will be started.
*/
public void setMonteCarloInitialization(boolean mc) {
this.monteCarlo = mc;
}
/**
* @param individual
*/
public void evaluate(AbstractEAIndividual individual) {
double fitness[] = this
.doEvaluation(((InterfaceDataTypeDouble) individual)
.getDoubleData());
individual.SetFitness(fitness);
if (this.best == null) {
this.best = (AbstractEAIndividual) individual.clone();
} else if (this.best.getFitness(0) > individual.getFitness(0)) {
this.best = (AbstractEAIndividual) individual.clone();
}
}
/**
* @param x
* @return
*/
public double[] doEvaluation(double[] x) {
double freturn[] = new double[1], fitness = 0;
double[][] result = model(x);
if (result == null) {
freturn[0] = Double.MAX_VALUE;
return freturn;
}
freturn[0] = 0;
if (this.solver != null)
if (this.solver.isUnstable()) {
freturn[0] += 10000;
}
// for all metabolites
for (int i = 0; (i < metabolites.size())
&& (freturn[0] < Double.MAX_VALUE); i++)
try {
/*
* Metabolites, which have not been considered in the
* measurements should also be able to be simulated. They
* shouldn't contribute to the overall fitness.
*/
if (!timeSeries.containsMetabolite(metabolites.get(i)
.toString())
|| (timeSeries.numberOfMeasurements(metabolites.get(i)
.toString()) == 0))
continue;
double[] times = this.timeSeries.getTimes(this.metabolites.get(
i).toString()), values = this.timeSeries
.getValues(this.metabolites.get(i).toString()), mvalue = new double[times.length];
// model
// values
// tests:
// boolean name = false;
// for all measured times for the current metabolite
for (int j = 0, min = 0, max = result.length - 1; j < times.length; j++) {
/*
* This happens, if the parameters are that bad that the
* model function doesn't even produce sensefull values.
*/
if (result[result.length - 1][0] < times[j]) {
mvalue[j] = Math.max(10000, 10000 * values[j]);
continue;
}
if (times[j] != result[j][0]) {
/*
* Seek the right time value from the model with an
* approach in logarithmic time. The variables min and
* max have been defined as an additional start
* condition of the for loop.
*/
int t = Math.round((max + min) / 2), t_old = t;
do {
t_old = t;
if (result[t][0] < times[j]) {
if (t < max)
min = t + 1;
else
min = t;
} else if (result[t][0] > times[j]) {
if (t > min)
max = t - 1;
else
max = t;
} else
break;
t = Math.round((max + min) / 2);
} while (t != t_old);
/*
* Set min and max for the search of the next time
* (measured times are assumed to be ordered).
*/
min = t;
max = result.length - 1;
/*
* The model value for the i-th metabolite at time j is
* given by result[t][i+1] because the first column of
* result is the time.
*/
mvalue[j] = result[t][i + 1];
/*
* These steps are necessary if the resulting model
* contains more than one value for one time step. In
* this case for the fitness the model value with the
* greatest distance to the measured value should be
* considered.
*/
int up = t, down = t;
while (up < result.length - 1)
if (result[up][0] == result[up + 1][0])
up++;
else
break;
while (down > 0)
if (result[down][0] == result[down - 1][0])
down--;
else
break;
if (up != down)
for (int k = down; k < up; k++)
if (Math.abs(result[k][i + 1] - values[j]) > Math
.abs(mvalue[j] - values[j]))
mvalue[j] = result[k][i + 1];
if ((result[t][0] > times[j]) && (t > 0)) {
if (result[t - 1][0] < times[j]) {
/*
* if (!name) {
* System.out.println(metabolites.get(i)+":");
* name=true; } System.out.println(
* result[t-1][0]+" = result["+(t-1)+"][0] <
* times["+j+"] = "+times[j]+" & "
* +result[t][0]+" = result["+t+"][0] >
* times["+j+"] = "+times[j]);//
*/
mvalue[j] = Mathematics.linearInterpolation(
times[j], result[t - 1][0],
result[t][0], result[t - 1][i + 1],
mvalue[j]);
} // otherwise we don't want to interpolate with
// the value before
// down.
} else if ((result[t][0] < times[j])
&& (t < result.length - 1)) {
if (result[t + 1][0] > times[j]) {
mvalue[j] = Mathematics.linearInterpolation(
times[j], result[t][0],
result[t + 1][0], mvalue[j],
result[t + 1][i + 1]);
} /*
* we don't want to interpolate with the value
* after up. This would propably smooth the
* error function.
*/
}
/*
* We are lucky: the model already contains this time
* point. So we just need to use the given model value.
*/
} else {
mvalue[j] = result[j][i + 1];
}
/*
* This also happens with bad parameter values. Give a heavy
* weight to the current metabolite.
*/
if ((mvalue[j] == Double.NaN) || (mvalue[j] < 0))
mvalue[j] = Math.max(10000, 10000 * values[j]);
} // end for all times
// average distance over all times
/*
* fitness = Mathematics.dist(mvalue, values, 2)/times.length;
* freturn[0] += fitness;//
*/
// fitness = Mathematics.dist(mvalue, values, 2);
fitness = Mathematics.relDist(mvalue, values, 10000);
System.out.println(metabolites.get(i) + "\t" + fitness);
if (!Double.isNaN(fitness) && !Double.isInfinite(fitness)) {
freturn[0] += fitness;
} else
freturn[0] += 99999999999999999.; // TODO this should be
// managed
// differently?
} catch (Exception exc) {
exc.printStackTrace();
}
if (Double.isInfinite(freturn[0])
|| (freturn[0] > 99999999999999999999.))
freturn[0] = 99999999999999999999.;
return freturn;
}
/**
* This method returns the best individual of the complete optimization
* process, which is not neccessarily identical to the best individual of
* the final population.
*
* @return best The best Individual of the complete optimization process
*/
public AbstractEAIndividual getOverallBestIndividual() {
return this.best;
}
/**
* Computes the differential equation describing the decrease or increase of
* the current metabolite in the metabolic network.
*
* @param x
* The current parameters of the differential equation describing
* the metabolic network.
* @return
*/
public abstract double[][] model(double[] x);
public int getProblemDimension() {
return m_ProblemDimension;
}
public double[][] getInitRange() {
return initRange;
}
public void SetInitRange(double[][] initRange) {
this.initRange = initRange;
}
}

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src/eva2/server/go/problems/inference/metabolic/AbstractValineCurveFittingProblem.java
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package eva2.server.go.problems.inference.metabolic;
import java.io.Serializable;
import java.util.LinkedList;
import wsi.ra.math.RNG;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.ESIndividualDoubleData;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.inference.metabolic.odes.AbstractValineSystem;
import eva2.tools.des.RKSolver;
/**
* Super class for many simulation problems of the valine and leucine reaction
* network in C. glutamicum.
*
* @since 2.0
* @version
* @author Andreas Dr&auml;ger (draeger) <andreas.draeger@uni-tuebingen.de>
* Copyright (c) ZBiT, University of T&uuml;bingen, Germany Compiler:
* JDK 1.6.0
* @date Sep 6, 2007
*/
public abstract class AbstractValineCurveFittingProblem extends
AbstractCurveFittingProblem implements Serializable {
/**
* Start values for: DHIV, IPM, AcLac, Val, Leu, KIV, KIC in this order.
*/
protected final double[] y = { 0.132, 0.0227, 0.236, 29.4, 0.209, 13.1,
0.0741 };
protected AbstractValineSystem system;
/**
* Generated serial id.
*/
private static final long serialVersionUID = -194867821991525140L;
public AbstractValineCurveFittingProblem(
AbstractValineCurveFittingProblem avcfp) {
if (avcfp.m_Template != null)
this.m_Template = (AbstractEAIndividual) ((AbstractEAIndividual) avcfp.m_Template)
.clone();
this.system = avcfp.system;
this.m_ProblemDimension = this.system.getNumberOfParameters();
}
/**
* Default constructor for simulation of the valine data.
*/
public AbstractValineCurveFittingProblem(AbstractValineSystem system) {
this.solver = new RKSolver(.01);
this.system = system;
this.m_ProblemDimension = system.getNumberOfParameters();
solver.setWithLinearCalc(false);
m_Template = new ESIndividualDoubleData();
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.OptimizationProblems.AbstractOptimizationProblem#initProblem()
*/
public void initProblem() {
this.best = null;
this.metabolites = new LinkedList<String>();
this.timeSeries = new TimeSeries();
// this.metabolites.add("PYR"); // 46
// this.metabolites.add("AKG"); // 47
// this.metabolites.add("ALA"); // 47
// this.metabolites.add("NAD"); // 47
this.metabolites.add("DHIV"); // 44
// this.metabolites.add("nadp"); // 45
// this.metabolites.add("Glut"); // 47
this.metabolites.add("2IPM"); // 45
this.metabolites.add("AcLac"); // 40
this.metabolites.add("Val"); // 46
this.metabolites.add("Leu"); // 47
this.metabolites.add("KIV"); // 47
this.metabolites.add("KIC"); // 47
this.t0 = -3.894;
this.t1 = 20.643;
this.xOffSet = RNG.gaussianDouble(0.1);
this.yOffSet = RNG.gaussianDouble(0.1);
this.noise = RNG.gaussianDouble(0.1);
initRange = new double[m_ProblemDimension][2];
for (int i = 0; i < initRange.length; i++) {
initRange[i][0] = 0;
initRange[i][1] = 2;
}
this.SetInitRange(initRange);
}
public void initPopulation(Population population) {
int i;
AbstractEAIndividual tmpIndy;
best = null;
population.clear();
((InterfaceDataTypeDouble) m_Template)
.setDoubleDataLength(initRange.length);
// set the range
double[][] range = getParameterRanges();
((InterfaceDataTypeDouble) m_Template).SetDoubleRange(range);
// for (i = 0; i < population.getPopulationSize(); i++) {
Population tmpPop = new Population();
if (population.getPopulationSize() < 250)
tmpPop.setPopulationSize(250);
else
tmpPop.setPopulationSize(population.getPopulationSize());
for (i = 0; i < tmpPop.getPopulationSize(); i++) {
tmpIndy = (AbstractEAIndividual) ((AbstractEAIndividual) m_Template)
.clone();
// tmpIndy.init(this);
// ////// B E G I N N ///////////////
int j;
double params[] = new double[m_ProblemDimension];
for (j = 0; j < params.length; j++) {
if (monteCarlo)
params[j] = RNG.randomDouble(range[j][0], range[j][1]);
else
params[j] = RNG.gaussianDouble(1) + 1;
if (params[j] < range[j][0]) {
params[j] = range[j][0];
}
if (params[j] > range[j][1]) {
params[j] = range[j][1];
}
}
tmpIndy.initByValue(params, this);
// ////////E N D E /////////////////
tmpPop.add(tmpIndy);
// population.add(tmpIndy);
// /////// B E G I N N //////////////
((InterfaceDataTypeDouble) tmpIndy).SetDoublePhenotype(params);
tmpIndy.resetConstraintViolation();
params = ((InterfaceDataTypeDouble) tmpIndy).getDoubleData();
for (j = 0; j < params.length; j++)
if ((params[j] < ((InterfaceDataTypeDouble) m_Template)
.getDoubleRange()[j][0])
|| (params[j] > ((InterfaceDataTypeDouble) m_Template)
.getDoubleRange()[j][1]))
System.out.println("Verletzung!\t" + params[j]);// */
if (tmpIndy.violatesConstraint())
System.out.println("Constraint violation: "
+ tmpIndy.getConstraintViolation());
// ////// E N D E /////////////////
}
tmpPop.init();
for (i = 0; i < population.getPopulationSize(); i++)
population.add(tmpPop.remove(tmpPop.getIndexOfBestIndividual()));
population.init();
}
/**
* This method returns a matrix whose number of lines equals the number of
* parameters and which has two columns. In each line of this matrix the
* lower and the upper bound for the corresponding parameter are defined.
*
* @return
*/
protected abstract double[][] getParameterRanges();
/*
* (non-Javadoc)
*
* @see simulation.AbstractCurveFittingProblem#model(double[])
*/
public double[][] model(double[] x) {
try {
system.setParameters(x);
return solver.solveAtTimePointsIncludingTime(system, y,
this.timeSeries.getTimes());
} catch (Exception e) {
e.printStackTrace();
return null;
}
}
}

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src/eva2/server/go/problems/inference/metabolic/AbstractValineSplineFittingProblem.java
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package eva2.server.go.problems.inference.metabolic;
import java.io.Serializable;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.problems.inference.metabolic.odes.AbstractValineSystem;
// Created at 2007-01-23
/**
* @author Andreas Dr&auml;ger
*/
public abstract class AbstractValineSplineFittingProblem extends
AbstractValineCurveFittingProblem implements Serializable {
/**
* Default Serial Verison ID
*/
private static final long serialVersionUID = 1L;
/**
* Constructor
*
* @param s
*/
public AbstractValineSplineFittingProblem(AbstractValineSystem s) {
super(s);
}
public AbstractValineSplineFittingProblem(GMAKiProblem problem) {
super(problem);
}
@Override
public void evaluate(AbstractEAIndividual individual) {
double fitness[] = new double[] { 0 }, result[][] = model(((InterfaceDataTypeDouble) individual)
.getDoubleData());
if (result == null) {
fitness[0] = Double.MAX_VALUE;
} else {
if (solver != null)
if (solver.isUnstable()) {
fitness[0] += 10000;
}
for (int i = 0; i < result.length; i++) { // for all times
double spline[] = new double[] { // get the spline values at
// the
// current time point.
system.getDHIV(result[i][0]),
system.getIPM(result[i][0]),
system.getAcLac(result[i][0]),
system.getVal(result[i][0]),
system.getLeu(result[i][0]),
system.getKIV(result[i][0]),
system.getKIC(result[i][0]) };
for (int j = 1; j < result[i].length
&& (fitness[0] < Double.MAX_VALUE); j++)
// for all metabolites
if (spline[j - 1] != 0)
fitness[0] += Math
.pow(
((result[i][j] - spline[j - 1]) / spline[j - 1]),
2);
else
fitness[0] += 10000;
}
}
individual.SetFitness(fitness);
if (this.best == null) {
this.best = (AbstractEAIndividual) individual.clone();
} else if (this.best.getFitness(0) > individual.getFitness(0)) {
this.best = (AbstractEAIndividual) individual.clone();
}
}
}

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src/eva2/server/go/problems/inference/metabolic/CKMMiProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.inference.metabolic.odes.CKMMiSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
/**
* Created at 2007-02-04
*
* @author Andreas Dr&auml;ger
*/
public class CKMMiProblem extends AbstractValineCurveFittingProblem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = 3459101339325025847L;
public CKMMiProblem() {
super(new CKMMiSystem());
}
public CKMMiProblem(CKMMiProblem ckmmiProblem) {
super(ckmmiProblem);
}
public String getName() {
return "CKMMiProblem";
}
protected double[][] getParameterRanges() {
int i;
double[][] range = new double[m_ProblemDimension][2];
for (i = 0; i < 9; i++) { // double[] kcat = new double[9]; // ->
// p[0..9]
range[i][0] = 0;
range[i][1] = 2000;
}
for (i = 9; i < 12; i++) { // double[] ki = new double[3]; // ->
// p[9..11]
range[i][0] = 0;
range[i][1] = 2000;
}
for (i = 12; i < 28; i++) { // double[] km = new double[16]; // ->
// p[12..27]
range[i][0] = 1E-8;
range[i][1] = 2000;
}
for (i = 28; i < 31; i++) { // double[] vm = new double[3]; // ->
// p[28..30]
range[i][0] = 0;
range[i][1] = 2000;
}
for (i = 31; i < 34; i++) { // double[] kmm = new double[3]; // ->
// p[31..33]
range[i][0] = 1E-8;
range[i][1] = 2000;
}
for (i = 34; i < 37; i++) { // double[] kia = new double[3]; // ->
// p[34..36]
range[i][0] = 0;
range[i][1] = 1E+8;
}
for (i = 37; i < 40; i++) { // double[] kib = new double[3]; // ->
// p[37..39]
range[i][0] = 0;
range[i][1] = 1E+8;
}
range[40][0] = 1E-8; // double acCoA = 0; // -> p[40]
range[40][1] = 2000;
return range;
}
@Override
public Object clone() {
return new CKMMiProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where 7 reactions are modeled using convenience kinetics"
+ " and 3 reactions with Michaelis Menten kinetics. Only two reactions"
+ " are considered reversible.";
}
}

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src/eva2/server/go/problems/inference/metabolic/CKMMrProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.InterfaceMultimodalProblem;
import eva2.server.go.problems.inference.metabolic.odes.CKMMrSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
/**
* This class is a problem of the valine/leucine biosynthesis in C. glutamicum
* and uses a combination of Michaelis-Menten and Convenience Kinetic, both
* reversible.
*
* @date 2007-02-05
* @author Andreas Dr&auml;ger
*/
public class CKMMrProblem extends AbstractValineCurveFittingProblem implements
InterfaceMultimodalProblem {
/**
*
*/
private static final long serialVersionUID = -3120557413810402464L;
public CKMMrProblem() {
super(new CKMMrSystem());
}
public CKMMrProblem(CKMMrProblem ckmmrprob) {
super(ckmmrprob);
}
public String getName() {
return "CKMMrProblem";
}
/*
* (non-Javadoc)
* @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges()
*/
public double[][] getParameterRanges() {
int i;
// double[] kcat = null, // <- params[0..6]
// bcat = null, // <- params[7..13]
// km = null, // <- params[14..43]
// ki = null, // <- params[44..46]
// vm = null, // <- params[47..50]
// kia = null, // <- params[51..53]
// kib = null; // <params[54..56]
// double acCoA = 0, // <- params[57]
// coA = 0; // <- params[58]
// set the range
double[][] range = new double[m_ProblemDimension][2];
for (i = 0; i < range.length; i++) {
// Reaktionen, bei denen CO2 entsteht, sind wahrscheinlich
// irreversibel.
if ((i < 14) || ((43 < i) && (i < 51)))
range[i][0] = 0;
else
range[i][0] = 1E-8;
if ((i > 50) && (i < 57))
range[i][1] = 1E+8;
else
range[i][1] = 2000;
}
return range;
}
@Override
public Object clone() {
return new CKMMrProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where 7 reactions are modeled using convenience kinetics"
+ " and 3 reactions with Michaelis Menten kinetics. Only two reactions"
+ " are considered irreversible.";
}
}

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src/eva2/server/go/problems/inference/metabolic/GMAKiProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.inference.metabolic.odes.GMAKiSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
// Created at 2007-02-02
/**
* @author Andreas Dr&auml;ger
*/
public class GMAKiProblem extends AbstractValineSplineFittingProblem {
/**
* Generated serial version id.
*/
private static final long serialVersionUID = -3635573692736142863L;
/**
* Default constructor for simulation of the valine data.
*/
public GMAKiProblem() {
super(new GMAKiSystem());
m_ProblemDimension = system.getNumberOfParameters();
}
public GMAKiProblem(GMAKiProblem gmakiProblem) {
super(gmakiProblem);
this.m_ProblemDimension = system.getNumberOfParameters();
}
public String getName() {
return "GMAKiProblem";
}
/*
* (non-Javadoc)
* @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges()
*/
protected double[][] getParameterRanges() {
int i;
// set the range
double[][] range = new double[this.m_ProblemDimension][2];
for (i = 0; i < 12; i++) {
range[i][0] = 0;
range[i][1] = 2000;
}
for (i = 12; i < this.m_ProblemDimension; i++) {
range[i][0] = 0;
range[i][1] = 8;
}
return range;
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.problems.AbstractOptimizationProblem#clone()
*/
public Object clone() {
return new GMAKiProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where all reactions are modeled using generalized"
+ " mass-action kinetics. Only two reactions are considered irreversible.";
}
}

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src/eva2/server/go/problems/inference/metabolic/GMAKrProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.InterfaceMultimodalProblem;
import eva2.server.go.problems.inference.metabolic.odes.GMAKrSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
/**
* In this problem, the valine/leucine reaction network in C. glutamicum is
* simulated using a generalized mass-action approach.
*
* @since 2.0
* @version
* @author Andreas Dr&auml;ger (draeger) <andreas.draeger@uni-tuebingen.de>
* Copyright (c) ZBiT, University of T&uuml;bingen, Germany Compiler:
* JDK 1.6.0
* @date Sep 6, 2007
*/
public class GMAKrProblem extends AbstractValineCurveFittingProblem implements
InterfaceMultimodalProblem {
/**
* Generated id.
*/
private static final long serialVersionUID = 3423204672190772267L;
/**
* Default constructor for simulation of the valine data.
*/
public GMAKrProblem() {
super(new GMAKrSystem());
}
public GMAKrProblem(GMAKrProblem gmakrprob) {
super(gmakrprob);
}
public String getName() {
return "GMAKrProblem";
}
/*
* (non-Javadoc)
* @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges()
*/
protected double[][] getParameterRanges() {
int i;
double[][] range = new double[m_ProblemDimension][2];
for (i = 0; i < 18; i++) {
range[i][0] = 0;
range[i][1] = 2000;
}
for (i = 18; i < m_ProblemDimension; i++) {
range[i][0] = 0;
range[i][1] = 2000;
}
return range;
}
@Override
public Object clone() {
return new GMAKrProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where all reactions are modeled using generalized"
+ " mass-action kinetics. Only two reactions are considered irreversible.";
}
}

111
src/eva2/server/go/problems/inference/metabolic/GMMiProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.inference.metabolic.odes.GMMiSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
// Created at 2007-01-18
/**
* @author Andreas Dr&auml;ger
*/
public class GMMiProblem extends AbstractValineCurveFittingProblem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = -7344294007783094303L;
public GMMiProblem() {
super(new GMMiSystem());
}
public GMMiProblem(GMMiProblem gmmiproblem) {
super(gmmiproblem);
this.m_ProblemDimension = system.getNumberOfParameters();
}
public String getName() {
return "GMMiProblem";
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges()
*/
protected double[][] getParameterRanges() {
int i;
double[][] range = new double[m_ProblemDimension][2];
// BRENDA: k+1 k+2 k+4 k+5 k+7 k+8 k+9 l1 l2 Ki1 Ki2 Ki3 vm1 vm2 vm3
// kia1
// kia2 kia3 kib1 kib2 kib3 km1 km2 km3
double[] min = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1E-8, 1E-8, 1E-8 }, max = { 2000, 2000, 2000, 2000,
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000,
2000, 1E+8, 1E+8, 1E+8, 1E+8, 1E+8, 1E+8, 2000, 2000, 2000 };
/*
* for (i = 0; i < 7; i++) { // k, rate constants for forward reaction
* range[i][0] = 0.01; range[i][1] = 2000; } for (i = 7; i < 9; i++) { //
* l, rate constants for backward reaction range[i][0] = 0.01;
* range[i][1] = 2000; } for (i = 9; i < 12; i++) { // ki, Rate constant
* for inhibition of uncertain mechanism range[i][0] = 0.01; range[i][1] =
* 100; } for (i = 12; i < 15; i++) { // vm, Maximal reaction velocity
* range[i][0] = 0.1; range[i][1] = 2000; } for (i = 15; i < 18; i++) { //
* kia' == km/kia, Rate constant for inhibition of the enzyme
* range[i][0] = 0; range[i][1] = 100; } for (i = 18; i < 21; i++) { //
* kib' == 1/kib, Rate constant for inhibition of the enzyme substrate
* complex range[i][0] = 0; range[i][1] = 100; } for (i = 21; i < 24;
* i++) { // km, the Michaelis-Menten constant range[i][0] = 0.01;
* range[i][1] = 2000; } // AcCo, Constant specifying the concentration
* of Acetyl-CoA // This is not needed because p_4 already includes
* Acetyl-CoA implicitely. //range[24][0] = 0.01; //range[24][1] = 100; //
*/
for (i = 0; i < range.length; i++) {
range[i][0] = min[i];
range[i][1] = max[i];
}
/*
* double initial[] = new double[] { 15.625991725476922, 2000.0, 0.0010,
* 0.0010, 0.0014167666438540366, 0.03914648793059087,
* 0.0012346931715111, 0.0010, 0.002922248833329009, 50.00050096623751,
* 25.00075000582071, 87.5001338911313, 0.0010, 0.0010, 0.0010,
* 99.99692409764904, 99.9470190144952, 100.0, 99.99999925494194,
* 99.99994374811648, 99.9999922933057, 2000.0, 1999.9999939464062,
* 2000.0,
*
* 30.76270291064184, 1000.0004988358473, 0.0010, 0.0010,
* 0.0017147897187887668, 0.04677587864891996, 0.0010852159729891494,
* 0.0010, 0.0025497199896605963, 96.87503280922212, 12.500875002910353,
* 93.75044398773962, 0.0010, 0.0010, 0.0010, 99.9999962747097,
* 99.98389044776184, 100.0, 99.90234374997726, 99.9999973224476,
* 99.99389646109051, 1999.9999701976926, 1999.9999664724041, 2000.0
*
*
* //10.773224990548886, 505.8899553355387, 0.0010, 0.0010, 0.0010,
* 0.0010, 0.0010, 0.0010, 0.0010, 100.0, //100.0, 100.0, 0.0010,
* 0.0010, 0.0010, 100.0, 0.0, 100.0, 100.0, 100.0, 0.0, 2000.0, 2000.0,
* 2000.0,
*
* //0.016, 6.1, 0.001, 0.001, 0.001, 0.01, 0.005, 0.7, 0.011, 0.011,
* 0.012, 35.3, 0.4, 0.001, 0.001, 0.001, 5, 5, 0.001, 5, 5, 10.9, 5, 5 };
* double choice = RandomNumberGenerator.randomDouble(0, 1);//
*/
return range;
}
@Override
public Object clone() {
return new GMMiProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where 7 reactions are modeled using generalized"
+ " mass-action kinetics and 3 reactions using Michaelis-Menten kinetics. "
+ "Only two reactions are considered reversible.";
}
}

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src/eva2/server/go/problems/inference/metabolic/GMMrProblem.java
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package eva2.server.go.problems.inference.metabolic;
import eva2.server.go.problems.inference.metabolic.odes.GMMrSystem;
import eva2.server.go.strategies.InterfaceOptimizer;
/**
* Created at 2007-02-04.
*
* @author Andreas Dr&auml;ger
*/
public class GMMrProblem extends AbstractValineCurveFittingProblem {
/**
* Generated serial version id.
*/
private static final long serialVersionUID = -3889975340670999788L;
public GMMrProblem() {
super(new GMMrSystem());
}
public GMMrProblem(GMMrProblem gmmrproblem) {
super(gmmrproblem);
}
public String getName() {
return "GMMrProblem";
}
/*
* (non-Javadoc)
* @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges()
*/
protected double[][] getParameterRanges() {
int i;
/*
* a // <- params[0..6] 7 b // <- params[7..13] 7 vm // <-
* params[14..17] 4 km // <- params[18..21] 4 kia // <- params[22..24] 3
* kib // <- params[25..27] 3 ki // <- params[28..30] 3
*/
double[][] range = new double[m_ProblemDimension][2];
double min[] = { 0.000000000, 0.000000000, 0.000000000, 0.000000000,
0.000000000, 0.000000000, 0.000000000, 0.000000000,
0.000000000, 0.000000000, 0.000000000, 0.000000000,
0.000000000, 0.000000000, 0.000000000, 0.000000000,
0.000000000, 0.000000000, 0.000000000, 0.000000000,
0.407343614, 0.059890210, 0.008423761, 0.000000000,
0.000000000, 0.000000000, 0.432428055, 0.000000000,
0.000000000, 0.000000000, 0.000000000 };
double max[] = { 16.012590, 10.398386, 14.983890, 9.367657, 16.011989,
7.229141, 409.395094, 3.348859, 65.532900, 424.407762,
426.881040, 2.753995, 6.234599, 8.552800, 2.216418, 16.623249,
1031.701261, 9.238867, 9.110233, 6.765434, 8.693675, 6.798338,
5.628473, 5.364593, 8.438638, 9.185120, 6.459422, 12.470121,
426.124248, 335.241456, 638.222487 };
for (i = 0; i < range.length; i++) {
// Reaktionen, bei denen CO2 entsteht, sind wahrscheinlich
// irreversibel.
// if (i < 18) //((i == 7) || (i == 12))
range[i][0] = min[i]; // 0;
/*
* else range[i][0] = 1E-8;/
*/
if ((21 < i) && (i < 28))
range[i][1] = max[i]; // 1E+8;
else
range[i][1] = max[i]; // 2000;
}
return range;
}
@Override
public Object clone() {
return new GMMrProblem(this);
}
public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
return "Parameter optimization problem for the valine and leucine biosynthesis"
+ " in C. glutamicum, where 7 reactions are modeled using generalized"
+ " mass-action kinetics and 3 reactions using Michaelis-Menten kinetics. "
+ "Only two reactions are considered irreversible.";
}
}

243
src/eva2/server/go/problems/inference/metabolic/TimeSeries.java
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@@ -1,243 +0,0 @@
package eva2.server.go.problems.inference.metabolic;
import java.io.Serializable;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.StringTokenizer;
/**
* This class is able to parse and store the contents of a time series data
* file. These files are supposed to be a matrix, whose columns are separated by
* spaces or tabs. Every row is supposed to contain a time value in the first
* column. Every other column contains the concentration of the appropriate
* metabolite. Because the concentrations of different metabolites are not
* measured at the same time, it is possible that some values equal -1 or "NaN".
* For a given metabolite this class provides methods to extract the appropriate
* times as well as the concentration values from this file.
*
* @since 2.0
* @version
* @author Andreas Dr&auml;ger (draeger) <andreas.draeger@uni-tuebingen.de>
* Copyright (c) ZBiT, University of T&uuml;bingen, Germany Compiler:
* JDK 1.6.0
* @date Sep 6, 2007
*/
public class TimeSeries implements Serializable {
/**
*
*/
private static final long serialVersionUID = 5744947450930268722L;
String dataFile[] = new String[] {
"\"Time\" \"PYR\" \"AKG\" \"ALA\" \"NAD\" \"DHIV\" \"nadp\" \"Glut\" \"2IPM\" \"AcLac\" \"Val\" \"Leu\" \"KIV\" \"KIC\"",
"-3.894 0.69956151035 4.640691888659999 0.8149815245099999 0.43831117728999996 0.12919945973 NaN 39.91322431689 0.01634219825 0.22846321864000002 25.300234279730002 0.31457001595 13.06542276153 0.12828553615000002",
"-3.429 0.6887305946 4.4648219135 0.9733354471500001 0.46283738647 0.09269418599 0.01751164422 34.689755620279996 0.016622587119999997 0.26529057205 27.855794163480002 0.21601742448 12.27325468611 0.08806661914",
"-2.964 0.648489239 5.16446833661 1.08501337472 0.510398731 0.09668719327 0.015292064690000001 35.94424701067 0.0218616059 0.24372308822 27.512009946459997 0.19358187491999998 11.83868901432 0.08060740248 ",
"-2.499 0.66263247645 5.46655875364 1.25007291486 0.54709779337 0.27832465706 0.02017254508 42.09304165779 0.02271131235 0.31096389668 31.734637769419997 0.26547698208000003 9.74938023117 0.09527994438000001 ",
"-2.037 0.78148116407 5.01023940504 1.1274704877 0.54374513457 0.10951589105 0.023434610419999998 38.623872567220005 0.02310058527 0.23193180999000002 28.68948017027 0.27338539200999995 9.8790791622 0.06856701949 ",
"-1.572 0.7976018096 4.50327160849 0.9657423536899999 0.58804343197 0.07731788865 0.021456078269999998 39.65098562534 0.02122962586 NaN 23.23780871601 0.21372010324 7.35589658416 0.04408823098 ",
"-1.107 0.62138120097 5.527992933249999 1.11320796018 0.55468774709 0.19127059402000002 0.02151432978 37.43103867786 0.021760915119999998 0.1961057295 28.06503640484 0.25571800238 7.77390798856 0.08440327591999999 ",
"-0.642 0.61440001723 5.6582676904 1.0934342424799999 0.57892866745 0.29234911798 0.02163427084 41.73288066597 0.02358943064 0.23458068462 31.36134966529 0.28291670151 8.77177024347 0.0732470093 ",
"0.363 0.70849102753 5.6485532671600005 1.07913785266 0.5494837929699999 0.22848890345 0.03264048454 40.837490521439996 0.02034557524 0.25195151103 27.336329975730003 0.1981060288 6.68584901645 0.07329936145 ",
"0.828 0.7284264133299999 5.24081938936 1.20656221781 0.56038053457 0.50828902006 0.02799224444 43.839863948079994 0.02365648322 0.33595864068 30.40658463215 0.25328480297 7.46481961657 0.06727204061 ",
"1.293 2.35072982926 12.865197464340001 2.91425720335 0.6174561459100001 0.51680611101 0.0180555805 45.00192868082 0.02789099302 0.27282017381999996 35.47319021015 0.34012252203000004 12.4841096615 0.12075887125 ",
"1.758 1.9467590874099998 9.85083850897 2.38135380833 0.47990344534 0.36137563687 0.02083824064 39.874833705 0.02603135155 0.24703878422 35.24799025209 0.25499939033 10.58660287792 0.12011356083 ",
"2.220 1.76234822871 12.57168005228 2.535743239 0.58312865514 0.17860360497 0.018047886200000002 48.82282962176 0.02531203769 NaN 31.84083292177 0.21013809416 8.79825645884 0.10474495756 ",
"2.685 1.92439252352 11.2260038878 2.8335955021 0.49220203429 0.16592713605 0.0278326992 39.75506759172 0.02115183034 0.24700121622 29.23941353308 0.22169631684000002 8.94277598226 0.09683684583 ",
"3.150 1.92008237217 10.682979534100001 2.63737602928 0.36735949621999997 0.1546055768 0.01515236183 39.012881840679995 NaN 0.46143297886 35.812552779769995 0.28387244697 14.20243053826 0.10933158105 ",
"4.620 1.81992606678 11.067551454270001 2.70934921435 0.40725580208999995 0.24849740356 0.027964720069999997 40.42309102465 0.0170493166 0.42824885292 37.17528380543 0.33573709864 15.33969385816 0.10861095009 ",
"5.085 1.45412618172 8.37506633272 2.5510691881399996 0.5402956812899999 0.29017599822 0.029667666019999997 52.86610489844 0.01551224716 0.37018809756000004 42.07773461152 0.32912302835999996 16.97282365372 0.1127063518 ",
"5.550 1.7963431670299999 10.9951402722 3.2730762781499996 0.49909124182 0.36584625016 0.031689318279999996 49.69757534082 0.030182574599999998 0.50230756817 44.18764066063 0.34318150693 14.45568137413 0.13087029789 ",
"6.015 1.79488675167 10.58210664143 2.82815659112 0.42028499769 0.33884287302 0.02904992542 47.618463449870006 0.02065291348 0.38243112459999995 41.68555649409 0.31270596242 14.18407754513 0.12297689406000001 ",
"6.477 1.71658915979 13.051632359080001 2.86429255047 0.69044126446 0.2906864281 0.02542790495 52.905505205989996 0.0363051233 0.46612294623 51.36020781111 0.52323119427 15.99708653385 0.13462198459000002 ",
"6.942 1.70200349375 11.29511994965 2.47410519106 0.61838724543 0.74141022319 0.02954918008 52.05561320903 0.03626130189 0.36020464648 47.08381733066 0.39496256108 15.29065040412 0.12714131781000002 ",
"7.407 1.4797133761099999 10.85092666902 2.73282139173 0.60893255673 0.91712444816 0.031866304570000004 54.69762263264 0.034354864900000004 0.50457338093 42.85485451463 0.35559022922000005 11.0783278302 0.11004224548000001 ",
"7.872 1.9687388421799998 12.632535436100001 3.38129932903 0.6733727859399999 1.19712426006 0.02287925851 69.03104100976 0.0346858365 0.6115046939 52.213982982809995 0.44352030561 14.17030298056 0.11817042216 ",
"8.877 1.66191235556 10.202950777509999 2.7018080554500004 0.65431385574 0.8630518573 0.03842550936 55.18740808596 0.03100906854 0.43636326995 44.94372991982 0.39590424030000004 11.87915174242 0.12055380252 ",
"9.342 1.79446423883 9.223736902639999 2.82086676673 0.46928184702 0.25036756575999997 0.0261580927 44.218411563610005 0.02307492312 0.37136132062 34.8715067452 0.23048150172999998 9.83227607934 0.08369892896 ",
"9.807 2.02936743557 10.926146897779999 3.40942786382 0.4943626997 0.41616779600000003 0.020775668350000002 57.63290012324 0.02453204368 0.59384364452 41.567799898539995 0.37314942280999996 11.524438278929999 0.10958751164 ",
"10.272 1.96339182674 11.26182399865 3.6725323889699997 0.51033452936 0.38195138421 0.02349833226 68.82889744295 0.02130776115 0.56596184217 43.85824526299 0.3563165939 10.882216090570001 0.11761378372 ",
"10.734 1.69522699956 9.28644900764 3.09521900994 0.46748976233 0.25486955073 0.024124165519999997 56.23949510944 0.01806823073 NaN 36.05982284756 0.25298007274 8.86586676025 0.07157556397999999 ",
"11.199 1.87021691917 9.68208305253 3.2919709268000004 0.5270903229299999 0.42322311943 0.0327964463 59.54087126072 0.022691202100000002 0.47737124454999996 41.842194656909996 0.28466758982 11.91006067162 0.09872917755999999 ",
"11.664 2.23471175262 12.19887299453 3.6051454388299997 0.54389398527 0.54313316601 0.01479252184 63.04737515095 0.02776861734 0.48410774037 46.409590689800005 0.44546516811 12.6862786223 0.10475246765999999 ",
"12.129 1.58626042008 9.99097297113 3.7513050785999997 0.42925918395999996 0.44080053731999996 0.013039703409999999 54.332315720649994 0.02735050961 0.31900140121 39.37877381533 0.32147277213000003 10.09758737431 0.08824555916 ",
"13.134 1.09179740401 6.491353226309999 2.26052958713 0.5481437170200001 NaN 0.01664214786 49.99620609066 0.02341309598 NaN 32.90242406736 0.29822072293 7.4630383928 0.0570285326 ",
"13.599 1.3879954566700001 9.38597812226 3.67292411291 0.41493699655 0.35712124297000003 0.01725904447 49.842043735 0.025759816390000002 0.40749356039 36.65749859265 0.31221753187 8.49907762956 0.08041935436999999 ",
"14.064 1.46398956252 9.514586394050001 3.8008622291900003 0.48941052945 0.43946239095 0.01718538349 54.47374486523 0.02895368422 0.48952197341 40.26813532416 0.40474806982 9.42390966864 0.07994405853 ",
"14.529 1.38755206855 8.484590611449999 3.2508142214399998 0.4940005127 0.48337445569 0.01893519951 47.84282639242 0.026125384369999997 0.4407703548 41.362606442570005 0.27053364357 11.06193869222 0.1028701703 ",
"14.991 0.88675119894 6.09938485345 2.0966046727900003 0.51403159937 0.25811120551 0.0228857864 44.11169057688 0.021193232819999998 NaN 31.027494313570003 0.22906790417 7.52510837768 0.053950203 ",
"15.456 1.8352414024800001 8.469270941569999 3.3301897663099997 0.4957035248 0.26361946777 0.025116875570000002 36.08517318062 0.025039514409999998 0.38916760052 38.21581762714 0.2746950311 11.731711424339998 0.12009641433 ",
"15.921 1.3283163392300001 8.17980680802 3.0409809543 0.38135485122 0.31612171511000003 0.01517163163 32.72595199009 0.02671292861 0.34622186149 42.07802536984 0.2542175654 12.23730313735 0.13746139785 ",
"16.386 1.0127505969 4.742491586150001 1.84597095725 0.34553761760999996 NaN NaN 23.37116254961 NaN NaN NaN 0.15254582637 6.9247716185999995 0.07113160166999999 ",
"17.391 1.3310395982 7.94156759798 2.53879431682 0.48983728463 0.28795700924 0.026527055879999997 26.57827416636 0.02478540202 0.32631108383999996 33.95632215865 0.23618178628 10.48083524196 0.12515650878 ",
"17.856 0.95469028533 6.2343780867100005 2.17715476263 0.38729195765999996 0.28723013923 0.02123345169 24.49108607053 0.02618426224 0.29861161455 32.8592195428 0.2472225415 10.106162722479999 0.10923677735 ",
"18.321 NaN 5.49182392648 1.46886777519 0.34474738841999997 0.40646871383 0.01547566737 20.63720807926 0.02820014831 0.24058040600000002 40.71516409524 0.24270642545999999 14.12503277772 0.12899961175000002 ",
"18.786 0.82619105906 5.95530539146 2.299362329 0.44373946309 0.41075036223 0.02555677606 38.20802369971 0.02900285112 0.32959703389 34.213028316030005 0.25023635697 9.12222153162 0.10197379988000001 ",
"19.248 1.25881942787 6.53214282703 2.7302141736600003 0.50467802709 0.41526312678 0.02242466065 36.837815626659996 0.02483324467 0.38040913803 33.23783851764 0.23737761148 9.48079376972 0.11263783924000001 ",
"19.713 1.18507811227 5.4310205666 2.1360162267800002 0.46222885974 NaN 0.03397622205 28.19338823663 0.02104977904 NaN 25.47234120703 0.14638363385 7.60817737184 0.08542327693999999 ",
"20.178 0.98680703323 6.8729957498 2.34703189167 0.36189835991 0.35197395012 0.0170510439 26.43911126279 0.03024185129 0.30588350463 36.72827117308 0.23660664184000002 13.6943897464 0.16017717532 ",
"20.643 0.97377667062 6.15710096877 2.10492373702 0.46580796436000005 0.38984610765 0.020551778160000003 28.19084298145 0.02817732147 0.31660453741 33.563565688000004 0.17354025424 11.137671980450001 0.11278907400999999"};
private int dataFileLineCnt = dataFile.length;
private Map<String, Object> names;
public static void main(String args[]) {
new TimeSeries();
}
/**
* This constructs a new instance of this class from the given file. All
* entries of the given file are stored in a <java>String</java> matrix. The
* names of the metabolites are stored in a hash for easy retrival.
*
* @param fileName
*/
public TimeSeries() {
try {
int i = 0, j = 0;
this.names = new HashMap<String, Object>();
StringTokenizer st = new StringTokenizer(dataFile[0]);
// Lesen zum ersten Mal und initialisieren Matrix.
for (j = 0; st.hasMoreElements(); st.nextElement(), j++);
String data[][] = new String[dataFileLineCnt - 1][j];
// Jetzt in Matrix einlesen.
for (i = 0; i < dataFileLineCnt; i++) {
j = 0;
st = new StringTokenizer(dataFile[i]);
while (st.hasMoreElements())
if (i > 0)
data[i - 1][j++] = st.nextElement().toString();
else names.put(st.nextElement().toString().replaceAll("\"", ""),
new Integer(j++));
}
/*
* for (i=0; i<data.length; i++) { System.out.print("\n"+data[i][0]+" ");
* for (j=1; j<data[0].length; j++) { double val =
* Double.parseDouble(data[i][j])/1000; System.out.print(val+"\t"); } }
*/
/*
* names becomes a hash pointing to matrices.
*/
Iterator<String> iter = names.keySet().iterator();
while (iter.hasNext())
try {
double values[][];
int tcol = 0, count = 0;
String name = iter.next().toString();
i = 0;
j = ((Integer) names.get(name)).intValue();
for (i = 0; i < data.length; i++)
if (!data[i][j].equals("-1") && !data[i][j].equals("NaN")) count++;
values = new double[2][count];
// /*
// * The times are supposed to be the first column. However, this
// * enables the times to be any column.
// */
// try {
// tcol = ((Integer) this.names.get("Time")).intValue();
// } catch (Exception exc) {
// };
for (i = 0, count = 0; i < data.length; i++)
if (!data[i][j].equals("-1") && !data[i][j].equals("NaN")) {
values[0][count] = Double.parseDouble(data[i][tcol]);
values[1][count++] = Double.parseDouble(data[i][j]);
}
names.put(name, values);
} catch (Exception exc) {
exc.printStackTrace();
}
data = null;
} catch (Exception exc) {
exc.printStackTrace();
}
}
/**
* With this method one retrives the time data of the given metabolite or
* throws an <java>exception</java> if the given metabolite does not exist.
*
* @param metabolite
* The name of the metabolite, whose time data are desired.
* @return The time data.
* @throws Exception
* @throws If
* there is no entry for the desired metabolite, an exeption
* is thrown.
*/
public double[] getTimes(String metabolite) throws Exception {
if (names == null) System.err.println("getTimes: names is null");
if (!names.containsKey(metabolite))
throw new Exception("No data measured for this metabolite");
if (names.get(metabolite) == null) System.err.println("get(metabolite) is null");
return ((double[][]) names.get(metabolite))[0];
}
/**
* This method returns all measured times.
*
* @return An array containing all measurement times.
* @throws Exception
* If there is no column called "Time" in the data matrix.
*/
public double[] getTimes() throws Exception {
return getTimes("Time");
}
/**
* This method returns the concentration values of the given metabolite or
* throws an exception if there is no measurement for the desired metabolite.
*
* @param metabolite
* The name of the desired metabolite.
* @return an array of concentration values.
* @throws Exception
* @throws If
* there is no entry for the desired metabolite, an exception
* is thrown.
*/
public double[] getValues(String metabolite) throws Exception {
if (!names.containsKey(metabolite))
throw new Exception("No data measured for this metabolite");
return ((double[][]) names.get(metabolite))[1];
}
/**
* With this method it can be checked wheather measurements have been
* perforemd for the given metabolite.
*
* @param name
* The name of the metabolite
* @return <code>true</code> if an entry for this metabolite exists or
* <code>false</code> otherwise.
*/
public boolean containsMetabolite(String name) {
return names.keySet().contains(name);
}
/**
* Returns the number of measurements taken for this specific metabolite or
* zero if there are no measurements.
*
* @param name
* The name of the metabolite
* @return The number of available measurement data for this metabolite
*/
public int numberOfMeasurements(String name) {
try {
return getTimes(name).length;
} catch (Exception exc) {
return 0;
}
}
}

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src/eva2/server/go/problems/inference/metabolic/odes/AbstractValineSystem.java
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156
src/eva2/server/go/problems/inference/metabolic/odes/CKMMiSystem.java
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@@ -1,156 +0,0 @@
/**
*
*/
package eva2.server.go.problems.inference.metabolic.odes;
/**
* Created at 2007-02-03
*
* This class describes the valine/leucine biosynthesis in <it>Corynebacterium
* glutamicum</it> acording to the convenience kinetics as proposed by
* Liebermeister and Klipp. Three reaction velocities follow the traditional
* Michaelis Menten scheme the others are given in the convenience kinetics. All
* reactions are considered to be ehter reversible or irreversible according to
* what is written in the KEGG database.
*
* @author Andreas Dr&auml;ger
*
*/
public class CKMMiSystem extends AbstractValineSystem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = 8595111459805099502L;
/**
*
*/
public CKMMiSystem() {
this.p = null;
}
/**
* @param params
* The parameters of this system.
*/
public CKMMiSystem(double[] params) {
this.p = params;
}
/*
* (non-Javadoc)
*
* @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
// double[] kcat = new double[9]; // -> p[0..9]
// double[] ki = new double[3]; // -> p[9..11]
// double[] km = new double[16]; // -> p[12..27]
// double[] vm = new double[3]; // -> p[28..30]
// double[] kmm = new double[3]; // -> p[31..33]
// double[] kia = new double[3]; // -> p[34..36]
// double[] kib = new double[3]; // -> p[37..39]
// double acCoA = 0; // -> p[40]
double pyr = getPyr_2(t), nadp = getNADP_2(t), nadph = 0.04 - nadp, glut = getGlut_2(t), ala = getAla_2(t);
/*
* Y[0] DHIV Y[1] IPM Y[2] AcLac Y[3] Val Y[4] Leu Y[5] KIV Y[6] KIC
*/
return linearCombinationOfVelocities(new double[] {
/* Korrigiert: */
// v_1: AHAS: convenience (ge<67>ndert)
(p[0] * p[9] * Math.pow(pyr / p[12], 2))
/ ((1 + pyr / p[12] + Math.pow(pyr / p[12], 2)) * (p[9] + Y[3])),
// v_2: AHAIR: convenience (ge<67>ndert)
(p[1] * Y[2] / p[13] * nadph / p[14] * p[10] - p[2] * Y[0]
/ p[15] * nadp / p[16] * p[10])
/ ((1 + Y[2] / p[13] + nadph / p[14] + (Y[2] * nadph)
/ (p[13] * p[14]) + Y[0] / p[15] + nadp / p[16] + (Y[0] * nadp)
/ (p[15] * p[16])) * (p[10] + Y[3])),
// v_3: DHAD: reversible Michaelis Menten with two inhibitions
// (ge<67>ndert)
(p[28] * Y[0])
/ (p[31] + Y[0] + Y[3] * (p[31] * p[34] + p[37] * Y[0])),
// v_4: BCAAT_ValB: convenience (ge<67>ndert)
(p[3] * Y[5] / p[17] * glut / p[18])
/ (1 + Y[5] / p[17] + glut / p[18] + (Y[5] * glut)
/ (p[17] * p[18])),
// v_5: BCAAT_ValC: convenience (ge<67>ndert)
(p[4] * Y[5] / p[19] * ala / p[20])
/ (1 + Y[5] / p[19] + ala / p[20] + (Y[5] * ala)
/ (p[19] * p[20])),
// v_6: Trans_Val: irreversible Michaelis Menten with two
// inhibitions
(p[29] * Y[3])
/ (p[32] + Y[3] + p[32] * p[35] * Y[4] + p[38] * Y[3]
* Y[4]),
// v_7: IPMS: convenience (AcCoA/K_m = p40) (ge<67>ndert)
(p[5] * Y[5] / p[21] * p[40] * p[11])
/ ((1 + Y[5] / p[21] + p[40] + p[40] * Y[5] / p[21]) * (p[11] + Y[4])),
// v_8: IPMDH: convenience (ge<67>ndert)
(p[6] * Y[1] / p[22] * nad / p[23])
/ (1 + Y[1] / p[22] + nad / p[23] + (Y[1] * nad)
/ (p[22] * p[23])),
// v_9: BCAAT_LeuB: convenience (ge<67>ndert)
(p[7] * Y[6] / p[24] * glut / p[25] - p[8] * Y[4] / p[26] * akg
/ p[27])
/ (1 + Y[6] / p[24] + glut / p[25] + (Y[6] * glut)
/ (p[24] * p[25]) + Y[4] / p[26] + akg / p[27] + (Y[4] * akg)
/ (p[26] * p[27])),
// v_10: Trans_Leu: irreversible Michaelis Menten with two
// inhibitions
(p[30] * Y[4])
/ (p[33] + Y[4] + p[33] * p[36] * Y[3] + p[39] * Y[3]
* Y[4])
/*
* zuvor: // AHAS: convenience (p[0] * pyr)/(p[9] * (1 + pyr/p[12] +
* Math.pow(pyr/p[12], 2)) + Y[3] * (1 + pyr/p[12] + Math.pow(pyr/p[12],
* 2))), // AHAIR: convenience (p[1] * Y[2] * nadph - p[2] * Y[0] *
* nadp)/((1 + Y[2]/p[13] + nadph/p[14] + (Y[2]*nadph)/(p[13]*p[14]) +
* Y[0]/p[15] + nadp/p[16] + (Y[0] * nadp)/(p[15] * p[16])) * p[10] *
* Y[3]), // DHAD: reversible Michaelis Menten with two inhibitions
* (p[28]* Y[0])/(p[31] + Y[0] + Y[3] * (p[28] * p[34] + p[37] * Y[0])), //
* BCAAT_ValB: convenience (p[3] * Y[5] * glut)/(1 + Y[5]/p[17] +
* glut/p[18] + (Y[5] * glut)/(p[17] * p[18])), // BCAAT_ValC:
* convenience (p[4] * Y[5] * ala)/(1 + Y[5]/p[19] + ala/p[20] + (Y[5] *
* ala)/(p[19] * p[20])), // Trans_Val: irreversible Michaelis Menten
* with two inhibitions (p[29] * Y[3])/(p[32] + Y[3] + p[32] * p[35] *
* Y[4] + p[38] * Y[3] * Y[4]), // IPMS: convenience (p[5] * Y[5])/((1 +
* Y[5]/p[21] + p[40] + (p[40] * Y[5])/p[21]) * (p[11] + Y[4])), //
* IPMDH: convenience (p[6] * Y[1] * nad)/(1 + Y[1]/p[22] + nad/p[23] +
* (Y[1] * nad)/(p[22] * p[23])), // BCAAT_LeuB: convenience (p[7] *
* Y[6] * glut - p[8] * Y[4] * akg)/(1 + Y[6]/p[24] + glut/p[25] + (Y[6] *
* glut)/(p[24] * p[25]) + Y[4]/p[26] + akg/p[27] + (Y[4] * akg)/(p[26] *
* p[27])), // Trans_Leu: irreversible Michaelis Menten with two
* inhibitions (p[30] * Y[4])/(p[33] + Y[4] + p[33] * p[36] * Y[3] +
* p[39] * Y[3] * Y[4])
*/
});
}
public void getValue(double t, double[] Y, double[] resultVector) {
double tmp[] = getValue(t, Y);
System.arraycopy(tmp, 0, resultVector, 0, tmp.length);
}
@Override
public int getNumberOfParameters() {
return 41;
}
}

117
src/eva2/server/go/problems/inference/metabolic/odes/CKMMrSystem.java
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@@ -1,117 +0,0 @@
package eva2.server.go.problems.inference.metabolic.odes;
/**
* This class describes the valine/leucine biosynthesis in <it>Corynebacterium
* glutamicum</it> acording to the convenience kinetics as proposed by
* Liebermeister and Klipp. Three reaction velocities follow the traditional
* Michaelis Menten scheme the others are given in the convenience kinetics.
* However, in this class all reactions are assumed to be reversible.
*
* @since 2.0
* @version
* @author Andreas Dr&auml;ger (draeger) <andreas.draeger@uni-tuebingen.de>
* Copyright (c) ZBiT, University of T&uuml;bingen, Germany Compiler:
* JDK 1.6.0
* @date 2007-02-05
*/
public class CKMMrSystem extends AbstractValineSystem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = 726678613269764327L;
transient protected double velocities[] = null;
/**
* @param x
*/
public CKMMrSystem(double[] x) {
this.p = x;
}
public CKMMrSystem() {
super();
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
double[] res = new double[7];
getValue(t, Y, res);
return res;
}
public void getValue(double t, double[] Y, double[] res) {
double pyr = getPyr_2(t), nadp = getNADP_2(t), nadph = 0.04 - nadp, glut = getGlut_2(t), akg = getAKG_2(t), ala = getAla_2(t), nad = getNAD_2(t), nadh = 0.8 - nad;
if (velocities == null)
velocities = new double[10];
// v_1
velocities[0] = ((p[0] * p[44] * Math.pow(pyr, 2)) / Math.pow(p[14], 2) - (p[7]
* p[44] * Y[2])
/ p[15])
/ ((1 + pyr / p[14] + Math.pow(pyr / p[14], 2) + Y[2] / p[15]) * (p[44] + Y[3]));
// v_2
velocities[1] = ((p[1] * p[45] * Y[2] * nadph) / (p[16] * p[17]) - (p[8]
* p[45] * Y[0] * nadp)
/ (p[18] * p[19]))
/ ((1 + Y[2] / p[16] + nadph / p[17] + (Y[2] * nadph)
/ (p[16] * p[17]) + Y[0] / p[18] + nadp / p[19] + (Y[0] * nadp)
/ (p[18] * p[19])) * (p[45] + Y[3]));
// v_3
velocities[2] = ((p[47] * Y[0]) / p[20] - (p[48] * Y[5]) / p[21])
/ (1 + p[51] * Y[3] + (Y[0] / p[20] + Y[5] / p[21])
* (1 + p[54] * Y[3]));
// v_4
velocities[3] = ((p[2] * Y[5] * glut) / (p[22] * p[23]) - (p[9] * Y[3] * akg)
/ (p[24] * p[25]))
/ (1 + Y[5] / p[22] + glut / p[23] + (Y[5] * glut)
/ (p[22] * p[23]) + Y[3] / p[24] + akg / p[25] + (Y[3] * akg)
/ (p[24] * p[25]));
// v_5
velocities[4] = ((p[3] * Y[5] * ala) / (p[26] * p[27]) - (p[10] * Y[3] * pyr)
/ (p[28] * p[29]))
/ (1 + Y[5] / p[26] + ala / p[27] + (Y[5] * ala)
/ (p[26] * p[27]) + Y[3] / p[28] + pyr / p[29] + (Y[3] * pyr)
/ (p[28] * p[29]));
// v_6
velocities[5] = (p[49] * Y[3])
/ (p[30] + p[30] * p[52] * Y[4] + Y[3] + p[55] * Y[3] * Y[4]);
// v_7
velocities[6] = ((p[4] * p[46] * Y[5] * p[57]) / (p[31] * p[32]) - (p[11]
* p[46] * Y[1] * p[58])
/ (p[33] * p[34]))
/ ((1 + Y[5] / p[31] + p[57] / p[32] + (Y[5] * p[57])
/ (p[31] * p[32]) + Y[1] / p[33] + p[58] / p[34] + (Y[1] * p[58])
/ (p[33] * p[34])) * (p[46] + Y[4]));
// v_8
velocities[7] = ((p[5] * Y[1] * nad) / (p[35] * p[36]) - (p[12] * Y[6] * nadh)
/ (p[37] * p[38]))
/ (1 + Y[1] / p[35] + nad / p[36] + (Y[1] * nad)
/ (p[35] * p[36]) + Y[6] / p[37] + nadh / p[38] + (Y[6] * nadh)
/ (p[37] * p[38]));
// v_9
velocities[8] = ((p[6] * Y[6] * glut) / (p[39] * p[40]) - (p[13] * Y[4] * akg)
/ (p[41] * p[42]))
/ (1 + Y[6] / p[39] + glut / p[40] + (Y[6] * glut)
/ (p[39] * p[40]) + Y[4] / p[41] + akg / p[42] + (Y[4] * akg)
/ (p[41] * p[42]));
// v_10
velocities[9] = (p[50] * Y[4])
/ (p[43] + p[43] * p[53] * Y[3] + Y[4] + p[56] * Y[3] * Y[4]);
linearCombinationOfVelocities(velocities, res);
}
@Override
public int getNumberOfParameters() {
return 59;
}
}

69
src/eva2/server/go/problems/inference/metabolic/odes/GMAKiSystem.java
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@@ -1,69 +0,0 @@
/**
*
*/
package eva2.server.go.problems.inference.metabolic.odes;
// Created ad 2007-01-13
/**
* In this class the valine/leucine biosynthesis in Corynebacterium glutamicum
* is modeled to be in many reactions irreversible according to what is written
* in the KEGG database. Inhibition is modeled as negative exponential function.
*
* @author Andreas Dr&auml;ger
*
*/
public class GMAKiSystem extends AbstractValineSystem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = -5893476466596099997L;
/**
*
* @param params
*/
public GMAKiSystem(double params[]) {
this.p = params;
}
public GMAKiSystem() {
this.p = null;
}
/*
* (non-Javadoc)
*
* @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
return linearCombinationOfVelocities(new double[] {
p[0] * Math.pow(getPyr_2(t), 2)
* Math.exp((-p[12]) * Y[3]),
(p[1] * Y[2] * (0.04 - getNADP_2(t)) - p[10] * Y[0]
* getNADP_2(t))
* Math.exp((-p[13]) * Y[3]),
p[2] * Y[0] * Math.exp((-p[14]) * Y[3]),
p[3] * getGlut_2(t) * Y[5],
p[4] * getAla_2(t) * Y[5],
p[5] * Y[3] * Math.exp((-p[15]) * Y[4]),
p[6] * Y[5] * Math.exp((-p[16]) * Y[4]),
p[7] * getNAD_2(t) * Y[1],
p[8] * getGlut_2(t) * Y[6] - p[11] * getAKG_2(t)
* Y[4],
p[9] * Y[4] * Math.exp((-p[17]) * Y[3]) });
}
public void getValue(double t, double[] Y, double[] resultVector) {
double tmp[] = getValue(t, Y);
System.arraycopy(tmp, 0, resultVector, 0, tmp.length);
}
@Override
public int getNumberOfParameters() {
return 18;
}
}

116
src/eva2/server/go/problems/inference/metabolic/odes/GMAKrSystem.java
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@@ -1,116 +0,0 @@
package eva2.server.go.problems.inference.metabolic.odes;
/**
* This class describes the valine/leucine biosynthesis in Corynebacterium
* glutamicum. All 10 reactions are assumed to be reversible and modeled
* following the generalized mass action kinetic, where inhibition is modeled
* using the function 1/(1 + ki*[Inhibitor]).
*
* @date 2007-02-04
* @author Andreas Dr&auml;ger
*/
public class GMAKrSystem extends AbstractValineSystem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = -5668558229555497614L;
transient double[] veloc = null;
public GMAKrSystem() {
super();
}
/**
* @param params
*/
public GMAKrSystem(double params[]) {
super();
this.p = params;
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
// a <- params[0..9]
// b <- params[10..17]
// g <- params[18..23]
double[] res = new double[7];
getValue(t, Y, res);
return res;
// return linearCombinationOfVelocities(new double[] {
// (params[0] * Math.pow(getPyr_2(t), 2) - params[10] * Y[2])/ (1 +
// params[18] * Y[3]), // Math.exp((-params[18]) * Y[3]),
// (params[1] * Y[2] * (.04 - getNADP_2(t)) - params[11] * Y[0] *
// getNADP_2(t)) / (1 + params[19] * Y[3]), // * Math.exp((-params[19])
// *
// Y[3]),
// (params[2] * Y[0] - params[12] * Y[5]) / (1 + params[20] * Y[3]), //
// *
// // Math.exp((-params[20])
// // *
// // Y[3]),
// params[3] * getGlut_2(t) * Y[5] - params[13] * getAKG_2(t) * Y[3],
// params[4] * getAla_2(t) * Y[5] - params[14] * getPyr_2(t) * Y[3],
// (params[5] * Y[3]) / (1 + params[21] * Y[4]), // *
// // Math.exp((-params[21])
// // * Y[4]),
// (params[6] * Y[5] - params[15] * Y[1]) / (1 + params[22] * Y[4]), //
// *
// // Math.exp((-params[22])
// // *
// // Y[4]),
// params[7] * getNAD_2(t) * Y[1] - params[16] * Y[6] * (.8 -
// getNAD_2(t)),
// params[8] * getGlut_2(t) * Y[6] - params[17] * getAKG_2(t) * Y[4],
// (params[9] * Y[4]) / (1 + params[23] * Y[3]) // *
// // Math.exp((-params[23])
// // * Y[3])
// });
}
public void getValue(double t, double[] Y, double[] res) {
if (veloc == null)
veloc = new double[10];
veloc[0] = (p[0] * Math.pow(getPyr_2(t), 2) - p[10] * Y[2])
/ (1 + p[18] * Y[3]); // Math.exp((-params[18]) * Y[3]),
veloc[1] = (p[1] * Y[2] * (.04 - getNADP_2(t)) - p[11] * Y[0]
* getNADP_2(t))
/ (1 + p[19] * Y[3]); // * Math.exp((-params[19]) * Y[3]),
veloc[2] = (p[2] * Y[0] - p[12] * Y[5])
/ (1 + p[20] * Y[3]); // *
// Math.exp((-params[20])
// *
// Y[3]),
veloc[3] = p[3] * getGlut_2(t) * Y[5] - p[13] * getAKG_2(t)
* Y[3];
veloc[4] = p[4] * getAla_2(t) * Y[5] - p[14] * getPyr_2(t)
* Y[3];
veloc[5] = (p[5] * Y[3]) / (1 + p[21] * Y[4]); // *
// Math.exp((-params[21])
// * Y[4]),
veloc[6] = (p[6] * Y[5] - p[15] * Y[1])
/ (1 + p[22] * Y[4]); // *
// Math.exp((-params[22])
// *
// Y[4]),
veloc[7] = p[7] * getNAD_2(t) * Y[1] - p[16] * Y[6]
* (.8 - getNAD_2(t));
veloc[8] = p[8] * getGlut_2(t) * Y[6] - p[17] * getAKG_2(t)
* Y[4];
veloc[9] = (p[9] * Y[4]) / (1 + p[23] * Y[3]); // *
linearCombinationOfVelocities(veloc, res);
}
@Override
public int getNumberOfParameters() {
return 24;
}
}

120
src/eva2/server/go/problems/inference/metabolic/odes/GMMiSystem.java
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@@ -1,120 +0,0 @@
package eva2.server.go.problems.inference.metabolic.odes;
// Created at 2007-01-18
/**
* This class implements a system of ordinary differential equations describing
* the valine and leucin biosynthesis in <it>Corynebacterium gluthamicum</i>.
*
* @author Andreas Dr&auml;ger
*/
public class GMMiSystem extends AbstractValineSystem {
/**
* Generated version id.
*/
private static final long serialVersionUID = 6119930633376953563L;
/**
* <p>
* A vector of parameters it needed to initialize this class. The parameters
* are given as:
* </p>
* <ul>
* <li>params[0-6] = k, Rate constant for the forward reaction</li>
* <li>params[7-8] = l, Rate constant for the backward reaction</li>
* <li>params[9-11] = ki, Rate constant for inhibition of uncertain
* mechanism</li>
* <li>params[12-14] = vm, Maximal reaction velocity</li>
* <li>params[15-17] = kia, Rate constant for inhibition of the enzyme</li>
* <li>params[18-20] = kib, Rate constant for inhibition of the enzyme
* substrate complex</li>
* <li>params[21-23] = km, the Michaelis-Menten constant</li>
* </ul>
*
* @param params
*/
public GMMiSystem(double[] params) {
if (params != null)
if (params.length != 24)
throw new Error(
"This ODE system needs exactly 24 parameters. Given are "
+ params.length);
this.p = params;
}
/**
*
*
*/
public GMMiSystem() {
this.p = null;
}
/*
* (non-Javadoc)
*
* @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
double[] v = new double[] {
// v_1
(p[0] * Math.pow(getPyr_2(t), 2)) / (1 + p[9] * Y[3]),
// v_2
(p[1] * Y[2] * (0.04 - getNADP_2(t)) - p[7] * Y[0]
* getNADP_2(t))
/ (1 + p[10] * Y[3]),
// v_3
// ((p[12] * Y[0])/p[21])/(1 + p[15] * Y[3] + (Y[0] + p[18] *
// Y[3] *
// Y[0])/p[21]),
(p[12] * Y[0])
/ (p[21] + Y[0] + p[21] * p[15] * Y[3] + p[18] * Y[0]
* Y[3]),
// v_4
p[2] * Y[5] * getGlut_2(t),
// v_5
p[3] * Y[5] * getAla_2(t),
// v_6
// (p[13]/p[22] * Y[3])/(1 + p[16] * Y[4] + (Y[3] + p[19] * Y[4]
// *
// Y[3])/p[22]),
(p[13] * Y[3])
/ (p[22] + Y[3] + p[22] * p[16] * Y[4] + p[19] * Y[3]
* Y[4]),
// v_7
(p[4] * Y[5]) / (1 + p[11] * Y[4]),
// v_8
p[5] * Y[1] * getNAD_2(t),
// v_9
p[6] * Y[6] * getGlut_2(t) - p[8] * Y[4] * getAKG_2(t),
// v_10
// ((p[14] * Y[4])/p[23])/(1 + p[17] * Y[3] + (Y[4] + p[20] *
// Y[3] *
// Y[4])/p[23])
(p[14] * Y[4])
/ (p[23] + Y[4] + p[17] * p[23] * Y[3] + p[20] * Y[3]
* Y[4]) };
/*
* if ((Double.isNaN(v[0]) || Double.isInfinite(v[0])) && (t < -1.784)) {
* System.out.println(t+". Nenner: "+(1 + p[9] * Y[3])+"\tY[3] =
* "+Y[3]+"\tparam9 = "+p[9]); }//
*/
return linearCombinationOfVelocities(v);
}
public void getValue(double t, double[] Y, double[] resultVector) {
double tmp[] = getValue(t, Y);
System.arraycopy(tmp, 0, resultVector, 0, tmp.length);
}
@Override
public int getNumberOfParameters() {
return 24;
}
}

87
src/eva2/server/go/problems/inference/metabolic/odes/GMMrSystem.java
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@@ -1,87 +0,0 @@
package eva2.server.go.problems.inference.metabolic.odes;
/**
* This class describes the valine/leucine biosynthesis in Corynebacterium
* gluthamicum where three reactions are modeled using traditional reversible
* Michaelis Menten kinetics and the remaining reactions are modeled using
* reversible generalized mass action kinetics, where inhibition is modeled
* using 1/(1 + ki*[Inhibitor]). Created at 2007-02-04.
*
* @author Andreas Dr&auml;ger
*/
public class GMMrSystem extends AbstractValineSystem {
/**
* Generated serial id.
*/
private static final long serialVersionUID = 7461506193192163451L;
/**
*
*/
public GMMrSystem() {
}
/**
* @param params
*/
public GMMrSystem(double[] params) {
this.p = params;
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double,
* double[])
*/
public double[] getValue(double t, double[] Y) {
/*
* double[] a = null, // <- params[0..6] 7 b = null, // <- params[7..13]
* 7 vm = null, // <- params[14..17] 4 km = null, // <- params[18..21] 4
* kia = null, // <- params[22..24] 3 kib = null, // <- params[25..27] 3
* ki = null; // <- params[28..30] 3 //
*/
return linearCombinationOfVelocities(new double[] {
// v_1
(p[0] * Math.pow(getPyr_2(t), 2) - p[7] * Y[2])
/ (1 + p[28] * Y[3]),
// v_2
(p[1] * Y[2] * (.04 - getNADP_2(t)) - p[8] * Y[0]
* getNADP_2(t))
/ (1 + p[29] * Y[3]),
// v_3
((p[14] * Y[0]) / p[18] - (p[15] * Y[5]) / p[19])
/ (1 + p[22] * Y[3] + (Y[0] / p[18] + Y[5] / p[19])
* (1 + p[25] * Y[3])),
// v_4
p[2] * Y[5] * getGlut_2(t) - p[9] * Y[3] * getAKG_2(t),
// v_5
p[3] * Y[5] * getAla_2(t) - p[10] * Y[3] * getPyr_2(t),
// v_6
(p[16] * Y[3])
/ (p[20] + p[20] * p[23] * Y[4] + Y[3] + p[26] * Y[4]
* Y[3]),
// v_7
(p[4] * Y[5] - p[11] * Y[1]) / (1 + p[30] * Y[4]),
// v_8
p[5] * Y[1] * getNAD_2(t) - p[12] * Y[6] * (.8 - getNAD_2(t)),
// v_9
p[6] * Y[6] * getGlut_2(t) - p[13] * Y[4] * getAKG_2(t),
// v_10
(p[17] * Y[4])
/ (p[21] + p[21] * p[24] * Y[3] + Y[4] + p[27] * Y[3]
* Y[4]) });
}
public void getValue(double t, double[] Y, double[] resultVector) {
resultVector = getValue(t, Y);
}
@Override
public int getNumberOfParameters() {
return 31;
}
}