private PhysicalObject findPhysicalNode(double[] actualCoord) {
PhysicalObject node = null;
double distance = Double.MAX_VALUE;
for (PhysicalNode pn : ECM.getInstance().physicalNodeList) {
if (!pn.isAPhysicalObject()) continue;
double[] coordOfPn = pn.getSoNode().getPosition();
double ypn = coordOfPn[1];
// if it is a slice view, we can exclude everyone outside of the slice
if (view.representationType == View.OM_SLICE_TYPE
|| view.representationType == View.EM_SLICE_TYPE) {
if (ypn > (view.sliceYPosition + 15) || ypn < (view.sliceYPosition - 15)) {
continue;
}
}
double[] mySomaMassLocation = Matrix.mult(view.V, coordOfPn);
double[] newCoord = view.getDisplayCoord(mySomaMassLocation);
double distancepn = Matrix.distance(newCoord, actualCoord);
if (distancepn < distance) {
distance = distancepn;
node = (PhysicalObject) pn;
}
}
return node;
}
public static void main(String[] args) {
ECM.setRandomSeed(2L);
Cell c = CellFactory.getCellInstance(new double[] {0.0, 0.0, 0.0});
c.addCellModule(new DividingModule());
Scheduler.simulateOneStep();
Scheduler.simulateOneStep();
ini.cx3d.utilities.SystemUtilities.tic();
for (int i = 0; i < 5000; i++) {
Scheduler.simulateOneStep();
}
ini.cx3d.utilities.SystemUtilities.tac();
}
public static void main(String[] args) {
// 1) Prepare the environment :
// get a reference to the extracelular matrix (ECM)
ECM ecm = ECM.getInstance();
// add additional PhysicalNodes (for diffusion)
int nbOfAdditionalNodes = 100;
for (int i = 0; i < nbOfAdditionalNodes; i++) {
double[] coord = randomNoise(500, 3);
ecm.getPhysicalNodeInstance(coord);
}
// 2) Create some artificial chemical gradients
// horizontal gaussian (peak concentration, peak coordinate, variance)
ecm.addArtificialGaussianConcentrationZ(new Substance("A", Color.red), 1, 300, 100);
ecm.addArtificialGaussianConcentrationZ(new Substance("B", Color.blue), 1, 00, 100);
ecm.addArtificialGaussianConcentrationZ(new Substance("C", Color.green), 1, -300, 100);
// horizontal linerar gradient
ecm.addArtificialLinearConcentrationZ(new Substance("D", Color.cyan), 1, 300, -300);
// vertical gaussian
ecm.addArtificialGaussianConcentrationX(new Substance("E", Color.red), 1, 0, 100);
ecm.addArtificialGaussianConcentrationX(new Substance("F", Color.green), 1, 300, 100);
// 3) Create a 4-uple Cell-SomaElement-PhysicalSphere-SpaceNode at the desired location
double[] cellLocation = new double[] {0, 0, 0};
Cell cell = CellFactory.getCellInstance(cellLocation);
cell.setColorForAllPhysicalObjects(Param.RED);
// 4) Extend an axon from the cell
NeuriteElement neurite = cell.getSomaElement().extendNewNeurite(new double[] {0, 0, 1});
neurite.getPhysical().setDiameter(1.0);
neurite.getPhysicalCylinder().setDiameter(3);
// 5) Put a movementReceptor
X_Movement_Module r = new X_Movement_Module();
r.linearDiameterDecrease = 0.01;
r.addAttractant("A");
neurite.addLocalBiologyModule(r);
// 6) Simulate
Scheduler.simulate();
}
public void run() {
PhysicalCylinder cyl = (PhysicalCylinder) cellElement.getPhysical();
// Juste temporary for getting a picture :
// if(cyl.getMassLocation()[2]<-100){
// return;
// }
double lengthBefore = cyl.getLength();
// not to thin?
if (cyl.getDiameter() < minimalBranchDiameter) {
if (cyl.lengthToProximalBranchingPoint()
> 7 + 10 * ECM.getRandomDouble()) { // so we don't end with short segments
return;
}
}
// can't leave
if (cantLeave != null) {
double currentCantLeaveConcntration = cyl.getExtracellularConcentration(cantLeave);
if (currentCantLeaveConcntration > maxConcentration) {
maxConcentration = currentCantLeaveConcntration;
}
if (currentCantLeaveConcntration < 0.95 * maxConcentration) {
cyl.setColor(Color.black);
return;
}
}
// find the gradients
double[] totalGradient = {0.0, 0.0, 0.0};
for (String s : attractants) {
double[] normalizedGrad = normalize(cyl.getExtracellularGradient(s));
totalGradient = add(totalGradient, normalizedGrad);
}
for (String s : repellents) {
double[] normalizedAntiGrad = scalarMult(-1.0, normalize(cyl.getExtracellularGradient(s)));
totalGradient = add(totalGradient, normalizedAntiGrad);
}
// if no gradient : go straight
if (attractants.isEmpty() && repellents.isEmpty()) {
totalGradient = movementDirection;
}
double[] newStepDirection =
add(
movementDirection,
scalarMult(directionWeight, totalGradient),
randomNoise(randomness, 3));
cyl.movePointMass(speed, newStepDirection);
movementDirection = normalize(add(scalarMult(5, movementDirection), newStepDirection));
// decrease diameter setDiameter
double lengthAfter = cyl.getLength();
double deltaL = lengthAfter - lengthBefore;
if (deltaL < 0) deltaL = 0;
cyl.setDiameter(cyl.getDiameter() * (1 - deltaL * linearDiameterDecrease));
}
public static void main(String[] args) {
Param.NEURITE_MAX_LENGTH = 20;
double pi = Math.PI;
// get a 2.5D ECM
ECM ecm = ECM.getInstance();
ECM.setRandomSeed(5L);
ecm.setArtificialWallsForCylinders(true);
ecm.setArtificialWallsForSpheres(true);
ecm.setBoundaries(-10000, 10000, -10000, 10000, -5, 5);
// eighteen extra PhysicalNodes :
for (int i = 0; i < 12; i++) {
double[] loc = concat(randomNoise(600, 2), randomNoise(100, 1));
ecm.getPhysicalNodeInstance(loc);
}
// set the inter object force
X_Adhesive_Force nogo = new X_Adhesive_Force();
nogo.setAttractionRange(3);
nogo.setAttractionStrength(5);
PhysicalObject.setInterObjectForce(nogo);
// generate cells
int nbOfCells = 20;
int minNbOfNeurites = 4;
int maxNbOfNeurites = 8;
for (int i = 0; i < nbOfCells; i++) {
Color c = Param.X_SOLID_GRAY;
double[] cellLocation =
new double[] {
-200 + ECM.getRandomDouble() * 400,
-200 + ECM.getRandomDouble() * 400,
-5 + ECM.getRandomDouble() * 10
};
if (i == 0) {
c = Param.X_SOLID_RED;
cellLocation = new double[] {0, 0, 0};
}
Cell cell = CellFactory.getCellInstance(cellLocation);
SomaElement soma = cell.getSomaElement();
PhysicalSphere sphere = soma.getPhysicalSphere();
if (i == 0) {
cell.setNeuroMLType(Cell.InhibitoryCell);
} else {
cell.setNeuroMLType(Cell.ExcitatoryCell);
}
sphere.setColor(c);
sphere.setAdherence(100);
int nbOfNeurites =
minNbOfNeurites + ((int) ((maxNbOfNeurites - minNbOfNeurites) * ECM.getRandomDouble()));
for (int j = 0; j < nbOfNeurites; j++) {
double angleOfAxon = pi * 2 * ECM.getRandomDouble();
double growthSpeed = 75;
double probaToBranch = 0.003;
double linearDiameterDecrease = 0.001;
NeuriteElement ne;
if (j == 0) {
ne = cell.getSomaElement().extendNewNeurite(3.0, Math.PI * 0.5, angleOfAxon);
ne.setIsAnAxon(true);
growthSpeed = 150;
probaToBranch = 0.009;
linearDiameterDecrease = 0;
ne.getPhysicalCylinder().setDiameter(1.5);
} else if (j == 1) {
ne =
cell.getSomaElement()
.extendNewNeurite(
3.0, Math.PI * 0.5, angleOfAxon + Math.PI - 0.5 + ECM.getRandomDouble());
ne.setIsAnAxon(false);
} else {
ne =
cell.getSomaElement()
.extendNewNeurite(3.0, Math.PI * 0.5, Math.PI * 2 * ECM.getRandomDouble());
ne.setIsAnAxon(false);
}
X_Bifurcation_Module br = new X_Bifurcation_Module();
br.shift = probaToBranch;
ne.addLocalBiologyModule(br);
X_Movement_Module mr = new X_Movement_Module();
mr.setRandomness(0.7);
mr.setSpeed(growthSpeed);
mr.setLinearDiameterDecrease(linearDiameterDecrease);
ne.addLocalBiologyModule(mr);
}
}
for (int i = 0; i < 350; i++) { // 350
Scheduler.simulateOneStep();
}
TestSynapses.extendExcressencesAndSynapseOnEveryNeuriteElement(0.4);
Exporter.saveExport();
}