Jan Verwer CWI and Univ. of Amsterdam A Scientific Computing Framework for Studying Axon Guidance Computational Neuroscience Meeting, NWO, December 9, 2005 Centrum voor Wiskunde en Informatica
Scientific Computing
Computer based applied mathematics
Scientific Computing Computer based applied mathematics, involving Modelling Analysis Simulation
Scientific Computing Computer based applied mathematics, involving Modelling Prescription of a given problem in formulas, relations, equations. Approximating reality. Here the application is prominent. Analysis Simulation
Scientific Computing Computer based applied mathematics, involving Modelling Prescription of a given problem in formulas, relations, equations. Approximating reality. Here the application is prominent. Analysis Study of mathematical and numerical issues (stability, conservation rules, etc). Here the mathematics is prominent. Simulation
Scientific Computing Computer based applied mathematics, involving Modelling Prescription of a given problem in formulas, relations, equations. Approximating reality. Here the application is prominent. Analysis Study of mathematical and numerical issues (stability, conservation rules, etc). Here the mathematics is prominent. Simulation Programming, benchmark selection, testing, visualization, interpretation. Here the computer is prominent.
Scientific Computing Computer based applied mathematics, involving Modelling Prescription of a given problem in formulas, relations, equations. Approximating reality. Here the application is prominent. Analysis Study of mathematical and numerical issues (stability, conservation rules, etc). Here the mathematics is prominent. Simulation Programming, benchmark selection, testing, visualization, interpretation. Here the computer is prominent.
Scientific Computing Computer based applied mathematics, involving ModellingThis is critical. AnalysisThis is fun. Simulation This is hard work.
Axon Guidance
Results from the PhD thesis of J. Krottje (CWI): On the numerical solution of diffusion systems with localized, gradient-driven moving sources, UvA, November 17, 2005 Axon Guidance
Joint project between CWI (Verwer), NIBR (van Pelt) and VU (van Ooyen), carried out at CWI and funded by Results from the PhD thesis of J. Krottje (CWI): On the numerical solution of diffusion systems with localized, gradient-driven moving sources, UvA, November 17, 2005 Axon Guidance
Axon Guidance Modelling
A first PDE model was built by Hentschel & van Ooyen ‘99 The model moves particles (axon heads) in attractant-repellent gradient fields Axon Guidance Modelling
A first PDE model was built by Hentschel & van Ooyen ‘99 The model moves particles (axon heads) in attractant-repellent gradient fields Axon Guidance Modelling
A first PDE model was built by Hentschel & van Ooyen ‘99 The model moves particles (axon heads) in attractant-repellent gradient fields Axon Guidance Modelling
A first PDE model was built by Hentschel & van Ooyen ‘99 The model moves particles (axon heads) in attractant-repellent gradient fields Axon Guidance Modelling Krottje generalized their model and has developed the Matlab package: AG-tools
Axon Guidance Modelling
Mathematical Framework
Three basic ingredients Domain States Fields
Mathematical Framework Three basic ingredients Domain Physical environment of axons, neurons, chemical fields. Domain in 2D with smooth complicated boundary, possibly with holes. States Fields
Mathematical Framework Three basic ingredients Domain Physical environment of axons, neurons, chemical fields. Domain in 2D with smooth complicated boundary, possibly with holes. States Growth cones, target cells, axon properties, locations. Particle dynamics modelled by ordinary differential equations. Fields
Mathematical Framework Three basic ingredients Domain Physical environment of axons, neurons, chemical fields. Domain in 2D with smooth complicated boundary, possibly with holes. States Growth cones, target cells, axon properties, locations. Particle dynamics modelled by ordinary differential equations. Fields Changing concentrations of guidance molecules due to diffusion, absorption, moving sources. Modelled by partial differential equations.
Three basic ingredients Domain States Fields Mathematical Framework
Three basic ingredients Domain States Fields Mathematical Framework
Three basic ingredients Domain States Fields Mathematical Framework
Three basic ingredients Domain States Fields Mathematical Framework - Local function approximations - Arbitrary node sets - Unstructured Voronoi grids - Local refinement - Implicit-explicit Runge-Kutta integration
AGTools Example
Ilustration of topographic mapping with 5 guidance fields (3 diffusive and 2 membrane bound) and 200 growth cones
Topographic Mapping Equations
No hard laws. Phenomenal setup.
Neuro Scientific Computing Challenges Modelling Analysis Simulation
Neuro Scientific Computing Challenges Modelling Here major steps are needed: Analysis Simulation
Neuro Scientific Computing Challenges Modelling Here major steps are needed: - e.g., dimensioned wires instead of point particles, - in general, a less phenomenal setup, - realistic data (coefficients, parameters). Analysis Simulation
Neuro Scientific Computing Challenges Modelling Here major steps are needed: - e.g., dimensioned wires instead of point particles, - in general, a less phenomenal setup, - realistic data (coefficients, parameters). Analysis Higher modelling level will require participation of PDE analysts. Simulation
Neuro Scientific Computing Challenges Modelling Here major steps are needed: - e.g., dimensioned wires instead of point particles, - in general, a less phenomenal setup, - realistic data (coefficients, parameters). Analysis Higher modelling level will require participation of PDE analysts. Simulation 3D-model with many species and axons. Will require huge computer resources, and presumably a different grid approach.