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Heart Simulator

Heart Simulator. FISIOCOMP - Laboratory of Computational Physiology Computer Science Department Universidade Federal de Juiz de Fora (UFJF) Juiz de Fora - MG - Brazil Gustavo Miranda Teixeira Ricardo Silva Campos. Undergraduate Students Caroline Costa Gustavo Miranda * Ricardo Campos *

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Heart Simulator

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  1. Heart Simulator FISIOCOMP - Laboratory of Computational Physiology Computer Science DepartmentUniversidade Federal de Juiz de Fora (UFJF)Juiz de Fora - MG - Brazil Gustavo Miranda Teixeira Ricardo Silva Campos

  2. Undergraduate Students Caroline Costa Gustavo Miranda * Ricardo Campos * Guilherme Montebrune Former Master Students Rafael Sachetto Oliveira Fernando Otaviano Campos Bernardo Rocha Daves Martins Ely Fonseca Group Professors Prof. Rodrigo Weber dos Santos, Dr. Math. * Prof. Marcelo Lobosco, Dr. Comp. Sci. * Prof. Ciro Barros Barbosa, Dr. Comp. Sci. Prof. Rubens Oliveira, Dr. Eng. Prof. Luis Paulo Barra, Dr. Eng. Prof. Elson Toledo, Dr. Eng. Master Students Carolina Xavier Ronan M. Amorim Franciane Peters * Grid team

  3. Overview Computational physiology The heart Heart models Computational Framework Inverse Problems Gridification Goals

  4. Computational Physiology • Physiology: The study of the (bio) functions • Computational Physiology: The use and development of mathematical and computational models to describe biological functions

  5. Computational Physiology • The bad news: • It is a wide gap connecting multiple scales, genes, proteins, cells, tissues, organs...; • multiple physics: quantum, molecular dynamics, chemistry, electro-mechanics…;

  6. Computational Physiology • The models representation are based and depend on multiple and diverse data MODEL

  7. The Heart

  8. The Heart • The blood pump • Cells contract changing the organ geometry and the blood is expelled

  9. The Heart • Cellular contraction: • An electric potential difference develops across the cell membrane and triggers a chain of electrochemical reactions that results in cellular contraction (intracellular Calcium spike, ATP, etc)

  10. The Heart • The interior of the cells are connected by special proteins that allow the electric potential to propagate. A fast electric wave propagates and triggers heart contraction.

  11. Models of Cardiac Electro-Mechanics • Cardiac disease is the #1 cause of death in the globe (30%) • Today, computational models of the heart provide a better understanding of the complex phenomenon and support the development of new drugs, therapies, biomedical equipments and clinical diagnostic methods

  12. ModelsofCardiacElectro-Mechanics • Bottom-up design • Sub-cellular and cellular mathematical models

  13. Models of Cardiac Electro-Mechanics • Bottom-up design • Tissue mathematical models: electric activity

  14. Models of Cardiac Electro-Mechanics • Bottom-up design • Tissue mathematical models: mechanical coupling

  15. Models of Cardiac Electro-Mechanics • Bottom-up design • Organ modeling

  16. Introduction to cardiac modelling Two basic components: 1) A cell model that describes the electric behavior of a single cell; 2) A tissue model which describes how the cardiac electric wave propagates from one cell to another

  17. Cell model • Bi-lipid layer: • Ionic channels: Special arrangement of proteins cut thru the membrane and allow the flow of specific ions, such as Sodium, Potassium and Calcium. Intracellular space Extracellular space Ionic channel

  18. e Cm Ic Iion i Cardiac cell models • Hodgkin-Huxley based models • Membrane works as a capacitor, isolating charges • The ionic channel currents and the transmembrane potential satisfy a set of ordinary differential equations

  19. Cell models • Canine ventricular model: Beeler-Reuter (9 eqs) • Rabbit atrial model: Lindblad (27 eqs) • Rat ventricular model: Panditetal (26 eqs) • Human atrial model: Nygrenetal (30 eqs) • Simplified ventricular modelbasedon FHN (2 eqs) • Guineapig ventricular model: Luo-Rudy II (14 eqs) • Human atrial model: Courtemancheetal (20 eqs)

  20. Cardiac Bidomain Model Tissue Model for cardiac electrophysiology Intracellurar and extracellular spaces (domains) modeled from an electrostatic point of view The coupling of the two domains is via non-linear cell modeling. Total cell membrane current spreads to both intracellurar and extracellular spaces

  21. Cardiac Bidomain Model

  22. Complex Models • Involves the coupling of several components (submodels) and data (geometry, biophysical parameters) • Each component is a complex mathematical formulation, typically with tens of variables and hundreds of parameters • New detailed models (components) are created and validated every week

  23. Complex Models • Modeling Challenges: Multi-scale and Multiphysics • Computational Challenges: Simulations are computationally expensive (one heart beat = a couple of days in a parallel machine) • Integration Challenge: Patient Specific Heart Model

  24. Results

  25. We have a 2D simulator • We needed a computational framework that would facilitate, stimulate and broadcast the use and benefits of cardiac modeling. • The framework combines: • The parallel simulator for bidomain-based models • Cluster Computing • An automatic code generator for models described by CellML • User-Friendly Graphical Interfaces • Web Server Results

  26. CellML • XML based language (machine-readable) • Describes mathematical models (MathML) • Repository contains over 300 biophysical models • A model is described via the connection of units, variables and components, in a hierarchical fashion

  27. CellML • The goal: • Accelerating the development of new models • Computational Frameworks and tools • On the way: • Ontology and web semantic • Grid Computing

  28. CellML-basedtools • A couple of tools exist for edition, validation and simulation of models described in CellML • Today two CellMl-based frameworks provide both cell and tissue level simulations: • COR, a MS-Windows based environment, from the University of Oxford (cor.physiol.ox.ac.uk) • AGOS, A web-based framework from FISIOCOMP-UFJF

  29. Agos Framework • Goal: Reach the biologists • Computational Framework that hides many of the technical issues of cardiac modeling

  30. TheComputational Framework • It provides support to cardiac electrophysiology modeling • A editor to CellML language • A translator of CellML code into C++ code • A user-friendly Web form to setup parameters and visualize results • Web Server • Cluster Computing

  31. AgosTranslator • API Generator to ODE Solutions • Cellular models are described in CellML/MathML • It translates CellML code into a object oriented C++ code • Through the API generated, it is possible to simulate the model and setup parameters

  32. Tissue Model

  33. Inverse Problem • Forward Models of Cardiac Physiology • Inverse Problem

  34. Inverse Problem • The forward problem • The user has to know all parameters, such as geometry of the organ and values of conductivity • It returns the potential diference along the time and space • Inverse problem • The user knows the potential diference • He or she may want calculate the geometry and all another parameters

  35. Inverse Problems • Estimate the values of electrical activity on the cardiac tissue • Given a number of observed transmural electrograms estimate possible changes on the conductivity (,) of a known and specific region of the heart.

  36. Inverse Problems Pathological Tissue Region with altered conductivity (,) • Motivation: focal variations of tissue conductivity values (both intra and extra) are observed in many different cardiac diseases: • Acute ischemia, Infarct, Chagas Disease, Myocarditis

  37. Inverse Problem • More computational costly than the foward problem • It solves the forward problem lots of time sequentionally • InvCell and InvTissue

  38. INVCell • We are adjusting a model which GA takes one day long to run. • Asynchronous x Synchronous. • Heterogeneity x Homogeneity. • It uses the AGOS API lots of times • ODEs are solved sequentionally

  39. InvCell • Genetic algorithm • Based on Darwin’s evolutionary theory • Aims to optmization (maximize/minimize) • It works simulating the process of natural reproduction, mutation, and selecting the fittest individual

  40. INVCell • GA implementation: • The individuals are the parameters • We know the solution – calculated by the simulator • Each iteration gets more closer to the final solution • Parallel GA – master-slaves. • Floating point representation; • Elitist selection; • The initial population is randomly generated ; • A new generation depends of their parents;

  41. INVTissue • It solves an inverse problem associated to the simulation of cardiac tissue models. • It also has an implementation of a Genetic Algorithm parallelized with MPI. • It runs the simulator to each individual • Quite slow!

  42. INVTissue Investigate the solution of an inverse problem associated to cardiac electrophysiology The goal is to estimate values for the electrical conductivity of cardiac tissue, taking as known some information concerning the electrical activity of the heart Asynchronous non generational GA Parallelized using master-slave

  43. Goals • Porting InvCell • It should be the easiest; • Porting InvTissue • More complicated – lots of dependencies; • Porting of a basic version of the Heart Simulator • Hardest problem;

  44. Goals • The heart simulator uses : • C code • Petsc library • MPI • Numerical methods to solve lots of equations • Each iteration have lots of dependencies on the previous one

  45. Questions …

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