Research in the Biomathematics and Bioinformatics group of Maastricht University

1. Research in the Biomathematics and Bioinformatics group of Maastricht University Ronald Westra Department of Knowledge Engineering Maastricht University WARWICK University Presentation, May 28, 2010

2. Overview 1. Department of Knowledge Engineering (DKE) 2. Signal and image processing and analysis 3. Complex Systems and Cell Models

3. 1. Department of Knowledge Engineering (DKE) * Established 1987 as Department of Mathematics and Department of Computer Science, since 2009: “DKE” * Houses the school of Knowledge Engineering BSc, MSc * Head: prof.dr.ir. Ralf Peeters * Three research groups: 1. Robots, Agents and Interaction (RAI) 2. Networks and Strategic Optimization (NSO) 3. BioMathematics and BioInformatics (BMI)

4. DKE

5. 2. Biomathematics and Bioinformatics group OUR BASIC PHILOSOPHY A multidimensional and integrative approach to biomedical problems: from molecule to patient

6. Members of the BMI group Scientific staff Dr. Ronald L. Westra (Group Leader) Dr. Joël Karel Dr. Mihaly Petrezcky Dr. Evgueni Smirnov * Prof. dr. ir. Ralf Peeters (Head of DKE) * Dr. Frank Thuijsman (Group Leader of NSO) Postdocs Dr. Martin Hoffmann Dr. Georgi Nalbantov (Dr) Ivo Bleylevens Ph.D. Students Matthijs Cluitmans M.Sc. (Analysis of complex dynamics on the heart using real-time ECGI, 2010-2014) Jordi Heijman M.Sc. (Computational modeling of compartmentalized myocytes and beta-adrenergic signalling pathways’, 2007-2011), Stephan Jansen M.Sc.(Video eye tracking and intravital microscopy)

7. Biomathematics and Bioinformatics group Research Themes : 1. Signal and image processing and analysis 2. Complex Systems and Cell Models

8. THEME 1: Signal and image processing and analysis 1. 1D EXG signal analysis using tailor made multi-wavelets 2. Texture analysis using 2D-wavelets 3. ECGI 3D analysis and construction

9. THEME 1: Signal and image processing and analysis

10. THEME 1-A: (Multi) wavelet filtering NWO-STW BIOSENS 2004 – 2009 Biomedical Signal Processing Platform for Low-Power Real-Time Sensing of Cardiac Signals

11. Multi-wavelet design

12. Multi wavelet filtering of ECGsimultaneous detection of QRS and T waves

13. THEME 1-B: 3D Imaging of the heart: ECGI

14. Our goal To obtain: A three dimensional heart With heart-surface potentials Based on: Many ECGs And a CT scan 25 February, 2010 MSc Presentation Matthijs Cluitmans 14

15. The First Human Reconstructionsat the the R peak 25 February, 2010 MSc Presentation Matthijs Cluitmans 15

16. THEME 2: Complex Systems and Cell Models 1. Gene-protein interaction networks 2. Single cell models3. Multiple cell, tissue and organ models 4. Complex Biological Systems

17. Degree distributions in human gene coexpression network. Coexpressed genes are linked for different values of the correlation r, King et al, Molecular Biology and Evolution, 2004

18. Objective Reconstruct gene-protein networks from experimental (e.g. micro array) data

19. Major Problem in reconstruction of sparse networks The system is severely under-constrained as there are typically far more model parameters than there is experimental dataD.

20. Result: Above a minimum number Mmin of measurements and with a maximum number kC of non-zeros the reconstruction is perfect. Mmin is much smaller than in L2-regression, Mmin and kC depend on N.

21. Critical number Mminversus the problem size N,

22. THEME 2-B: Single and Multiple Cell Models

23. MYOCYTE CELL MODELS 1. Single Myocyte cell models are simplified mathematical-computational models that exhibit specific properties of the myocyte. > 30 years of myocyte models from Hodgkin-Huxley to Hund-Rudy 2. These are phenomenological/heuristic models, build bottom-up and but extremely well validated. 3. But still they are simplifications that can not account for many observed phenomena, e.g. beat-to-beat instability 4. Central in function of the myocyte model are the ION-CHANNELS (just as in neurons)

24. From Molecule to Patient • Multiscale integrative modeling of the Cardiac System • PhD Project Jordi Heijman, : Computational modelling of compartmentalized myocytes and adrenergic signalling pathways’, (jointly with CBAC 2007-2011), • PhD ProjectMatthijs Cluitmans : ‘Analysis of complex dynamics on the heart using real-time 3D-Electrocardiographic-Imaging’ (jointly with CBAC 2010-2014).

25. Experimental facilities (CARIM) computational tissue/whole heart model (DKE /BMI) computational physico-chemical model (DKE /BMI)

27. COMPLEXITY RESEARCH The emergence of synchronization and self-organization on the heart Principal research-question : To understand and predict observed complex macroscopic electrophysiological phenomena (instability, synchronization, memory) in and on the heart in terms of their constituent microscopic (molecular, genetic, cellular) processes.

28. COMPLEXITY RESEARCH The emergence of synchronization and self-organization on the heart Secondary research-objectives 1: temporal electrophysiological variability and transition to instability in the single cardiac myocyte; 2: formation of deterministic chaos, and the self-organization –or breakdown–of synchronization; 3: understand ‘Long-Term-Cardiac-Memory’( LTCM) as emergent property of microscopic processes (including the genetic pathways).

29. individual IKS ion channel Markov model of conformational states ion channels in cell membrane The emergence of synchronization and self-organization on the heart Microscopic-Scale: Variability and instability in the single cell Epicardial Myocyte

30. Multiple Cell and Tissue Models

31. Microscopic-Scale: Variability and instability in the single cell Figure 1. A Schematic of our single venticular myocyte model. B. Steady-state action potentials from canine ventricular myocytes (top), our recently published deterministic canine model (middle), and the canine model with a preliminary stochastic ICal model (bottom). Cycle length = 1000 ms. Action potential duration is indicated below each beat. C. Poincaré maps of 30 successive action potentials for each setting.

32. Macroscopic-Scale: Spontaneous order and self-organization Figure 2. 1. Synchronization and deterministic-chaos Chaotic EAD dynamics in isolated cardiac myocytes and in an AP model A/C Experimental data B/D/E Single Myocyte model data

33. Macroscopic-Scale: Spontaneous order and self-organization Figure 3. Partial regional synchronization of chaotic EADs, causing APD dispersion From Sato et al, PNAS 2009

34. Macroscopic-Scale: Spontaneous order and self-organization Figure 4. Partial regional synchronization of chaos generates PVCs initiating reentry From Sato et al, PNAS 2009

35. Macroscopic-Scale: Spontaneous order and self-organization 2. Long-Term Cardiac Memory LTCM is an learned change of the propagation induced by a temporarily altered activation . It involves the CREB-genes, which also have a well-documented role in neuronal plasticity and long-term memory formation in the brain

36. RELATION GENETIC CONDITIONS AND CARDIOPATHOLOGY Certain known cardio-pathologies relate to genetic dispositions. Currently we study the relation between the V341A mutation in the KCNQ1 gene that codes for the IKS channel and causes a severe long QT syndrome.

37. THEME 2c: Modeling of Mesenchymal Stem Cells • Modeling of Cell Expansion and Mobility • Model for mesenchymal stem cell expansion • extendable to: • neuronal tissue morphogenesis • neuroplasticity.

38. simulation of mesenchymal stem cell cultures cell-cell alignment similar to magnetic spin domains

39. factor 2 in cell number results quantitative agreement with experiment?! guided expansion results in later contact inhibition

40. Conclusions • Mathematical and computational research in three area’s • Multi-wavelet filtering and analysis of 1-2-3 D signals/images • Machine Learning-based approach to Pattern Recognition, Clustering and Classification • Modelling of complex dynamical biological systems from molecule to patient

41. Thanks for your attention … Ronald Westra BMI Group Maastricht University