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Research in the
Biomathematics and Bioinformatics group of Maastricht University
Ronald WestraDepartment of Knowledge Engineering
Maastricht University
WARWICK University Presentation,
May 28, 2010
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1. Department of Knowledge Engineering (DKE)
2. Signal and image processing and analysis
3. Complex Systems and Cell Models
Overview
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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)
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DKE
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2. Biomathematics and Bioinformatics group
OUR BASIC PHILOSOPHY
A multidimensional and integrative approach to biomedical problems:
from molecule to patient
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Members of the BMI group
Scientific staffDr. Ronald L. Westra (Group Leader)Dr. Joël KarelDr. Mihaly PetrezckyDr. Evgueni Smirnov* Prof. dr. ir. Ralf Peeters (Head of DKE)* Dr. Frank Thuijsman (Group Leader of NSO)
PostdocsDr. Martin HoffmannDr. Georgi Nalbantov(Dr) Ivo Bleylevens
Ph.D. StudentsMatthijs 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)
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Biomathematics and Bioinformatics group
Research Themes :
1. Signal and image processing and analysis
2. Complex Systems and Cell Models
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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
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THEME 1: Signal and image processing and analysis
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THEME 1-A: (Multi) wavelet filtering
Biomedical Signal Processing Platform for Low-Power Real-Time Sensing of Cardiac Signals
NWO-STW BIOSENS 2004 – 2009
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Multi-wavelet design
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Multi wavelet filtering of ECGsimultaneous detection of QRS and T waves
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THEME 1-B: 3D Imaging of the heart: ECGI
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25 February, 2010 MSc Presentation Matthijs Cluitmans 14
Our goal
• To obtain:– A three dimensional heart– With heart-surface potentials
• Based on:– Many ECGs– And a CT scan
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25 February, 2010 MSc Presentation Matthijs Cluitmans 15
The First Human Reconstructionsat the the R peak
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THEME 2: Complex Systems and Cell Models
1. Gene-protein interaction networks
2. Single cell models
3. Multiple cell, tissue and organ models
4. Complex Biological Systems
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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
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Reconstruct gene-protein networks from experimental (e.g. micro array) data
Objective
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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 data D.
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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.
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Critical number Mmin versus the problem size N,
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THEME 2-B: Single and Multiple Cell Models
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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)
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From Molecule to PatientMultiscale 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 Project Matthijs Cluitmans : ‘Analysis of complex dynamics on the heart using real-time 3D-Electrocardiographic-Imaging’ (jointly with CBAC 2010-2014). -
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computational tissue/whole heart model
(DKE /BMI)
Experimental facilities (CARIM)
computational physico-chemical model (DKE /BMI)
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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.
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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).
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The emergence of synchronization and self-organization on the heart
Microscopic-Scale: Variability and instability in the single cell
Epicardial Myocyte
ion channels in cell membrane
individual IKS ion channel
Markov model of conformational states
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Multiple Cell and Tissue Models
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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.
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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
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Macroscopic-Scale: Spontaneous order and self-organization
Figure 3. Partial regional synchronization of chaotic EADs, causing APD dispersion
From Sato et al, PNAS 2009
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Macroscopic-Scale: Spontaneous order and self-organization
Figure 4. Partial regional synchronization of chaos generates PVCs initiating reentry
From Sato et al, PNAS 2009
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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
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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.
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Modeling of Cell Expansion and Mobility
Model for mesenchymal stem cell expansion
extendable to: - neuronal tissue morphogenesis- neuroplasticity.
THEME 2c: Modeling of Mesenchymal Stem Cells
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simulation of mesenchymal stem cell cultures
cell-cell alignment similar to magnetic spin domains
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results
quantitative agreement with experiment?!
factor 2 in cell number
guided expansion results in later contact inhibition
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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
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Thanks for your attention …
Ronald Westra
BMI Group
Maastricht University
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