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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