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Peter Hilbers
BioModeling & bioInformatics
Dept. BioMedical Engineering
Trends in Healthcare Technology
Overview
• Introduction
• General trends in Healthcare Technology
• Dept BioMedical Engineering(TU/e)
• Computational Biology example(s)
PAGE 3
Eindhoven University of Technology
PAGE 4
Departments
Architecture, Building and Planning
Chemical Engineering and Chemistry
Applied Physics
Mathematics and Computer Science
Industrial Design
Electrical Engineering
BiomedicalEngineering
MechanicalEngineering
TechnologyManagement
PAGE 5
TU/e key Figures (2015)
Staff• Full professors 140• Part-time professors 125• Research staff 2000• Total staff 3150
Students 9.900• BSc-students 6000 ( 2% international)• MSc-students 3.900 (16% international)• Exchange students 400 per annum• Ph.D students 840 (>30% international)
05/20/14PAGE 6April 2009
Welcome
BioMedicalEngineering
05/20/14
History of dept BioMedical Engineering
• Educational program: start 1997
• Department: 1999− First dean: Jan Jansen, 1999-2003
− Second dean: Frank Baaijens: 2003-2007
− Third dean: Peter Hilbers: 2007-
Mission statement
• To be an internationally leading research institute that offers
(post)graduate programs to educate scientists and engineers
for advanced biomedical research and development, who
master a cross disciplinary approach.
• To advance and apply engineering principles and tools
• to unravel the pathophysiology of diseases, and
• to enhance prevention, diagnostics, intervention and
treatment of these diseases by combining natural sciences
and engineering.
/ Biomedische Technologie
Costs of Health Care in the Netherlands
www.kostenvanziekten.nl (RIVM)
Need for technology to get healthy older
Trends in HealthcareWe’re getting older and sicker Demand for care is growing
We don’t take good care of ourselves We expect better choicesNeed for technology to get healthy older
humanstissues / organscellspathwaysmolecules
Seconds 10-6 102 104 105 109
Meters 10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 1
From molecule to cell to tissue to human
Biological sytems are networks of molecules, cells, tissues and organs that interact in space and time
Healthcare-transforming technologies
Imaging
Earlier diagnosis saves lives and reduces
costs
Minimally Invasive surgery
Reducing patient trauma and reduces costs
Clinical IT Right Information at the right time enables best treatment and reduces
costs
Molecular Medicine
Preventing disease from happening and reduces costs
Regenerative medicine
Implants taking over vital bodily functions, improving quality of life
Healthcare-transforming technologies
Imaging
Classically: whole body, X-rayTrends:
• non-invasive, • combining modalities, CT, MRI, PET• ultrasound• biosensors• molecular
Mathematics, digital revolution, computational modeling
Earlier diagnosissaves lives and reduces costs
Healthcare-transforming technologies
Minimally Invasive surgery
Reducing patient trauma and reduces costs
Trends: • robotics (example da Vinci), • image guided• combination of diagnosis and intervention• computational modeling
Healthcare-transforming technologies
Trends: • digital hospital• home monitoring systems• decision support systems • electronic patient systems• workflow systems• big data, healthcare data, clinical informatics
Information technology, communications
Clinical IT Right Information at the right
time enables best treatment and reduces costs
Healthcare-transforming technologies
Trends: • human genome, Virtual Physiological Human (VPH)• Metabolomics: (Recon2 1,789 enzyme-encoding
genes, 7,440 reactions and 2,626 unique metabolites)
• biosensors• personalized molecular medicine
bioinformatics, systems biology, chemical biology
Molecular Medicine
Preventing disease from happening and reduces costs
Healthcare-transforming technologies
Trends: • stem cells• tissue engineering• gene therapy• cell growth, differentiation• biomechanics
Computational modeling, imaging
Regenerative medicine
Implants taking over vital bodily functions, improving quality of life
Imaging
Earlier diagnosis saves lives and reduces costs
Minimally Invasive surgery
Reducing patient trauma and reduces costs
Systems Medicine
Preventing disease from happening and reduces costs
Regenerative medicine
Implants taking over vital bodily functions, improving quality of life
/ Biomedical Engineering
Clinical IT Right Information
at the right time enables
best treatment and
reduces costs
Collaborations at TU/e
Biology/Medicine
Physics
Electrical engineering
Chemistry
Mathematics
BMT
Computer science
Mechanical engineering
Biomechanics & Tissue Engineering (BMTE) ● Orthopaedic Biomechanics Keita Ito
• Cardiovascular Biomechanics Frans van de Vosse● Soft Tissue Biomechanics & Tissue Engineering Frank Baaijens● Cell-Matrix interaction in Cardiovascular Regeneration Carlijn Bouten
Biomedical Imaging & Modeling (BIOMIM)
• Image Analysis and Interpretation Josien Pluim
• Biomodeling & Bioinformatics Peter Hilbers
Molecular Bioengineering & Molecular Imaging (MBEMI)
• Biomedical Chemistry Bert Meijer (0.5)
• Biomedical NMR=> Image Formation Klaas Nicolay => ?
• Chemical Biology Luc Brunsveld
• Molecular Biosensing for Medical Diagnostics Menno Prins
Disciplines plans
Proposals new chairs
● Biomaterials
● Neuro-engineering
● Immuno-engineering
/ Biomedische Technologie
Research quality BMT
Educational Programs
In all programs: students asap research involvement
• BSc in BME• Major BioMedical Engineering
• Major Medical Sciences and Technology
• MSc in BME• BioMedical Engineering
• Joint master with UU/UMCU: Regenerative Medicine & Technology
• MSc in Medical Engineering: UM
• SUMMA(-T), AKO
• Postmaster education: SMPE, Clinical Physics
High(Top) Rankings
/ Biomedische Technologie
Onderwijs (3)
2012 start MWT
Your carreer as biomedical engineer
/ Biomedische Technologie
A master (B)ME at the TU/e provides you skills and knowledge for an excellent position in a still growing jobmarket in Health & Technology
Start working:Examples of companies:•Shering-Plough•Yacht Interim Professionals •Fortimedix •Philips Medical Systems•Pie Medical Imaging•Shell Global Solutions •Medtronic•Occam International•TNO-sport•Bavaria•Pharmascope
• PhD student: 4 year specialised research
• Hospital-based, university-managed training program that leads to:
– Specialist Medical Physicist: (2+2 years)
– Qualified Medical Engineer: 2 year
– Qualified Medical Physicist: (2 year)
• Design and Technology of Instrumentation: 2 year training (Stan Ackermans Institute).
More learning:
BioMedical Engineering Core Data
• 1000 students in BSc and MSc phases• About 90 PhD students and 20 post-docs• About 45 fte Scientific Staff, several VENI's(4), VIDI's(4), ERC grants(3),
in 2006-2016• Small Administrative and Technical Staff
• Shared Laboratories• Budget >16 Meuro
/ Biomedische Technologie
Regenerative medicine
ComputationalDiagnostics
Chemical Biology
Biomechanics & Tissue Engineering (BMTE) ● Orthopaedic Biomechanics Keita Ito
• Cardiovascular Biomechanics Frans van de
Vosse● Cell-Matrix interaction in Cardiovascular Regeneration Carlijn Bouten
Biomedical Imaging & Modeling (BIOMIM)
• Image Analysis and Interpretation Josien Pluim
• Biomodeling & Bioinformatics Peter Hilbers
Molecular Bioengineering & Molecular Imaging (MBEMI)
• Biomedical Chemistry Bert Meijer (0.5)
• Biomedical NMR=> Image Formation Klaas Nicolay
• Chemical Biology Luc Brunsveld
• Molecular Biosensing for Medical Diagnostics Menno Prins
Disciplines and Group Leaders
Cluster: Regenerative medicine
Biomechanics & Tissue Engineering (BMTE)
● Orthopaedic Biomechanics Keita Ito
● Cardiovascular Biomechanics Frans van de Vosse
● Cell-Matrix interaction in Cardiovascular Regeneration Carlijn Bouten
● Biomechanics of Soft Tissues Cees Oomens
Orthopaedic Biomechanics - Keita ItoBone adaptation in health, disease and
regenerationIntervertebral disc
degeneration and regeneration
Osteoarthrosis and cartilage
tissue engineering
disc
Spinal motion
segment
knee
hip
bone TE
Develop new technology for mathematical modelling and clinical measurementof cardiovascular physiology to enhance diagnosis and predict outcome of medical
intervention by means of computer simulations
Predictive Model Patient
reference
intervention
outcomemeasurements
patient caremedicaltechnology
Cardiovascular Biomechanics /F.N. van de Vosse
Measurements: sensors, ultra sound, photo acousticsModels: finite element fluid-structure interaction, (0D/1D/3D)
aneurysms vascular access coronary disease carotid plaques
heart failure neurovascular diseases perinatal care
Applications
Cardiovascular Biomechanics /F.N. van de Vosse
Basic investigations
• Cell-scaffold interaction
• Tissue Engineering
• Tissue remodeling & growth
• Mechanical characterization
• Cell & tissue mechanobiology
• Mechanoregulation of cell fate
Approach
• in-vitro, in-vivo and in-silico modeling
• sub-cell to tissue level
Applications
• Cardiac regeneration
• valve & vessel tissue engineering
• Engineered disease models
• Cellular niches and biomaterial design
cell screening
niche
design
Cell-Matrix interaction in Cardiovascular Regeneration
Carlijn Bouten
Regenerative therapies for the heart Carlijn Bouten
in-situ tissue engineering / endogenous tissue regeneration rebuild original structure and function
• strong, durable tissues
• continuous cyclic loading
• contact with blood
Biomechanics of Soft Tissues – Cees Oomens
Trans-epidermal
drug delivery
Pressure Ulcers
Cluster: Chemical Biology
Molecular Bioengineering & Molecular Imaging (MBEMI)
● Biomedical Chemistry Bert Meijer (0.5)
● Biomedical NMR=> Image Formation Klaas Nicolay
● Chemical Biology Luc Brunsveld
● Molecular Biosensing for Medical Diagnostics Menno Prins
Functional life-like systems
and
How far can we push chemical
self-assembly?
Non-covalent synthesis of functional supramolecular
materials and systems Bert Meijer
Architectural integrity at different length scales
Dynamic adaptivity at different time scales
Out-of-equilibrium systems , kinetic control
Non-homogeneous distribution of components
And many more, like buffering & autoregulation
Non-covalent synthesis of functional
supramolecular materials and systems
Meijer Lab – TU/e
New technologies by mastering the complexity
Chemical Biology - Luc Brunsveld
From the molecule to the cell
Novel chemistry within a biology setting is applied to biomedical problems.
Three lines of applications are being pursued - Diagnostics (clinical chemistry, molecular devices)- Drug discovery (small molecules, protein research)- Biomaterials (cell adhesion, molecular imaging)
magnetics
plasmonics
fluorescence
microscopies
proteins
DNA
molecular function
near-patient testing
blood diagnostics
monitoring
on-body in-body
modelling
nano-micro
particles
Prof. Menno Prins
Dr. Leo van IJzendoorn
Dr. Arthur de Jong
Dr. Peter Zijlstra
Nano-Physics Molecular Engineering Applications
enzymes
Dr. Junhong Yan
hydrogel
+ Students & Collaborators
Molecular Biosensing for Medical Diagnostics - Menno Prins
Dr. Adam Taylor
Our New Solution - LUMABS
40
BRET: Bioluminescence Resonance Energy Transfer
• LUMinescent AntiBody Sensor Proteins
• Ratiometric bioluminescent detection of antibodies directly in clinical
samples (no washing, no calibration)
• Modular sensor platform: plug-and-play substitution of epitope
sequences -> applicable to any antibody
• Bright NanoLuc luciferase allows direct detection in blood plasma using
smartphone camera
• Arts et al (2016) Anal. Chem. 88: 4525-4531
• Switchable reporter enzymes for homogenous antibody detection; EP 2900830 A1; US 20150285818
Highlights
New concept to quantify magnetic particle interactions in blood plasma Relevant for Minicare of Philips
Handheld Diagnostics
New detection techniques under investigation for on-body biomolecular monitoring Plasmonic particles Particle motion analysis
New international competition in the field of molecular biosensors
www.SensUs.org
part of the Philips-TU/e Impuls program
Engineering intelligent biomolecularsensors and switches
42
• Protein engineering, chemical
biology, synthetic biology
• Sensors for intracellular imaging
• Point of care diagnostics using
your mobile phone!
- infectious diseases
- therapeutic antibody monitoring
- drugs screening
• Smart antibody-based drugs
Prof. Dr. Maarten Merkx
Protein Engineering – Maarten Merkx
Cluster: Computational Diagnostics
Biomedical Imaging & Modeling (BIOMIM)
● Image Analysis and Interpretation Josien Pluim
● Biomodeling & Bioinformatics Peter Hilbers
Development + application of image analysis methods that support clinicians in all aspects of clinical care
SCREENING – DIAGNOSIS – PROGNOSIS – TREATMENT PLANNING / GUIDANCE / MONITORING
www.tue.nl/image
Medical Image Analysis - Josien Pluim
Prognosis of breast cancer
MEDICAL IMAGE ANALYSIS – IMAG/e
HISTOLOGY
NUCLEI SIZE
MITOSES
Computational Biology
Systemsbiology
Molecularsimulations
Biomedical Engineering
Computational Biology - Peter Hilbers
Systemsbiology
Syntheticbiology
Computational Biology
Molecularsimulations
Biomedical Engineering
Computational Biology=Systemsbiology
Syntheticbiology
+ +
Some highlights:● Korevaar Peter A., George Subi J., Markvoort Albert J., Smulders Maarten M. J., Hilbers Peter A. J., Schenning Albert P. H. J., De Greef Tom F. A., Meijer E. W., Pathway complexity in supramolecular polymerization, NATURE, 481(7382):492-U103, 2012, 10.1038/nature10720● Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PAJ, et al. (2013) Parameter Trajectory Analysis to Identify Treatment Effects of
Pharmacological Interventions. PLoS Comput Biol 9(8): e1003166. doi:10.1371/journal.pcbi.1003166● van Roekel H.W.H., Stals P.J.M., Gillissen M.A.J., Hilbers P.A.J., Markvoort A.J., de Greef T.F.A., Evaporative self-assembly of single-chain, polymeric nanoparticles.CHEMICAL COMMUNICATIONS, 49(30):3122-3124, 2013.
●Tom de Greef: ECHO Stip 260.000 euro, ERC Starting Grant 2016● Natal van Riel: EU Resolve 1 M euro
April 20, 2016Natal van Riel(also prof at AMC)Peter Hilbers
Eindhoven University of Technology, the NetherlandsDepartment of Biomedical EngineeringSystems Biology and Metabolic [email protected]
@nvanriel
Quantification of variability and uncertainty in systems medicine models
Computational modelling
• Explaining the data & understanding the biological system
49
Wolkenhauer, Front Physiol. 2014; 5:21.
TOP-DOWN
BOTTOM-UP
Developing models of dynamical systems
Explaining the data & understanding the system• Estimating models
• Comparing alternative hypotheses (differences in model structure)
• Given a fixed model structure, find sets of parameter values that accurately describe the data
• Evaluate the capability of the model to reproduce the measured data and the complexity of the model
50
Model complexity / granularity
^
arg min Description of Data Penalty on FlexibilityModelClass
Model
Model Errors
The error in an estimated model has two sources:
1. Too much constraints and restrictions; “too simple model sets". This gives rise to a bias error or systematic error.
2. Data is corrupted by noise, which gives rise to a variance error or random error.
51 Adapted from Ljung & Chen, 2013
^
arg min Description of Data Penalty on FlexibilityModelClass
Model
Model calibration
Parameter identification• Maximum likelihood techniques
• Implemented using nonconvex optimization
• Error model
52Quantitative and Predictive Modelling
2
2
1 1
( ) ( | )( )
n Ni i
i k ik
d k y k
2
ˆ 0
ˆ arg min ( )
( ) ( | )i id k y k
( | ) ( )i iy k k
Information-rich data
It is often not trivial to find a mechanistic (mechanism-based) model that can describe information-rich data of an interconnected system
• If the measurements provide sufficient coverage of the system components (details)
• Under (multiple) physiological, in vivo conditions (operational context)
53
measurements
No.
of c
ompo
nent
sNo. of observations per component
Rethinking Maximum Likelihood Estimation
54
• The bias - variance trade-off is often reached for rather large bias
• Typically, we are far away from the asymptotic situation in which Maximum Likelihood Estimation (MLE) provides the best possible estimates
Tiemann et al. (2011) BMC Syst Biol, 5:174Van Riel et al, Interface Focus 3(2): 20120084, 2013Tiemann et al. (2013) PloS Comput Biol, 9(8):e1003166
Room for more flexibility
• Instead of increasing structural complexity (increasing model size)• Introduce more freedom in model parameters to compensate for
bias (‘undermodelling’) in the original model structure• Increasing model flexibility using time-varying parameters
•ADAPTAnalysis of Dynamic Adaptations in Parameter Trajectories
55
Disease progression and treatment of T2DM
• 1 year follow-up of treatment-naïve T2DM patients (n=2408)• 3 treatment arms: monotherapy with different hypoglycemic agents
– Pioglitazone – insulin sensitizer• enhances peripheral glucose uptake• reduces hepatic glucose production
– Metformin - insulin sensitizer• decreases hepatic glucose production
– Gliclazide - insulin secretogogue• stimulates insulin secretion by the pancreatic beta-cells
56
FPG
[mmol/L]
Schernthaner et al, Clin. Endocrinol. Metab. 89:6068–6076 (2004)Charbonnel et al, Diabetic Med. 22:399–405 (2004)
Glucose-insulin homeostasis model
• Pharmaco-Dynamic model • 3 ODE’s, 15 parameters
57De Winter et al. (2006) J Pharmacokinet Pharmcodyn, 33(3):313-343
FPG: fasting plasma glucoseFSI: fasting serum insulinHbA1c: glycosylated hemoglobin A1c
T2DM disease progression model
• Fixed parameters
• Adaptive changes in -cell function B(t) and insulin sensitivity S(t)
• Parameter trajectories
58Nyman et al, Interface Focus. 2016 Apr 6;6(2): 20150075
Reducing bias while controlling variance
• The common way to handle the flexibility constraint is to restrict / broaden the model class
• If an explicit penalty is added, this is known as regularization
59 Cedersund & Roll (2009) FEBS J 276: 903
Progressive changes in lipoprotein metabolism
60
Rader & Daugherty, Nature 451,2008
Lipolysis
• Lipoprotein distribution (LPD) codetermines metabolic and cardio-vascular disease risks
• Liver X Receptor (LXR, nuclear receptor),induces transcription of multiple genes modulating metabolism of fatty acids, triglycerides, and lipoproteins
• LXR agonists increase plasma high density lipoprotein cholesterol (HDLc)
• LXR as target for anti-atherosclerotic therapy?
Levin et al, (2005) Arterioscler Thromb Vasc Biol. 25(1):135-42
Progressive changes in lipoprotein metabolism after pharmacological intervention
• LXR activation in C57Bl/6J mice leads to complex time-dependent perturbations in cholesterol and triglyceride metabolism
• Dynamic model of lipid and lipoprotein metabolism• ADAPT: time-varying metabolic parameters to accommodate
regulation not included in the metabolic model
• Hepatic steatosis: Increased influx of free fatty acids from plasma is the initial and main contributor to hepatic triglyceride accumulation
61Tiemann et al., PLOS Comput Biol 2013 9(8):e1003166
Hijmans et al. (2015) FASEB J. 29(4):1153-64
Model: the darker the more likely
Quantification of Identifiability and Uncertainty
Verification, Validation, and Uncertainty Quantification (VVUQ)
• Profile Likelihood Analysis (PLA)
• Prediction Uncertainty Analysis (PUA)– Ensemble modelling
• Uncertainty quantification: the elephant in the room
62
Raue.et al 2009 Bioinformatics, 25(15): 1923-1929Vanlier et al. 2012 Bioinformatics, 28(8):1130-5
“Uncertainty quantification is an underdeveloped science, emerging from real-life problems.” Bassingthwaighte JB. Biophys J. 2014 Dec 2;107(11):2481-3
“Uncertainty quantification is an underdeveloped science, emerging from real-life problems.” Bassingthwaighte JB. Biophys J. 2014 Dec 2;107(11):2481-3
Vanlier et al. Math Biosci. 2013 Mar 25
Vanlier et al. Bioinformatics. 2012, 28(8):1130-5
Conclusions
• The network structure of the biological systems imposes strong constraints on possible solutions of a model
• The bias - variance trade-off is often reached for rather large bias, not favoring MLE
• Systems Biology / Systems Medicine is entering an era in which dynamic models, despite their size and complexity, are not flexible enough to correctly describe all data
• Computational techniques to introduce more degrees of freedom in models, but simultaneously enforcing sparsity if extra flexibility is not required (ADAPT)
• Model estimation tools are complemented with ‘regularization’ methods to reduce the error (bias) in models without escalating uncertainties (variance)
63
64
Systems Biology of Disease Progression - ADAPT modelinghttp://www.youtube.com/watch?v=x54ysJDS7i8