Open Lecture Peter Hilbers - July 8, 2016

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)

Eindhoven University of Technology

Departments

Architecture, Building and Planning

Chemical Engineering and Chemistry

Applied Physics

Mathematics and Computer Science

Industrial Design

Electrical Engineering

BiomedicalEngineering

MechanicalEngineering

TechnologyManagement

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

http://www.kostenvanziekten.nl/

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


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




Trends: • robotics (example da Vinci), • image guided• combination of diagnosis and intervention• computational modeling


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


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



Trends: • stem cells• tissue engineering• gene therapy• cell growth, differentiation• biomechanics

Computational modeling, imaging



Imaging

Earlier diagnosis saves lives and reduces costs



Systems Medicine




/ 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


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


Onderwijs (3)

2012 start MWT

Your carreer as biomedical engineer


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



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


• Image Analysis and Interpretation Josien Pluim

• Biomodeling & Bioinformatics Peter Hilbers


• 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


● 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


● 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

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Systems Biology of Disease Progression - ADAPT modelinghttp://www.youtube.com/watch?v=x54ysJDS7i8

http://www.youtube.com/watch?v=x54ysJDS7i8

Open Lecture Peter Hilbers - July 8, 2016

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Transcript of Open Lecture Peter Hilbers - July 8, 2016