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Open Lecture Peter Hilbers - July 8, 2016

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BioModeling & bioInformatics: Trends in Healthcare Technology

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Open Lecture Peter Hilbers - July 8, 2016

  1. 1. Peter Hilbers BioModeling & bioInformatics Dept. BioMedical Engineering Trends in Healthcare Technology
  2. 2. Overview • Introduction • General trends in Healthcare Technology • Dept BioMedical Engineering(TU/e) • Computational Biology example(s)
  3. 3. PAGE 3 Eindhoven University of Technology
  4. 4. PAGE 4 Departments Architecture, Building and Planning Chemical Engineering and Chemistry Applied Physics Mathematics and Computer Science Industrial Design Electrical Engineering Biomedical Engineering Mechanical Engineering Technology Management
  5. 5. 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)
  6. 6. 05/20/14 PAGE 6April 2009 Welcome BioMedical Engineering
  7. 7. 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-
  8. 8. 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
  9. 9. Costs of Health Care in the Netherlands www.kostenvanziekten.nl (RIVM) Need for technology to get healthy older
  10. 10. Trends in Healthcare We’re getting older and sicker Demand for care is growing We don’t take good care of ourselves We expect better choices Need for technology to get healthy older
  11. 11. 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
  12. 12. 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
  13. 13. Healthcare-transforming technologies Imaging Classically: whole body, X-ray Trends: • non-invasive, • combining modalities, CT, MRI, PET • ultrasound • biosensors • molecular Mathematics, digital revolution, computational modeling Earlier diagnosis saves lives and reduces costs
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. Collaborations at TU/e Biology/ Medicine Physics Electrical engineering Chemistry Mathematics BMT Computer science Mechanical engineering
  20. 20. 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
  21. 21. / Biomedische Technologie Research quality BMT
  22. 22. 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
  23. 23. Onderwijs (3) 2012 start MWT
  24. 24. 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:
  25. 25. 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 Computational Diagnostics Chemical Biology
  26. 26. 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
  27. 27. 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
  28. 28. Orthopaedic Biomechanics - Keita Ito Bone adaptation in health, disease and regeneration Intervertebral disc degeneration and regeneration Osteoarthrosis and cartilage tissue engineering disc Spinal motion segment knee hip bone TE
  29. 29. Develop new technology for mathematical modelling and clinical measurement of cardiovascular physiology to enhance diagnosis and predict outcome of medical intervention by means of computer simulations Predictive Model Patient reference intervention outcome measurements patient caremedical technology Cardiovascular Biomechanics /F.N. van de Vosse Measurements: sensors, ultra sound, photo acousticsModels: finite element fluid-structure interaction, (0D/1D/3D)
  30. 30. aneurysms vascular access coronary disease carotid plaques heart failure neurovascular diseases perinatal care Applications Cardiovascular Biomechanics /F.N. van de Vosse
  31. 31. 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
  32. 32. 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
  33. 33. Biomechanics of Soft Tissues – Cees Oomens Trans-epidermal drug delivery Pressure Ulcers
  34. 34. 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
  35. 35. Functional life-like systems and How far can we push chemical self-assembly? Non-covalent synthesis of functional supramolecular materials and systems Bert Meijer
  36. 36. 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
  37. 37. 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)
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. Engineering intelligent biomolecular sensors 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
  42. 42. Cluster: Computational Diagnostics Biomedical Imaging & Modeling (BIOMIM) ● Image Analysis and Interpretation Josien Pluim ● Biomodeling & Bioinformatics Peter Hilbers
  43. 43. 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
  44. 44. Prognosis of breast cancer MEDICAL IMAGE ANALYSIS – IMAG/e HISTOLOGY NUCLEI SIZE MITOSES
  45. 45. Computational Biology Systems biology Molecular simulations Biomedical Engineering Computational Biology - Peter Hilbers Systems biology Synthetic biology
  46. 46. Computational Biology Molecular simulations Biomedical Engineering Computational Biology= Systems biology Synthetic biology + + 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
  47. 47. April 20, 2016 Natal van Riel(also prof at AMC) Peter Hilbers Eindhoven University of Technology, the Netherlands Department of Biomedical Engineering Systems Biology and Metabolic Diseases n.a.w.v.riel@tue.nl @nvanriel Quantification of variability and uncertainty in systems medicine models
  48. 48. Computational modelling • Explaining the data & understanding the biological system 49 Wolkenhauer, Front Physiol. 2014; 5:21. TOP-DOWN
  49. 49. 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 Flexibility ModelClass Model  
  50. 50. 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 Flexibility ModelClass Model  
  51. 51. Model calibration Parameter identification • Maximum likelihood techniques • Implemented using nonconvex optimization • Error model 52 Quantitative and Predictive Modelling 2 2 1 1 ( ) ( | ) ( ) n N i i i k ik d k y k              2 ˆ 0 ˆ arg min ( )       ( ) ( | )i id k y k   ( | ) ( )i iy k k  
  52. 52. 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.ofcomponents No. of observations per component
  53. 53. 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
  54. 54. Tiemann et al. (2011) BMC Syst Biol, 5:174 Van Riel et al, Interface Focus 3(2): 20120084, 2013 Tiemann 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 •ADAPT Analysis of Dynamic Adaptations in Parameter Trajectories 55
  55. 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 FP G [m m ol/ L] Schernthaner et al, Clin. Endocrinol. Metab. 89:6068–6076 (2004) Charbonnel et al, Diabetic Med. 22:399–405 (2004)
  56. 56. Glucose-insulin homeostasis model • Pharmaco-Dynamic model • 3 ODE’s, 15 parameters 57 De Winter et al. (2006) J Pharmacokinet Pharmcodyn, 33(3):313-343 FPG: fasting plasma glucose FSI: fasting serum insulin HbA1c: glycosylated hemoglobin A1c
  57. 57. T2DM disease progression model • Fixed parameters • Adaptive changes in -cell function B(t) and insulin sensitivity S(t) • Parameter trajectories 58 Nyman et al, Interface Focus. 2016 Apr 6;6(2): 20150075
  58. 58. 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
  59. 59. 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
  60. 60. 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 61 Tiemann 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
  61. 61. 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): 1923-1929 Vanlier 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
  62. 62. 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
  63. 63. 64 Systems Biology of Disease Progression - ADAPT modeling http://www.youtube.com/watch?v=x54ysJDS7i8

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