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Curriculum Vitae

Professional Achievements

15 Years in Life Science

4 Key Principles

Innovation

Advanced Data Analytics

New Regulatory Pathways

Leading Key Publications

Drug-, CDx-, IVD- and Biomarker-Deverlopment

Collaboration

Founder of the IMI SAFE-T Consortium

First Successful Biomarker Qualification

Innovation Through In-licensing

Collaboration with BMGF

Implementation

Translational Medicine and Personalized Medicine

Developing Companion Diagnostics Tests

Development of Niji PoC IVD Platform

Launch of Niji and Total IgE IVD Tests

Leadership

Acquisition of the Biotech Vivacta

Building-up the Novartis Kent Site

Leading the Change Management of NPT

Outlook

Ph. D. Thesis

Abstract

Abstract in German (Zusammenfassung)

Table of Contents

1. Introduction

1.1. Outline

2. Theory – Fundamentals of the Multivariate Data Analysis

2.1. Overview of the Multivariate Quantitative Data Analysis

2.2. Experimental Design

2.3. Data Preprocessing

2.4. Data Splitting and Validation

2.4.1. Crossvalidation

2.4.2. Bootstrapping

2.4.3. Random Subsampling

2.4.4. Kennard Stones

2.4.5. Kohonen Neural Networks

2.4.6. Conclusions

2.5. Calibration of Linear Relationships

2.5.1. PLS

2.6. Calibration of Nonlinear Relationships

2.7. Neural Networks – Universal Calibration Tools

2.7.1. Principles of Neural Networks

2.7.2. Topology of Neural Networks

2.7.3. Training of Neural Networks

2.8. Too Much Information Deteriorates Calibration

2.8.1. Overfitting, Underfitting and Model Complexity

2.8.2. Neural Networks and the Complexity Problem

2.8.3. Brute Force Variable Selection

2.8.4. Variable Selection by Stepwise Algorithms

2.8.5. Variable Selection by Genetic Algorithms

2.8.6. Variable Selection by Simulated Annealing

2.8.7. Variable Compression by Principal Component Analysis

2.8.8. Topology Optimization by Pruning Algorithms

2.8.9. Topology Optimization by Genetic Algorithms

2.8.10. Topology Optimization by Growing Neural Network Algorithms

2.9. Measures of Error and Validation

3. Theory – Quantification of the Refrigerants R22 and R134a: Part I

3.1. Experimental

3.2. Single Analytes

3.3. Sensitivities

3.4. Calibrations of the Mixtures

3.5. Variable Selection by Brute Force

3.6. Conclusions

4. Experiments, Setups and Data Sets

4.1. The Sensor Principle

4.2. SPR Setup

4.3. RIfS Sensor Array

4.4. 4l Miniaturized RIfS Sensor

4.5. Data Sets

4.5.1. Refrigerants R22 and R134a

4.5.1.1. R22 and R134a by the SPR Setup

4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup

4.5.2. Homologous Series of the Low Alcohols

4.5.2.1. Methanol and Ethanol by the SPR Setup

4.5.2.2. Methanol, Ethanol and Propanol by the SPR Setup

4.5.2.3. Methanol, Ethanol and Propanol by the RIfS Array and the 4l Setup

4.5.2.4. Quaternary Mixtures of Alcohols by the RIfS Array and the SPR Setup

5. Results – Kinetic Measurements

5.1. Static Sensor Measurements

5.2. Time-resolved Sensor Measurements

5.3. Makrolon – A Polymer for Time-resolved Measurements

5.3.1. General Properties of Makrolon

5.3.2. Time-resolved Measurements

5.3.3. Thickness of the Sensitive Layer

5.3.4. Influence of the Carrier Gas

5.4. Conclusions

6. Results – Multivariate Calibrations

6.1. PLS Calibration

6.1.1. Wald-Wolfowitz Runs Test

6.1.2. Durbin-Watson Statistics

6.1.3. Results of Statistical Tests

6.2. Box-Cox Transformation + PLS

6.3. INLR

6.4. QPLS

6.5. CART

6.6. Model Trees

6.7. MARS

6.8. Neural Networks

6.9. PCA-NN

6.10. Neural Networks and Pruning

6.11. Conclusions

7. Results – Genetic Algorithm Framework

7.1. Single Run Genetic Algorithm

7.2. Genetic Algorithm Framework - Theory

7.3. Genetic Algorithm Framework - Results

7.4. Genetic Algorithm Framework – Conclusions

8. Results – Growing Neural Network Framework

8.1. Modifications of the Growing Neural Network Algorithm

8.2. Application of the Growing Neural Networks

8.3. Growing Neural Network Algorithm Frameworks

8.4. Applications of the Growing Neural Network Frameworks

8.4.1. Parallel Framework

8.4.2. Loop-based Framework

8.5. Conclusions and Comparison of the Different Methods

9. Results – All Data Sets

9.1. Methanol and Ethanol by SPR

9.1.1. Single Analytes

9.1.2. Parallel Growing Neural Network Framework

9.1.3. Sensitivity Analysis

9.1.4. Brute Force Variable Selection

9.1.5. Conclusions

9.2. Methanol, Ethanol and 1-Propanol by SPR

9.2.1. Single Analytes

9.2.2. Multivariate Calibrations of the Mixtures

9.2.3. Genetic Algorithm Framework

9.2.4. Parallel Growing Neural Network Framework

9.2.5. PCA-NN

9.2.6. Conclusions

9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup

9.3.1. Signals and Data Preparation

9.3.2. Mixtures by the RIfS Array

9.3.3. Mixtures by the 4l Setup

9.3.4. Conclusions

9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array

9.4.1. Introduction

9.4.2. Results

9.4.3. Conclusions

9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II

10. Results – Various Aspects of the Frameworks and Measurements

10.1. Single or Multiple Analyte Rankings

10.2. Stopping Criteria for the Parallel Frameworks

10.3. Optimization of the Measurements

10.4. Robustness and Comparison with Martens' Uncertainty Test

11. Summary and Outlook

12. References

13. Acknowledgements

Publications

Publications and Patents

Poster Gallery

Research Tutorials

Metabonomics

Biology and "-omics"

Definition of Metabonomics

Methods

In-life phase

Animal Study

Human Study

Analytical Techniques

Structure Elucidation

Pre-processing

Spectral Exclusion Regions

Peak Shifts

Equidistant Binning

Non-equidistant Binning

Normalization

Probabilistic Normalization

Data Analysis

Principles of PCA

PCA in Metabonomics

Metabolite Projection Analysis

Biochemical Interpretation

Applications in Drug Development

Possiblities and Examples

Specific Biomarkers

Phospholipidosis

PAG as Biomarker for Phospholipidosis

Amiodarone Study

Monitoring Drug Candidates

Patterns of Biomarkers

Competitive Study

Compound A

Compound B

Interpretations for Compound B

Predictions and Results

Gut Microflora

Normal Gut Microflora

Abnormal Gut Microflora

Structure Elucidation of New Biomarkers

Competitive Study

Metabolite Enrichement

HNBC 2D-NMR

Biochemical Interpretation

Ranking Study

Influences of Study Protocol

Clustering of Compounds

Statistical Identification of Correlated Signals

HSQC 2D-NMR

Validation of Structure Identity

Biochemical Interpretation

Acknowledgements

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