Introduction

Johan Westerhuis studied Analytical Chemistry/Chemometrics at the Rijks Hogeschool Groningen where he received his BSc degree in 1989. He then went to the Catholic University of Nijmegen to study Chemistry/Chemometrics in the group of Dr. L.M.C. Buydens, where he received his MSc degree in 1992. From 1992-1997 he worked towards a PhD at the University Centre for Pharmacy at the University of Groningen on Multivariate Statistical Modeling of the Pharmaceutical Process of Wet Granulation and Tableting.

From 1997 until 1998 he worked as a postdoctoral researcher at the Chemical Engineering Department of McMaster University , Hamilton, ON, Canada in the group of Dr. JF MacGregor. In October 1998 he joined the former Process Analysis & Chemometrics group (PAC), now the Biosystems Data Analysis group at the Universiteit van Amsterdam where he works as an assistent professor.

In 2010 he won the prestigious EAS Award for Outstanding Achievements in Chemometrics.

In 2012 he became an extraordinary professor at the BMI group of the North-West University in Potchefstroom, South Africa. Here he supervises projects in the metabolomics laboratory.

In 2013 he won two publication awards from the Metabolomics Society. 

Metabolomics Data Analysis

Functional genomics approaches are increasingly being used for the elucidation of complex biological questions with applications that range from human health via plants to microbial strain improvement . Metabolomics aims to measure the complete metabolomic response of an organism to the environmental conditions of interest. The main goal is to understand the cell in terms of metabolic and regulation networks, how are the different pathways connected and how are they regulated.

In our data analysis approach we aim at using prior information of the cells to improve the analysis results. Such an approach is called Grey Modelling . Grey models give a global overview of the cell with improved interpretability and they are able to reveal new molecular level information.

The development of data analysis tools to make such grey models is the key part of our research. How to incorporate prior knowledge on detailed kinetic models with different levels of confidence, how to include nonlinear metabolite/protein interactions and how to learn from the grey models ?
Recently, we developed Nutrikinetics , a new research field that studies what the body does to nutrients and their metabolites using specific kinetic models.
To learn about Nutrikinetics and How to deal with biological variation take a look at the following links:

Biosystems Data Analysis Master Course

Files needed for the master course on Biosystems Data Analysis can be found here.

Research Projects

• Biologically Relevant Analysis of Metabolomics Data; Polina Reshetova
• User-driven Development of Statistical Methods for Experimental Planning, Data Gathering, and Integrative Analysis of Next Generation Sequencing, Proteomics and Metabolomics data (STATegra, FP7 Health project)
• Low level data fusion approaches to combine multiple functional genomics data sets of the same experiment for improved integration; Frans van der Kloet
• Combining metabolomics, proteomics and genome sequencing data using dynamic flux models for experimental design purposes; Theo Reijmers
• The metabolic consequences of acute alcohol abuse; Mari van Reenen (NWU, South Africa)

Selected Key Publications

• Westerhuis JA, Hoefsloot HCJ, Smit S, Vis DJ, Smilde AK, van Velzen EJJ, van Duijnhoven JPM, van Dorsten FA, Assessment of PLSDA cross validation, Metabolomics, (2008), 4, 81-89.

• Velzen EJJ, Westerhuis JA, van Duijnhoven JPM, van Dorsten FA, Grün C, Jacobs DM, Duchateau GSMJE, Vis D, Smilde AK, Phenotyping tea consumers by nutrikinetic analysis of polyphenol end metabolites, Journal of Proteome Research, (2009), 8, 3317-3330.

• Reshetova P, Smilde AK, van Kampen AHC, Westerhuis JA, Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data  BMC Systems Biology, 8(Suppl 2):S2 (2014)