NMC

Biostatistics Project summaries

Global analysis of time resolved data from different compartments and treatments
The goal of this project is to develop metabolomics data analysis tools to be able to integrate metabolic profiles originating from different body fluids and organs. The combined analysis of profiles from different body fluids and organs will result in better descriptions of the biological process studied, or enhance the discovery of biomarkers, especially when this analysis is coupled with pharmacokinetic or physiological information.

Data fusion of complex-structured metabolomics data
This project will result in tools and algorithms to combine data obtained from different sources such that bias within statistical algorithms towards a subset of data is eliminated. We aim at the fusion of datasets obtained from different metabolomics platforms but also at the fusion of datasets obtained at different levels of X-omics (metabolomics, proteomics and transcriptomics) and generated from different biological sources such as plasma and tissue. This project will result in the combinatory tools that do right to the information content of each individual dataset.

Power analysis of metabolomics studies
In this project we aim at providing a protocol to design a metabolomics study given the specifications and requirements of the study. This protocol comprises the type of design, the number of levels for each factor and the number of samples and individuals to use. Furthermore the preferred data analysis method will be determined.

Experimental designs for time-resolved metabolomics experiments
How often and when do experiments in a time-resolved study need to be taken, how many individuals are needed and how many repeats are necessary. What type of design has to be selected and how are the results analyzed afterwards.

Combining metabolomics data with physico-chemical, thermo-dynamical and biochemical knowledge
In this project we will incorporate prior knowledge of molecular relationships already during the explorative analysis of the metabolomics data. This will result in explorative models that are much closer to the molecular biology. This approach may fill the gap between top-down and bottom up metabolomics based systems biology approaches and therefore improve the chances of obtaining biologically relevant models.

Discovering hidden dynamics by combining metabolomics data and physiological models
The aim of this project is to model dynamic measured metabolomic profiles, combine this information with quantitative and qualitative a priori knowledge, and infer from this dynamic information the hidden (non-measurable) dynamic processes of a system. Using dynamic measurements and a priori information we want to obtain a mechanistic understanding of the dynamics of regulation and communication of biological processes.

Emerging time-resolved metabolomics data structures from in-silico networks
In this project the aim is to use computer models to simulate dynamic metabolomics datasets, to investigate the role of different types of disturbances and experimental setup on the ability to infer topology and stoichiometry of metabolic networks and association (relevance) networks. This will give guidelines on the types of experiments that have to be performed in order to obtain the information needed to infer networks.

Inferring network information from time resolved metabolomics data
In this project we will develop methods to reconstruct networks based on real (or in-silico) dynamic metabolomics datasets. The main challenges in this project are on the inference of larger (parts of) biological networks. This includes the study of differences in networks due to genetic modifications, or the study of diseased systems.


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Biostatistics Project summaries Global analysis of time resolved data from different compartments and treatments The goal of this project is to develop metabolomics data analysis tools to be able to integrate metabolic profiles originating from different body fluids and organs. The combined analysis of profiles from different body fluids and organs will result in better descriptions of the biological process studied, or enhance the discovery of biomarkers, especially when this analysis is coupled with pharmacokinetic or physiological information. Data fusion of complex-structured metabolomics data This project will result in tools and algorithms to combine data obtained from different sources such that bias within statistical algorithms towards a subset of data is eliminated. We aim at the fusion of datasets obtained from different metabolomics platforms but also at the fusion of datasets obtained at different levels of X-omics (metabolomics, proteomics and transcriptomics) and generated from different biological sources such as plasma and tissue. This project will result in the combinatory tools that do right to the information content of each individual dataset. Power analysis of metabolomics studies In this project we aim at providing a protocol to design a metabolomics study given the specifications and requirements of the study. This protocol comprises the type of design, the number of levels for each factor and the number of samples and individuals to use. Furthermore the preferred data analysis method will be determined. Experimental designs for time-resolved metabolomics experiments How often and when do experiments in a time-resolved study need to be taken, how many individuals are needed and how many repeats are necessary. What type of design has to be selected and how are the results analyzed afterwards. Combining metabolomics data with physico-chemical, thermo-dynamical and biochemical knowledge In this project we will incorporate prior knowledge of molecular relationships already during the explorative analysis of the metabolomics data. This will result in explorative models that are much closer to the molecular biology. This approach may fill the gap between top-down and bottom up metabolomics based systems biology approaches and therefore improve the chances of obtaining biologically relevant models. Discovering hidden dynamics by combining metabolomics data and physiological models The aim of this project is to model dynamic measured metabolomic profiles, combine this information with quantitative and qualitative a priori knowledge, and infer from this dynamic information the hidden (non-measurable) dynamic processes of a system. Using dynamic measurements and a priori information we want to obtain a mechanistic understanding of the dynamics of regulation and communication of biological processes. Emerging time-resolved metabolomics data structures from in-silico networks In this project the aim is to use computer models to simulate dynamic metabolomics datasets, to investigate the role of different types of disturbances and experimental setup on the ability to infer topology and stoichiometry of metabolic networks and association (relevance) networks. This will give guidelines on the types of experiments that have to be performed in order to obtain the information needed to infer networks. Inferring network information from time resolved metabolomics data In this project we will develop methods to reconstruct networks based on real (or in-silico) dynamic metabolomics datasets. The main challenges in this project are on the inference of larger (parts of) biological networks. This includes the study of differences in networks due to genetic modifications, or the study of diseased systems.		Life Sciences Momentum NGI The Hague, 23 November 2010 Johan Westerhuis receives EAS Award Amsterdam, March 2010 30th ISPPP - Call for Papers Bologna, Italy, 6-8 September 2010 NGI sponsors Postdoc Retreat The Hague, 21-23 April 2010 Pre-Seed Grant NGI The Hague, January 2010 Metabolomics in Nature's vision 2020 Nature Magazine 7 January 2010 Metabolomics receives first ever Impact Factor Rating UK 17 August 2009 NMC makes the difference NMC, Leiden Puzzling with metabolites NMC, NBIC, May 2009 First NMC paper published Amsterdam, Utrecht, January 2009 Robert Hall elected as secretary Metabolomics Society 11 December 2008

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