Difference: SystemsGenomicsBioLab (1 vs. 25)

Revision 25
14 Mar 2016 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.

  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, Nature Biotechnology, 2013).

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.

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Revision 24
29 Jan 2015 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
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  The Systems Genomics research is led by Perry Moerland .

Umesh Nandal is a PhD student working on the comparison of model systems with human.

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Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.

Miranda Stobbe succesfully defended her PhD thesis on the comparison of public metabolic pathway databases (October 18, 2012).
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Herman Sontrop successfully defended his PhD thesis on compendium analyses of microarray breast cancer datasets (January 15, 2015).

Miranda Stobbe succesfully defended her PhD thesis on the comparison of public metabolic pathway databases (October 18, 2012).
 
Revision 23
27 Aug 2014 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
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The Systems Genomics research is led by Perry Moerland .

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Umesh Nandal is a PhD student working on the comparison of model systems with human.

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Umesh Nandal is a PhD student working on the comparison of model systems with human.

  Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.

Miranda Stobbe succesfully defended her PhD thesis on the comparison of public metabolic pathway databases (October 18, 2012).
Revision 22
31 Jul 2014 - Main.AngelaLuijf
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META TOPICPARENT name="ResearchBioLab"
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Medical Bioinformatics

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Revision 21
31 Jul 2014 - Main.AngelaLuijf
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.

  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, Nature Biotechnology, 2013).

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.

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Revision 20
18 Jul 2014 - Main.AdminUser
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Systems Genomics People
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Systems Genomics People .

  High-throughput experimental methods in molecular biology provide the quantitative basis for gaining a better understanding of human disease. However, multifactorial diseases such as diabetes and atherosclerosis are complex disorders involving hundreds of genes and many developmental and environmental factors. Therefore, computational methods are needed that can uncover the molecular networks perturbed by disease. In the Systems Genomics group our goal is to develop methods and software that can be used in this endeavour. Methods and software developed in our group are generic, but we are particularly interested in their application to metabolic syndrome (obesity, insulin resistance, diabetes). We collaborate with several AMC groups who are studying metabolic syndrome in mice and human patients using transcriptome and metabolome data.

Our research comprises three interrelated lines of investigation:
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.

  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, Nature Biotechnology, 2013).

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.

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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.

  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, Nature Biotechnology, 2013).

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.

 
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  The Systems Genomics research is led by Perry Moerland .

Umesh Nandal is a PhD student working on the comparison of model systems with human.

Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.

Miranda Stobbe succesfully defended her PhD thesis on the comparison of public metabolic pathway databases (October 18, 2012).
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Revision 19
18 Jul 2014 - Main.UnknownUser
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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Revision 18
10 Mar 2013 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Systems Genomics
 
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Revision 17
09 Mar 2013 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
Changed:
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, accepted for publication in Nature Biotechnology, 2013).
>
>
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, Nature Biotechnology, 2013).
 
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  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
>
>
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
 

Revision 16
19 Dec 2012 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Our research comprises three interrelated lines of investigation:
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
>
>
  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE, 2011). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
 
Changed:
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network.
>
>
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology, 2011). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network (Thiele et al, accepted for publication in Nature Biotechnology, 2013).
 
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  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
>
>
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry, 2011). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
 

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The Systems Genomics research is led by Perry Moerland .

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Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases.

  Umesh Nandal is a PhD student working on the comparison of model systems with human.

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Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
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Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.

Miranda Stobbe succesfully defended her PhD thesis on the comparison of public metabolic pathway databases (October 18, 2012).
 .
Revision 15
14 Oct 2011 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Our research comprises three interrelated lines of investigation:
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies (Sontrop et al, PLoS ONE). For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
 
Changed:
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network.
>
>
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, BMC Systems Biology). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network.
 
Changed:
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  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload. An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
>
>
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload (Hakvoort et al, Journal of Biological Chemistry). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
 

Revision 14
15 Apr 2011 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network.

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload. An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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Revision 13
21 Mar 2011 - Main.AngelaLuijf
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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System Genomics
 
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Revision 12
21 Mar 2011 - Main.AngelaLuijf
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Systems Genomics
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High-throughput experimental methods in molecular biology provide the quantitative basis for gaining a better understanding of human disease. However, multifactorial diseases such as diabetes and atherosclerosis are complex disorders involving hundreds of genes and many developmental and environmental factors. Therefore, computational methods are needed that can uncover the molecular networks perturbed by disease. In the Systems Genomics group our goal is to develop methods and software that can be used in this endeavour. Methods and software developed in our group are generic, but we are particularly interested in their application to metabolic syndrome (obesity, insulin resistance, diabetes). We collaborate with several AMC groups who are studying metabolic syndrome in mice and human patients using transcriptome and metabolome data.
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The Systems Genomics research is led by Perry Moerland .

Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases.

Umesh Nandal is a PhD student working on the comparison of model systems with human.

Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
>
>
The Systems Genomics research is led by Perry Moerland .

Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases.

Umesh Nandal is a PhD student working on the comparison of model systems with human.

Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
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Revision 11
11 Feb 2011 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Our research comprises three interrelated lines of investigation:
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we will develop software for the construction of disease-specific compendia of high-throughput data.
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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we develop software for the construction of disease-specific compendia of high-throughput data.
 
Changed:
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).
>
>
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted). We are also working on the integration of our findings in an international effort to build a consensus human metabolic network.
 
Changed:
<
<
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We will focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload. An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
>
>
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload. An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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Revision 10
31 Jan 2011 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we will develop software for the construction of disease-specific compendia of high-throughput data.
Changed:
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted to Molecular Systems Biology). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).
>
>
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).
 
Changed:
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<
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We will focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting (Sokolovic et al, in prep) and nutritional overload (grant in prep). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
>
>
  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We will focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting and nutritional overload. An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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The Systems Genomics research is led by Perry Moerland . Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases. Umesh Nandal is a PhD student working on the comparison of model systems with human. Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
>
>
The Systems Genomics research is led by Perry Moerland .

Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases.

Umesh Nandal is a PhD student working on the comparison of model systems with human.

Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
 .
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Revision 9
05 Oct 2010 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

Line: 17 to 17
 

  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we will develop software for the construction of disease-specific compendia of high-throughput data.
Changed:
<
<
  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted to Genome Biology). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).
>
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  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted to Molecular Systems Biology). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).
 

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We will focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting (Sokolovic et al, in prep) and nutritional overload (grant in prep). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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08 Aug 2010 - Main.AntoineVanKampen
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Medical Bioinformatics

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High-throughput experimental methods in molecular biology provide the quantitative basis for gaining a better understanding of human disease. However, multifactorial diseases such as diabetes and atherosclerosis are complex disorders involving hundreds of genes and many developmental and environmental factors. Therefore, computational methods are needed that can uncover the molecular networks perturbed by disease. In the Systems Genomics group our goal is to develop such methods and tools. We focus on the
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High-throughput experimental methods in molecular biology provide the quantitative basis for gaining a better understanding of human disease. However, multifactorial diseases such as diabetes and atherosclerosis are complex disorders involving hundreds of genes and many developmental and environmental factors. Therefore, computational methods are needed that can uncover the molecular networks perturbed by disease. In the Systems Genomics group our goal is to develop methods and software that can be used in this endeavour. Methods and software developed in our group are generic, but we are particularly interested in their application to metabolic syndrome (obesity, insulin resistance, diabetes). We collaborate with several AMC groups who are studying metabolic syndrome in mice and human patients using transcriptome and metabolome data.
 

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  1. construction of disease-specific microarray compendia for the integration of multiple gene expression studies (diabetes, breast cancer)
  2. comparison and consensus reconstruction of human metabolic pathway databases
  3. development of methods for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks.
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Our research comprises three interrelated lines of investigation:

  • A recurring theme in systems biology research is the integration of large amounts of high-throughput data, for example from gene expression studies. For this purpose we will develop software for the construction of disease-specific compendia of high-throughput data.

  • Next to the integration of high-throughput measurements, the collection of prior biological knowledge, e.g., published information about biochemical pathways is another key component in systems biology research. We recently performed a systematic comparison of five comprehensive and often used metabolic networks and uncovered surprisingly large differences between these databases (Stobbe et al, submitted to Genome Biology). We will be working on the integration of our findings in an international effort to build a consensus human metabolic network (see Jamborees).

  • The previous two research lines provide the building blocks for the development of computational methods to uncover molecular networks perturbed by disease. Disease-specific networks are constructed using our compendium database and curated pathway databases. We will focus on the development of network-based methods for the analysis of gene expression data across (a) different tissues and (b) different organisms. An example of the former is our ongoing collaboration with the Verhoeven group (Medical Biochemistry) on the interorgan coordination in response to fasting (Sokolovic et al, in prep) and nutritional overload (grant in prep). An example of the latter is our recently granted NBIC project for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks. Our goal is to provide a systematic framework that will be useful for understanding the applicability of rodent models for human metabolic syndrome.
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28 Jan 2010 - Main.PerryMoerland
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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25 Jan 2010 - Main.PerryMoerland
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Medical Bioinformatics

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High-throughput experimental methods in molecular biology provide the quantitative basis for gaining a better understanding of human disease. However, multifactorial diseases such as diabetes and atherosclerosis are complex disorders involving hundreds of genes and many developmental and environmental factors. Therefore, computational methods are needed that can uncover the molecular networks perturbed by disease. In the Systems Genomics group our goal is to develop such methods and tools. We focus on the

  1. construction of disease-specific microarray compendia for the integration of multiple gene expression studies (diabetes, breast cancer)
  2. comparison and consensus reconstruction of human metabolic pathway databases
  3. development of methods for investigating similarities and differences between model organisms and human using omics data and disease-specific functional networks.

 
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24 Jan 2010 - Main.PerryMoerland
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Medical Bioinformatics

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The Systems Genomics research is led by PerryMoerland . Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases. Umesh Nandal is a PhD student working on the comparison of model systems with human.
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The Systems Genomics research is led by Perry Moerland . Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases. Umesh Nandal is a PhD student working on the comparison of model systems with human. Herman Sontrop is a PhD student at Philps Research working on compendium analyses of microarray breast cancer datasets.
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22 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Systems Genomics People
Under construction. The Systems Genomics research is led by PerryMoerland . Miranda Stobbe is a PhD student working on the comparison of public metabolic pathway databases. Umesh Nandal is a PhD student working on the comparison of model systems with human.
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21 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Revision 2
19 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="EbioscienceBioLab"
Medical Bioinformatics

Systems Genomics
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META TOPICPARENT name="ResearchBioLab"
Medical Bioinformatics

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Systems Genomics
 

Under construction
 
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