258 Hits in 5.0 sec

Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

Jan Krumsiek, Karsten Suhre, Thomas Illig, Jerzy Adamski, Fabian J Theis
2011 BMC Systems Biology  
In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations  ...  Results: In our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables.  ...  Jan Krumsiek is supported by a PhD student fellowship from the "Studienstiftung des Deutschen Volkes". Thanks to Harold Gutch for critically proofreading and correcting this manuscript.  ... 
doi:10.1186/1752-0509-5-21 pmid:21281499 pmcid:PMC3224437 fatcat:miqrtrmeinesfjevlr6mbps5we


Jörg Bartel, Jan Krumsiek, Fabian J. Theis
2013 Computational and Structural Biotechnology Journal  
Targeted metabolomics is Citation Bartel J, Krumsiek J, Theis FJ (2013) Statistical methods for the analysis of high-throughput metabolomics data.  ...  Metabolomics in the field of biomedical research With the advent of metabolomics, a new, important milestone in the endeavor to fully measure a biological system could be achieved.  ...  Gaussian graphical models applied to metabolomics data. A) Network representation of a Gaussian graphical model.  ... 
doi:10.5936/csbj.201301009 pmid:24688690 pmcid:PMC3962125 fatcat:ybh5eka5mfekzfwpsz53aqajca

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks

Liam G Fearnley, Michael Inouye
2016 International Journal of Epidemiology  
. • Recent advances in high-throughput technologies now allow generation of population-scale metabolomics and other 'omics' data. • Parallel advances in computational and statistical approaches enable  ...  the integration of these data. • Consequently, analytical approaches that consider single concentration-based metabolites can now integrate additional omics data and existing databases to build reaction  ...  association studies Direct analogue of GWAS studies; testing of metabolites for association with phenotype 24 Gaussian graphical modelling Inference and reconstruction of metabolic pathways where reactions  ... 
doi:10.1093/ije/dyw046 pmid:27118561 pmcid:PMC5100607 fatcat:ql53omilqjeozby3mlsdkcgduu

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Jan Krumsiek, Karsten Suhre, Anne M. Evans, Matthew W. Mitchell, Robert P. Mohney, Michael V. Milburn, Brigitte Wägele, Werner Römisch-Margl, Thomas Illig, Jerzy Adamski, Christian Gieger, Fabian J. Theis (+2 others)
2012 PLoS Genetics  
Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites.  ...  Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.  ...  We combine high-throughput metabolomics and genotyping data in Gaussian graphical models (GGMs) [21] and in genome-wide association studies (GWAS) [5] in order to produce testable predictions of the  ... 
doi:10.1371/journal.pgen.1003005 pmid:23093944 pmcid:PMC3475673 fatcat:jj4oufgjzrbbfg4usakhq42ydq

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology [chapter]

Stéphane Ballereau, Enrico Glaab, Alexei Kolodkin, Amphun Chaiboonchoe, Maria Biryukov, Nikos Vlassis, Hassan Ahmed, Johann Pellet, Nitin Baliga, Leroy Hood, Reinhard Schneider, Rudi Balling (+1 others)
2013 Systems Biology  
how recent technological advances in these fields have moved the bottleneck from data production to data analysis.  ...  Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.  ...  Methods and Tools Current high-throughput technologies produce very large data sets and have shifted the bottleneck from data production to data analysis.  ... 
doi:10.1007/978-94-007-6803-1_1 fatcat:toqji65vxzbejo6wdfxuncwnmq

Omics meet networks—using systems approaches to infer regulatory networks in plants

Miguel A Moreno-Risueno, Wolfgang Busch, Philip N Benfey
2010 Current opinion in plant biology  
Networks and pathways have been reconstructed using transcriptome, genome-wide transcription factor binding, proteome and metabolome data, and subsequently used to infer functional interactions among genes  ...  However, more sophisticated systems biology approaches are needed to integrate different omics data sets.  ...  Work in the Benfey lab on regulatory networks in plants is funded by grants from the NIH, NSF and DARPA.  ... 
doi:10.1016/j.pbi.2009.11.005 pmid:20036612 pmcid:PMC2862083 fatcat:o4ogc3aiizg6hlnw3ba4u3heee

Reconstruction of Metabolic Association Networks Using High-throughput Mass Spectrometry Data [chapter]

Imhoi Koo, Xiang Zhang, Seongho Kim
2012 Lecture Notes in Computer Science  
Graphical Gaussian model (GGM) has been widely used in genomics and proteomics to infer biological association networks, but the relative performances of various GGM-based methods are still unclear in  ...  These approaches then are applied to simulated data and real metabolomics data.  ...  On the other hand, graphical Gaussian models (GGMs) reveal direct associations with conditional independences/dependences among variables, using partial correlation coefficients that are calculated by  ... 
doi:10.1007/978-3-642-31588-6_21 fatcat:plhqxk4225g2dpbtriwjqdmwwm

Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

Tunahan Çakır, Mohammad Jafar Khatibipour
2014 Frontiers in Bioengineering and Biotechnology  
Flux-Balance Analysis to elucidate the active network.The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract  ...  Keywords: constraint-based models, metabolic network inference, active metabolic state, metabolome, network biology, reverse engineering, flux-balance analysis  ...  Graphical Gaussian modeling was also applied to metabolome data from blood serum samples to reconstruct human fatty acid metabolism (Krumsiek et al., 2011) .  ... 
doi:10.3389/fbioe.2014.00062 pmid:25520953 pmcid:PMC4253960 fatcat:l2tsj6oflretrh2a436hoza32u

Databases and tools for constructing signal transduction networks in cancer

Seungyoon Nam
2017 BMB Reports  
One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype  ...  CONCLUSIONS Systems biology is a general modeling framework that utilizes high-throughput data and prior knowledge, to result in network inference and hypotheses suggestions.  ...  Systems biology often begins from high-throughput experimental data.  ... 
doi:10.5483/bmbrep.2017.50.1.135 pmid:27502015 pmcid:PMC5319659 fatcat:pwjpj6my6bblzj6lcu4urbxxkm

Genetics of the human metabolome, what is next?

Harish Dharuri, Ayşe Demirkan, Jan Bert van Klinken, Dennis Owen Mook-Kanamori, Cornelia M. van Duijn, Peter A.C. 't Hoen, Ko Willems van Dijk
2014 Biochimica et Biophysica Acta - Molecular Basis of Disease  
graphical modeling; PSEA, phenotype set enrichment analysis; GSMM, genome scale metabolic model; CBA, constraint-based analysis ☆  ...  Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples.  ...  Gaussian Graphical Modeling Gaussian Graphical Modelling (GGM) is an unbiased and database independent approach to reconstruct metabolic networks from largescale metabolomics data sets [47] .  ... 
doi:10.1016/j.bbadis.2014.05.030 pmid:24905732 fatcat:xduqiio2dvd2hk3zb3ogmacdre

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

Andreas Kuehne, Urs Mayr, Daniel C. Sévin, Manfred Claassen, Nicola Zamboni, Christos A. Ouzounis
2017 PLoS Computational Biology  
We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential  ...  or serial metabolomics data.  ...  Acknowledgments We thank Tobias Fuhrer for providing the metabolomics data for the KEIO knock-out library. Author Contributions Conceptualization: AK MC NZ.  ... 
doi:10.1371/journal.pcbi.1005577 pmid:28598965 pmcid:PMC5482507 fatcat:ddgyqbhbw5b4zoiasrfs7gp3se

Identification of aberrant pathways and network activities from high-throughput data

J. Wang, Y. Zhang, C. Marian, H. W. Ressom
2012 Briefings in Bioinformatics  
In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities.  ...  This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities.  ...  We appreciate the organizers and participants of the Workshop on Identification of Aberrant Pathway and Network Activity from High-Throughput Data at the 2011 Pacific Biocomputing Symposium for providing  ... 
doi:10.1093/bib/bbs001 pmid:22287794 pmcid:PMC3404398 fatcat:woielqwwtbc6bhdoub3hycnu6a

Network Biology in Medicine and Beyond

B. Zhang, Y. Tian, Z. Zhang
2014 Circulation: Cardiovascular Genetics  
It is challenging to discern patterns and distill knowledge from massive amount of data in a high-dimensional space.  ...  and develop new treatments. 1 This data-intensive paradigm has fundamentally transformed biomedical science and holds great promise for the betterment of human health. 2 These high-throughput biotechnologies  ...  A special case of a Markov network is a Gaussian graphical model, where the distribution of the variables in the graph is assumed to be multivariate Gaussian. 39 Learning graphical models from data includes  ... 
doi:10.1161/circgenetics.113.000123 pmid:25140061 pmcid:PMC4333150 fatcat:bobjv3d5xzcttgtvng5vcf7x2a

Statistical Challenges in Biological Networks

George Michailidis
2012 Journal of Computational And Graphical Statistics  
INTRODUCTION High-throughput techniques have enabled biomedical researchers to obtain large quantities of data at the genomic, transcriptomic, proteomic, and metabolomic level.  ...  Subsequently, work focused on conditional independence models, especially Gaussian graphical models that are computationally tractable, since one needs to estimate from data the zeros in the inverse covariance  ... 
doi:10.1080/10618600.2012.738614 fatcat:3vvduwsaibgolom7dyv46salsy

Observing and interpreting correlations in metabolomic networks

R. Steuer, J. Kurths, O. Fiehn, W. Weckwerth
2003 Bioinformatics  
Based on their pair-wise correlations, the data obtained from metabolomic experiments are organized into metabolic correlation networks and the key challenge is to deduce unknown pathways based on the  ...  In a second step, we investigate to what extent our result is applicable to the problem of reverse engineering, i.e. to recover the underlying enzymatic reaction network from data.  ...  Discussion and conclusions In this work, we have discussed the analysis and interpretation of metabolomic data sets acquired by high-throughput measurements.  ... 
doi:10.1093/bioinformatics/btg120 pmid:12761066 fatcat:ccqlj23kpvhyzbyamwt2we55qi
« Previous Showing results 1 — 15 out of 258 results