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10.1093/bioinformatics/btu069 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu069 C4071196!4071196 !24578401     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24578401 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (13 ): 1892-8 Nephropedia Template TP {{tp|p=24578401 |t=2014. Derivative processes for modelling metabolic fluxes |pdf=|usr=}}{{24578401 }} gab.com Text Derivative processes for modelling metabolic fluxes JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24578401 #FOAvip MOTIVATION: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. RESULTS: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. AVAILABILITY AND IMPLEMENTATION: R code is available from the authors upon request. Twit Text FOAVip Derivative processes for modelling metabolic fluxes ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24578401 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24578401 #FOAvip English Wikipedia <ref>{{Cite pmid|24578401 }}</ref> Derivative processes for modelling metabolic fluxes #MMPMID24578401 Zurauskien? J ; Kirk P ; Thorne T ; Pinney J ; Stumpf M Bioinformatics 2014[Jul]; 30 (13 ): 1892-8 PMID24578401 show ga MOTIVATION: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. RESULTS: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. AVAILABILITY AND IMPLEMENTATION: R code is available from the authors upon request. |*Metabolic Networks and Pathways [MESH] |Bayes Theorem [MESH] |Escherichia coli/metabolism [MESH] |Models, Biological [MESH]Derivative processes for modelling metabolic fluxes (epmc).pdf DeepDyve Pubget Overpricing