Literature DB >> 9664759

Mathematical models of purine metabolism in man.

R Curto1, E O Voit, A Sorribas, M Cascante.   

Abstract

Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.

Entities:  

Mesh:

Substances:

Year:  1998        PMID: 9664759     DOI: 10.1016/s0025-5564(98)10001-9

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  21 in total

1.  Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data.

Authors:  Zhen Qi; Eberhard O Voit
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2016-07-07       Impact factor: 3.710

Review 2.  Biological systems modeling and analysis: a biomolecular technique of the twenty-first century.

Authors:  Gautam Goel; I-Chun Chou; Eberhard O Voit
Journal:  J Biomol Tech       Date:  2006-09

3.  Fuzzy optimization for detecting enzyme targets of human uric acid metabolism.

Authors:  Kai-Cheng Hsu; Feng-Sheng Wang
Journal:  Bioinformatics       Date:  2013-09-26       Impact factor: 6.937

4.  Metabolic modeling helps interpret transcriptomic changes during malaria.

Authors:  Yan Tang; Anuj Gupta; Swetha Garimalla; Mary R Galinski; Mark P Styczynski; Luis L Fonseca; Eberhard O Voit
Journal:  Biochim Biophys Acta Mol Basis Dis       Date:  2017-10-22       Impact factor: 5.187

Review 5.  The best models of metabolism.

Authors:  Eberhard O Voit
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-05-19

6.  A two-way interface between limited Systems Biology Markup Language and R.

Authors:  Tomas Radivoyevitch
Journal:  BMC Bioinformatics       Date:  2004-12-07       Impact factor: 3.169

Review 7.  Recent developments in parameter estimation and structure identification of biochemical and genomic systems.

Authors:  I-Chun Chou; Eberhard O Voit
Journal:  Math Biosci       Date:  2009-03-25       Impact factor: 2.144

Review 8.  Modeling cellular compartmentation in one-carbon metabolism.

Authors:  Marco Scotti; Lorenzo Stella; Emily J Shearer; Patrick J Stover
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2013-02-13

9.  Identification of cancer mechanisms through computational systems modeling.

Authors:  Zhen Qi; Eberhard O Voit
Journal:  Transl Cancer Res       Date:  2014-06-01       Impact factor: 1.241

10.  Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses.

Authors:  Gonzalo Guillén-Gosálbez; Albert Sorribas
Journal:  BMC Bioinformatics       Date:  2009-11-24       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.