A mathematical model of the methionine cycle

Reed, Michael C.; Nijhout, H. Frederik; Sparks, Rachel; Ulrich, Cornelia M.

doi:10.1016/j.jtbi.2003.08.001

Cited by 89 publications

(93 citation statements)

References 30 publications

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“…The results of both the present work and Gutenkunst et al (2007) have clear implications for robustness: at some level, the methionine cycle model used here (Reed et al, 2004), as well as the models studied by Gutenkunst et al, are robust to parameter value variation. However, robustness generally implies similar behavior with parameter value variation in any direction, whereas Gutenkunst et al showed that parameter value variation in only certain directions is tolerated without markedly altering the quantitative output of the model.…”

Section: Discussionsupporting

confidence: 60%

“…With a focus on the parameter identification problem and its importance in metabolic modeling, here we examined the ranges of specific model parameter values capable of producing particular quantitative behavior of a previously published methionine cycle model (Reed et al, 2004). Using a nonlinear global parameter identification algorithm, we found that remarkably broad ranges of parameter values were consistent with simulated metabolite concentration and flux data within modest error estimates.…”

Section: Discussionmentioning

confidence: 99%

“…A previously published mathematical model of the methionine cycle developed initially by Martinov et al (2000) and expanded by Reed et al (2004) was used to generate the simulated data. The model consists of four non-linear ordinary differential equations describing the rate of change of methionine (Met), S-adenosylmethionine (AdoMet), S-adenosylhomocysteine (AdoHyc), and homocysteine (Hyc) (see Figure 1).…”

Section: Methionine Cycle Modelmentioning

confidence: 99%

“…These four metabolites interconvert via eight different enzyme catalyzed reactions: Table 1 lists the functional forms of the flux expressions for each of the eight reactions. The formulation of the model here is identical to that of Reed et al (2004), except for elimination of any over-parameterization. Specifically, in the expression for V METH , the parameter K METH m /[A] from the original publication was absorbed into V METH max , where [A] represents an arbitrary methylation substrate.…”

Section: Methionine Cycle Modelmentioning

confidence: 99%

“…In this work, we apply this method to parameter estimation for a metabolic network model that simulates the methionine cycle (Martinov et al, 2000;Reed et al, 2004). The focus is to understand the properties of the extracted parameter families (including the reliability of the predictions that they make) as a function of the input data provided.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Diverse metabolic model parameters generate similar methionine cycle dynamics

Piazza

Xiao

Rabinowitz

et al. 2008

Journal of Theoretical Biology

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Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine-cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.

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Section: Discussionsupporting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Methionine Cycle Modelmentioning

confidence: 99%

Section: Methionine Cycle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Diverse metabolic model parameters generate similar methionine cycle dynamics

Piazza

Xiao

Rabinowitz

et al. 2008

Journal of Theoretical Biology

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First steps in computational systems biology: A practical session in metabolic modeling and simulation

Reyes-Palomares

Sánchez‐Jiménez

Medina

2009

Biochem Molecular Bio Educ

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A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever faster numerical simulations of mathematical models. Mathematical modeling plays an essential role in new systems biology approaches. As a complex, integrated system, metabolism is a suitable topic of study for systems biology approaches. However, up until recently, this topic has not been properly covered in biochemistry courses. This communication reports the development and implementation of a practical lesson plan on metabolic modeling and simulation.

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Incorporation of guanosine gels into sieving matrices for length‐ and sequence‐based separation of DNA in capillary electrophoresis

Dong

McGown

2011

Electrophoresis

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Sieving gels are used in capillary gel electrophoresis to resolve DNA strands of different lengths. For complex samples, however, such as those encountered in metagenomic analysis of microbial communities or biofilms, length-based separation may mask the true genetic diversity of the community since different organisms may contribute same-length DNA with different sequences. There is a need, therefore, for DNA separations based on both the length and sequence. Previous work has demonstrated the ability of guanosine gels (G-gels) to separate four single-stranded DNA 76-mers that differ by only a few A/G base substitutions. The goal of the present work is to determine whether G-gels could be combined with commercial sieving gels in order to simultaneously separate DNA based on both length and sequence. The results are given for the four 76-mers and for a standard dsDNA ladder. Commercial sieving gels were used alone and in combination with G-gels. For the 76-mers, the combined medium was less efficient than the G-gel alone but was able to achieve partial resolution. The combined medium was at least as effective as the sieving gel alone at resolving the denatured DNA ladder and showed indications of sequence-based resolution as well, as supported by MALDI-MS. The results show that the combined sieving gel/G-gel medium retains the selectivity of the individual media, providing a promising approach to simultaneous length- and sequence-based DNA separation for metagenomic analysis of complex systems.

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A mathematical model of the methionine cycle

Cited by 89 publications

References 30 publications

Diverse metabolic model parameters generate similar methionine cycle dynamics

Diverse metabolic model parameters generate similar methionine cycle dynamics

First steps in computational systems biology: A practical session in metabolic modeling and simulation

Incorporation of guanosine gels into sieving matrices for length‐ and sequence‐based separation of DNA in capillary electrophoresis

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