A mathematical model of glutathione metabolism

Reed, Michael C.; Thomas, Rachel L.; Pavisic, Jovana; James, S. Jill; Ulrich, Cornelia M.; Nijhout, H. Frederik

doi:10.1186/1742-4682-5-8

Cited by 148 publications

(184 citation statements)

References 71 publications

(98 reference statements)

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“…Narrow black arrows indicate biochemical reactions and the broad gray arrows indicate allosteric activation or inhibition or dimerization reactions. Names of acronyms used in this paper are in the text, others can be found in Reed et al (2008). Data on the structure of the mathematical model can also be found in Reed et al (2008).…”

Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

“…Names of acronyms used in this paper are in the text, others can be found in Reed et al (2008). Data on the structure of the mathematical model can also be found in Reed et al (2008).…”

Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

“…2), in which metabolites activate or inhibit enzymes in the network. Our analysis has shown that these regulatory interactions serve to stabilize a few specific reactions in the system, namely: the TS reaction which is the rate-limiting step for DNA synthesis, the AICART reaction which is an early step in the de novo synthesis of nucleotides, the DNMT reaction which ensures correct methylation of newly replicated DNA, and also the synthesis of glutathione (not shown) (Nijhout et al 2004(Nijhout et al , 2006bReed et al 2006Reed et al , 2008. As amino acid input into the system varies, for instance with meals, these homeostatic mechanisms maintain stability of these critical reactions.…”

Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

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Systems Biology of Phenotypic Robustness and Plasticity

Nijhout

Sadre-Marandi

Best

et al. 2017

Integrative and Comparative Biology

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Synopsis Gene regulatory networks, cellular biochemistry, tissue function, and whole body physiology are imbued with myriad overlapping and interacting homeostatic mechanisms that ensure that many phenotypes are robust to genetic and environmental variation. Animals also often have plastic responses to environmental variables, which means that many different phenotypes can correspond to a single genotype. Since natural selection acts on phenotypes, this raises the question of how selection can act on the genome if genotypes are decoupled from phenotypes by robustness and plasticity mechanisms. The answer can be found in the systems biology of the homeostatic mechanisms themselves. First, all such mechanisms operate over a limited range and outside that range the controlled variable changes rapidly allowing natural selection to act. Second, mutations and environmental stressors can disrupt homeostatic mechanisms, exposing cryptic genetic variation and allowing natural selection to act. We illustrate these ideas by examining the systems biology of four specific examples. We show how it is possible to analyze and visualize the roles of specific genes and specific polymorphisms in robustness in the context of large and realistic nonlinear systems. We also describe a new method, system population models, that allows one to connect causal dynamics to the variable outcomes that one sees in biological populations with large variation.

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Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

“…Names of acronyms used in this paper are in the text, others can be found in Reed et al (2008). Data on the structure of the mathematical model can also be found in Reed et al (2008).…”

Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

Section: Robustness and Cryptic Genetic Variationmentioning

confidence: 99%

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Systems Biology of Phenotypic Robustness and Plasticity

Nijhout

Sadre-Marandi

Best

et al. 2017

Integrative and Comparative Biology

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“…These examples include the kinetic analysis and even multicompartment models of lipoprotein metabolism [7,21], or even computational models to analyse human energy metabolism during semi-starvation and in cancer cachexia [9,10]. Examples to describe mammalian nutrient metabolism in quantitative kinetic models have been put forward for folate and most recently for glutathione [22,23]. Despite the lack of examples it seems that nutritional science is ready to move forward to the systems approaches.…”

Section: Resultsmentioning

confidence: 99%

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

et al. 2008

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Nutritional systems biology may be defined as the ultimate goal of molecular nutrition research, where all relevant aspects of regulation of metabolism in health and disease states at all levels of its complexity are taken into account to describe the molecular physiology of nutritional processes. The complexity spans from intracellular to interorgan dynamics, and involves iterations between mathematical modelling and analysis employing all profiling methods and other biological read-outs. On the basis of such dynamic models we should be enabled to better understand how the nutritional status and nutritional challenges affect human metabolism and health. Although the achievement of this proposition may currently sound unrealistic, many initiatives in theoretical biology and biomedical sciences work on parts of the solution. This review provides examples and some recommendations for the molecular nutrition research arena to move onto the systems level.

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“…The folate cycle completes when 5-methyl-THF is demethylated to produce methionine and THF. This model is composed of 7 species and 13 enzymatic reactions where their rates are modelled by the Michaelis-Menten kinetics 30,31 .…”

Section: A Rssa Formulations Performance On Large Modelsmentioning

confidence: 99%

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Thanh

Zunino

Priami

2015

The Journal of Chemical Physics

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Stochastic simulation for in-silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [J. Chem. Phys. 141(13):134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

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A mathematical model of glutathione metabolism

Cited by 148 publications

References 71 publications

Systems Biology of Phenotypic Robustness and Plasticity

Systems Biology of Phenotypic Robustness and Plasticity

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

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