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Mathematical modelling of metabolic regulation in aging.

Auley MT, Mooney KM, Angell PJ, Wilkinson SJ - Metabolites (2015)

Bottom Line: These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1.We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled.We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science & Engineering, University of Chester, Thornton Science Park, CH2 4NU, UK. m.mcauley@chester.ac.uk.

ABSTRACT
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.

No MeSH data available.


Related in: MedlinePlus

Proposed reduced model of SIRT1 and mTOR interaction based on Ghosh et al. (2010) [31]. Insulin binds to IRs resulting in autophosphorylation. IRs can be phosphorylated or unphosphorylated [123]. The active IR induces the phosphorylation of IRS-1 via the activity of protein tyrosine kinase. This signal depends on phosphorylated IR. IRS1p docks with PI3-kinase. This results in the formation of the IRS-1/activated PI3-kinase complex (IRS1pPI3). Dissociation of the complex is dependent on IRS1pPI3. Phosphorylation of Akt depends on IRS1pPI3 and is reversible. Formation of the TSC1/2 complex depends on phosphorylated Akt. The TS1/2 complex inhibits the conversion of inactive Rheb to active Rheb (Rheb-GTP). RhebA activates mTORC1. Activation of mTORC1 affects S6K, while the activity of S6K is triggered by phosphorylation [124]. The activity of S6 kinase triggers feedback mechanisms inhibiting IRS-1. mTORp provokes cell metabolism and growth. We explore the proposed interaction between mTOR and SIRT1 by having SIRT1 bind with the TSC1/2 complex to inhibit active Rheb formation. Thus, the SIRT1 TSC1/2 complex association is included as an inhibitor of active Rheb formation and subsequent mTOR signaling, as suggested by Ghosh and colleagues.
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metabolites-05-00232-f003: Proposed reduced model of SIRT1 and mTOR interaction based on Ghosh et al. (2010) [31]. Insulin binds to IRs resulting in autophosphorylation. IRs can be phosphorylated or unphosphorylated [123]. The active IR induces the phosphorylation of IRS-1 via the activity of protein tyrosine kinase. This signal depends on phosphorylated IR. IRS1p docks with PI3-kinase. This results in the formation of the IRS-1/activated PI3-kinase complex (IRS1pPI3). Dissociation of the complex is dependent on IRS1pPI3. Phosphorylation of Akt depends on IRS1pPI3 and is reversible. Formation of the TSC1/2 complex depends on phosphorylated Akt. The TS1/2 complex inhibits the conversion of inactive Rheb to active Rheb (Rheb-GTP). RhebA activates mTORC1. Activation of mTORC1 affects S6K, while the activity of S6K is triggered by phosphorylation [124]. The activity of S6 kinase triggers feedback mechanisms inhibiting IRS-1. mTORp provokes cell metabolism and growth. We explore the proposed interaction between mTOR and SIRT1 by having SIRT1 bind with the TSC1/2 complex to inhibit active Rheb formation. Thus, the SIRT1 TSC1/2 complex association is included as an inhibitor of active Rheb formation and subsequent mTOR signaling, as suggested by Ghosh and colleagues.

Mentions: There a number of issues that can impede the development of a model in this area. Firstly, a large number of biological systems remain poorly understood mechanistically. This makes an integrated mathematical description of their mechanisms inherently challenging. Moreover, this problem feeds forward into parameter selection; if the mechanisms have not been fully delineated, then it is probable that there will be a paucity of measurements with which to infer accurate parameter values. A variety of techniques have been developed for estimating parameters that are not identifiable in the strict mathematical sense [114,115]. Residual parameter uncertainty, however, has an impact on model extension, because if the parameters of the original model are unclear, this raises questions over the value of extending the model further. This is particularly frustrating from the perspective of the aging process, as due to its intrinsic complexity, holistic models are a necessity if we are to improve our understanding of this process. To help overcome these difficulties, Kriete et al. (2010) created a noteworthy rule-based cell systems model of aging that included the mTOR pathway along with several other pathways related to aging [116]. This semi-quantitative model enabled the authors to represent aging in a holistic fashion that was defined by connectivity/rules among all components involved. Another promising direction for future research is the development of algorithmic methods for reducing model complexity and identifying, from the ensemble of uncertain model parameters, the critical components that demand further investigation. A good example of this is sensitivity analysis to determine those parameters that exert the most influence over a particular model output [117]. The performance of formal techniques for automated model reduction of systems biology models has also been studied by Dokoumetzidis and Aarons [118]. Recently, Rao and colleagues (2014) developed a model reduction method for biochemical reaction networks [119]. The method employed a stepwise reduction in the number of complexes, defined as the left- and right-hand sides of the reactions in the network. The reduced network has fewer complexes, reactions, variables and parameters when compared to the original network, and yet, the behaviour of a preselected set of significant metabolites in the reduced network closely resembles that of the original network. Moreover, the reduced network largely retains the structure and kinetics of the original model. A yeast model of glycolysis was reduced from 12 to seven variables. This reduction was able to improve the understanding of the dynamics of the network and presents a means of facilitating model parameterization or to embed a detailed model of interest in a more coarse-grained, yet realistic environment [120]. This type of approach could be important for modelling the interaction between mTOR and SIRT1, because their metabolic activities comprise a multitude of relationships that encapsulates the dynamic behaviour between enzymes, metabolites and cofactors. Given the current limited understanding of the mechanisms that underpin these pathways and the cloudiness surrounding their interactivity, constructing a detailed model of their circuitry is to a large extent futile. Rather, it is vital that computational models are informed by recent experimental evidence and that they are used to test a particular idea or hypothesis relating to a component or components of a reasonably well-defined system. The incorporation of model entities needs to be informed by metabolic circuitry that has been at least partly characterized experimentally. As an example, we created a diagram that could be used as a starting point to model the interaction of mTOR and SIRT1 (Figure 3). This diagram is informed by the hypothesis outlined by Ghosh et al. (2010) [31], in which the authors suggest SIRT1 negatively regulates mTOR signaling through inhibition of the TSC1/2 complex. Our idea centres on capturing this effect together with the key components of this pathway. The next step would be to code our putative model using a software tool, such as CellDesigner [121] or Copasi [122], both of which have been designed to support the SBML exchange framework, which would facilitate model updating. It would then be necessary to parameterize the model. Following this, simulations could be conducted along with a sensitivity analysis. The model could then be ultimately used to explore the relationship between mTOR and SIRT1 based on current experimental knowledge.


Mathematical modelling of metabolic regulation in aging.

Auley MT, Mooney KM, Angell PJ, Wilkinson SJ - Metabolites (2015)

Proposed reduced model of SIRT1 and mTOR interaction based on Ghosh et al. (2010) [31]. Insulin binds to IRs resulting in autophosphorylation. IRs can be phosphorylated or unphosphorylated [123]. The active IR induces the phosphorylation of IRS-1 via the activity of protein tyrosine kinase. This signal depends on phosphorylated IR. IRS1p docks with PI3-kinase. This results in the formation of the IRS-1/activated PI3-kinase complex (IRS1pPI3). Dissociation of the complex is dependent on IRS1pPI3. Phosphorylation of Akt depends on IRS1pPI3 and is reversible. Formation of the TSC1/2 complex depends on phosphorylated Akt. The TS1/2 complex inhibits the conversion of inactive Rheb to active Rheb (Rheb-GTP). RhebA activates mTORC1. Activation of mTORC1 affects S6K, while the activity of S6K is triggered by phosphorylation [124]. The activity of S6 kinase triggers feedback mechanisms inhibiting IRS-1. mTORp provokes cell metabolism and growth. We explore the proposed interaction between mTOR and SIRT1 by having SIRT1 bind with the TSC1/2 complex to inhibit active Rheb formation. Thus, the SIRT1 TSC1/2 complex association is included as an inhibitor of active Rheb formation and subsequent mTOR signaling, as suggested by Ghosh and colleagues.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495371&req=5

metabolites-05-00232-f003: Proposed reduced model of SIRT1 and mTOR interaction based on Ghosh et al. (2010) [31]. Insulin binds to IRs resulting in autophosphorylation. IRs can be phosphorylated or unphosphorylated [123]. The active IR induces the phosphorylation of IRS-1 via the activity of protein tyrosine kinase. This signal depends on phosphorylated IR. IRS1p docks with PI3-kinase. This results in the formation of the IRS-1/activated PI3-kinase complex (IRS1pPI3). Dissociation of the complex is dependent on IRS1pPI3. Phosphorylation of Akt depends on IRS1pPI3 and is reversible. Formation of the TSC1/2 complex depends on phosphorylated Akt. The TS1/2 complex inhibits the conversion of inactive Rheb to active Rheb (Rheb-GTP). RhebA activates mTORC1. Activation of mTORC1 affects S6K, while the activity of S6K is triggered by phosphorylation [124]. The activity of S6 kinase triggers feedback mechanisms inhibiting IRS-1. mTORp provokes cell metabolism and growth. We explore the proposed interaction between mTOR and SIRT1 by having SIRT1 bind with the TSC1/2 complex to inhibit active Rheb formation. Thus, the SIRT1 TSC1/2 complex association is included as an inhibitor of active Rheb formation and subsequent mTOR signaling, as suggested by Ghosh and colleagues.
Mentions: There a number of issues that can impede the development of a model in this area. Firstly, a large number of biological systems remain poorly understood mechanistically. This makes an integrated mathematical description of their mechanisms inherently challenging. Moreover, this problem feeds forward into parameter selection; if the mechanisms have not been fully delineated, then it is probable that there will be a paucity of measurements with which to infer accurate parameter values. A variety of techniques have been developed for estimating parameters that are not identifiable in the strict mathematical sense [114,115]. Residual parameter uncertainty, however, has an impact on model extension, because if the parameters of the original model are unclear, this raises questions over the value of extending the model further. This is particularly frustrating from the perspective of the aging process, as due to its intrinsic complexity, holistic models are a necessity if we are to improve our understanding of this process. To help overcome these difficulties, Kriete et al. (2010) created a noteworthy rule-based cell systems model of aging that included the mTOR pathway along with several other pathways related to aging [116]. This semi-quantitative model enabled the authors to represent aging in a holistic fashion that was defined by connectivity/rules among all components involved. Another promising direction for future research is the development of algorithmic methods for reducing model complexity and identifying, from the ensemble of uncertain model parameters, the critical components that demand further investigation. A good example of this is sensitivity analysis to determine those parameters that exert the most influence over a particular model output [117]. The performance of formal techniques for automated model reduction of systems biology models has also been studied by Dokoumetzidis and Aarons [118]. Recently, Rao and colleagues (2014) developed a model reduction method for biochemical reaction networks [119]. The method employed a stepwise reduction in the number of complexes, defined as the left- and right-hand sides of the reactions in the network. The reduced network has fewer complexes, reactions, variables and parameters when compared to the original network, and yet, the behaviour of a preselected set of significant metabolites in the reduced network closely resembles that of the original network. Moreover, the reduced network largely retains the structure and kinetics of the original model. A yeast model of glycolysis was reduced from 12 to seven variables. This reduction was able to improve the understanding of the dynamics of the network and presents a means of facilitating model parameterization or to embed a detailed model of interest in a more coarse-grained, yet realistic environment [120]. This type of approach could be important for modelling the interaction between mTOR and SIRT1, because their metabolic activities comprise a multitude of relationships that encapsulates the dynamic behaviour between enzymes, metabolites and cofactors. Given the current limited understanding of the mechanisms that underpin these pathways and the cloudiness surrounding their interactivity, constructing a detailed model of their circuitry is to a large extent futile. Rather, it is vital that computational models are informed by recent experimental evidence and that they are used to test a particular idea or hypothesis relating to a component or components of a reasonably well-defined system. The incorporation of model entities needs to be informed by metabolic circuitry that has been at least partly characterized experimentally. As an example, we created a diagram that could be used as a starting point to model the interaction of mTOR and SIRT1 (Figure 3). This diagram is informed by the hypothesis outlined by Ghosh et al. (2010) [31], in which the authors suggest SIRT1 negatively regulates mTOR signaling through inhibition of the TSC1/2 complex. Our idea centres on capturing this effect together with the key components of this pathway. The next step would be to code our putative model using a software tool, such as CellDesigner [121] or Copasi [122], both of which have been designed to support the SBML exchange framework, which would facilitate model updating. It would then be necessary to parameterize the model. Following this, simulations could be conducted along with a sensitivity analysis. The model could then be ultimately used to explore the relationship between mTOR and SIRT1 based on current experimental knowledge.

Bottom Line: These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1.We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled.We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science & Engineering, University of Chester, Thornton Science Park, CH2 4NU, UK. m.mcauley@chester.ac.uk.

ABSTRACT
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.

No MeSH data available.


Related in: MedlinePlus