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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

A simple pathway illustrating a metabolic flux model.
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metabolites-05-00232-f002: A simple pathway illustrating a metabolic flux model.

Mentions: One important method that has been successfully applied to metabolic networks is flux balance analysis (FBA). In this approach, each intermediate metabolite pool is assumed to be in a steady state. This defines a set of mass balance constraints that must be satisfied by any feasible flux pattern and that can be solved efficiently for large, even genome-scale models, to understand global interactions [86]. Figure 2 provides a simple pathway to illustrate the concept. Metabolite B, for example, is produced by flux and consumed by fluxes and . If the concentration of B is assumed not to vary over time, its rate of change (first derivative with respect to time) can be set to zero, which implies the following simple relationship between these fluxes: . A similar analysis of Metabolite C gives another constraint: . For larger networks, the balancing of hundreds or thousands of metabolites gives the same number of constraints that the fluxes must satisfy. This “constraint space” can be efficiently searched by mathematical optimization algorithms to find a flux pattern that optimizes a certain objective, such as maximization of the cell growth rate. FBA has been widely used to interpret metabolic data, for example in a study on the effect of aging on key metabolite fluxes in hypoxia tolerance in Drosophila [87]. Although originally devised for steady-state metabolic networks, these methods have found some limited application for dynamic signaling networks of the type considered here [88] and also gene regulation [89]. From an aging perspective, several other models are worth mentioning; for example, the FBA model of mitochondrial energy metabolism by Ramakrishna and colleagues [90]. This model was used to characterize the optimal flux distributions for maximal ATP production in the mitochondrion. The model predicted the expected ATP yields for glucose, lactate and palmitate alongside the secretion of TCA-cycle intermediates, which is observed during mitochondrial disease [90]. More recently, Nogiec and colleagues (2015) used FBA modelling to create a systems-level model of insulin resistance, under a variety of nutrient conditions. The metabolic network was probed to isolate reactions that replicate the clinical manifestations of an insulin resistance-linked metabolic state [91]. Yizhak et al. (2013) also focused on the area of healthy ageing and generated a metabolic transformation algorithm [92]. This algorithm has the goal of identifying health states within a metabolic network that has been perturbed by disease. According to the authors, the algorithm was able to predict novel drug targets for human ageing based on analyses of several genes associated with lifespan extension in yeast. In terms of disadvantages, flux models are not based on mechanistic biochemical kinetics; therefore, they are limited at predicting metabolic conditions. This means that although the “transportation infrastructure” of the cell is quite well characterized by FBA, the kinetic rates, information exchange and regulation that control the flow of molecules around these metabolic networks are far less well understood. They are also solely based on model steady states, and fluxes are inferred based on steady states. This is not a true reflection of reality, as regulatory processes are inherently dynamic in nature and, therefore, require an alternative dynamic or time-dependent modelling approach [93].


Mathematical modelling of metabolic regulation in aging.

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

A simple pathway illustrating a metabolic flux model.
© Copyright Policy
Related In: Results  -  Collection

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

metabolites-05-00232-f002: A simple pathway illustrating a metabolic flux model.
Mentions: One important method that has been successfully applied to metabolic networks is flux balance analysis (FBA). In this approach, each intermediate metabolite pool is assumed to be in a steady state. This defines a set of mass balance constraints that must be satisfied by any feasible flux pattern and that can be solved efficiently for large, even genome-scale models, to understand global interactions [86]. Figure 2 provides a simple pathway to illustrate the concept. Metabolite B, for example, is produced by flux and consumed by fluxes and . If the concentration of B is assumed not to vary over time, its rate of change (first derivative with respect to time) can be set to zero, which implies the following simple relationship between these fluxes: . A similar analysis of Metabolite C gives another constraint: . For larger networks, the balancing of hundreds or thousands of metabolites gives the same number of constraints that the fluxes must satisfy. This “constraint space” can be efficiently searched by mathematical optimization algorithms to find a flux pattern that optimizes a certain objective, such as maximization of the cell growth rate. FBA has been widely used to interpret metabolic data, for example in a study on the effect of aging on key metabolite fluxes in hypoxia tolerance in Drosophila [87]. Although originally devised for steady-state metabolic networks, these methods have found some limited application for dynamic signaling networks of the type considered here [88] and also gene regulation [89]. From an aging perspective, several other models are worth mentioning; for example, the FBA model of mitochondrial energy metabolism by Ramakrishna and colleagues [90]. This model was used to characterize the optimal flux distributions for maximal ATP production in the mitochondrion. The model predicted the expected ATP yields for glucose, lactate and palmitate alongside the secretion of TCA-cycle intermediates, which is observed during mitochondrial disease [90]. More recently, Nogiec and colleagues (2015) used FBA modelling to create a systems-level model of insulin resistance, under a variety of nutrient conditions. The metabolic network was probed to isolate reactions that replicate the clinical manifestations of an insulin resistance-linked metabolic state [91]. Yizhak et al. (2013) also focused on the area of healthy ageing and generated a metabolic transformation algorithm [92]. This algorithm has the goal of identifying health states within a metabolic network that has been perturbed by disease. According to the authors, the algorithm was able to predict novel drug targets for human ageing based on analyses of several genes associated with lifespan extension in yeast. In terms of disadvantages, flux models are not based on mechanistic biochemical kinetics; therefore, they are limited at predicting metabolic conditions. This means that although the “transportation infrastructure” of the cell is quite well characterized by FBA, the kinetic rates, information exchange and regulation that control the flow of molecules around these metabolic networks are far less well understood. They are also solely based on model steady states, and fluxes are inferred based on steady states. This is not a true reflection of reality, as regulatory processes are inherently dynamic in nature and, therefore, require an alternative dynamic or time-dependent modelling approach [93].

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