The disposable soma theory suggests that aging occurs because natural selection favors a strategy in which fewer resources are invested in somatic maintenance than are necessary for indefinite survival. However, laboratory rodents on calorie-restricted diets have extended life spans and retarded aging. One hypothesis is that this is an adaptive response involving a shift of resources during short periods of famine away from reproduction and toward increased somatic maintenance. The potential benefit is that the animal gains an increased chance of survival with a reduced intrinsic rate of senescence, thereby permitting reproductive value to be preserved for when the famine is over. We describe a mathematical life-history model of dynamic resource allocation that tests this idea. Senescence is modeled as a change in state that depends on the resources allocated to maintenance. Individuals are assumed to allocate the available resources to maximize the total number of descendants. The model shows that the evolutionary hypothesis is plausible and identifies two factors, both likely to exist, that favor this conclusion. These factors are that survival of juveniles is reduced during periods of famine and that the organism needs to pay an energetic "overhead" before any litter of offspring can be produced. If neither of these conditions holds, there is no evolutionary advantage to be gained from switching extra resources to maintenance. The model provides a basis to evaluate whether the life-extending effects of calorie-restriction might apply in other species, including humans.

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset. Ageing is characterised by a gradual loss of homeostasis within organs, which is known to be driven by the accumulation of senescent cells. Cellular senescence helps prevent cells from becoming cancerous, but their detrimental effect on organ function becomes debilitating once they accumulate. These cells are particularly difficult for the body to remove, and therefore understanding what controls their survival and interactions within the organ is important to combat age-related disease. We present a mathematical model for cellular senescence. This model is used for predicting drug interventions for restoring function in cellular senescence. Whilst these interventions were predicted and tested in vitro, showing improved function and phenotype, none was able to restore cells to a pre-senescent state. Our model includes mitochondria, the power-plants of the cell, and we identify impairment of their turnover coupled with increased mitochondrial biogenesis as a mechanism which explained the long-term failure in drug intervention. Finally, we predict that the system dynamics stabilise in a new late-senescence state, characterised by limited network response to treatment and increased system vulnerability. This study shows formally for the first time the dynamics of cellular senescence as a system network and proves the requirement of early intervention in order to delay cellular senescence.

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational–experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca²⁺/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

Modeling and testing network structures reveal a distinct mechanism of mTORC2 activation by insulin.