Age is a major risk factor for chronic disease, and chronic diseases are the major cause of death and disability in the world, estimated at around 70% (WHO 2005). Molecular damage is the underlying factor in all of these (DNA damage (cancer), dementia and more). Ageing results from the accumulation of molecular damage. There is an irreversible accumulation of macromolecular damage, even though we have ameliorating systems such as the antioxidant systems, some damage escapes repair and builds up. Levels of oxidised protein, mutational frequency in nuclear DNA and mutational frequency in mDNA all increase exponentially with age. This underlying damage gives rise to cellular senescence. Cells which go into a permanent state of cell cycle arrest are called senescent, and they secrete a number of chemicals into the surrounding environment. The number of these cells increase with age. If you remove these senescent cells (e.g. from mice) there is a definite survival enhancement, though we don’t really understand why. So, although overall there aren’t many of them, they do seem to have quite an impact.
The good news is that there is plasticity in ageing. For instance, caloric restriction in mice does allow them to live longer (almost double). In part, this is due to them overeating if they’re allowed to free eat, but these undernourished mice aren’t healthy – they’re infertile, for example (it’s not a “natural state”). Mutations that bring longer life are in genes associated with nutrition – they’re signalling to the organism that there is less food available. This signal is somehow reducing molecular damage. However, it’s hard to test this in humans…
If we build models of known mechanisms, we can explore interventions, and with known interventions we can explore mechanisms. With a lot of background information, we can use the models to optimise synergy/antagonism, dose and timing. Ageing is caused by multiple mechanisms, and most damage increases exponentially – can the cycle be slowed or broken – there is an implication of positive feedback.
After existing knowledge and data has been used to create a calibrated model, then we perform sensitivity analysis and validate the model. Once all that has been done, then you can start using the model to make the predictions you’d like to see. It’s a long journey for a single model! They’ve created a set of Python modules for COPASI called PyCoTools, which allows you to compare models by generating other alternative models based on a starting model.
They are using a systems approach to model the development of the senescent phenotype with a view to find interventions to prevent progression and reverse the phenotype. They’d already been working on the processes involved in this with earlier models of insulin signalling, stress response, DNA damage, mitochondrial dynamics and ROSs.
Bringing all of these models together into an integrative dynamic model for cellular senescence is just the first task; they also needed to create an independent in vitro data set for estimating the integrated model parameters. This data was then used to fit their model. They had to infer what was going on inside the mitochondria, by inferring the internal states for ‘new’ and ‘old’ mitochondria. Then the model was used to make interventions for improving mito function and its phenotype, especially via combinations that would be difficult to perform in the lab.
If you reduce ROS in the model, it has an impact on the entire network. The results can be used to inform later experimental designs. Then there was in vitro confirmation of increased mitochondrial membrane potential during ROS inhibition. The model matched initially, but at a later date it diverged from the lab. When you go back and look at the cells, you find that there was very little movement among the senescent cells, which hampers autophagy. This is why the autophagy/mitophagy was predicted in the model, but wasn’t being seen in the lab. It’s a quality of the senescent cell which is blocking their removal from the cell. Mitochondrial dynamics are reduced over time, driven by an inability to remove the network of dysfunctional mitochondria.
Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!