The virtual ageing cell will allow key processes to be represented either
in relatively simple terms or to be expanded into more detailed structures
as hypotheses and/or actual knowledge permits.
The majority of modelling work to date has concentrated on the intracellular
mechanisms that result in the cell's degeneration and death. This research
has emphasized that cellular mechanisms cannot be taken individually, but
rather must be taken in the context of interacting forces.
For example, our model of mitochondrial dynamics [1] predicts very
different outcomes between dividing and non-dividing cells for accumulation
of mtDNA mutations, as has been observed experimentally.
Other processes that experimental data indicate as important include the
role of oxidative stress in accelerating the progressive erosion of
telomeres during replication of telomerase-negative somatic cells [2]
and the role of intrinsic stochastic variation in cell ageing [3,4].
Our stochastic model of cell replicative senescence gave simulation
results that are in good agreement with published data on intraclonal
variability in cell doubling potential [5].
Experimental Data [4]
Simulated Data [5]
The virtual ageing cell enables key cellular processes to be represented
as simple terms or to be expanded into detailed structure. The advantage
of this flexibility is three-fold.
1) A skeleton model can easily be constructed before adding the specific
modelling details. This has the added benefit of highlighting gaps in
biological knowledge of the user.
2) Depending on the modeller's aims a simple term maybe all that is
required. For example, in representing the cell's antioxidant enzyme
defences it could be sufficient to include simply a generic antioxidant
enzyme that represents the overall activity of the antioxidant complex.
For other purposes, e.g. where the effect of upregulating a particular
enzyme is of interest, it is essential to incorporate greater mathematical
detail.
3) By introducing transparency into the modelling process, the non-
mathematical biologist will gain greater insight, understanding and confidence
in the outcomes of the model.
This flexible structure is complemented by taking both a deterministic
and stochastic approach to modelling (see tutorial for more details).
By taking this two-pronged approach, we are able to analysis both the
'average' and random behaviour of cellular mechanisms.
Overall, the virtual ageing cell presents the opportunity to explore
cellular relationships on a level not yet achieved simultaneously opening
up the modelling process to other users.
References:
1. Kowald A, Kirkwood TBL. 2000. Accumulation of defective mitochondria
through delayed degradation of damaged organelles and its possible role
in the ageing of post-mitotic and dividing cells. Journal of Theoretical
Biology, 202, 145-160.
2. von Zglinicki T, Burkle A, Kirkwood TBL, 2001. Stress, DNA damage
and ageing - an integrative approach. Experimental Gerontology, 36,
1049-1062.
3. Holliday R, Huschtscha LI, Tarrant GM, Kirkwood TBL, 1977. Testing
the commitment theory of cellular ageing. Science, 198, 366-372.
4. Smith JR, Whitney RG, 1980. Intraclonal variation in proliferative
potential of human diploid fibroblast cells: stochastic mechanism for
cellular aging. Science, 207, 82-84.
5. Sozou PD, Kirkwood TBL, 2001. A stochastic model of cell replicative
senescence based on telomere shortening, oxidative stress, and somatic
mutations in nuclear and mitochondrial DNA. Jounal of Theoretical Biology,
213, 573-586.
