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.


    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.
C 2002-2003 basis



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