Exploratory Simulation of Cell Ageing Using Hierarchical Models

Marija Cvijovic [1] (cvijovic@molgen.mpg.de)
Hayssam Soueidan[2] (soueidan@labri.fr)
David James Sherman[3] (david.sherman@labri.fr)
Edda Klipp[1] (klipp@molgen.mpg.de)
Macha Nikolski[2] (macha@labri.fr)

[1] Max-Planck Institute for Molecular Genetics, Ihnestraße 63, D-14195 Berlin, Germany
[2] LaBRI, Université Bordeaux 1, 351 cours de la Libération, 33405 Talence, France
[3] INRIA Team Magnome, 351 cours de la Libération, 33405 Talence, France


Thorough knowledge of the model organism S. cerevisiae has fueled efforts in developing theories of cell ageing since the 1950s. Models of these theories aim to provide insight into the general biological processes of ageing, as well as to have predictive power for guiding experimental studies such as cell rejuvenation. Current efforts in in silico modeling are frustrated by the lack of efficient simulation tools that admit precise mathematical models at both cell and population levels simultaneously. We developed a novel hierarchical simulation tool that allows dynamic creation of entities while rigorously preserving the mathematical semantics of the model. We used it to expand a single-cell model of protein damage segregation to a cell population model that explicitly tracks mother-daughter relations. Large-scale exploration of the resulting tree of simulations established that daughters of older mothers show a rejuvenation effect, consistent with experimental results. The combination of a single-cell model and a simulation platform permitting parallel composition and dynamic node creation has proved to be an efficient tool for in silico exploration of cell behavior.

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Japanese Society for Bioinformatics