Section: New Results

Logical Modeling of the Mammalian Cell Cycle

Participants : François Fages, Pauline Traynard, Denis Thieffry.

The molecular networks controlling cell cycle progression in various organisms have been previously modelled, predominantly using differential equations. However, this approach meets various difficulties as one tries to include additional regulatory components and mechanisms. This led to the development of qualitative dynamical models based on Boolean or multilevel frameworks, which are easier to define, simulate, analyse and compose. In a poster presented at ECCB 2014, we revisit the Boolean model of Fauré et al. for the core network controlling G/S transition in mammalian cell cycle, taking into account recent advances in the characterisation of the underlying molecular networks to obtain a better qualitative consistency between model simulations and documented mutants features. In particular, we introduced Skp2, the substrate recruiting component of the SCFSkp2 complex, which targets cell cycle control elements, such as p27, and is repressed by the tumour suppressor protein Rb. Furthermore, to supersede the limitations inherent to the Boolean simplifications, we have considered the association of multilevel logical components with key cell cycle regulators, including the tumour suppressor protein Rb. Indeed, it is well established that differently phosphorylated forms of Rb result in different effects on other components of the network, which can be faithfully modelled using a multilevel rather than a Boolean variable. To evaluate the dynamical properties of the resulting models, we perform synchronous and asynchronous simulations using the software GINsim (http://www.ginsim.org ), for both the wild-type case and documented perturbations (e.g. combinations of loss- or gain-of-function mutations). In addition, we have designed a series of temporal logic queries (expressed in the CTL language), which enable an efficient and automatic verification of key dynamical properties (existence of a cyclic attractor or of a stable state, conditions on the order of changes of component levels, etc.), using the popular symbolic model checker NuSMV. This strategy greatly facilitates the dynamical analysis of increasingly detailed and complex cell cycle models. Our goal is to obtain a core cell cycle model consistent with the most relevant experimental results on mammalian cells, which will then be used as a module in more comprehensive cellular models, including cross-talks with the circadian clock network and key signalling pathways, whose deregulation underlies the development of various cancers.