Functional Analysis of Mammalian Cell Cycle using A Computational Model of Hybrid Petri Net.

Shuji Kotani1, Takashi Yoshioka2, Kaoru Takahashi, Akihiko Konagaya, RIKEN Genomic Sciences Center ;, NTT Data Corp

Mammalian cells reproduce by passing correctly through the cell cycle. The activity and quantitative changes of several Cyclin-dependent kinases (Cdks), currently called the "cell cycle engine", are found to be important to the cell-cycle progression. To correctly describe the molecular mechanism of the mammalian cell cycle, we developed a new computational model by using Hybrid Petri Nets (HPN), a software tool for numerical analysis or simulation. This model shows the concentration and activity of various functional proteins and the dynamic changes in organelles at each stage of the cell cycle in silico. Referring to the results of past biological experiments, we modeled the change of activity and quantity of each Cdk in the progression of the cell cycle using HPN, connected the Cdk models to one another functionally, and then verified the accuracy of each model experimentally. Furthermore, the state of the organelles in the progression of the cell cycle was changed into a formula expression and incorporated into the model. Finally the somatic cell cycle of mammalian cells was reproducible in silico. Knockout or over-expression of specific gene products can be easily reproduced in the model. As over-expression or reduction of specific gene products related to the cell cycle control has been seen in cells extracted from patients suffering from cancer or congenital diseases, we simulated some cases of this using the model. Each time we individually simulated the reduction of Cyclin B, Cdc20 or Emi1 in silico, the cell cycle was arrested at a different phase respective to the specific protein reduction simulated. The progression through the cell cycle was not affected when one of their proteins was over-expressed in the model. We confirmed the accuracy of these simulation results by biological experiments. Using the model, we can easily presume how the change in a gene product influences the cell cycle progression. Therefore, this model may be useful for exploring the relationship between gene functions and diseases.