Formal verification confirms the role of p53 protein in cell fate decision mechanism

Abstract

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision.

Design patterns for the construction of computational biological models

“…However, we also discuss how a quantitative model can be derived from this structure. Interested readers may wish to consult the many publications that discuss the use of Petri nets to model biological reaction networks, for example: modelling break-repair systems, circadian oscillation and T7 phage model in [ 26 ], modelling Eukaryotic cell cycle in [ 10 ], modelling yeast cell cycles in [ 27 ], modelling the cyanobacterial circadian gene clock system in [ 20 ], modelling the Brusselator in [ 23 ], modelling bacterial colonies with phase variable genes in [ 29 ], modelling the cell fate decision mechanism in [ 33 ] or modelling intracellular calcium dynamics in [ 34 ].…”

Verifiable biology

“…Due to its novel features to infer information about system behaviour, there is growing interest in applying this technique in systems biology [38,39]. Model checking has been applied to the analysis of various biological systems such as ERK/MAPK or FGF signalling pathway [40][41][42], EGFR pathway [43,44], T-cell receptor signalling pathway [45][46][47][48], cell cycle in eukaryotes [49,50], cell cycle control [51][52][53][54][55], mammalian cell cycle regulation [56,57], apoptosis network [58,59], bladder tumorigenesis [60], quorum sensing [61][62][63], biological control mechanisms [64], DNA computing [65][66][67], genetic oscillator [68,69], genetic Boolean gates [61,[70][71][72][73] and switches [73][74][75].…”