Stochastic Model of p53-Mdm2


The p53-Mdm2 network is one of the most widely studied gene regulatory networks. Abou-Jaude, Ouattara and Kauffman proposed a logical four-variable model to describe the dynamics of the tumor suppressor protein p53 and its negative regulator Mdm2 in the presence and absence of DNA damage.


node1 = P (protein p53)
node2 = Mc (cytoplasmic Mdm2)
node3 = Mn (nuclear Mdm2)
node4 = Dam (DNA damage)

Transition Table

Click to see the transition table of p53-Mdm2 model

Propensity Parameters

0.9 0.9
0.9 0.9
0.9 0.9
1.0 0.05

Initial State

0 0 1 1 (representing the state when DNA damage is introduced)

Nodes of Interest

1, 2, 3, 4

Number of States


Number of Steps


Number of Simulations


Key Dynamic Features

Steady States

There is only 1 steady state in the system, which is 0 0 1 0.

Cell Population Simulation

Probability Distribution

Transition Matrix

Click to see the transition matrix of p53-Mdm2 model

Other examples for Stochastic Discrete Dynamical Systems

Stochastic Model of Lambda Phage Infection (Lysis)
Stochastic Model of Lambda Phage Infection (Lysogeny)


D. Murrugarra, A. Veliz-Cuba, B. Aguilar, S. Arat, R. Laubenbacher "Modeling Stochasticity and Variability in Gene Regulatory Networks" (under review)


Seda Arat

Last Updated

February 2012