Stochastic Model of p53-Mdm2
Description
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.
Variables
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
3
Number of Steps
50
Number of Simulations
100
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)
References
D. Murrugarra, A. Veliz-Cuba, B. Aguilar, S. Arat, R. Laubenbacher "Modeling Stochasticity and Variability in Gene Regulatory Networks" (under review)
Author
Seda Arat
Last Updated
February 2012