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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