Modelling Stochasticity and Robustness in Gene Regulatory Networks

Abhishek Garg ****

Takes a Boolean modelling approach. Stochastic Boolean Modelling

• T-helper cell differentiation gene regulatory network Mendoza and Xenarios 2006
• T-cell activation network Stephan Klamt
  

Enviromental input changes the nature of the differentiated cell. Stochastic behaviour is common in biological models.

Robustness maintains functionality over perturbations. In gene networks do they move between steady states (can they change attractor), and give different cell type.

Stochasticity can be applied to the nodes or the functions.
Not Probabalistic Boolean Networks - Datta group.
Use Boolean functions AND, OR etc.

Stochasticity in nodes flip the output using a probability distribution.
Kauffman, Willanda etc lots of literature.
Over-represents noise by placing it at the end and not at the inputs/intermediates.

More stochastic the more interaction are involved so allosteric and protein localisation has high noise.
http://si2.epfl.ch/~garg/