Speaker
Prof.
Elena Dubrova
(KTH)
Description
Random Boolean Networks (RBNs) were introduced by Kaufmann
in 1969 in the context of gene expression and fitness
landscapes. They were applied to the problems of cell
differentiation, immune response, evolution, and neural
networks. They have also attracted the interest of
physicists due to their analogy with the disordered systems
studied in statistical mechanics, such as the mean field
spin glass. An RBN is a synchronous Boolean automaton. Each
vertex has k predecessors, selected at random, and an
associated Boolean function of k variables. Kauffman has
shown that it is possible to tune the parameters of an RBN
so that the network exhibits self-organized critical
behavior ensuring both stability and evolutionary
improvements. This talk focuses on computational aspects of
RBNs. First, we give an introduction to RBNs and show how
they can be used for the modeling of gene regulatory
networks of living cells. Then, we describe three basic
steps of the analysis of dynamical behavior of RBNs:
redundancy removal, partitioning, and computation of
attractors. Finally, we discuss open problems and outline
prospectives of RBNs.
