The RBN Toolbox is a powerful
tool for the simulation of Random Boolean Networks with low
connectivity. However, if we choose to simulate large networks
with high connectivity (typically N~K), we rapidly have to deal
with huge matrices of some megabytes and even more.
Especially the Rules-Matrix
grows exponentially in K as it is of dimensions 2^K x N. For a
fully connected network with parameters N=K=30, we already
have (considering that one matrix-element needs one byte of
memory) a matrix of 2^30*30/(2^20) = 30'720 megabytes!
Unfortunately, these memory ressources cannot be provided by
As about 50% of the Rules-Matrix are non-zero entries, using the
Matlab sparse-matrices would not reduce the memory needed for the
storage of the matrix; on the contrary, some tests showed that by
using sparse-matrices the memory ressources even grow.
The most effective way to solve this problem, would probably be to
change to model / internal representation of networks with high
connectivity. By randomly generating the rules for each node ad
hoc, instead of saving them permanently in a matrix, we could
avoid dealing with huge matrices. However, this approach would
generate some additional computational overhead while evolving the
network. Furthermore, by randomly choosing the logic transition
rules, we loose the property that a node has a fixed rule during
evolution and it is unclear how this would affect the evolution af