Recent experiments have unambiguously established that
biological systems can have significant cell to cell variations in gene
expression levels even in isogenic populations. Computational approaches
to studying gene expression in cellular systems should capture such
biological variations for a more realistic representation. We present a
simple, fully probabilistic approach to the modeling of gene regulatory
networks that allows for fluctuations in the gene expression levels. The
new algorithm uses a very simple representation for the genes, and
accounts for the repression or induction of the genes and for the
biological variations among isogenic populations simultaneously. We have
tested the new algorithm on a recently bioengineered synthetic gene
network library, and found a good agreement between model predictions
and experimental results.