report

Although nodes represent genes and the connections among them in gene regulatory networks, networks having long term memory cannot be approximated well with a 1st order Boolean network. Higher order Boolean networks are employed in SIAN by using recurring representations of nodes in order to realize  more life-like regulations among nodes. In other words, higher order Boolean networks can track a single node at various time intervals as opposed to just one. For example, instead of node “x” being a function of its parent(s) at the definite time of [t-1], its parents may be tracked at different periods such as [t+1], [t], or [t-2] to expand to all possibilities in analyzing a Boolean network. Furthermore, higher order levels of Boolean networks act as boundaries for SIAN in order to produce feasible networks

Kth -order Boolean networks for modeling gene regulatory networks are first implemented in this project as far as we know from published literature on Boolean networks.  In the past, only 1st-order Boolean networks have been used, mainly due to lack of computational power.  SIAN uses higher order Boolean networks to allow a much more complex model to be derived for gene interactions, including time-delayed effects as well as instantaneous ones.