Boolean Nested Effects Models (B-NEM) are used to infer signalling pathways. In different experiments (conditions) members of a pathway (S-genes) are stimulated or inhibited, alone and in combination. In each experiment transcriptional targets (E-genes) of the pathway react differently and are higher or lower expressed depending on the condition. From these differential expression profiles B-NEM infers Boolean functions presented as hyper-edges of a hyper-graph connecting parents and children in the pathway. For example if the signal is transducted by two parents A and B to a child C and the signal can be blocked with a knock-down of either one, they are connected by a typical AND-gate. If the signal is still transduced during a single knock-down, but blocked by the double knock-down of A and B, they activate C by an OR-gate. In general the state of child C is defined by a Boolean function

f: {0,1}^n -> {0,1}, C = f(A_1, ... , A_n)

with its parents A_i, i ∈ {1,...,n}.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("bnem")

Most recent (devel) version:

install.packages("devtools")

library(devtools)

install_github("MartinFXP/bnem")

library(bnem)

Then check out the vignette for working examples.

vignette("bnem")

Use the function ?processDataBCR to reproduce the data analysed in the publication (Pirkl et. al., 2016).

Pirkl, Martin, Hand, Elisabeth, Kube, Dieter, & Spang, Rainer. 2016. Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models. \textit{Bioinformatics}, 32(6), 893–900.

Pirkl, Martin. 2016. Indirect inference of synergistic and alternative signalling of intracellular pathways. University of Regensburg.