B-NEM

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}.

Install:

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")

Publication result:

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

References:

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.