BooleanNet

[…] and (ii) the state‐space is generated with all the transition history of a sufficient number of consecutive steps from a sufficient number of random initial states. r‐abs was performed using the booleannet ver.1.2.6 python package (http://code.google.com/p/booleannet/), with 700 consecutive update steps from each of 700 random initial states per condition. five independent simulations per […]

[…] and other genes: “klk3 low => x low” and its counterpart “x high => klk3 high” (figure ). the resulting gene list included 57 transcripts (using s > 10, p < 0.01 threshold for the booleannet analysis). to filter this list, we assembled three independent publicly available prostate cancer datasets that were annotated for recurrence-free survival and tested whether […]

[…] signal transduction pathways [], particularly when, as in the present case, relative timescales of internal processes are largely unknown. we implemented the model using the python software library booleannet []. we used the stochastic asynchronous update algorithm, in which the nodes are updated in a randomly selected order in each time step. due to the stochasticity introduced by the update […]

[…] time description, thus providing more understandable information about general pathway dynamics under different conditions. to support these studies, publicly available software tools such as booleannet [], pathologic-s [] and odefy [] have been developed; they offer a scalable boolean framework for modeling cellular signaling., following these considerations, in this work we present […]

[…] is not unique and can be unstable in relation to small interaction alterations. there are also software tools that can be used to run such simulations, including cellnetanalyzer [], cellnopt [], booleannet [], and cpn tools []., furthermore, all the existing approaches, differential equations, boolean, or petri net, require a complete and exact knowledge of the network, including […]