Fig. 1: General principle of the conducted inference of ensembles of Boolean networks. | npj Systems Biology and Applications

Fig. 1: General principle of the conducted inference of ensembles of Boolean networks.

From: Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming

Fig. 1

By offering a generic modeling language, BoNesis enables integrating prior knowledge on regulation mechanisms with different types of experimental data, after qualitative interpretation, which may depend on biological hypotheses and experimental systems. These inputs specify what an admissible model is. Then, employing logic programming, BoNesis can generate ensembles of Boolean networks that fulfill the structural and dynamical properties and, by combining with combinatorial optimization technologies, enables the prediction of important genes and derive predictions for attractor reprogramming.

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