Exploring attractor bifurcations in Boolean networks

Background: Boolean systems (BNs) offer an effective modelling formalism for a number of complex biochemical phenomena. Their lengthy term conduct is symbolized by attractors-subsets from the condition space towards that the BN eventually converges. They are then typically associated with different biological phenotypes. Based on various logical parameters, the dwelling and excellence of attractors can undergo a substantial change, referred to as a bifurcation. We present a technique for analysing bifurcations in asynchronous parametrised Boolean systems.

Results: Within this paper, we advise a computational framework employing advanced symbolic graph algorithms which allow case study of huge systems with countless Boolean variables. To visualise the outcomes of the analysis, we created a novel interactive presentation technique according to decision trees, allowing us to rapidly uncover parameters essential to the alterations within the attractor landscape. In general, the methodology is implemented within our tool AEON. We assess the method’s applicability on the complex human cell signalling network describing the game of type-1 interferons and related molecules getting together with SARS-COV-2 virion. Particularly, case study concentrates on explaining the possibility suppressive role from the lately suggested drug molecule GRL0617 on replication from the virus.

Conclusions: The suggested method results in a working example to the idea of bifurcation analysis broadly utilized in kinetic modelling to show the outcome of parameters around the system’s stability. The key feature in GRL0617 our tool is its capacity to operate fast with large-scale systems having a relatively large extent of unknown information. The outcomes acquired within the situation study have been in agreement using the recent biological findings.