Publications by authors named "Priyotosh Sil"
Boolean models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. Their overall dynamics are embodied in a state transition graph (STG). Indeed, two Boolean networks (BNs) with the same network structure and attractors can have drastically different STGs depending on the type of Boolean functions (BFs) employed.
View Article and Find Full Text PDFBoolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models.
View Article and Find Full Text PDFArticle Synopsis
- Boolean models are used to represent developmental gene regulatory networks (DGRNs) to understand how cells acquire identities, but many combinations of Boolean functions can yield the same cell fates.
- Researchers explore the concept of relative stability of biological attractors to improve model selection, highlighting the mean first passage time (MFPT) as an effective measure that also reveals cell state transitions.
- The study reveals shortcomings in recent Boolean models of Arabidopsis thaliana root development and introduces a new algorithm for finding models that align better with expected hierarchies of cell states, ultimately advancing the accuracy of DGRN reconstructions.