(658d) Modeling the Stochastic Dynamics of Gene Regulatory Networks Using Probabilistic Boolean Networks

Gene regulatory networks (GRN) control nearly every aspect of a cell’s behavior. There are numerous fields that can benefit from increased knowledge of the underlying mechanisms of gene regulation or the ability to manipulate behavior of GRNs. These networks are comprised of many layers of complexity and interwoven connections, making it a challenge to compute or simulate their dynamics. Current modeling methods leave the end user with a catch-22 of trying to choose between computational cost and system resolution.

Modeling by simulating or solving the Chemical Master Equation (CME) has the benefit of describing the system in high molecular detail and accounting for stochastic molecular processes. However, the high computational cost and large number of unknown parameters that come along with treating every molecule can overwhelm even state of the art computing clusters. It is possible to use the Boolean Network (BN) framework to model larger GRNs but this can result in an oversimplification of both the gene states and their interactions.

This work discusses using a Probabilistic Boolean Network (PBN) approach to approximating a CME model of GRN dynamics. Using the network’s eigenvalues and proposed global metrics to define the dynamics of the GRN, this method combines stochastic modeling and BNs to reduce computational cost while maintaining model accuracy.