| The ability to model biological networks has profoundly impacted our comprehension of complex biological systems. Systems of ordinary differential equations (ODEs) have been the common approach to model such networks. As biological systems contain many sources of randomness and variation, these complexities should ideally be included in model development. As the dynamic relationships become more complex, in the case of biological networks, ODE models involving randomness may become tedious and difficult to simulate. Queueing theory, a common tool for modeling random dynamic processes, has been widely applied in communication networks, operations research, and computer science. More recently, the application of queueing theory has emerged as an avenue to model complex dynamics within varying biological systems. Queuing theory has promising characteristics that may allow investigators to overcome limitations that may persist in traditional modeling approaches such as nonlinear relationships and naturally occurring random variations. By representing nodes as queues, and reaction rates as arrival/service rates, biological networks can be modeled through queueing networks. This enables efficient simulations of biological system dynamics, with applications including, but not limited to, metabolism, signaling, drug delivery, and endosomal trafficking. Importantly, the efficiency exhibited in queueing theory models may pave the way for modeling and simulating large, complex biological networks. |
