Yet, the static pictures these experiments give cannot provide insight on the dynamic behavior of gene networks.
Towards analyzing the dynamic behavior of gene networks, a range of mathematical and computational modeling methods have been developed, including Boolean networks, Petri nets, state-charts, ordinary differential equations, and stochastic simulation algorithms [4], [20] [30].
Sophisticated algorithms can accurately capture the dynamical behavior of gene networks.
Our model captures essential features of gene network behavior (e.g. threshold response) and emphasizes the importance of transcription regulation in evolution [ 20, 53, 54] resulting in neutral space properties that apply to real gene regulatory networks [ 28].
It can be used to study damped vibration behavior of the gene network and reveal how fast the gene networks respond to perturbation of environments and how accurately the networks can finally achieve the designed steady-state values.
Methods such as the transfer function (frequency domain) and linear state-space (time domain) were applied to reveal inherent characteristics and predict the dynamic behavior of various gene network topologies, including cascade/parallel forms, feedback loops, and feedforward loops.
Ideally, the computational approach will be able to investigate thoroughly all possible alternatives and adjust the dynamical behavior of a gene network to fit certain demands.
When studying robustness, the typical question is how sensitive the behavior of a gene network is to kinetic parameter perturbations in the model [ 13].
Firstly, a nonlinear stochastic time-delayed model is developed to mimic the realistic dynamic behavior of a gene network under process delays, random intrinsic and extrinsic molecular noises.
Dynamical properties succinctly characterize dynamical behaviors, and differential dynamical properties of gene networks can be seen as a natural extension of differentially expressed genes.
