A fundamental challenge in computational biology is to understand the complex dynamics of biological systems, and system-level approaches that combine high-throughput experimental data with mathematical and computational modeling are emerging as a comprehensive way to study these systems’ dynamics. Biological processes involve complexes of proteins whose interaction characteristics, such as randomness of duration and of interaction timing, are best captured by stochastic models, and a common modeling approach is the use of systems of stochastic differential equations that describe the evolution of the biochemical reaction networks. Stochastic simulation and stochastic control have been recently used to infer cellular networks structure, to study cell decisions and to understand complex dynamics of biological processes.

Noise is an intrinsic aspect in the dynamics of biochemical networks. While solving the dynamics of biochemical reactions at equilibrium, many random effects that cannot be modeled may affect the analysis of nonlinear dynamics of large biochemical reaction networks. Recent work has generated new insights into the complex dynamics of biological systems and the control processes that drive their dynamics.

The aim of this special issue is to attract original research contributions and comprehensive reviews on dynamical systems analysis, stochastic modeling, and control theory focusing on methods for dynamical systems analysis (stochastic differential equations, bifurcation analysis and chaotic behavior, and multitime stochastic methods), modeling and analysis of biological systems (developmental dynamics, cellular differentiation, and immune system dynamics), and control of biological processes (stochastic control analysis, cell decision processes, transcription, translation, and epigenetic control).

We encourage submissions of theoretical as well as applied investigations on the dynamical analysis of biological systems, bifurcation analysis, and chaotic behavior in biological systems, network dynamics analysis, stochastic reaction networks with time-scale separation, and numerical methods for simulations and analysis of biological networks.

Potential topics include but are not limited to the following:

• Dynamical systems and systems biology
• Bifurcation analysis in biological systems
• Chaotic behavior in biological systems
• Stochastic differential equations for analysis of biological systems
• Dynamics of cellular process using time-varying multiscale models
• Multitime methods for system dynamics analysis
• Multitime evolution and optimal control in biological systems
• Numerical methods for computation of steady-state dynamics
• Numerical methods for large scale simulations of biochemical networks dynamics