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 modelling. These methods are becoming a more comprehensive way to study the dynamics of these systems. Biological processes involve complexes of proteins whose interaction characteristics, such as randomness of duration and of interaction timing. These are best captured by stochastic models. A common modelling approach is the use of systems of stochastic differential equations that describe the evolution of biochemical reaction networks. Stochastic simulation and stochastic control have been recently used to infer cellular network structure, to study cell decisions, and to understand the 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 modelled may affect the analysis of non-linear 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 articles and review articles on dynamical systems analysis, stochastic modelling and control theory focusing on methods for dynamical systems analysis (stochastic differential equations, bifurcation analysis and chaotic behaviour, multi-time stochastic methods), modelling and analysis of biological systems (developmental dynamics, cellular differentiation, 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 behaviour in biological systems, network dynamics analysis, stochastic reaction networks with time-scale separation and numerical methods for simulation 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 behaviour in biological systems
• Dynamical systems analysis for epidemiology
• Dynamical systems and ecological modelling
• Stochastic differential equations for the analysis of biological systems
• Multi-time methods for system dynamics analysis
• Multi-time evolutions and optimal control in biological systems
• Dynamics of cellular process using time-varying multi-scale models
• Parameter estimation for complex biological systems
• Numerical methods for computation of steady-state dynamics
• Methods for large-scale simulation of biochemical network dynamics