With the deepening of our understanding of biological systems, from both macro and micro aspects, these systems have shown strong complexity, including as non-linear, multi-layered, self-organized, open, and dynamic. Complex biological systems can occur at all levels of the biological world, including molecular, cellular, tissue and organ levels, individual levels, and population levels. The problem of dealing with complexity arises when we fail to achieve a desired behaviour of biological systems (for example, in cancer treatment).

Modelling, analysing, and simulating of complex biological systems can replace complex, long-term, expensive, and even unachievable experiments, greatly improving research efficiency and quantification, and studying artificially imposed control conditions to affect biological system operations.

This Special Issue aims to introduce and discuss novel models, results, control techniques, and circuit simulations for complex nonlinear biological systems. We welcome original research and review articles relating to the themes of this special issue.

Potential topics include but are not limited to the following:

• Modelling and analysis of complex biological systems
• Optimization and control of complex biological systems
• Evolutionary analysis of complex biological systems
• Parameter identification of complex biological systems
• Data-driven modelling and simulation of complex biological systems
• Machine learning techniques in model and simulation of biological systems
• Medical imaging technologies and biological modelling
• Machine learning (data mining) and medical data analysis