With the deepening of our understanding of biological systems, from both macro and micro aspects, these systems have shown strong complexity, including 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 the desired behavior of biological systems (for example, in cancer treatment).

Modeling, analyzing, and simulating 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:

• Modeling 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 modeling and simulation of complex biological systems
• Machine learning techniques in model and simulation of biological systems
• Medical imaging technologies and biological modeling
• Machine learning (data mining) and medical data analysis