Mixed quantitative/qualitative modeling and simulation of the cardiovascular system

Area of Science:

• Physiology
• Systems Biology
• Computational Neuroscience

Background:

• The cardiovascular system involves hemodynamics and central nervous system (CNS) control.
• While hemodynamics are well-modeled, CNS control remains poorly understood with limited deductive models.
• Qualitative methodologies offer an alternative for modeling complex, unknown systems like CNS control.

Purpose of the Study:

• To develop a qualitative model of CNS control for the cardiovascular system.
• To utilize the fuzzy inductive reasoning (FIR) methodology for this modeling task.
• To integrate the CNS control model with a hemodynamic model for holistic cardiovascular system analysis.

Main Methods:

• Employed fuzzy inductive reasoning (FIR), a technique based on general system problem solving (GSPS).
• Developed five independent controller models for distinct control actuations using FIR.
• Integrated these FIR models with a differential equation-based hemodynamic model to form a complete cardiovascular system model.

Main Results:

• Successfully developed five distinct qualitative controller models for CNS cardiovascular control.
• Integrated these models with a hemodynamic model to simulate the entire cardiovascular system.
• The developed model was validated on a single patient case.

Conclusions:

• Fuzzy inductive reasoning (FIR) is applicable for modeling the complex and not fully understood CNS control of the cardiovascular system.
• The integrated model provides a framework for studying cardiovascular system dynamics.
• Further validation across diverse patient populations is warranted.