Building up a model family for inflammations.

Area of Science:

• Mathematical Biology
• Computational Science
• Life Sciences

Background:

• Life science applications often face challenges due to unknown biological mechanisms and scarce quantitative data.
• Modeling complex biological systems, particularly in disease progression, requires robust and adaptable methodologies.

Purpose of the Study:

• To develop a flexible modeling framework for life science applications with incomplete data.
• To investigate the hierarchical order of biological mechanisms driving disease chronification.

Main Methods:

• Constructing a model family of reaction-diffusion equations on a mesoscopic scale.
• Defining reaction and taxis terms using classes of feasible functions derived from biological knowledge and mathematical analysis.
• Employing numerical simulations to compare model predictions with qualitative observational data.

Main Results:

• The approach successfully generates a hierarchy of biological mechanisms.
• It allows for the comparison of individual models within the family against observational data.
• The method provides a framework for understanding complex biological processes where data is limited.

Conclusions:

• This modeling strategy offers a powerful tool for studying complex biological systems with unknown mechanisms and limited data.
• The approach is particularly relevant for investigating liver inflammation, such as metabolic dysfunction-associated steatohepatitis and viral hepatitis, where disease chronification factors are unclear.