FAMoS: A Flexible and dynamic Algorithm for Model Selection to analyse complex systems dynamics.

Area of Science:

• Systems Biology
• Computational Biology
• Immunology

Background:

• Biological systems possess complex dynamics due to numerous interacting components, making analysis challenging.
• Existing model selection algorithms often struggle with large biological model spaces or rely on exhaustive searches, limiting their applicability.
• Identifying relevant factors and processes in complex biological systems requires sophisticated modeling approaches.

Purpose of the Study:

• To develop a novel algorithm for efficient model selection in complex biological systems.
• To enable dynamic searching of large model spaces, overcoming limitations of existing methods.
• To apply the algorithm to analyze cell proliferation dynamics and evaluate its flexibility.

Main Methods:

• Developed a dynamic model selection algorithm incorporating local and non-local search strategies.
• Validated the algorithm using simulated data across various model structures.
• Applied the algorithm to experimental data on CD4+ and CD8+ T cell proliferation in different culture conditions.

Main Results:

• The algorithm successfully performed model selection in complex systems and navigated large model spaces.
• Validation with simulated data demonstrated the algorithm's flexibility with different model structures.
• Analysis of T cell proliferation revealed reduced proliferation potential in 3D ex vivo cultures compared to suspension cultures.

Conclusions:

• The developed algorithm offers a flexible and efficient tool for data-oriented evaluation of complex biological model spaces.
• It aids in identifying critical factors and dynamics within biological systems.
• The findings highlight the impact of 3D culture environments on T cell proliferation dynamics.