Rheological Method for Determining the Molecular Weight of Collagen Gels by Using a Machine Learning Technique

Area of Science:

• Biomaterials Science
• Rheology
• Biochemistry

Background:

• Determining molecular weights of complex biomolecules like collagen gels is challenging due to their hierarchical structure and high viscosity.
• Traditional methods may be insufficient for characterizing these sensitive biomaterials under mechanical stress.

Purpose of the Study:

• To present a novel rheological technique for measuring molecular weights (Mw) and molecular weight distributions (MwD) of non-hydrolyzed collagen gels.
• To investigate the influence of concentration on rheological measurements of viscous collagen gels.
• To develop a machine learning-assisted data analysis approach for rheological frequency sweeps.

Main Methods:

• Application of rheological techniques to collagen gels.
• Investigation of concentration effects on rheological properties.
• Development and implementation of machine learning algorithms for data analysis of frequency sweeps.
• Validation using chromatography and electrophoresis.

Main Results:

• Successfully measured molecular weights (Mw) and distributions (MwD) of collagen fibers, identifying species around 600 kDa.
• Validated the rheological method against traditional techniques like chromatography and electrophoresis.
• Demonstrated the method's ability to provide microstructural information on biomolecules.

Conclusions:

• The proposed rheological method, enhanced by machine learning, is a simple and effective technique for characterizing the molecular weights of hierarchical biomolecules.
• This approach facilitates the acquisition of microstructural information, aiding in the design of novel structural biomaterials.
• The study validates a new approach for analyzing sensitive biomaterials under mechanical stress.