Microbe cultivation guidelines to optimize rhamnolipid applications

Area of Science:

• Biotechnology and biochemical engineering
• Surfactant chemistry
• Computational modeling

Background:

• Selecting natural surfactants for specific applications is challenging due to complex production variables.
• Knowledge on microbial surfactants, particularly rhamnolipids (RLs), is often limited to specific experimental conditions.
• Triglyceride (TG) solubilization by RLs is an underrepresented area in current literature.

Purpose of the Study:

• To develop a computational framework for biosynthesizing rhamnolipids with targeted properties.
• To create predictive models for rhamnolipid characteristics and solubilization efficiency.
• To facilitate the selection of optimal biosurfactants for various applications.

Main Methods:

• Amassed literature data on RL biosynthesis and micellar solubilization.
• Augmented literature data with experimental results on triglyceride solubilization.
• Constructed mathematical models (logP_RL and logMSR) to predict RL properties and performance.

Main Results:

• Developed predictive models with robust R² values (0.581-0.997 for RL characteristics, 0.804 for solubilization efficiency).
• Identified key descriptors influencing RL properties and solubilization, ranking their impact.
• Translated models into user-friendly calculators for biosurfactant selection.

Conclusions:

• Computational models can effectively predict rhamnolipid characteristics and solubilization efficiency.
• The developed calculators streamline the process of selecting appropriate microbial biosurfactants.
• This approach bridges the gap between scientific knowledge and practical application of microbial surfactants.