Quantum modeling simulates nutrient effect of bioplastic polyhydroxyalkanoate (PHA) production in Pseudomonas putida

Area of Science:

• Biotechnology
• Bioprocess Engineering
• Quantum Biology

Background:

• Sustainable plastics derived from polyhydroxyalkanoates (PHAs) face challenges in accurate biosynthetic modeling, particularly for medium-chain-length PHA (mcl-PHA).
• Optimizing PHA yield requires precise understanding of gene regulation and dynamic responses to nutrient limitations, such as carbon-to-nitrogen (C/N) ratios.
• Current modeling approaches struggle with complexity and uncertainty in simulating these bioprocesses.

Purpose of the Study:

• To develop a novel quantum-like decision-making model for simulating PHA biosynthesis.
• To accurately model the dynamic regulation mechanisms of mcl-PHA production in Pseudomonas putida.
• To enhance the prediction and optimization of PHA yield for sustainable bioplastics.

Main Methods:

• Encoding gene expression and regulation as hidden layers using quantum-like density matrix transformations.
• Implementing a quantum formalism to describe PHA biosynthesis empirically.
• Modeling mcl-PHA production in Pseudomonas putida in response to varying external C/N ratios.

Main Results:

• The quantum-like model successfully simulated mcl-PHA biosynthesis in Pseudomonas putida.
• Optimal PHA production reached 13.81% cell dry mass (CDM) at a C/N ratio of 40:1.
• The study revealed Pseudomonas putida's adaptive strategies for carbon channeling towards PHA under nutrient stress, interpreted through quantum formalism.

Conclusions:

• Quantum theory offers a novel perspective for modeling and optimizing PHA production.
• The developed quantum-like model provides a powerful tool for enhancing bioprocesses.
• This approach demonstrates potential applications beyond PHA synthesis, applicable to other microbial bioprocesses.