A paper recently published in the journal Scientific Reports proposed a quantum-like decision-making model capable of modeling bacterial polyhydroxyalkanoate (PHA) biosynthesis.
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Background
PHAs are promising materials for producing biodegradable and sustainable plastics. These naturally occurring biopolymers are synthesized by specific microorganisms as intracellular energy reserves under nutrient-stress conditions, such as high carbon-to-phosphorus (C/P) or high carbon-to-nitrogen (C/N) ratios.
PHAs offer a viable alternative to conventional petroleum-based plastics. They are carbon-neutral, easily biodegradable, chemically stable, and exhibit properties suitable for various applications. However, the high production costs, stemming from the complexity of optimizing biosynthesis in industrial bacterial populations, particularly for medium-chain-length PHAs (mcl-PHAs), are a significant barrier to their widespread commercialization.
One key challenge in advancing PHA production is developing precise mechanistic biosynthesis models to improve yields. Current modeling approaches struggle with uncertainty and excessive complexity in simulating the dynamic regulatory mechanisms underlying PHA bioprocesses.
Quantum theory has shown promise in modeling probabilistic behavior and has been applied to describe macro-level classical processes in biological systems. For example, a quantum-like decision-making model has been developed to simulate the genetic regulation of Escherichia coli (E. coli) in its adaptive diauxie behavior, where it utilizes glucose and lactose. This approach could potentially be leveraged to enhance our understanding and optimization of PHA biosynthesis.
The Study
In this study, researchers developed a model for mcl-PHA biosynthesis using quantum formalism instead of using classical decision-making approaches. They introduced the nutrient condition detection "preparation" and gene regulation activation in response to C/N ratios and a hidden genetic preference parameter/environment inputs, which was followed by the physiological outputs' "transformation" and "measurement" responding to PHA production/previous inputs.
The proposed quantum-like decision-making model for modeling bacterial PHA biosynthesis can encode gene regulation and expression events as hidden layers by a density matrix's general transformation. Specifically, the formulation of "transformation," "measurement," and "preparation" embedded gene regulation and expression events into a 2-stage quantum-like model.
The density matrix utilized the probability amplitudes' interference to provide an empirical-level description of PHA biosynthesis. Researchers implemented their framework, modeling the mcl-PHA biosynthesis in Pseudomonas putida (P. putida) concerning external C/N ratios.
They performed several experiments on the bacterium by feeding different C/N ratios, which represented a range of nutrient conditions simulating high to low nutrient stress environments, and quantified the PHA accumulation response related to each C/N ratio to validate the model.
In the quantum-like decision-making model, a bacterial cell was assumed to be a system that processes the external environmental information in a "quantum-like" manner. The term "quantum-like" implied that researchers had only drawn parallelisms between biological complexities, quantum probability, and its philosophy to employ advanced quantum theories for biology problems instead of coupling the actual quantum effects that are observed in sub-atomic scale events to biological processes.
Study Contributions
The simulation results were consistent with the experimental results for C/N ratios between 10:1 and 50:1. For instance, the PHA production was negligible/0.06 % cell dry mass (CDM) at a low 10:1 C/N ratio, which corresponded to a nutrient-rich condition. However, the intracellular PHA levels increased at higher C/N ratios, corresponding nutrient stress, and peaked at 13.8 % CDM at a 40:1 C/N ratio. A further rise in the C/N ratio to 50:1 led to a substantial decrease in PHA accumulation/2.51 % CDM.
Such a narrow C/N ratio window with high PHA accumulation has been extensively reported and well-known. In another PHA-producing and styrene-degrading bacterium, P. putida CA-3, PHA production was almost negligible until the C/N ratio exceeded 14:1, and a peak production was observed at 28:1 C/N ratio.
This complex response of PHA production across species indicated the randomness underlying the biological dynamics at the macro-scale. Thus, the P. putida NBUS12's PHA biosynthesis must be modeled as a quantum-like system where both nutrient-rich and nutrient stress conditions affect the PHA formation outcome instead of modeling it as a classical stochastic system with discrete input state spaces.
Additionally, the results highlighted the degree to which P. putida prefers to channel carbon toward PHA production as part of its adaptive response to nutrient stress. This preference was modeled using quantum formalism, reflecting the bacterium's behavior under varying nutrient conditions.
Researchers discussed several generic parameters derived from the quantum formulation, including kN, kD, and θ. These parameters provided insights into the bacterium's metabolic strategies and its efficiency in utilizing carbon for PHA production.
They also determined a new invariant quantity, given by kD/kN ratio = 0.29512 and θ = π. The kD to kN ratio indicated the bacteria's nutrient condition preference for PHA production. These parameters defined the interference between the events' probability amplitudes during adaptive dynamics and enabled researchers to quantitatively estimate the PHA production adaptation of P. putida NBUS12 to extracellular C/N ratios.
To summarize, this study provided an accurate empirical-level description of the environmental nutrient effect on PHA production in P. putida and offered a new perspective on using quantum theory for PHA production.
Journal Reference
Ho, L. et al. (2024). Quantum modeling simulates the nutrient effect of bioplastic polyhydroxyalkanoate (PHA) production in Pseudomonas putida. Scientific Reports, 14(1), 1-9. DOI: 10.1038/s41598-024-68727-7, https://www.nature.com/articles/s41598-024-68727-7
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