Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

Area of Science:

• Quantum biology
• Theoretical biology
• Biophysics

Background:

• Quantum mechanics plays a role in various biological processes, including photosynthesis and genetics.
• Existing quantum biological channel models are memoryless, limiting their ability to describe mutation propagation, aging, and genetic evolution.
• There is a need for quantum models that incorporate memory to accurately represent these dynamic biological phenomena.

Purpose of the Study:

• To develop novel quantum mechanical models with memory for biological channels.
• To investigate the aging and evolution of quantum biological channel capacity over generations.
• To provide a more comprehensive framework for studying quantum effects in biology.

Main Methods:

• Derived operator-sum representation of a biological channel.
• Developed Markovian classical, Markovian-like quantum, and hybrid quantum-classical models with memory.
• Applied these models to study aging and evolution of quantum biological channel capacity.
• Compared proposed models with existing Markovian and Kimura models.

Main Results:

• Proposed novel quantum mechanical models with memory to describe mutation creation and propagation.
• Demonstrated that the quantum Master equation is a first-order approximation of the proposed quantum Markov chain-like model.
• Showed that aging phenotype is determined by transition probabilities in coupled programmed and damage models of aging.

Conclusions:

• The developed models offer a more accurate description of biological processes influenced by quantum mechanics, particularly those involving memory effects.
• These models have broad applicability to open problems in biology beyond just biological channel capacity.
• The quantum Markov chain-like model provides a more refined understanding of aging and genetic information evolution.