Information Gain Limit of Biomolecular Computation

Area of Science:

• Biophysics
• Theoretical Biology
• Information Theory

Background:

• Biomolecules exist in various functional states, determined by their configurations.
• Biochemical changes alter the probability distribution of these configurations, enabling computation.
• Cellular computations often involve thermodynamic forces, leading to energy expenditure.

Purpose of the Study:

• To investigate the information-theoretic advantage of energy-intensive biomolecular computations.
• To develop a theoretical framework quantifying the role of thermodynamic forces in information gain.
• To relate energy expenditure to information processing capabilities in biological systems.

Main Methods:

• Developed a theoretical framework to analyze information gain from thermodynamic forces.
• Quantified information gain by changes in probability distributions.
• Derived a general expression linking thermodynamic force to maximum information gain.

Main Results:

• Thermodynamic forces enable information gain beyond equilibrium levels.
• A universal bound relates force to maximum information gain for arbitrary computations.
• Small input variations can lead to exponential output changes.

Conclusions:

• Energy expenditure is crucial for achieving high information processing in biomolecular systems.
• Biological systems can approach theoretical limits of information gain.
• The framework explains the necessity of costly computational paradigms in nature.