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Cells solved the Gibbs paradox by learning to contain entropic forces
1University of Nevada, Reno School of Medicine, Reno, NV, 89521, USA. jebaker@unr.edu.
Scientific Reports
|October 3, 2023
Summary
Evolution harnesses increased chemical entropy (disorder) to generate order, using muscle proteins as a model. This process, akin to machine learning, explains how life creates order from disorder.
Area of Science:
- Biophysics
- Evolutionary Biology
- Biochemistry
Background:
- Evolution acts as a natural machine learning process.
- Life generates order from disorder, a complex problem.
- Understanding this process is key to fundamental biological questions.
Purpose of the Study:
- To describe the mechanism by which life creates order from disorder.
- To use muscle as a model system to explain this mechanism.
- To connect evolutionary solutions to fundamental thermodynamic principles.
Main Methods:
- Utilizing muscle as a model biological system.
- Analyzing the physical properties of proteins involved in energy transduction.
- Examining how proteins interact with chemical entropy.
Main Results:
- Evolution has optimized protein physical properties to manage changes in chemical entropy.
- This protein tuning allows for the generation of directed chemical forces from disorder.
- The findings align with Gibbs' postulates on 'sensible' properties.
Conclusions:
- Life's ability to create order from disorder is achieved through evolution-tuned protein properties.
- This mechanism provides a solution to a long-standing scientific and philosophical paradox.
- The study elucidates a fundamental principle of biological energy conversion.
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