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Limits on the computational expressivity of non-equilibrium biophysical processes
Carlos Floyd1,2, Aaron R Dinner3,4,5, Arvind Murugan4,6
1The Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, IL, USA. csfloyd@uchicago.edu.
Nature Communications
|August 5, 2025
Summary
Biological systems face limitations in classifying chemical states due to thermodynamic constraints. Input multiplicity, like enzymes targeting multiple substrates, can overcome these limits, enhancing information processing capabilities.
Area of Science:
- Biophysics
- Biochemical Networks
- Computational Biology
Background:
- Biological decision-making often involves classifying complex chemical states.
- The underlying biophysical and computational mechanisms for this classification are not well understood.
Purpose of the Study:
- To investigate the fundamental limitations on classification ability in generic biophysical processes.
- To identify mechanisms that can overcome these limitations in biological systems.
Main Methods:
- Utilized Markov jump processes as a mathematical abstraction for biochemical networks.
- Derived a novel non-equilibrium thermodynamic constraint.
Main Results:
- Revealed universal limitations on the classification ability of biophysical processes arising from a thermodynamic constraint.
- Demonstrated that input multiplicity, such as enzymes with multiple targets, can overcome these limitations.
Conclusions:
- Tuning input multiplicity can exponentially increase a biological system's capacity for information classification and processing.
- Findings offer insights analogous to how network depth enhances neural network capabilities.
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