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Intrinsic Computation of a Monod-Wyman-Changeux Molecule
1Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
Statistical complexity, a measure of intrinsic computation, can jump to infinity with small changes in biological models. This contrasts with excess entropy and transfer functions, impacting our understanding of biological systems.
Area of Science:
- Theoretical biology
- Computational neuroscience
- Information theory
Background:
- Causal states represent minimal sufficient statistics for predicting stochastic processes.
- Statistical complexity quantifies the coding cost of these causal states, reflecting a process's intrinsic computation.
- Biological systems, like the Monod-Wyman-Changeux molecule, can be modeled as dynamical systems with intrinsic computational properties.
Purpose of the Study:
- To investigate how statistical complexity changes in response to perturbations of a biologically-motivated dynamical model.
- To compare the behavior of statistical complexity with other information-theoretic measures like excess entropy and transfer functions.
- To explore the implications for the relationship between intrinsic and functional computation in biological sensory systems.
Main Methods:
- Analysis of a biologically-motivated dynamical model (Monod-Wyman-Changeux molecule).
- Perturbation of kinetic rates within the model.
- Calculation and comparison of statistical complexity, excess entropy, and transfer function.
- Evaluation of information-theoretic measures under model variations.
Main Results:
- Perturbations to kinetic rates in the Monod-Wyman-Changeux model caused statistical complexity to transition from finite to infinite.
- Excess entropy and the molecule's transfer function did not exhibit this abrupt change under the same perturbations.
- This highlights a unique sensitivity of statistical complexity to model dynamics.
Conclusions:
- Statistical complexity offers a distinct perspective on intrinsic computation, sensitive to subtle model changes.
- The findings suggest a potential divergence between intrinsic computational properties (statistical complexity) and functional measures (transfer function) in biological systems.
- This has implications for understanding how biological sensory systems perform computation and adapt.
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