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Probabilistic Inference with Polymerizing Biochemical Circuits.

Yarden Katz1, Walter Fontana2

  • 1Digital Studies Institute, University of Michigan, Ann Arbor, MI 48109, USA.

Entropy (Basel, Switzerland)
|May 28, 2022
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Summary
This summary is machine-generated.

Biochemical circuits using polymerizing polymers can perform probabilistic inference in real-time. This research explores how cellular machinery may achieve predictive behaviors by recording environmental changes.

Keywords:
biological computationchanging environmentsmolecular information-processingprobabilistic inferencesingle-celled organisms

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Area of Science:

  • Biochemistry
  • Computational Neuroscience
  • Systems Biology

Background:

  • Probabilistic inference models cognitive processes like learning and memory.
  • While animal nervous systems are studied for inference, single-celled organisms also exhibit predictive behaviors.
  • The biochemical mechanisms for cellular inference remain largely unknown.

Purpose of the Study:

  • To investigate how biochemical machinery can perform probabilistic inference.
  • To demonstrate a real-time biochemical realization of inference in a simple Markov model.

Main Methods:

  • Utilizing polymerizing biochemical circuits to approximate inference.
  • Assembling linear polymers to record environmental history.
  • Generating molecular complexes reflecting posterior probabilities through polymerization.

Main Results:

  • Demonstrated real-time approximate inference using polymerizing biochemical circuits.
  • Showcased how polymer assembly can encode environmental history.
  • Established a link between polymerization products and posterior probabilities.

Conclusions:

  • Biochemical circuits can perform probabilistic inference, challenging previous assumptions.
  • Polymerization offers a novel mechanism for biological information processing.
  • This work opens avenues for understanding cellular predictive capabilities.