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Emergent programmable behavior and chaos in dynamically driven active filaments.

Deepak Krishnamurthy1, Manu Prakash2

  • 1Department of Bioengineering, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|July 5, 2023
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Summary
This summary is machine-generated.

Scientists modeled the active filament in Lacrymaria olor cells to understand how programmed forces create cell neck movement. This research reveals how simple rules can generate complex behaviors like searching and homing.

Keywords:
active matterbiophysicscell behaviorchaosnonlinear dynamics

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Area of Science:

  • Biophysics
  • Cellular dynamics
  • Active matter physics

Background:

  • Understanding how subcellular components generate cell behavior is a major challenge in biology and physics.
  • The ciliate Lacrymaria olor exhibits complex hunting behaviors using a slender, ciliated neck.
  • The mechanism by which cells program active filament structures for specific behaviors remains unclear.

Purpose of the Study:

  • To develop an active filament model to link programmed forcing to filament shape dynamics.
  • To investigate how time-varying activity patterns and follower forces influence cell neck behavior.
  • To explore the potential for simple programs to generate complex behaviors like homing and searching.

Main Methods:

  • Development of an active filament model incorporating time-varying activity and follower force constraints.
  • Analysis of filament dynamics under deterministic, time-varying follower forces.
  • Identification of nonlinear iterated maps to predict long-term filament behavior.
  • Statistical measurement of biological programs in Lacrymaria olor for experimental comparison.

Main Results:

  • Active filaments under time-varying follower forces exhibit complex periodic and aperiodic dynamics.
  • A transition to chaos was observed in biologically accessible parameter spaces, explaining aperiodicity.
  • A simple nonlinear iterated map was identified, predicting long-term filament behavior.
  • The model's predictions were compared with statistical properties of biological programs in L. olor.

Conclusions:

  • Active filament models can elucidate the relationship between programmed forces and emergent cell behavior.
  • Time-varying follower forces can generate rich and complex dynamics, including chaotic regimes.
  • Simple predictive maps suggest the possibility of designing artificial programs for specific cellular functions.
  • The study provides a framework for comparing theoretical models with experimental observations of cellular behavior.