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Published on: May 8, 2021
From a homeostatic to a homeodynamic self
Takashi Ikegami1, Keisuke Suzuki
1Department of General Systems Sciences, The Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Tokyo 153-8902, Japan. ikeg@sacral.c.u-tokyo.ac.jp
Bio Systems
|September 4, 2007
Summary
This study explores how autonomous homeostatic systems achieve self-movement. Two computational cell models demonstrate that membrane shape and chaotic population dynamics can replace random searching, enabling sensor and motor emergence.
Area of Science:
- Systems biology
- Computational modeling
- Theoretical biology
Background:
- Homeostatic systems maintain stability.
- Autonomous systems exhibit self-movement.
- Ashby's ultrastability describes system adaptation under constraint violation.
Purpose of the Study:
- To investigate the mechanism driving homeostatic systems towards autonomous self-movement.
- To model this transition using computational cell models.
- To explore the emergence of sensors and motors in such systems.
Main Methods:
- Developed two computational cell models.
- Simulated systems under viability constraints.
- Replaced random parameter searching with specific mechanisms (membrane shape, chaotic population dynamics).
Main Results:
- Demonstrated a mechanism transitioning homeostatic states to autonomous self-moving states.
- Showcased membrane shape as a driver in the first model.
- Illustrated chaotic population dynamics as a driver in the second model.
- Observed the emergence of sensors and motors.
Conclusions:
- Autonomous homeostatic systems can naturally develop self-movement capabilities.
- Emergent sensors and motors are coupled recursively.
- Computational models provide insights into system autonomy and adaptation.
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