From deterministic to fuzzy decision-making in artificial cells

Area of Science:

• Synthetic Biology
• Biophysics
• Systems Biology

Background:

• Autonomous artificial cells require regulatory networks for information processing and decision-making, which traditionally consume time and energy.
• Making decisions with limited molecular components is challenging due to potential inaccuracies, despite being fast and energy-efficient.

Purpose of the Study:

• To demonstrate decision-making in artificial cells using a bistable gene network with constant protein turnover.
• To investigate the impact of gene copy number on decision-making speed, accuracy, and energy consumption in artificial cells.

Main Methods:

• Engineered artificial cells with a bistable gene network.
• Varied gene copy numbers from high (10^5) to low (approx. 10 per cell).
• Observed gene regulation dynamics and protein concentrations.

Main Results:

• A transition from slow, deterministic decision-making to rapid, fuzzy decision-making dominated by small-number fluctuations was observed as gene copy number decreased.
• Gene regulation occurred at lower DNA and protein concentrations than equilibrium predictions, suggesting rate enhancement via co-expressional localization.
• High-copy regimes showed sharp transitions and hysteresis, while low-copy regimes exhibited fluctuations, state switching, and cellular individuality.

Conclusions:

• Artificial cells can achieve rapid, low-power information processing and decision-making using minimal molecular components.
• Small-number fluctuations play a crucial role in rapid decision-making dynamics in low-copy number systems.
• The study highlights the potential for designing efficient regulatory networks in synthetic cells.