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This study explores eco-evolutionary control, guiding biological systems like viruses and cells toward desired outcomes. Understanding control mechanisms enhances predictability and efficacy in applications from drug design to synthetic biology.

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Synthetic biology

Background:

  • Control interventions are crucial for directing the evolution of biological systems, including molecules, viruses, and cells.
  • Applications span from engineering biomolecules and synthetic organisms to designing drugs, therapies, and vaccines against pathogens and cancer.

Purpose of the Study:

  • To synthesize the objectives, mechanisms, and dynamics of eco-evolutionary control across diverse biological systems.
  • To explore how control systems learn and process information about target systems for effective intervention.
  • To differentiate human-led pre-emptive control from biotic feedback control mechanisms.

Main Methods:

  • Review and synthesis of existing literature on eco-evolutionary control strategies.
  • Analysis of information processing in control systems through sensing, measurement, adaptive evolution, and computational prediction.
  • Development of a cost-benefit calculus for evaluating and optimizing control protocols.

Main Results:

  • Eco-evolutionary control involves altering the trajectory of target systems to induce new functions or prevent escape evolution.
  • Information flow is key, distinguishing human pre-emptive control from natural biotic feedback control.
  • A cost-benefit framework can optimize control protocols based on evolutionary predictability.

Conclusions:

  • Effective eco-evolutionary control relies on understanding and manipulating the information flow within biological systems.
  • The predictability of evolutionary trajectories is fundamentally linked to the efficacy of pre-emptive control strategies.
  • This framework provides insights for designing robust interventions in medicine, biotechnology, and evolutionary engineering.