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Updated: May 19, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

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Published on: December 15, 2017

Optimal isn't good enough.

Gerald E Loeb1

  • 1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA. gloeb@usc.edu

Biological Cybernetics
|August 17, 2012
PubMed
Summary
This summary is machine-generated.

Biological systems often use "good-enough" control strategies, not globally optimal ones, for sensorimotor behavior. This trial-and-error learning approach enhances robustness and aligns better with neural processing than strict optimal control theory.

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Last Updated: May 19, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Area of Science:

  • Neuroscience
  • Control Engineering
  • Evolutionary Biology

Background:

  • The concept of biological systems achieving optimal solutions is often linked to evolutionary principles and applied to sensorimotor behaviors.
  • Optimal control theory offers an attractive engineering framework for resolving motor redundancy by identifying a single best strategy.

Purpose of the Study:

  • To evaluate the applicability of formal optimal control engineering tools for a reductionist understanding of biological systems.
  • To determine if optimal control theory generates testable hypotheses for biological sensorimotor control.

Main Methods:

  • Critically assessed the core assumptions of optimal control theory (known cost function, invertible model, simple noise) against biological realities.
  • Considered an alternative "good-enough" control strategy based on trial-and-error learning and useful, rather than optimal, behaviors.

Main Results:

  • The strict conditions for optimal control are unlikely to be met by biological organisms, whose motivation is often "good-enough" rather than globally optimal.
  • Biological performance frequently deviates from optimality due to continuous performance limit testing by the brain.
  • "Good-enough" control, involving learning and a repertoire of useful behaviors, promotes individual and evolutionary robustness.

Conclusions:

  • Optimal control serves as a useful metaphor for superficial aspects of motor psychophysics but is insufficient for understanding underlying neural mechanisms.
  • A shift towards understanding "good-enough" control strategies is necessary for reductionist approaches to biological sensorimotor systems.
  • The cerebral cortex's capacity for information classification and recall supports a learning-based "good-enough" control model over online optimal computation.