Optimization and variability can coexist

Area of Science:

• Systems biology
• Theoretical biology
• Biophysics

Background:

• Biological systems often operate near physical performance limits.
• The principle of optimality is difficult to establish due to apparent requirements for precise parameter tuning.

Purpose of the Study:

• To challenge the notion that optimality necessitates fine-tuning.
• To demonstrate how parameter variability can coexist with near-optimal performance.
• To provide a theoretical framework for understanding biological system diversity.

Main Methods:

• Analysis of functional performance across diverse biological systems.
• Mathematical modeling of parameter dependencies near performance optima.
• Investigation of parameter space entropy and its relation to performance.

Main Results:

• Functional performance near an optimum is characterized by "sloppy" parameter dependence.
• Weak constraints on some parameter combinations allow for extensive entropy in parameter space.
• Parameter variability is predicted even when average performance is near optimal.

Conclusions:

• The "sloppy" nature of performance near optima removes a key objection to optimization as a general principle.
• This framework rationalizes the widespread observation of parameter variability in biological systems.
• Optimality can be a general principle without requiring extreme parameter precision.