Dynamic metabolic resource allocation based on the maximum entropy principle.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Theoretical Biology

Background:

• Metabolic regulation models typically assume certainty, neglecting environmental unpredictability.
• Organisms employ diverse strategies to manage environmental fluctuations.
• Existing models lack a unified approach to dynamic metabolic regulation under uncertainty.

Purpose of the Study:

• To develop a dynamic metabolic modeling framework integrating resource allocation and optimal control.
• To incorporate the principle of maximum entropy for cellular resource allocation.
• To unify and generalize existing dynamic metabolic modeling approaches.

Main Methods:

• Synthesis of resource allocation theories and Ramkrishna's optimal control formulation.
• Application of the maximum entropy principle to elementary flux mode allocation.
• Development of a dynamic metabolic model interpolating between flux balance analysis and unregulated models.

Main Results:

• The framework successfully models 'bet-hedging' strategies in fluctuating environments.
• It explains heterogeneous resource investment and reserve accumulation under growth limitation.
• Observed yeast growth dynamics in batch and continuous cultures are accurately described.

Conclusions:

• The maximum entropy principle provides an optimal control law for resource partitioning.
• This approach unifies dynamic metabolic modeling, bridging deterministic and stochastic views.
• The framework has implications for model reduction, selection, and simulation in systems biology.