Stochastic control of metabolic pathways.

Area of Science:

• Biochemistry and Systems Biology
• Metabolic Network Analysis
• Stochastic Modeling

Background:

• Metabolic control analysis (MCA) traditionally assumes deterministic system behavior.
• Understanding the impact of noise on metabolic networks is crucial for realistic modeling.
• Extrinsic noise, or random fluctuations from external sources, can significantly influence cellular processes.

Purpose of the Study:

• To develop a stochastic generalization of metabolic control analysis (MCA) that incorporates extrinsic noise.
• To investigate how external random fluctuations at the kinetic level affect metabolic network behavior.
• To introduce and define new concepts such as control by noise.

Main Methods:

• Introducing external random fluctuations (extrinsic noise) at the kinetic level of metabolic networks.
• Deriving a stochastic version of metabolic control analysis.
• Defining new elasticity and response coefficients that account for noise intensity.

Main Results:

• Summation and connectivity theorems remain valid under extrinsic noise.
• Control coefficients explicitly depend on noise intensity.
• A new concept, 'control by noise,' is introduced, enabling tuning of metabolic system behavior.
• The framework is applicable to intrinsic noise under time-scale separation.

Conclusions:

• Extrinsic noise can be systematically incorporated into metabolic control analysis.
• Noise intensity is a tunable parameter for controlling metabolic network dynamics.
• The developed framework provides a more comprehensive understanding of metabolic regulation in the presence of fluctuations.