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This study introduces a hybrid reaction-diffusion model for enzyme kinetics. It shows that using more, less active enzymes can speed up biological responses by reducing metabolite levels.

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Area of Science:

  • Soft and biological physics
  • Biochemical reaction dynamics
  • Computational modeling

Background:

  • Many biological and soft matter systems involve multiple, distinct time and length scales.
  • Enzyme kinetics exemplifies this with large, slow enzymes and small, fast-diffusing metabolites.
  • Coupling different modeling techniques for separate scales is a natural approach.

Purpose of the Study:

  • To explore the coupling of different techniques for multi-scale systems.
  • To develop a stochastic-deterministic discrete-continuous reaction-diffusion model.
  • To model enzyme-catalyzed reactions with mobile sources/sinks and spatial heterogeneities.

Main Methods:

  • Construction of a stochastic-deterministic discrete-continuous reaction-diffusion model.
  • Incorporation of mobile sources and sinks to represent enzymes and metabolites.
  • Separation of different sources of stochasticity within the model.

Main Results:

  • Modeling enzyme-catalyzed reactions with freely diffusing enzymes and heterogeneous metabolite sources.
  • Calculations indicate that a higher quantity of less active enzymes reduces metabolite pool size and lag time.
  • This configuration leads to a faster response to external stimuli compared to fewer, more active enzymes.

Conclusions:

  • The developed hybrid modeling approach effectively separates sources of stochasticity.
  • Employing more, less active enzymes can enhance system responsiveness in biological contexts.
  • The methodology is extendable to complex systems involving spatial heterogeneities and discreteness.