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On the rejection-based algorithm for simulation and analysis of large-scale reaction networks.

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Area of Science:

  • Computational biology
  • Biochemical network simulation

Background:

  • Stochastic simulation of large biochemical networks is computationally intensive.
  • The rejection-based stochastic simulation algorithm (RSSA) was previously developed to improve performance by delaying propensity updates.

Purpose of the Study:

  • To analyze and improve the performance of RSSA for large-scale biochemical reaction networks.
  • To introduce a new algorithm, simultaneous RSSA (SRSSA), for efficient generation of multiple independent simulation trajectories.

Main Methods:

  • Detailed performance analysis of the existing RSSA algorithm.
  • Development of SRSSA utilizing a shared data structure across simulations for reaction selection and trajectory formation.
  • Collective updating of the data structure in a single operation.
  • Exploiting the rejection-based mechanism for exact and independent trajectory generation.

Main Results:

  • SRSSA significantly improves simulation performance for large biochemical networks.
  • Memory requirements are independent of the number of trajectories generated.
  • The algorithm demonstrates applicability and efficiency on diverse real biological systems.

Conclusions:

  • SRSSA offers an efficient and exact method for simulating multiple independent trajectories of large biochemical networks.
  • The developed algorithm reduces computational time and memory overhead, making it suitable for complex biological system analysis.