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Published on: November 25, 2015
Quantifying stochastic effects in biochemical reaction networks using partitioned leaping.
Leonard A Harris1, Aaron M Piccirilli, Emily R Majusiak
1School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA. lharris@pitt.edu
Summary
The partitioned leaping algorithm (PLA) accelerates biochemical simulations by capturing subtle stochastic effects. This study demonstrates PLA
Area of Science:
- Computational Biology
- Biochemical Reaction Networks
- Stochastic Simulation
Background:
- Stochastic simulation methods are crucial for analyzing complex biochemical systems.
- "Leaping" algorithms offer significant acceleration for these simulations.
- Practical applications of leaping methods remain limited in the literature.
Purpose of the Study:
- To apply the partitioned leaping algorithm (PLA) to investigate stochastic effects in model biochemical reaction networks.
- To demonstrate the PLA's capability in quantifying subtle and significant stochastic behaviors.
- To identify and discuss potential challenges and solutions for implementing the PLA.
Main Methods:
- Utilized the partitioned leaping algorithm (PLA), a multiscale leaping approach.
- Applied PLA to two representative, yet simplified, biochemical reaction networks.
- Compared PLA's performance against commonly used "exact" stochastic simulation methods.
Main Results:
- Successfully quantified subtle stochastic effects in the model systems that are difficult to ascertain with other methods.
- Revealed not-so-subtle behaviors that challenge conventional stochastic simulation techniques.
- Identified specific bottlenecks hindering the PLA's application and proposed mitigation strategies.
Conclusions:
- The PLA is a powerful tool for accelerating stochastic simulations of biochemical networks.
- This study provides practical insights into the benefits and challenges of applying leaping methods.
- The findings aim to encourage wider adoption of leaping algorithms in computational biology.
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