Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks

Area of Science:

• Biochemistry
• Computational Biology
• Systems Biology

Background:

• Stochastic fluctuations in biochemical reactions significantly impact biochemical network responses, especially in transcriptional regulation with low molecule counts.
• Small numbers of messenger RNA (mRNA) molecules in gene expression pathways necessitate computational tools for stochastic modeling.

Purpose of the Study:

• To develop a computational tool for simulating stochastic models of biochemical networks.
• To provide an efficient and accurate method for investigating the dynamics of biochemical systems with inherent randomness.

Main Methods:

• Developed the Biochemical Network Stochastic Simulator (BioNetS) software package.
• Implemented an efficient Gillespie algorithm for discrete variables.
• Utilized chemical Langevin equations for continuous variables.
• Enabled hybrid models with both discrete and continuous variables.

Main Results:

• BioNetS offers a graphical user interface for straightforward model entry.
• The simulator efficiently handles large-scale biochemical networks.
• Accurate simulation of stochastic dynamics is achieved through validated numerical methods.

Conclusions:

• BioNetS is a reliable tool for studying stochastic dynamics in large biochemical networks.
• The software supports hybrid models and incorporates cell growth and division.
• Accuracy and efficiency were confirmed using various test systems.