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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

Margaritis Voliotis¹, Philipp Thomas², Ramon Grima³

¹School of Mathematics, University of Bristol, Bristol, United Kingdom.

Plos Computational Biology

|June 2, 2016

Summary

This summary is machine-generated.

This study introduces the Extrande method for faster stochastic simulation of biomolecular networks in dynamic cellular environments. This advance enables new insights into biological systems and synthetic circuit design.

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Area of Science:

Systems Biology
Computational Biology
Biophysics

Background:

Biomolecular network simulation is crucial for understanding biological systems.
Existing simulation methods face computational challenges with dynamic environments and large cell populations.

Purpose of the Study:

To develop a novel, efficient method for stochastic simulation of biomolecular networks with time-varying propensities.
To address the limitations of current simulation approaches in dynamic cellular contexts.

Main Methods:

Introduced the Extrande method for stochastic simulation.
Sampled trajectories of the stochastic process described by the chemical master equation with time-varying propensities.
Compared Extrande with existing approaches.

Main Results:

The Extrande method provides a conditionally exact and significantly faster simulation solution (orders of magnitude).
Demonstrated feasibility using quorum sensing bacteria to show constraints on cell fate network design due to signaling fluctuations.
Existing methods can fail or be computationally prohibitive.

Conclusions:

The Extrande method offers a computationally feasible solution for simulating complex biomolecular networks in dynamic environments.
This approach can advance the understanding of molecular systems biology and the design of synthetic biological circuits.