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Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology

Lookup NU author(s): Daven Sanassy, Dr PaweĊ‚ Widera, Professor Natalio KrasnogorORCiD


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Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochemical systems at low species concentrations. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to determine which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochemical model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochemical model with the predicted fastest algorithm as the simulation engine.

Publication metadata

Author(s): Sanassy D, Widera P, Krasnogor N

Publication type: Article

Publication status: Published

Journal: ACS Synthetic Biology

Year: 2015

Volume: 4

Issue: 1

Pages: 39-47

Print publication date: 01/01/2015

Online publication date: 22/08/2014

Acceptance date: 07/03/2014

ISSN (electronic): 2161-5063

Publisher: American Chemical Society


DOI: 10.1021/sb5001406


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Funder referenceFunder name
EP/I031642/1Engineering and Physical Sciences Research Council
EP/J004111/1Engineering and Physical Sciences Research Council
EP/L001489/1Engineering and Physical Sciences Research Council