title: Meta-stochastic simulation of biochemical models for systems and synthetic biology.
abstract: 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. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.
authors.affiliation: Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University , Newcastle upon Tyne, Tyne and Wear, NE1 7RU, United Kingdom.
authors.forename: Daven
authors.initials: D
authors.lastname: Sanassy
authors.forename: Paweł
authors.initials: P
authors.lastname: Widera
authors.forename: Natalio
authors.initials: N
authors.lastname: Krasnogor
mesh_terms: D000465:Algorithms; D001669:Biochemical Phenomena; D003198:Computer Simulation; D020407:Internet; D016014:Linear Models; D008954:Models, Biological; D013269:Stochastic Processes; D058615:Synthetic Biology; D049490:Systems Biology
keywords: Web application; algorithm performance; model properties; stochastic simulation; synthetic biology; systems biology