Evaluation of deterministic and stochastic simulation tools for cellular signaling
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1
Tampere University of Technology, Finland
During the ongoing decade there has been a distinct rise in the number of simulation tools , i.e., software designed for studying cellular signaling systems in silico. Currently, there are numerous simulation tools, either freely or commercially available, with different features, capabilities, etc. From a scientist's point of view, this development, although desirable in principle, makes the selection of a tool more and more difficult. Therefore, to both highlight the current state and aid in the selection process, we have made three comparative evaluations of the simulation tools (Pettinen et al., Bioinformatics 2005; Manninen et al., Proc. of EMBC, 2006; Mäkiraatikka et al. Proc. of FOSBE, 2007), results of which we will summarize in this work to provide further insights and assistance for the development of new tools.
The simulation tools can be roughly divided into two categories, based on the type of the algorithm used for solving the model equations, i.e., deterministic or stochastic. Traditionally the modeling and simulation is done utilizing deterministic approaches, but with the growing knowledge of the modeled systems and their complexity, the use of stochastic approaches has increased. One example of such is the modeling and simulation of complex reaction-diffusion systems that often involve minute quantities of signaling molecules. In this case, the deterministic methods most likely are not adequate. From our studies listed above, the first (2005) focuses on deterministic simulation tools, whereas the latter two (2006 and 2007) focus on stochastic ones. Two of the studies (2005 and 2006) use the same test case, a model of the protein kinase C (PKC) signal transduction pathway, originally obtained from DOQCS-database (http://doqcs.ncbs.res.in). The other study (2007) uses a gene expression/protein function hybrid model combining two models from the DOQCS-database.
In the first study, a survey of the then existing and freely available simulation tools was conducted, and based on the survey, four tools, all deterministic, were selected for more detailed evaluation. The survey compared more than 20 different tools using more than 10 attributes per tool. In the more detailed evalution, the selected four tools were compared using the abovementioned PKC-pathway model, i.e., the model was implemented and simulated in each tool and the results and experiences were compared to each other. The other two studies (2006 and 2007), which use stochastic simulation tools, follow the same procedure. A larger survey has been made and specific tools are selected for more detailed evalution based on the survey.
When the results of the three studies are drawn together, the following conclusions can be drawn. Despite the recent development, the usability and user-friendliness of the simulation tools still leaves a lot to be desired. Proper manuals and analysis tools should also be available. The XML-based languages (e.g. NeuroML and SBML) will hopefully provide a solution for the current compatibility issues between simulation tools. Other important issues that too often are neglected are the possibility to utilize realistic external stimuli in the models, estimate model parameters, and perform sensitivity analysis.
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
Computational Neuroscience
Citation:
Pettinen
A,
Mäkiraatikka
E,
Manninen
T,
Smolander
O,
Ylipää
A,
Hituri
K and
Linne
M
(2008). Evaluation of deterministic and stochastic simulation tools for cellular signaling.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.033
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Received:
28 Jul 2008;
Published Online:
28 Jul 2008.
*
Correspondence:
Antti Pettinen, Tampere University of Technology, Tampere, Finland, antti.pettinen@tut.fi