The study of signaling networks often requires computational support to better deal with the complexity of biological interactions. The upcoming field of executable biology focuses on the design of executable computer algorithms that mimic biological phenomena[1]. Interacting biological entities are modelled both in their static and dynamic aspects, thus allowing the biologist to study the evolution of a system, playing it as a "movie"[2]. However, the user is frequently required to *learn complex mathematical formalisms* or modeling languages before starting with the real modeling experience. Moreover, the user will typically be asked to input *precise numerical data* to represent the speed of reactions, which are hardly ever known.
ANIMO[3-5] contributes to the study of signaling networks by providing a framework for modeling and analyzing the behavior of biological pathways in a **formal yet user-friendly** way. The idea is to let molecular biologists **play** with different possible configurations for a pathway, and thus help them to formulate new hypotheses, detect wrong or missing data, and identify informative wet-lab experiments. After having defined a qualitative topology for the pathway, the user can become more precise, fitting the model to existing experimental data. All throughout the process, the user is able to see how the model would evolve, and query it for simple behavioral properties. ANIMO is based on the powerful formalism of Timed Automata[6], while the user interface is implemented as a plugin to the widespread network visualization tool Cytoscape[7], thus allowing for transparent modeling and analysis of signaling networks.
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[1] Fisher, J and Henzinger, T.A., [http://www.ncbi.nlm.nih.gov/pubmed/17989686 Executable cell biology] (2007)
[2] Priami, C. [http://cacm.acm.org/magazines/2009/5/24650-algorithmic-systems-biology/fulltext Algorithmic systems biology: an opportunity for computer science] (2009)
[3] Schivo, S. et al. [http://www.ncbi.nlm.nih.gov/pubmed/24808226 Modeling biological pathway dynamics with timed automata] (2014)
[4] Scholma, J. et al. [http://www.ncbi.nlm.nih.gov/pubmed/24125950 Biological networks 101: computational modeling for molecular biologists] (2014)
[5] Schivo, S. et al. [http://dx.doi.org/10.4204/EPTCS.145.5 Setting Parameters for Biological Models With ANIMO] (2014)
[6] Bengtsson, J. and Yi, W. [http://link.springer.com/chapter/10.1007%2F978-3-540-27755-2_3 Timed automata: Semantics, algorithms and tools] (2004)
[7] http://www.cytoscape.org
