It is not, in any way, meant to be comprehensive on the very diverse simulation tools that already exist, but focuses on mechanistic, dynamic models. This blog, and this software list, is intended to guide the potential user of simulation models. (3) they enable connecting biology with other fields of study ranging from physiology to genomics (2) they are unique approaches to integrate (in the literal term of the word) biological knowledge, in the form of experimental results and (1) they enable exploration of hypotheses, and as such, have become invaluable means to guide research There are many reasons for this, but we emphasize three of the more important: Simulation models have become important tools in Bioinformatics studies. Within the overall task of simulation, there are three primary sub-fields: model design, model execution and model analysis Computer simulation is the electronic equivalent of this type of role playing and it serves to drive synthetic environments and virtual worlds. To understand reality and all of its complexity, we must build artificial objects and dynamically act out roles with them. As adults, we lose some of this childlike behavior but recapture it later on through computer simulation. Children understand the world around them by simulating (with toys and figurines) most of their interactions with other people, animals and objects. The use of simulation is an activity that is as natural as a child who role plays. Simulation embodies the principle of ``learning by doing'' - to learn about the system we must first build a model of some sort and then operate the model. Computer simulation is the discipline of designing a model of an actual or theoretical physical/biological system, executing the model on a digital computer, and analyzing the execution output.

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