GinSim (file format)

File format title

GinSim

Element type

Diagram

Plugin

biouml.plugins.ginsim (Support for GinSim models)

GINsim format

GINsim is a software supporting the definition, the simulation and the analysis of regulatory graphs¹. The underlying multilevel logical formalism leans on the definition of two types of graphs: regulatory graphs, which model regulatory networks, and state transition graphs, which represent their dynamical behaviour.

GINsim format (GINML) is the extension of the GXL (Graph eXchange Language) comprising new attributes and subelements for elements node and edge, and an additional element called parameter, where

• element node:
  1. a new attribute basevalue corresponds to the "based level of expression" of the corresponding component (default value 0). In other words, it is the value of the logical parameter corresponding to the case where none of the incoming interactions if functional,
  2. new subelements within a node:
     • a list of elements parameter corresponding to the user defined logical parameters for this node
     • an element intdefining the maximum level of expression of this node.
• element edge:
  1. a new attribute sign which gives the sign of the interaction (positive for an activation, negative for a repression, otherwise unknown),
  2. one or two subelements int (level or interval letting the interaction functional).
• element parameter, is empty and has two attributes:
  1. attribute idActiveInteractions gives the "name" of the parameter. It is the list of the functional interactions exerted upon the considered node,
  2. attribute val is the value of the logical parameter.

Regulatory graphs

Logical regulatory graphs are defined as follow:

1. The nodes (or vertices): G={G₁,G₂,...,G_n} representing n components (regulatory products, genes, proteins...) labelled by their names;
2. The labelled oriented graph R: vertices are G's elements, and arcs represent interactions between regulatory products, labelled with a sign when the interaction corresponds to an activation or an inhibition.
3. The products levels: for each node, a specific variable represents the level of expression (or of activity) of the corresponding node. This level (me g) is related to the threshold above which an outgoing interaction becomes functional. It is represented by an integer in {0,...,max}, where max equals, at most, the number of outgoing interactions from this particular component. By default, two extreme values are considered, 0 (none outgoing interaction functional) and max=1 (all interactions functional). The user can then define additional values to represent functional intermediate levels for the activity of the corresponding node.
4. The logical parameters: they allow the qualitative specification of the effects of combinations of interactions controlling a given element. A parameter is named by the set of incoming interactions which are functional. At present, a null value is assign to all parameters by default.

State transition graphs

Logical state transition graphs represent the dynamical behaviour of systems described by regulatory graphs, given a set of initial states. They are defined as follows,

1. The graph defined by a set of nodes (vertices) and a set of arcs connecting pairs of nodes.
2. The nodes represent states of the system, defined by a word resulting of ogique temporelle et model checking pour les rthe concatenation of the actual level of each regulatory product.
3. The arcs represent spontaneous transitions between pairs of states.

In addition, we have to define an updating method to specify the temporal ordering of the transitions. Under the synchronous assumption, all orders of commutation are executed simultaneously at each time step. From a biological point of view, this assumption implies that all macromolecular processes are realised in identical amounts of time, which is clearly unrealistic and often at the origin of simulation artefacts. Under the asynchronous assumption, when multiple orders of commutations occur at a given state, additional information is needed to select specific transitions. As we have no such information, all possible transitions are considered.

References

1. http://gin.univ-mrs.fr/