Figure 2.

The principle of automatic network reconstruction explained with the help of a trivial example. a) The input for the reconstruction algorithm is a time series data set that describes the time-course of the components of interest (A,B,C) with discrete values as a causal sequence of events. At time t2 the system reached its terminal state, i.e. the values of all components have reached their final level. In the simplest form, the entries are boolean (0,1). b) Shows the reaction vector of the transition in e). A reaction vector corresponds to the incidence matrix of an individual transition or to a column in the incidence matrix of a Petri net. c,d) The presence of the components at given time points is represented by tokens in places assigned to the components. The algorithm evaluates those places the marking of which has changed between two successive time points and e) connects these places with transitions that cause the observed flow of tokens in the reconstructed Petri net.

Durzinsky et al. BMC Systems Biology 2011 5:113   doi:10.1186/1752-0509-5-113
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