Parameter Identification and Model Ranking of Thomas Networks

We propose a new methodology for identification and analysis of discrete gene networks as defined by René Thomas, supported by a tool chain: (i) given a Thomas network with partially known kinetic parameters, we reduce the number of acceptable parametrizations to those that fit time-series measurements and reflect other known constraints by an improved technique of coloured LTL model checking performing efficiently on Thomas networks in distributed environment; (ii) we introduce classification of acceptable parametrizations to identify the most optimal ones; (iii) we propose a way of visualising parametrizations dynamics wrt time-series data. The methodology is validated on a rat neural development case study; (iv) finally we provide description of developed algorithms and evaluation of their performance.