As mentioned earlier, the second experiment consisted of identifying the optimal combination of both network connectivities and kinetic parameters that would approximate the data.
In the cellular realm, dynamic organization [6] encompasses the architectural and the topological views of network analysis, accounting for both the autonomous dynamics exhibited by their components (nodes) and their defined interactions (connectivity) based on kinetic and thermodynamic principles [42], [48].
The structure of mass action kinetic models is given by the connectivity and the stoichiometry of the network and the parameters are the kinetic rate constants.
The symbolic form of kinetic transfer functions is shown to reflect connectivity features of the reaction network, more specifically the total and minimum number of reaction intermediates between gas phase reactants and products.
The dynamics of this network reproduces the time sequence protein activation along the biological cell cycle, solely on the basis of the connectivity graph of the network, neglecting all biochemical kinetic parameters.
But most importantly, networks are rarely robust to the random rewiring of the underlying interconnection structure and hence determining the network connectivity is much more important than determining the kinetic parameters that fit the particular data.
To achieve this, we present a novel stochastic model with physical model parameters and mechanisms for chromatin binding, 1D diffusion, and recruitment of regulatory proteins, and gene connectivity based on biochemical mechanisms and literature-based kinetic parameters.
The transition point between transport and kinetic control appears to be related to a critical porosity (9%) at which pore connectivity is high enough to allow rapid transport.
Our model extends those studies and considers the kinetic descriptions of nucleosome modifications, that is, diffusive propagation and recruitment of modification enzymes, and chromatin connectivity.
The 'structure' of a reaction network can be understood as its connectivity, without considerations of reaction rates: in this view the network structure is a graph with no kinetic information.
