System Oriented Modelling and Simulation

Actually, the application of conceptual process oriented modelling leads to very large and complex networks. For each of the molecular objects the model provides information about processes that consume and produce it. Each process provides information about its materials flow, parameters and location. The motivation the last step in our workflow, called systems integration, is to discover networks formed by different models and to use these networks in the modelling of dynamic systems.

To enable the discussion about the occurrence of objects as potential agents, we have to model them conceptually as participants at processes in the model. Further more, recent information is hidden in the complexity of the total network. The task is now to analyse the network and to extract the parts of interest, while unnecessary parts will be hidden. A first reduction will be achieved by filtering the total network for location specific processes. Actually, modelling of organisms and pathways as locations of bioprocesses will result in networks of organism specific pathways, e.g. the Glycolysis pathway in the organism rat. Based upon the conceptual schema of Figure 11.12, the next Figure 11.13 visualizes the network of all biochemical reactions in the organism rat as a graph.

It is obvious that the filtering of networks by their location will still produce complex information, while it is not clear that the network is a closed graph. A first approach is the exploration of the network by hand by recursively traversing the database. Another is approach uses graph theory to discover complex relationships automatically.

What can we expect from this approach are qualitative statements. Simple knockout experiments can be executed by computing shortest and alternative pathways, while added and deleting molecular objects in the network. Furthermore, graph theory can be applied for the modelling of systems. To differ the elements of a system from its environment, closed networks need to be computed interconnecting an initial set of objects and processes. The automated reconstruction of networks can be used to compute the topology of a dynamic system. In cases where only a low amount information networks of similar biological systems can be merged. To continue the workflow with third party tools, e. g. visualization and simulation tools, the discovered networks can be stored within the model and exported into common graph based and systems biology file formats.

As explained in Section 11.1, the quantitative modelling of dynamic systems includes equations for each process, e. g. differential, difference or stochastic equations. Solving them as equation systems aims at the computation of initial state or optimisation problems. Actually, adding dynamics to a process requires the specifica-

Fig. 11.13 Visualization and exploration of materialized
Fig. 11.14 Quantitative simulation of integratively modelled systems.

tion of the equation itself, as well as the specification of the concentrations of the participating objects and the estimated parameters.

0 0

Post a comment