Notes and summary from Chapter 11 of Szallasi:
Regulatory networks consist of both network topology and dynamics. The term "network" emphasizes the topology, whereas the term "regulatory" emphasizes the dynamic interactions within the network, also called kinetics.
Experimental data can be used to capture both the topology, or architecture, and the dynamics of a cell. In the preceding chapter (and my corresponding write-up) data acquisition was discussed, whereas this chapter (and this write-up) focuses on using this data to model the topology and dynamics.
Szallasi mentions that, "engineering approaches have been instrumental in the reverse engineering effort". Reverse engineering or network inference in this context refers to identification of cellular networks from experiments. Various approaches are discussed in this chapter including Bayesian networks, iterative modeling, dynamic flux balance analysis, and
The reverse engineering of cellular networks is very complex. The kinetics/dynamics within the network are changing in time, which makes modeling incredibly challenging. The interactions change in complex manner that are often difficult to model by collecting data.
Szallasi identifies three challenges (within reverse engineering cellular networks) that will allow efficient and accurate dynamical modeling of networks:
(i) to improve the signal-to-noise ratio in the measurements
(ii) to develop new tools for measuring the cellular concentrations, fluxes, and interactions in both space and time
(iii) to incorporate model-based design of experiment protocol