Abstract

One of the fundamental interests in systems biology is the discovery of the specific biochemical mechanisms that explain the observed behaviour of a particular system. These mechanisms are composed of a complex network of reactions between various chemical species; detailing the species and interactions forming this network can be an overwhelming task, even for the simplest of biochemical systems.

In spite of the intrinsic difficulty of network reconstruction, our research outlines the experimentation process that makes such discovery possible. If nothing is known about the network between measured species, then experiments must be performed as follows:

1. for a network composed of p measured species, the same number of experiments p must be performed;
2. each experiment must independently control a measured specie, i.e., control input i must first affect measured specie i (e.g. experiments involving gene silencing or inducible overexpression).

If something is known about the system, then these experiments may be relaxed. The network representation resulting from this process yields a predictive model commensurate with the informativity of the data used to create it: steady-state data yield static network information, while time series data generate a dynamic representation of the system suitable for simulation.

Further, we demonstrate that in the absence of this essential experimental design, the network cannot be reconstructed and every conceivable network structure between species (e.g. a fully decoupled network or a fully connected network) can be equally descriptive of any particular set of input-output data. Unless this correct methodology is followed, any best-fit measure for network reconstruction can yield arbitrarily poor and misleading results.