Figure 1.
An Overview of the Combined Computational and Experimental Iterative Procedure
The computational algorithm utilizes a dynamic simulation of the current integrated transcriptional regulatory and metabolic network reconstruction to design experiments. The new regulatory logic rules discovered by the experiments are then added to the reconstruction.
TFi, transcription factor i; TFj, transcription factor j.
Figure 2.
A Graphical Depiction of the Computational Algorithm
The algorithm ultimately produces experiment designs ranked by their potential for producing the most new regulatory rules.
TFi, transcription factor i ; TFj, transcription factor j.
Figure 3.
A Graphical Depiction of How an Activity Profile Is Created for a Growth Simulation
The example model M in (A) contains genes for three transcription factors and two enzymes and shows the 0/1 (“off/on”) state of each gene in each time step tn of a simulation for a defined growth environment E. The dashed line in the model indicates that a regulatory connection between the two genes is not explicitly modelled, but is suspected to exist.
(B) shows how a “basic unit” is defined and shows the general formula for the parameterization of each cell. One basic unit exists for every known or suspected TF–target gene pair.
As shown in (C), for such regulatory connections explicitly modelled, each cell of the basic unit gets either a “0” or a “1,” depending on whether its associated transcription factor and target gene were observed to be in the indicated 0/1 combination in any simulation time step.
(D) illustrates how the inferred TF–target gene regulatory connections and logic derived from experimental and/or computational data are incorporated in a basic unit.
In (E) the basic units from (C) and (D) are concatenated to form the activity profile for the simulation.
Table 1.
Human-Made Designs of Double Perturbation Experiments
Table 2.
Computer-Generated Designs of Double Perturbation Experiments