Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach

Abstract

The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy) data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12)) that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

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. TF_i, transcription factor i; TF_j, 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. TF_i, transcription factor i ; TF_j, 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 t_n 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.