Transcriptional regulatory circuits: predicting numbers from alphabets

Abstract

Transcriptional regulatory circuits govern how cis and trans factors transform signals into messenger RNA (mRNA) expression levels. With advances in quantitative and high-throughput technologies that allow measurement of gene expression state in different conditions, data that can be used to build and test models of transcriptional regulation is being generated at a rapid pace. Here, we review experimental and computational methods used to derive detailed quantitative circuit models on a small scale and cruder, genome-wide models on a large scale. We discuss the potential of combining small- and large-scale approaches to understand the working and wiring of transcriptional regulatory circuits.

Fig. 1

Gene-regulation functions. (A) A gene-regulation function describes how trans inputs, such as transcription factors (I₁, I₂, … I_n), and cis inputs, such as regulatory elements, are transformed into a gene's mRNA level (O). (B) Different functions describe distinct scenarios, using distinct mathematical approaches. Most small-scale approaches rely on a thermodynamically motivated model (i), which explains the dependency on a single tunable trans input and fits the response to a Hill function. The extracted Hill parameters, maximum expression level (M), threshold (T), and sensitivity (n), become functions of the remaining trans- and cis-input variables. Large-scale approaches utilize a host of approaches. (ii) Linear models assume that the output is a linear combination of the cis or trans inputs. (iii) Bayesian networks give the probability distribution of the output given the input values. (iv) Logical (Boolean) circuits consider the output as a result of applying logical operations on the inputs. (Top) The assumption underlying each modeling approach; (middle) the gene-regulation function in this modeling approach; and (bottom) an equivalent mathematical formulation. All modeling approaches are applicable in both small- and large-scale studies.