Table 1.
Glossary of specialized terms used throughout this article.
Figure 1.
Workflow of generating the gene network model encoded in a DNA sequence.
The input for this process is a DNA sequence that is first broken down into parts by the scanner. The combination of the parts is validated by the parser according to a syntactic model. After validation by the parser, the sequence is translated by applying semantic actions attached to the rules to transform the series of parts into a set of chemical equations. The resulting equations can then be solved using existing simulation engines. Each step takes the output of the previous step as input, so the workflow can start from any step if the appropriate input is provided.
Figure 2.
Parse tree showing the derivation process of a two-cassette genetic construct.
In the derivation tree, terms in <> corresponds to the non-terminals in the grammar, while terms in [ ] are terminals, and the dashed lines indicate the transformation to terminals. The subscripts are used to distinguish different instances of the same category.
Figure 3.
An example of attribute grammar.
Table 2.
Attributes associated with non-terminals.
Figure 4.
Equation generators.
Figure 5.
Chemical equations translated from a DNA sequence.
Table 3.
Context-dependency of experimentally determined translation rates.
Figure 6.
Mapping the behavior of 384 genetic constructs.
Each section A to F indicates a different selection of repressors within a toggle switch: (A) tetR and lacI, (B) lacI and tetR, (C) lacI and cI, (D) cI and lacI, (E) cI and tetR, and (F) tetR and cI. Other networks that cannot give rise to bistability (e.g. a construct with tetR as both genes) are excluded as are designs that only vary the GFP RBS (see text). Each pair is explored by varying the RBS (ordered by translational efficiency from low (RBS H) to high (RBS B) as determined by a qualitative fit of the results of Gardner et al. [24] with consistent letter-based labels) and calculating the detectability ratio, defined as the steady state GFP concentration in the “on” state divided by the concentration in the “off” state. These ratios are displayed using a color map as indicated by the legend to the right. Monostable constructs have a ratio of 1 and are indicated by gray boxes. The ratio gives a measure of how easily the two steady states can be distinguished, which is important due to high experimental noise. Each pane also elucidates the traditional two-parameter bifurcation diagram of each gene pair as the translational rates are varied by changing RBSs. Constructs near the edge of the cusp operate near saddle-node bifurcations and are more prone to noise-induced switching. Thus, constructs from the cusp interior are preferred for robust behavior.