Biology Reference
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However, the variability of host physiology and its sensitive dependence on environmental
conditions, and the complex biochemistry of interactions among circuit elements and
products with each other and host, suggests that all uncertainty cannot be removed even
from the basal function of a circuit. Thus, a combination of rational design and screening
will likely be necessary.
CIRCUIT DESIGN
A major challenge in circuit engineering is the optimization of every component in the
circuit such that desired behavior is attained. So far, due to the lack of reliable genetic tools,
engineers often have to tinker the system via fine-tuning of every component, resulting
in design cycles that are laborious and cost-ineffective. 46,47 Conversely, the advent of
well-characterized parts libraries and the ability to assemble them in a predictable manner,
as discussed above, can dramatically shorten the design cycle for genetic circuits
by providing the designer with the ability to specifically target the desired circuit parameters
( Fig. 4.2 ).
Input 1
Legend
Genetic variant
Circuit function
Circuit
Output
Input 2
0%
100%
output = f ( Input 1 , Input 2 )
Circuit
behavior
known?
yes
no
72
yes
no
HT screening
available?
Input 2
Input 2
Input 2
Input 2
Random
Semirational
Design of
experiments
Model-based
Directed evolution
FIGURE 4.2
Comparison between the different methodologies for circuit design. An illustrative circuit composed by two inputs and one
output, wherein the circuit function is dependent on concentration of the two input genes, is depicted in the figure.
When circuit models are available, functional regions can be readily targeted to yield functional systems (heatmaps depict
circuit functionality). Conversely, when such models are unknown, one needs to employ discovery methods to characterize
circuit output as a function of the inputs. When high-throughput (HT) screening is available, we can take advantage of the
capacity to generate vast numbers of genetic variants provided by directed evolution. However, performing a random
mutagenesis strategy does not ensure an effective search of the solution space. Semirational approaches that more precisely
target features that efficiently alter inputs can provide a more comprehensive search. Conversely, when HT screening or
selection methods are not available, systematic design strategies, such as design of experiments (DoE), can be coupled with
reliable genetic parts to provide an effective search strategy.
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