406 9.3  Synthetic Biology, Biomimicry, and Bionanotechnology

KEY POINT 9.3

Biological circuits are optimized to be robust against changes in biochemical environ­

ment from cell to cell, which necessitates a gain function that depends on the output

response.

For example, several natural signal transduction pathways have key components that can be

adapted to be used for general synthetic biology biosensing. Many of these involve receptor

tyrosine kinases. These are structural motifs that contain the amino acid tyrosine, and

when a ligand binds to the receptors this induces a conformational change that stimulates

autophosphorylation of the receptor (i.e., the receptor acts as an enzyme that catalyzes the

binding of phosphate groups to itself). These in turn dock with a specific adapter protein and

in doing so activate signaling pathways that generate specific cellular responses depending on

the adapter protein. A schematic of this process is depicted in Figure 9.2b.

The point here is that the gene circuit for this response is generic, in that all that needs

to be changed to turn it into a detector for another type of biomolecule are the specifics

of the receptor complex and the adapter protein used, which thus offers the potential for

detecting a range of different biomolecule outside cells and bringing about different, non­

native responses of the host cell. Different combinations can potentially lead to cell survival

and proliferation, whereas others might lead to, for example, promoting apoptosis, or pro­

grammed cell death (see Chapter 2), which could thus have potential in destroying diseased

cells in a controllable way (also, see the section in this chapter on personalizing healthcare).

Similarly, the output can be tailored to express a TF that stimulates the production of a par­

ticular structural protein. For example, yeast cells have been used as a model organism to

design such a system using an actin regulatory switch known as N-​WASP to controllably

manufacture F-​actin filaments.

Many gene circuits also have logic gate features to them. For example, it is possible to

engineer an AND gate using systems that require activation from two inputs to generate an

output response. This is exemplified in the Y2H assay discussed previously (see Chapter 7),

and there are similar gene circuit examples of OR and NOT gates. This is particularly valuable

since it in principle then allows the generic design principles of electrical logic circuitry to be

aligned directly with these biological circuits.

One issue with gene circuit design is the approximations used in modeling their response.

For example, spatial effects are usually ignored in characterizing the behavior of gene circuits.

This assumes that biochemical reactions in the system occur on time scales much slower

than the typical diffusional time required for mixing of the reactants or, in other words, a

reaction-​limited regime (see Chapter 8). Also, stochastic effects are often ignored, that is,

instead of modeling the input and output of a gene circuit as a series of discrete events,

an approximation is made to represent both as continuous rather than discrete parameters.

Often, in larger scale gene circuit networks, such approximations are required to generate

computationally tractable simulations. However, these assumptions can often be flawed in

real, extensive gene circuit networks, resulting in emergent behaviors that are sometimes dif­

ficult to predict. However, relatively simple kinetics analysis applied to gene circuits can often

provide useful insight into the general output functions of a genetic module (see Worked

Case Example 9.1).

In practice, there are also more fundamental biological causes for design problems of

gene circuits. For example, there are sometimes cooperative effects that can occur between

different gene circuits that are not embodied in a simple Boolean logic design model. One

of these cooperative effects is mechanical in origin. For example, there is good evidence that

mechanical perturbations in DNA can be propagated over thousands of nucleotide base

pairs to change the state of a specific gene’s expression, that is, to turn it “on” or “off.” These

effects can be measured using, for example, single-​molecule force manipulation techniques

such as magnetic tweezers (see Chapter 6), and there is increasing evidence that mechanical

propagation is limited to the so-​called topological domains in DNA, so that certain protein