Next-generation synthetic gene networks

Abstract

Synthetic biology is focused on the rational construction of biological systems based on engineering principles. During the field's first decade of development, significant progress has been made in designing biological parts and assembling them into genetic circuits to achieve basic functionalities. These circuits have been used to construct proof-of-principle systems with promising results in industrial and medical applications. However, advances in synthetic biology have been limited by a lack of interoperable parts, techniques for dynamically probing biological systems and frameworks for the reliable construction and operation of complex, higher-order networks. As these challenges are addressed, synthetic biologists will be able to construct useful next-generation synthetic gene networks with real-world applications in medicine, biotechnology, bioremediation and bioenergy.

Figure 1

Tunable genetic filter. Filter characteristics can be adjusted by tuning the degradation of RNA and protein effectors in negative feedback loops. Examples of RNA effectors include small interfering RNAs, riboregulators, and ribozymes. Examples of protein effectors include transcriptional activators and repressors.

Figure 2

Genetic signal converters. (a) Analog-to-digital converter circuit that enables the discretization of analog inputs. The circuit is composed of a bank of toggle switches that have increasing response thresholds so that sequential toggling is achieved as input levels increase. The design could enable different natural or synthetic pathways to be activated depending on distinct input ranges, which may be useful in cell-based biosensing applications. Inputs into promoters and logic operations are shown explicitly except when the promoter name is italicized, which represents an inducible promoter. (b) Digital-to-analog converter circuit that enables the programming of defined promoter activity based on combinatorial inputs. The circuit is composed of a bank of recombinase-based switches, known as single-invertase memory modules (SIMMs). Each SIMM is composed of an inverted promoter and a recombinase gene located between its cognate recognition sites, indicated by the arrows. Upon the combinatorial addition of inducers that activate specific P_write promoters, different SIMMs will be flipped, enabling promoters of varying strength to drive GFP expression. This allows combinatorial programming of different levels of promoter activity.

Figure 3

Adaptive learning networks. (a) Associative memory circuit enables association between two simultaneous inputs (Activator A and Activator B) so that the subsequent presence of only a single input can drive its own pathway and the pathway of the other input. Associations between inputs are recorded by a promoter P_AND that is activated in the presence of Activator A and Activator B to toggle the memory switch. Inputs into promoters and logic operations are shown explicitly except when the promoter name is italicized, which represents an inducible promoter. (b) Winner-take-all circuit allows only one input out of many to be recorded. This effect is achieved by a global repressor protein that gates all inputs and prevents them from being recorded if there has already been an input recorded in memory.

Figure 4

Amyloid-based memory. (a) Amyloid-based memory can be implemented by fusing a prion-determining region (PD) to an effector gene, such as a transcriptional activator. (b) Overexpressing the prion-determining region via promoter P_OFF causes aggregation of the fusion protein, rendering the effector inactive. (c) Subsequent overexpression of chaperone proteins (HSP104), which act to disaggregate amyloids, via promoter P_ON releases the effector from the amyloid state and enables it to fulfill its function. Inputs into promoters and logic operations are shown explicitly except when the promoter name is italicized, which represents an inducible promoter.

Figure 5

Cell-cycle counter for biological containment. Cell-cycle counting is accomplished with a cascade of single recombinase-based memory units (e.g., SIMMs), each of which is driven by a cell cycle-dependent promoter. After N cell-cycle events are counted, the gene circuit unlocks the expression of a toxic protein triggering cell death.

Figure 6

Autonomous chemotaxis. (a) Chemotactic environment made up of three chemoattractant gradients (A, B, C). (b) The synthetic gene network, whereby toggle switches control the sequential expression of three chemotaxis sensor receptors, for autonomously navigating bacteria down three chemoattractant gradients. Inputs into promoters and logic operations are shown explicitly except when the promoter name is italicized, which represents an inducible promoter. (c) Boolean on/off values for the network genes illustrate the sequential order of operations.