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Foundations and Emerging Paradigms for Computing in Living Cells

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Genetic circuits, composed of complex networks of interacting molecular machines, enable living systems to sense their dynamic environments, perform computation on the inputs and formulate appropriate outputs. By rewiring and expanding these circuits with novel parts and modules, synthetic biologists have adapted living systems into vibrant substrates for engineering. Diverse paradigms have emerged for designing, modeling, constructing, and characterizing such artificial genetic systems. In this paper, we first provide an overview of recent advances in the development of genetic parts and highlight key engineering approaches. We then review the assembly of these parts into synthetic circuits from the perspectives of digital & analog logic, systems biology, and metabolic engineering, three areas of particular theoretical and practical interest. Finally, we discuss notable challenges that the field of synthetic biology still faces in achieving reliable and predictable forward-engineering of artificial biological circuits.
| Complementing metabolic engineering with synthetic circuits for dynamic control. (a) Synthetic circuits enable the dynamic correction of metabolite imbalances resulting in suboptimal flux, host toxicity, etc. A block diagram is used to illustrate this concept via a feedback loop where cellular information from the circuit input (e.g., a small-molecule inducer) and circuit biosensors are added at the summation point (e.g., through interactions of transcription factors) and result in expression of controller circuits that modulate metabolic processes. (b) An example of a dynamic controller by Xu et al. [300] where ACC represents the malonyl-CoA source pathway and FAS represents the malonyl-CoA sink pathway. The purpose of this circuit is to maintain requisite levels of malonyl-CoA for fatty acid production; excessive malonyl-CoA activates the sink pathway and insufficient malonyl-CoA results in activation of the source pathway. Behavior of the circuit is shown in panel e. Figure reprinted from Xu et al. [300]. (c) Left: static metabolic engineering using flux balance analysis to identify optimal mutants in the constrained solution space; blue and green dots represent two potential solutions along specific constraint boundaries (dotted lines) [301]. Right: a dynamic two-stage production strategy is instead utilized which allows for a time-dependent changing profile (dotted line connecting the two solutions) of an engineered strain (figure adapted from Venayak et al. [262]). (d) Metrics of interest for metabolic engineers include yield, the amount of product synthesized per the amount substrate, titer, and the amount of product per reaction volume. Biomass measurements are also important for understanding where yield and titers might be improved. Dynamically regulated circuits (right) can sometimes achieve relatively higher yields or titers (in blue) but lower biomasses (in green) due to the host burden of synthetic circuits [300,302]. (e) Malonyl-CoA profile from strains without (left) and with (right) the controller circuit in (b). Dynamic regulation can lead to fluctuations in the time-course of small molecule production. Figure adapted from Xu et al. [300].
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Foundations and Emerging Paradigms for
Computing in Living Cells
Kevin C. Ma, Samuel D. Perli and Timothy K. Lu
Synthetic Biology Group, Research Laboratory of Electronics, Department of Biological Engineering and Electrical Engineering & Computer
Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Correspondence to Timothy K. Lu: timlu@mit.edu
http://dx.doi.org/10.1016/j.jmb.2016.02.018
Edited by I. B. Holland
Abstract
Genetic circuits, composed of complex networks of interacting molecular machines, enable living systems to
sense their dynamic environments, perform computation on the inputs, and formulate appropriate outputs. By
rewiring and expanding these circuits with novel parts and modules, synthetic biologists have adapted living
systems into vibrant substrates for engineering. Diverse paradigms have emerged for designing, modeling,
constructing, and characterizing such artificial genetic systems. In this paper, we first provide an overview of
recent advances in the development of genetic parts and highlight key engineering approaches. We then
review the assembly of these parts into synthetic circuits from the perspectives of digital and analog logic,
systems biology, and metabolic engineering, three areas of particular theoretical and practical interest. Finally,
we discuss notable challenges that the field of synthetic biology still faces in achieving reliable and predictable
forward-engineering of artificial biological circuits.
© 2016 Published by Elsevier Ltd.
Introduction
Since the design of the bacterial toggle switch and
the bacterial oscillator in 2000 [1,2], researchers in
the multi-disciplinary field of synthetic biology have
developed innovations in the areas of cellular
computing [3,4], bio-sensing [514], biochemicals
[1520], therapeutics and diagnostics [2125], phar-
maceuticals manufacturing [2628], and biomaterials
[2935]. With the advent of high-throughput methods
to construct and characterize genetic circuits and the
continually decreasing costs of DNA synthesis and
sequencing [36], synthetic biology is well poised to
continue contributing to areas ranging from answering
unsolved questions of biology to generating novel
solutions for today's pressing challenges in healthcare
and the environment [37].
In this review, we begin with a look at the fundamental
engineering principles that have guided the early
phases of research in synthetic biology and highlight
recent major advances in part development at the DNA,
RNA, and protein level. We then turn to strategies that
have guided the assembly of these parts into high-
er-order constructs. Ideas and implementations of
novel synthetic circuits have routinely come from the
importation of ideas and design principles from other
fields; we apply particular focus on the intersection of
synthetic biology with digital and analog computing,
systems biology, and metabolic engineering. These
perspectives and specific applications often deter-
mine how a bioengineer will approach the design
process for a synthetic circuit, from core conceptual
models to methods of characterization to desired
outputs. Ultimately, as theability of synthetic biologists
to predictably engineer complex circuits continues to
increase, we expect to see even greater diversity in
the inspiration for and application of synthetic gene
circuits.
Engineering Components
Gene regulatory parts
Biological systems operate in dynamic environ-
mental contexts where they sense and respond to
various forms of stimuli. The inputs sensed by
engineered synthetic circuits are application specific
0022-2836/© 2016 Published by Elsevier Ltd. J Mol Biol (2016) 428, 893915
Article
and include signals represented by different combi-
nations of small-molecule chemicals, light, heat,
mechanical stimuli, and others. Synthetic circuits
translate inputs into specific outputs based on the
desired function. The outputs of synthetic circuits
have conventionally been RNA or proteins. Fluores-
cent proteins are typically used while testing
synthetic circuits, whereas proteins with physiolog-
ical, therapeutic or commercial relevance are select-
ed based on the application of interest [38,39].In
recent years, the number of parts developed to
enable the construction of a wide range of synthetic
gene circuits has become large. We briefly review
advances in the development of biological parts; for
another overview see Table 1 in Wang et al.[40].
Early synthetic circuits in prokaryotic systems
focused on regulation of transcription through well-
characterized, inducible transcription factors [1,2].
The activity of many of these transcription factors
can be controlled via small-molecule inducers, such
as the transcription factors LacI, AraC, and TetR,
which respond to chemical induction by isopropyl
β-D-1-thiogalactopyranoside, arabinose, and anhy-
drotetracycline, respectively [41,42]. Light-inducible
circuits in Escherichia coli have been constructed
by creating chimeric fusions of phytochromes
and histidine kinases as well as by heterologously
expressing cyanobacterial two-component systems
[43,44]. Bioinformatics has enabled the expansion
of this set of parts via genomic mining of novel
transcription factors in prokaryotic genomes [45].
Other small-molecule-inducible systems developed
over the past decade include the propionate-induci-
ble PrpR system, the rhamnose-inducible RhaRS
system, the cumate-inducible cym/cmt system from
Pseudomonas putida F1, and the toluate or benzoa-
te-inducible xylS system from P. putida [4649].
Recent work by Rogers et al. focused on expanding
this toolkit by characterizing the dynamics, host
toxicity, and orthogonality of AcuR, an acrylate-
inducible catabolism regulator in Rhodobacter
sphaeroides, CdaR, a glucarate-inducible transcrip-
tional activator, MphR, a macrolide-responsive tran-
scription factor, and TtgR, a flavonoid-responsive
efflux pump regulator in P. putida [50]. The continued
addition of new ligand-inducible transcription factors
to the synthetic biology toolbox will help expand the
range of practical applications that can be addressed
by the field.
In eukaryotic systems, early inducible systems
often involved the construction of chimeric proteins,
such as those composed of a hormone-binding
domain, the DNA-binding domain of the Gal4 protein
from yeast, and a transactivating domain from
herpes simplex virus protein VP16. These inducible
systems function via hormone-induced translocation
into the nucleus, which allows Gal4 to bind to its
cognate site and activate transcription [5154].
Another system consists of small-molecule inducible
bacterial transcription factors fused to eukaryotic
transactivation or transrepression (e.g., KRAB from
the human kox-1 gene) domains. The bacterial
portion of these chimeric proteins are derived from
antibiotic-resistance machinery (e.g., the pristinamycin-
resistance system of Streptomyces pristinaespiralis
and the tetracycline-resistance and erythromycin-
resistance systems from E. coli); in the presence of
antibiotic, the protein and the allosteric effector form
a complex resulting in a change of affinity of the
DNA-binding domain for its DNA target [5557].
Recent innovations in expanding the diversity of
inputs available for inducible circuits have further
exploited bacterialmammalian chimeric proteins.
For instance, Gitzinger et al. utilized the flavonoi-
d-responsive TtgR operon from P. putida to build a
mammalian system responsive to phloretin, a
component of some cosmetics [58]; Wang et al.
built a system responsive to paraben derivatives, a
common ingredient of skin-care products, and
demonstrated the ability of commercial cosmetics
to induce expression from subcutaneously im-
planted engineered cells [59].Inadditiontoyeast-
derived and bacteria-derived systems, human-
component-based inducible systems have been
constructed that function via rapamycin-inducible
assembly of a synthetic transcription factor; such
systems are thought to have a reduced risk of
undesired immunogenicity in a gene therapy
context [60]. Over the past decade, the eukaryotic
ligand-inducible transcription factor toolkit has
rapidly expanded to include expression systems
thatcanbecontrolledvia quorum-sensing (QS)
signal molecules, NADH, L-arginine, biotin, and
more [61]. In addition to harvesting parts from
nature, the continuing development of computa-
tional design methods and large-scale mutagene-
sis and screening techniques may expedite the
construction of new designerallosteric transcrip-
tion factors via the rational re-design of well-
characterized transcription factors to recognize
related, yet distinct, novel ligands [62].
Synthetic transcription factors based on DNA-
binding domains, such as zinc-finger nucleases,
transcriptional activator-like effectors (TALEs), and
the Clustered Regularly Interspaced Short Palin-
dromic Repeats (CRISPR)/Cas system, fused with
transcriptional effector domains have also been
constructed [6368]. Zinc-finger transcription factors
(ZF-TFs) rely on creating arrays of zinc-finger protein
domains where each domain individually binds to a
specific ~3 bp DNA sequence, allowing user-designed
specificity [69,70]. The refinement of methods for
assembling zinc-finger arrays (e.g., through oligomer-
ized pool engineering or context-dependent assembly
[71,72]) has enabled the large-scale development and
characterization of zinc-finger-based transcription fac-
tors [63]. By placing ZF-TFs downstream of well-
characterized endogenous promoters, it is furthermore
894 Computing in Living Cells
possible to couple natural sensing machinery capable
of detecting hypoxia and DNA damage with synthetic
actuators composed of transcriptional ZF-TF circuits
for sensing and reporting internal cell states [73].
Another approach to constructing synthetic TFs
relies on transcription activator-like effector (TALE)
proteins from Xanthomonas spp. that consist of
tandem arrays of 3335 aa domains with each
domain binding to a single nucleobase. Arrays of
TALE domains can be linked together to engineer
binding to user-specified sites [74,75]. Combinations
of TALE-TFs can be employed to further fine-tune
engineered regulatory transfer functions [64].By
swapping out the transcriptional effector domain for
epigenetic effector domains (e.g., the LSD1 histone
demethylase domain or the TET1 hydroxylase
domain which enables DNA demethylation), re-
searchers have also demonstrated the utility of
TALEs for efficient epigenome editing [76,77].In
comparison to TALEs, ZF-TFs can be more difficult
to design and suffer from a higher failure rate [78];
however, their smaller size and less repetitive coding
sequences may be beneficial for delivery in a
therapeutic setting.
The most recent advance in synthetic transcrip-
tional regulation has emerged from the adaptation of
prokaryotic CRISPR/Cas immune system. In its
most commonly used form, the CRISPR system
consists of the Cas9 protein (an RNA-guided
nuclease) and associated guide RNAs, which target
Cas9 to specified regions of the genome [79]. Given
its advantages over TALEs and ZF-TFs (including
the ease of design and the ability to multiplex
outputs), the CRISPR system with a catalytically
inactive Cas9 (dCas9) fused to either activator or
repressor domains has been widely applied to
regulate the transcription of synthetic and endoge-
nous genes in both prokaryotic and eukaryotic
systems [65,8082]. Cas9 can further be engineered
to detect small-molecule inputs via the incorporation
of ligand-responsive inteins or rapamycin-inducible
dimerization [83,84]. By integrating RNA-level ele-
ments for stability and recruitment of RNA-binding
transcriptional effectors, synthetic biologists have
expanded the capacity of the CRISPR system to
support multistage and multicomponent genetic
circuits [8587]. Furthermore, Kiani et al. demon-
strated that RNADNA base pairing properties of the
CRISPR guide RNAs can be exploited to allow
simultaneous genome editing and transcriptional
regulation with the basic Cas9 system, thus adding
to the versatility of this system [88]. Optogenetic
control over the CRISPR system has been demon-
strated via incorporation of the Arabidopsis thaliana
CRY2 and CIB1 system [89,90]; similar work had
previously been conducted using the same CRY2
and CIB1 system to enable light-inducible engi-
neered TALEs to modulate gene expression [91].
CRISPR, TALE, and ZF platforms will furthermore
likely continue to play a pivotal role in the develop-
ment of tools for editing and regulation at genetic and
epigenetic levels [9196].
RNA-based components have also been developed
which, through various mechanisms, can activate or
attenuate transcription and furthermore enable the
creation of RNA-only circuitry [97100].Thiscan,in
turn, open up new methods of incorporating inputs into
circuits. For instance, Hoynes-O'Connor et al.recently
demonstrated the de novo design of RNA thermo-
sensors in E. coli; under high temperatures, an RNase
E cleavage site on the mRNA of interest is exposed
and leads to mRNA cleavage and the loss of
expression [101]. Prior thermosensitive circuits relied
on protein control via a mutant form of the cI repressor
from the well-studied lambda bacteriophage, which
results in de-repression at 42 °C [1,102]. Additional
control can be derived from tuning post-transcriptional
RNA stability, by suppressing specific mRNA
molecules through RNA interference in eukaryotes,
and through synthetic small non-coding RNAs in
prokaryotes [103,104]. Important for the develop-
ment of these parts are computational models; for
instance, aptazymes and ribozymes enable tran-
script degradation in a predictable and robust
manner and can therefore be designed using kinetic
RNA folding simulations [105]. For a thorough
review of RNA-specific synthetic biology tools, see
Chappell et al.[106].
Translational control via RNA engineering serves as
yet another vital point of control for synthetic
biologists. Modulation of the strength of translation
via replacement and mutation of the ribosome-binding
sequence (RBS) in bacteria has long been a popular
mode of tuning protein expression levels. These
efforts have been aided by statistical thermodynamic
models of RBS efficacy that can predict protein coding
sequence translation rate over a 100,000-fold range
[107,108]. RNA devices such as the riboregulator,
consisting of a cis-repressed mRNA that prevents
translation via astemloop structure and a trans-
activating RNA that hybridizes with the stemloop
and exposes the RBS, as well as the similar
RNA-INRNA-OUT system, which inducibly blocks
ribosome access to the RBS via antisense stem
loop RNA expression, have allowed for additional
control over translation initiation and timing
[109,110]. Green et al. expanded on the conven-
tional riboregulator approach but instead utilized
linearlinear RNA interactions to create toehold
switches that have allowed for regulation of trans-
lation over a 400-fold dynamic range, which is on the
order of protein-based regulation [111]. The dynam-
ic range of toehold switches, as well as their ability to
be multiplexed due to limited crosstalk, makes them
attractive candidates for applications integrating
biosensing and computing. Coupling toehold
switches with the tna element adaptor (as Liu et al.
didwiththeRNA-INRNA-OUT system [98]) could
895Computing in Living Cells
furthermore allow for straightforward composition of
wide-dynamic-range toeholds into diverse transcrip-
tional regulatory functions.
Engineering the genetic code of translation is a
nascent area of synthetic biology tool development.
Expansions of the genetic code require an open
codon and (ideally orthogonal) machinery (i.e.,
aminoacyl-tRNA synthetase and tRNAs with elon-
gation factors and ribosomal machinery) capable of
incorporating noncanonical amino acids [112,113].
In an effort to more systematically address the initial
hurdle of freeing up codons for usage, Lajoie et al.
demonstrated that E. coli canbegenomically
recoded via the replacement of all known UAG
stop codons with UAA stop codons, allowing for
increased bacteriophage resistance and the ability to
incorporate non-standard amino acids [114]. The
incorporation of noncanonical amino acids has a
myriad of applications, including, for instance,
alternative methods to confer photoregulation of
biological processes (in both bacterial and mammalian
systems), biocontainment of genetically modified
organisms via engineered dependence on non-stan-
dard amino acids, modification of therapeutic proteins
for increasing substrate affinity or affecting pharmaco-
kinetic features, and more [115120,214]. However,
challenges still remain including fitness impairments in
recoded genes and generally lower efficiency of
translation with noncanonical amino acids [112,121];
for a thorough overview of the field and outstanding
challenges, see Lajoie et al.[122].
The development of novel orthogonal components
of translation has also contributed numerous well-
characterized parts to the synthetic biologist's toolkit.
Approaches to creating orthogonal translation sys-
tems have focused on evolving 16S rRNA variants
(which recognize different Shine-Dalgarno se-
quences) and 23S rRNA variants (via alteration of
the peptidyl transferase center) [123125], as well as
evolving ribosomes to recognize quadruplet codons
[126,127]. More recently, Orelle et al. created an
artificialribosome (called Ribo-T) comprising teth-
ered subunits capable of replacing the endogenous
system in E. coli and engineered to be orthogonal
using a Shine-Dalgarno sequence variant [128].
Directed evolution techniques can be used to further
refine the orthogonality of the various components of
these translation systems [129].
Post-translational control, such as protein degra-
dation, can also be inducibly activated in both
prokaryotic and eukaryotic systems [130132].
Recent work by Cameron and Collins demonstrates
the benefits of utilizing an inducible Lon protease
system from Mesoplasma florum in E. coli,which
degrades tmRNA-tagged substrates; such a system is
both tunable and a useful tool for the systematic
perturbation of natural protein networks [133].Fora
review of recent developments in the post-translational
toolkit, see Olson and Tabor [134].
Circuit characterization and tuning
Novel DNA assembly techniques (e.g., the Golden
Gate method and Gibson assembly as well as
genome engineering technologies such as conjuga-
tive assembly genome engineering) have greatly
reduced the time spent in the construction phase of
the synthetic circuit or synthetic genome engineering
cycle [135138]. However, there still exists a need
for higher-throughput and novel methods for char-
acterizing genetic parts and circuits. Conventionally,
part and circuit characterization relies on assessing
parameters such as the fold change and time course
of a fluorescent protein output from an upstream
synthetic component. The data from these charac-
terization experiments can be recorded in a data-
sheet format, which provides a quantitative
description of the behavior of a part. The Registry
of Standard Biological Parts, now over a decade old,
seeks to store information on characterized parts
and now contains over 20,000 documented entries
[139].
A core component of such datasheets is the
characterization of the transfer function, which
maps the range of input levels (e.g., concentrations
of small molecules or intensity of light) to a range of
output levels at steady state; empirical transfer
functions guide how synthetic biologists characterize
and optimize circuit performance. For instance, for
components of digital logic circuits (e.g., computing
on binary signals), a sharp transfer function is often
desired (see Fig. 1b), whereas for applications of
analog computing (e.g., computing on continuous
values), a wide-ranged transfer function quantita-
tively described by a linear or loglinear relationship
may be more appropriate (see Fig. 1f). Furthermore,
the process of composing genetic parts for high-
er-order synthetic systems necessitates that the
output range of one circuit fits well with the input
range of another. Similar to the problems that arise
from improper impedance matching in electronics,
improperly layered circuits will suffer from deviations
from expected behavior [140].
The incorporation of optogenetics as a means to
interrogate synthetic circuits can help expand circuit
characterization from the static perspective of a
transfer function to a dynamic perspective of its
behavior through time. For instance, Olson et al.
recently proposed an optogenetics-based gene circuit
characterization method utilizing light-inducible two-
component systems (comprising light-inducible ki-
nases and paired transcriptional response regulators);
they applied their system to better understand the
dynamical properties of synthetic inverter circuits
given analog light inputs [141]. In contrast to
typically used small-molecule-based characteriza-
tion methodologies, employing phytochrome-family
light-responsive proteins leverages the ease of
creating multiplexed light inputs to control biological
896 Computing in Living Cells
circuits. This biological function generatorenabled
Olson et al. to create a dynamical model of the system
parameterized by experimental data, which was then
used to design custom protein-expression dynamics
by iteratively adjusting the light input to minimize
the error (i.e., the difference between predicted and
desired expression levels) [141]. The continued
development of these and other dynamical circuit
characterization methods (an overview is given by
Castillo-Hair [142]) complement the static picture of
circuit behavior provided by empirical transfer func-
tions. To fully characterize a part may also require
explicit consideration of any context-dependent ef-
fects that might arise from functional couplings at
element-element junctions (see section on Modularity
for greater detail). Mutalik et al. worked to address this
by employing a statistical ANOVA-based framework
for assessing part quality,a metric that should help
Fig. 1. | Conceptual models and characterization of digital versus analog circuits. (a) Schematic of a typical input/output
transfer function, highlighting digital and analog regimes. When constructing digital synthetic gene circuits, synthetic
biologists aim to achieve a steep transfer function and minimize the range of the analogregion. An analog synthetic gene
circuit on the other hand has a more graded response with reduced step-like behavior. (bd) Dynamic switching between
analog and digital synthetic gene circuit behavior. (b) Using a positive-feedback loop, Daniel et al. used a CopyControl
(CC) induction solution to achieve dynamic adjustment of AHL-to-mCherry transfer functions between digital and analog
behaviors [212]. (c) To achieve dynamic behavior, Daniel et al. used a variable-copy-number plasmid (VCP) that is present
at low-copy numbers in the absence of CC, but induced to high copy numbers in the presence of CC. (d) The circuit exhibits
a digital-like transfer function at high copy numbers (CC ON, red diamonds). A Hill function fitted to the digital-like curve is
represented by a dotted red line. At low copy numbers (CC OFF, blue circles), the circuit exhibits a wide-dynamic-range
analog transfer function at low copy numbers (CC OFF, blue circles), and the dashed blue line shows that this behavior is
well-fitted by a ln(1 + x) function [212]. Figures bd reprinted from Daniel et al.[212].
897Computing in Living Cells
give circuit designers an impression of how well a
given part performs in different contexts and where
troubleshooting efforts may be best applied [143].
All characterization approaches will likely benefit
from the greater integration of high-throughput
characterization techniques such as microfluidics
and flow cytometry into the engineering process
[144146]. Automation of key steps in the cytometry
pipeline (e.g., Ref. [147]) and the continued auto-
mation of laboratory techniques in general through
robotics represent welcome steps toward a stream-
lined, computer-aided and machine-automated de-
sign process for building synthetic biological
systems. Fully realizing this goal will require both
data standards for communicating designs (e.g., the
Synthetic Biology Open Language) and software to
assist with the design and interpretation process
[148150].
It is important to note that current efforts to
construct circuits with desired behaviors almost
always require some fine tuning of the various
biological components. Such fine tuning might
include altering or swapping promoters via muta-
tions, changing plasmid copy number, mutagenizing
translational control elements, introducing DNA
looping to enhance cooperativity, adding operator
positions or any combinations thereof [140]. Alter-
natively, in contrast to precise rational design, some
synthetic biologists have opted to use directed
evolution to evolve desired circuits and parts
[151,152]. Mathematical models can be employed
to guide the design and troubleshooting process,
though challenges still remain for the development of
reliable predictive models [153,154].
Modularity
Modularity, which refers to the ability of parts to
achieve context-independent performance and to be
combined together without undesirable interactions,
has long been a desired goal of synthetic biologists.
Systems found in nature sometimes exhibit a
surprising degree of modularity; for instance, the
modularity of the mitogen-activated protein kinase
pathway has been utilized to rewire signaling
dynamics and introduce new inputs into the natural
mitogen-activated protein kinase pathway [155,156].
In viruses, synthetic biologists have taken advan-
tage of relatively modular capsid components to
create synthetic phages with altered host ranges
[157]. Engineered DNA-binding proteins, like
TALEs, are often employed in synthetic circuit
construction precisely because of their modular
nature [158].
Despite these examples, the majority of synthetic
parts and circuits do not exhibit ideal levels of
modularity.Attheleveloftranscription,context-
dependent effects can arise, for instance, from the
presence of cis-regulatory elements or from RNA
interactions between the 5-untranslated region
(UTR) and the nascent mRNA, which can affect
the initiation of translation [108].Earlyworkto
mitigate the effects of these interactions focused on
the rational design of promoters with well-defined
upstream and downstream components [159].
Another approach focuses on the inclusion of
RNA processing machinery (e.g., either the auto-
catalytic ribozyme RiboJ or the CRISPR-asso-
ciated endoribonuclease Csy4), which enables
modularity by cleavage and removal of the
5-UTR from nascent mRNA [160,161].
At the level of translation, it is now well recognized
that a given bacterial RBS element is coding-se-
quence-dependent and can result in varying output
levels based on the specified genetic context [108].
To address this, Mutalik et al. created a set of
translational insulator elements based off of bacterial
coupled translationsystems composed of overlap-
ping upstream coding regions and downstream RBS
regions [162,163]. In their system, this bicistronic
design for translation initiation (which significantly
reduced gene-of-interest-dependent translational
variance) was further coupled with a standardized
transcription library with defined 5-UTRs to achieve
more predictable gene expression [164].
At the level of protein engineering, even the
oft-cited modular nature of zinc-finger arrays was
brought into question when Ramirez et al. demon-
strated a ~ 76% failure rate of modular assembly for
a particular class of target site [165]. Mitigating this
particular mode of failure might necessitate switch-
ing to other more modular DNA-binding systems
(e.g., TALEs or CRISPR) due to the difficulty of
tuning and troubleshooting zinc-finger array
assembly.
Addressing context dependence at the parts level
will expedite the design process. However, to design
and construct synthetic systems composed of
layered genetic sub-modules will require a better
understanding of modularity at the circuit level.
Recent work by Mishra et al. sought to address this
challenge via the inclusion of a synthetic load driver
comprising a phosphotransfer cascade in between
the input and the output (downstream genes) of a
genetic circuit [166]. Mishra et al. demonstrated that
inclusion of their system significantly decreases
system response time and bandwidth in a circuit
undergoing substantial load. Further exploration of
modularity, insulation, and current efforts to address
these issues can be found in a recent review [167].
Host effects
Closely related to the pursuit of modularity is better
understanding and addressing the burden, or the
over-utilization of shared resources, that synthetic
heterologous systems impart on their hosts, which
can often produce surprising results in circuit
898 Computing in Living Cells
behavior [168,169]. Mathematical models of cell
growth and its effect on gene expression as a result
of the availability of cellular resources have been
constructed to assist with genetic circuit design
[170172]. Better methods for characterizing hos-
t-burden will be needed; often, circuit burden has
largely been characterized through simple measure-
ments of growth dynamics in cells with and without
the synthetic circuit. Ceroni et al. have recently
sought to address this with the development of a
capacity monitorconsisting of genomically inte-
grated constitutive green fluorescent protein as a
proxy for resource availability [173]. For a small
library of simple circuits, the authors found that
capacity and growth rate correlated strongly but
inversely so with output of the circuit. Model-driven
analysis then allowed them to select and character-
ize the difference between two circuits with similar
outputs but varying capacity levels. Incorporation of
synthetic parts or circuits specifically designed to
reduce host-burden may be another way to address
the challenge. For instance, using transposons,
Segall-Shapiro et al. constructed a fragmented T7
RNA polymerase where expression of all subunits is
necessary to activate downstream synthetic circuits.
Subunits comprise a core fragment that determines
the total amount of cellular resourcesavailable to be
allocated among multiple σfragments,which enable
binding to different promoters [174]. The authors
successfully demonstrated that such a partitioning
can be harnessed to coordinate and tune expression
levels to decrease toxicity. However, constructing split
proteins for usage in other circuits may prove to be
more challenging.
Efficiently adapting genetic circuits that function well
in one host to a different one is currently a topic of
great interest within the community, which, if able to be
conducted routinely, would allow for rapid prototyping
of circuits in model strains before porting of the system
over to more specialized chassis [175177].In
addition to employing new characterization tech-
niques, higher-complexity synthetic circuits may be
able to compensate for host-dependent effects. For
instance, Kushwaha et al. employed mixed feedback
loops to enable self-regulation of the expression of
heterologous transcriptional machinery, though the
system was limited to bacterial hosts [178]. A thorough
understanding of host effects of heterologous circuits
will be broadly useful for any application of synthetic
biology, ranging from fitness considerations of engi-
neered gut bacteria in the gut to optimization of
small-molecule production in bioreactors.
Digital and Analog Computing
In synthetic biology, abstracted mathematical
models of the desired input/output transfer functions
are realized through the design and implementation
of synthetic gene circuits. Molecular components are
synthetically wiredtogether to comprise a genetic
circuit that can realize the desired input/output
response. Inspired by the discipline of electrical
engineering, a growing group of circuit designers
have sought to create digital circuits and analog
circuits for computing applications. In the digital logic
paradigm, biochemical signals that serve as inputs
and outputs, such as concentrations, intensities,
etc., are arbitrarily categorized to represent ON(1
or High) and OFF(0or Low) states. By building
simple switch devices that switch between the ON
and OFFstates and by interconnecting multiple
such devices, complex computation can be
achieved. In the analog paradigm however, the
whole range of concentrations/intensities of the
input/output signals is used for computation (see
Fig. 1 for a comparison of the two approaches).
Computation within the analog paradigm is per-
formed by implementing abstracted mathematical
functions based on the biochemical and biophysical
laws governing the properties of the interacting
molecular components. Unlike in the digital logic
paradigm, complex computation can often be real-
ized by far fewer molecular components. For further
in-depth information, please refer to additional
reviews [179182].
Digital synthetic gene circuits
Over the past couple of decades, there have been
a wide variety of synthetic digital logic circuits
implemented at the DNA, RNA and the protein
level, across both prokaryotic and eukaryotic hosts.
While building digital synthetic gene circuits, regard-
less of the underlying biological implementation, one
has to bear in the mind the challenge of achieving
steep input/output transfer functions to result in
digital responses. Techniques often employed to
tune transfer functions to exhibit digital behavior
often involve exploiting positive cooperativity and
positive feedback loops [63,183].
One of the first synthetic gene circuits built was a
toggle switch in E. coli that employed two mutually
repressing genes to inhibit the expression of one
other, resulting in a bi-stable memory circuit [1]. The
state attained and maintained by the toggle switch
system can be switched by adding small molecule
inducers that regulate the activity of the genes
involved. Several variations of the bi-stable circuit
have been built and tested since then [183189].An
alternative approach that has been developed to
maintain stable states within cells is by changing the
configuration of intracellular synthetic DNA. This can
be accomplished using enzymes known as recom-
binases that are capable of flipping the DNA present
between att (attachmentsites) [190196].
Digital circuits capable of complex computation
have been built by combining simple memory
899Computing in Living Cells
switches. Counters that can count up to three input
pulses were built using recombinase-based Single
Invertase Memory Modules [193]. Using the unidi-
rectional recombinases BxbI and phiC31, Siuti el al.
[195] built all of the 16 possible two-input Boolean
logic gates. Using a similar approach, Bonnet et al.
[197] built logic gates implementing the digital
functions AND, NAND, and others. A large number
of logic gates have been created at the transcrip-
tional level by employing synthetic transcription
factors that are built by fusing transcriptional
effectors (VP16, p65, KRAB, etc.) with DNA-binding
domains (TetR, LacI, Zinc fingers, TALENs, and
CRISPR-Cas). By using the combined action of
gene activation and repression, Kramer et al.[198]
built NOT IF-, AND-, NOT IF IF-, NAND-, OR-, NOR-,
and INVERTER-type logic gates. More recently,
CRISPR-Cas-based digital logic gates that have a
high potential for scalability have been proposed
[199], although achieving high cooperativity and
sharp transfer functions may be challenging. In
addition to building logic gates at the transcriptional
level, RNA interference has been leveraged to build
logic gates in mammalian cells [200,201]. A micro-
RNA profile-based classifier that can distinguish
HeLa cells from HEK 293T cells was built by layering
multiple RNA interference-based logic gates in a
multi-input fashion [202]. Furthermore, additional
RNA-based logic can be enabled by synthetic
ribozymes and ribo-switches that trigger catalytic
RNA cleavage in a programmable fashion [203,204].
Approaches have been developed to build synthetic
switch circuits at the protein level which operate at
faster time scales compared with their transcriptional
and RNA-based counterparts [156,205].
By interconnecting multiple digital logic gates,
Auslander et al.[4] built a complex, half-adder circuit
that can add two digital logic bits together. Larger,
multi-input logic gates, such as a 4-input AND gate,
have been built by inter-connecting smaller, two- and
three-input AND gates within individual cells [206].
The approach of interconnecting logic gates into
higher-order computation has also leveraged inter-
cellular communication enabled by QS systems. For
instance, Tabor et al. combined a sensor circuit, a
QS system, and an X AND (NOT Y) gate (where X
represents AHL-bound activator LuxR and Y repre-
sents the cI repressor) to achieve spatial signal
processing capabilities in living cells [207]. Inter-
communicating cell populations implementing differ-
ent logic gates were combined to construct all of the
possible 16 two-input logic gates, 2-1 multiplexers
and half-adders [3,208].
Analog synthetic gene circuits
Due to the limited availability of cellular resources,
the complexity of digital synthetic gene circuits can
be hard to scale to perform higher-order calculations.
Analog synthetic gene circuits provide an alternative
design paradigm that enables implementation of
complex computation using the underlying mathe-
matical principles of biochemical processes within
cells. Analog synthetic gene circuits can be useful
when one desires graded responses to environmen-
tal signals such as those found in nature. However,
the rational design and implementation of analog
synthetic gene circuits is relatively new in the field of
synthetic biology.
By expressing the transcription factor, TetR, in a
negative auto-regulatory feedback loop, Nevozhay
et al., [211] linearized the doseresponse curve to
widen the input/output dynamic range from a typical
Hill-type response common in synthetic gene circuits
implementing digital logic. Daniel et al., built analog
synthetic gene circuits that exhibit loglinear behav-
ior over an input dynamic range of four orders of
magnitude (see Fig. 1)[212]. Using the wide log
linear transfer function, the authors implemented
subtraction and division operations amongst the
inputs. Moreover, the authors also built a feedback
circuit, which implements a power-law relationship
between the input and the output.
Hybrid circuits that incorporate both digital and
analog processing have also been described. While
operating in the digital paradigm, one is usually limited
by the number of possible states that can be specified
using distinct small molecules. A hybrid, digital-to-a-
nalog converter can help overcome this limitation by
specifying different output levels as a combinatorial
combination of the distinct input chemical species,
thereby scaling the maximum number of cell
states that can be specified [213]. A prototypical
digital-to-analog was built by Siuti et al.[195],wherein
multiple recombinases expressed by distinct inputs
independently turn ONthe expression of the same
output gene driven by different constitutive promoters.
By using constitutive promoters of different strength,
different levels of the output gene were achieved by
the combinatorial combination of the inputs. A different
approach to digital-to-analog computation employs
digital processing on a single-cell level (i.e., a given
cell is individually either ONor OFF) but analog
processing on the population level (i.e., the fraction of
cells in a population that is ONenables continuous
memory and logic due to the large population sizes of
bacteria). Farzadfard and Lu constructed such a
system by combining the ssDNA-mediated recombi-
nation system of bacteriophage λwith an ssDNA
expression cassette to enable inducible recording of
the magnitude and temporal dynamics of chemical
and light inputs into genomic memory [215].
Systems Biology
The relationship between systems biology and
synthetic biology has often been described as
900 Computing in Living Cells
synergistic: insights from natural networks provide
parts and modules for synthetic biologists and synthetic
circuits, in turn, are built to rigorously test our models of
natural systems. One area where this complementarity
is most evident is in the investigation of biological
networks, where the forward-engineeringperspective
of synthetic biologists has been both inspired by and a
contributor to studies of natural motifs. Models
incorporating circuit architecture and exploring alter-
nate topologies often lead to novel insights into the
robustness of natural biological networks [216,217].
Synthetic circuits can offer a means to test these
models. For instance, Çağatay et al. investigated the
Bacillus subtilis competence circuit using a synthetic
variant wherein the native negative feedback circuit
consisted of expression of an inhibitor (MecA) instead
of repression of an activator (ComS), as is present in
the wild-type strain [218]. This enabled researchers to
compare functional differences in competence dynam-
ics arising due to variation in circuit topology. In
comparison to the synthetic version, the natural system
exhibited greater variability in competence duration
times, thought to be advantageous in an unpredictable
environmental context [218]. A better understanding of
the effects of positive and negative feedback topolo-
gies can then, in turn, allows bioengineers to exert
greater degrees of control over the shape of circuit
transfer functions. For example, it is now clear that
appropriately tuned negative feedback loops can yield
behavior including faster response times, dynami-
c-range increases, and a linearization of the transfer
function for a TetR system, all of which are potentially
attractive features for gene expression or biosensing
applications [211,219,220].
A subset of systems biologists have focused in
particular on studying network motifs which occur
more often than expected and thus are thought to
carry specific information-processing capabilities; for
instance, depending on the particular configuration,
the feed-forward loop (FFL) can act to create
sign-sensitive delays or as a pulser [221,222].
Early work in synthetic biology often involved the
direct construction of these motifs in order to
recapitulate predicted behavior, often in conjunction
with mathematical modeling. For instance, Entus et
al. used a simple model of the type I incoherent
feed-forward loop (I1-FFL) to predict the effect of
mutations (represented by variations of key param-
eters) on the transfer function of their circuit and
verified their predictions experimentally [223]. Incor-
poration of basic network motifs into larger circuits
allows for more complicated processing capabilities;
Basu et al. integrated a synthetic I1-FFL circuit with a
QS circuit to enable population-level sensing and
pulse generation [224]. In these early years of
synthetic biology, the build to understand,forward
engineering perspective (see Fig. 2a) complemen-
ted studies to understand naturally occurring in-
stances of these circuits (e.g., Refs. [225,226]) and
taken together, conferred a broader, more holistic
understanding of the function of these motifs [227].
Synthetic biologists have complemented the build
to understandapproach with the build to demon-
strateapproach, where well-characterized motifs are
used as test beds for implementing engineering
concepts such as orthogonality or model-based
design. For instance, An and Chin created an
orthogonal expression system composed of an
orthogonal ribosome and mRNA pair coupled with
T7 RNAP, and arrayed their network in a FFL topology
to demonstrate the ability to mediate tunable delays
for orthogonal protein production [228]. Ellis et al.
created a library of genetic components and with
modeling, constructed various FFLs in a plug-and-
playnature [153]. The close alignment of experimen-
tal results and model predictions the authors found is,
however, not yet routine in synthetic biology and
further work will be necessary to understand where
predictability breaks down.
Oscillatory behavior and its mathematical analysis
are another domain of interest for systems-level
biologists. Focus is given to not only the composition
and description of the various submodules of an
oscillator but also its emergent behavior, character-
ized by its metrics such as its robustness and
periodicity [229]. Early efforts to study natural
oscillators and construct synthetic ones centered
on either the construction of dual-feedback circuits or
a three-repressor system (see Fig. 2b) [2,230,231].
The addition of QS machinery further allowed for the
creation of synchronized oscillations at the colony
level [232,233]. The applications are diverse: for
instance, the construction of genetic clocks provides
insights into what is needed for robust oscillatory
behavior, a core component of many fundamental
physicological and behavioral processes [234,235].
Transplantation of genetic oscillators (e.g., the
KaiABC cyanobacterial system) into other organisms
can help elucidate core components necessary for
recapitulating oscillatory behavior [236]. The opposite
also holds trueheterologous expression of genes
and circuits from other organisms can help elucidate
core components behind oscillatory behavior in their
native context. For instance, Pattanayak et al.
expressed the galP sugar symporter from E. coli in
cyanobacteria and demonstrated that rhythmic feed-
ing synchronizes the endogenous KaiABC clock
[237]. By rewiring the metabolism of cyanobacteria,
Pattanayak et al. were able to demonstrate another
avenue by which the cyanobacterial clock is synchro-
nized. Understanding oscillatory behavior and con-
structing synthetic oscillators can have broad
applications: for instance, synthetic oscillators con-
trolling metabolic pathways may be needed to
fine-tune the growth of engineered microbial consor-
tia, as studies of B. subtilis biofilm communities
suggest [238]. Furthermore, synthetic oscillators
may someday be applied to correct the loss of
901Computing in Living Cells
Fig. 2. |Abuild to understandapproach to systems biology employing synthetic circuits. Forward engineering and in silico characterization of systems biology
network motifs. (a) I1-FFL motif, biological implementation (TF = transcription factor), and time-course for a simplified model of the I1-FFL [223]. The I1-FFL exhibits
pulse generation and an accelerated response time (after normalizing with respect to steady-state output) [221,222,224,225]. For comparison, a TF2 mutant (TF2-Mut)
which is unable to bind to and repress the output promoter is shown. (b) Synthetic oscillator (repressilator) motif, biological implementation, and time-course.
Repressilator schematic and time-course adapted from DNAplotlib and Elowitz and Leibler [2]. Alternate oscillator topologies incorporating both positive and negative
feedback have been explored in recent years [240]. Circuit diagrams drawn according to SBOL Visual specification; part-to-part arrows indicate activation and bars
indicate inhibition.
902 Computing in Living Cells
microbiota oscillations and the resulting detrimental
effects on human health (e.g., dysbiosis from chronic
jet lag) [239]. A more comprehensive overview of
synthetic genetic oscillators from mathematical, com-
putational, and biological perspectives can be found in
Purcell et al.[240].
Perspectives and approaches derived from control
theory have likewise influenced both systems and
synthetic biologists. In particular, frequency domain
analysis has been used to understand phenomena
ranging from simple genetic circuits to osmo-adaptation
in yeast [241243]. Furthermore, in light of growing
recognition of homeostasis as a control circuit, with
disease as a consequence of its disruption [244],
synthetic biologists have designed control-theory-
inspired circuits to correct physiological imbalances.
For instance, Kemmer et al. engineered a mamma-
lian circuit to detect and correct imbalances in uric
acid levels and demonstrated that their system
worked as effectively as standard drug therapy in
urate deficient mice [245].Rossgeret al. coupled a
fatty acid sensor with the production of pramlintide, a
peptide hormone that suppresses appetite, and
demonstrated that compared to wild-type mice,
obese mice implanted with this synthetic circuit
exhibited lower levels of food consumption, blood fat
levels, and body weight under a high-fat diet [246].
These and other examples (e.g., Refs. [247,248])
demonstrate the immense therapeutic potential of
synthetic control circuits for adaptive medicines.
Finally, the DNA assembly strategies developed
for synthetic biology applications can be useful for
deciphering complex natural networks, which are
often combinatorial and intertwined. For example,
barcoded genetic perturbation libraries that target
individual genes can be rapidly assembled in pooled
format into high-order combinatorial genetic pertur-
bation libraries that simultaneously overexpress or
downregulate genetic combinations (e.g., pairwise,
three-way, and higher-order sets of genes). Tracking
the enrichment and deprivation of such barcodes in
pooled populations with next-generation sequencing
enables the rapid mapping of combinatorial genetic
interactions in microbial and human systems [249
252].
Metabolic Engineering
The overlap between the fields of metabolic
engineering and synthetic biology continues to
grow. Conventionally, metabolic engineering has
focused on improving product titer, yield and
productivity through the tuning of metabolic flux or
the introduction of heterologous genes; this system-
s-focused, industrial perspective might be thought to
differentiate metabolic engineering from synthetic
biology [253]. However, as Nielsen and Keasling
note, for many applications today in industrial
biology, a synergistic platform integrating both
metabolic engineering and synthetic biology ap-
proaches (the latter enabling environmentally re-
sponsive circuits and self-regulation to enhance
performance and reduce toxicity) may be required
(see Fig. 3)[254]. Applications of synthetic biology in
a therapeutic context will likewise increasingly
integrate tools of metabolic engineering; for instance,
synthetic biologists interested in engineering the
microbiome to inducibly produce therapeutic small
molecules or degrade toxic metabolites will depend
heavily on pathway optimization and the suite of tools
metabolic engineers have developed to address this
challenge. We briefly review examples of engineering
at the intersection of these two fields, with a particular
focus on circuits and genome-engineering tools that
have been developed to enable more dynamic and
high-throughput approaches to metabolic engineer-
ing. As the diversity and complexity of small molecules
of interest to bioengineers continues to grow along
with the need for dynamic control, we expect to see
further synergistic overlap between the two fields.
Growing recognition of the role gene expression
dynamics play in affecting metabolite production has
led to the development of models in E. coli and yeast
expanding traditional constraint-based frameworks
(e.g., flux-balance analysis) with time-dependent
constraints (representing transcriptional regulation)
(see Fig. 3c) [255257]. Incorporation of transcrip-
tional dynamics has enabled finer tuning of flux
profiles by suggesting optimal times for activation or
repression of a given metabolic pathway over the
time course of a batch culture as opposed to a
reliance on ab initio gene knockouts. This ONOFF
approach to optimizing production may be important
when gene knockouts to increase yield cause
notable growth impairment, decreasing productivity.
Anesiadis et al. explored this approach for ethanol
production in E. coli by utilizing a QS module for
density-dependent repression (via a toggle switch)
of phosphotransacetylase (pta), which leads to
inactivation of a competing acetate-production path-
way [258]. Their synthetic circuit increased produc-
tivity but decreased yield and differed in behavior
from predictive models that did not account for the
timescale of repression and protein degradation
[259]; nonetheless, their work highlights the impact
synthetic biology can have on actualizing dynamic
approaches to metabolic engineering. By further
employing, for instance, CRISPR-based transcrip-
tional regulation [20], one could conceivably achieve
multiplexed regulation at many genomic loci on
differing timescales.
Synthetic circuits have also been applied to
mitigate deleterious host effects due to the toxicity
of intermediates. Zhang et al. engineered a control
system comprising an engineered fatty acid/acyl-
CoA sensor to regulate the production of fatty acid
ethyl ester, a biofuel [260]. Fatty acid ethyl ester is
903Computing in Living Cells
Fig. 3. | Complementing metabolic engineering with synthetic circuits for dynamic control. (a) Synthetic circuits enable the dynamic correction of metabolite imbalances
resulting in suboptimal flux, host toxicity, etc. A block diagram is used to illustrate this concept via a feedback loop where cellular information from the circuit input (e.g., a
small-molecule inducer) and circuit biosensors are added at the summation point (e.g., through interactions of transcription factors) and result in expression of controller circuits
that modulate metabolic processes. (b) An example of a dynamic controller by Xu et al.[300] where ACC represents the malonyl-CoA source pathway and FAS represents the
malonyl-CoA sink pathway. The purpose of this circuit is to maintain requisite levels of malonyl-CoA for fatty acid production; excessive malonyl-CoA activates the sink pathway
and insufficient malonyl-CoA results in activation of the source pathway. Behavior of the circuit is shown in panel e. Figure reprinted from Xu et al.[300]. (c) Left: static metabolic
engineering using flux balance analysis to identify optimal mutants in the constrained solution space; blue and green dots represent two potential solutions along specific
constraint boundaries (dotted lines) [301]. Right: a dynamic two-stage production strategy is instead utilized which allows for a time-dependent changing profile (dotted line
connecting the two solutions) of an engineered strain (figure adapted from Venayak et al.[262]). (d) Metrics of interest for metabolic engineers include yield, the amount of product
synthesized per the amount substrate, titer, and the amount of product per reaction volume. Biomass measurements are also important for understanding where yield and titers
might be improved. Dynamically regulated circuits (right) can sometimes achieve relatively higher yields or titers (in blue) but lower biomasses (in green) due to the host burden of
synthetic circuits [300,302]. (e) Malonyl-CoA profile from strains without (left) and with (right) the controller circuit in (b). Dynamic regulation can lead to fluctuations in the
time-course of small molecule production. Figure adapted from Xu et al.[300].
904 Computing in Living Cells
synthesized by the enzyme wax-ester synthase
using substrates ethanol and fatty acyl-CoA; both
ethanol production and wax-ester synthase synthe-
sis are inhibited by fatty acid/acyl-CoA sensor in the
absence of fatty acid. By reducing imbalances in the
metabolic pathway, Zhang et al. were able to triple
the yield of their strain. In situations where well-
characterized sensors are not available, a systems
biology approach can identify promoters responsive
to toxic intermediate buildup that can then be
strategically employed to reduce host burden [261].
These and other examples of synthetic circuits
enabling dynamic control (see Venayak et al. for a
recent review [262]) hold great potential for a more
precise and robust approach for small molecule
production.
Rational design and predictive models will, at
times, fail to yield desired results due to gaps in our
understanding of the complex temporal and spatial
dynamics underlying metabolic pathways. In such
cases, metabolic engineers and synthetic biologists
have opted to use directed evolution techniques
consisting of iterative steps of mutagenesis, selec-
tion via screening, and amplification to enable the
testing of large, combinatorial hypotheses for im-
proving yield [263]. Directed evolution on the protein
scale in recent years has benefitted from a range of
innovations, including the usage of bacteriophage-
based selection, RNA aptamers for coupling metab-
olite concentrations to fluorescence, and emulsion
PCR-based amplification [152,264,265]. Genome
engineers have expanded directed evolution ap-
proaches to multiple sites within the genome via the
development of technologies such as multiplex-
automated genome engineering (MAGE), which
relies on incorporation of multiple single-strand
oligonucleotides introduced via electroporation into
daughter cell genomes [135,266]. MAGE, coupled
with co-selection, can reach incorporation efficien-
cies of greater than 70% and has been applied to
increase, by four to fivefold, the production of
lycopene as well as aromatic amino acid derivatives
[267,268].
It is important to note, however, that further
development of directed evolution methodseven
at the genome levelwill be limited in throughput by
downstream screening methodologies. For the
applications of MAGE listed above, visual screens
were utilized to assess intensity of pigmentation; this
will not always be an available option for pathways of
interest for metabolic engineers. The development of
circuits comprising biosensors for metabolites
coupled to fluorescent outputs can help address
this. Fluorescent read-outs can easily be sorted in a
high-throughput manner viafluorescence-activated
cell sorting. This mutagenesis combined with the
fluorescence-activated cell sorting approach was
used to select for higher productivity of amino acid
synthesis in the industrial microbe Corynebacterium
glutamicum via circuits incorporating endogenous
amino acid-mediated regulatory devices for biosens-
ing purposes [269,270]. Endogenous machinery is
often available and amenable for biosensor con-
struction in situations where the metabolite of
interest is core to the cell's survival. In situations
where this is not the case, biosensors can be
constructed by, for instance, engineering RNA
switches comprising aptamer and output modules,
engineering synthetic transcription factors (e.g.,
based off the CRISPR system), or re-designing
natural ones through high-throughput screening
[62,83,84,209,210,265].
Toward Synthetic Ecosystems
The assemblage of genetic parts into genetic
circuits has dominated much of early synthetic
biology efforts; more recently, there has been
growing interest in higher-order systems composed
of synthetic or engineered microbial and eukaryotic
communities. Early examples of synthetic commu-
nities include engineered cooperation within yeast
[271], a microbial predatorprey system utilizing
killer or antidote genes and a QS system [272],
tunable auxotrophic co-cultures [273,274], and a
QS-based band-detectcircuit capable of forming
patterns [275] (see Kong et al. for additional
examples [276]). Chen et al. recently demonstrated
the construction of a consortium of two strains of E.
coli capable of undergoing genetic oscillations only
when mutually present via the coupling of two QS
systems [277]. The development of synthetic com-
munities may have implications for metabolic engi-
neering; for instance, complex metabolites may be
more amenable for multi-stage production using
communities of engineered strains to offset the
per-cell metabolic load.
Despite these examples, there are still open
questions and challenges in engineering communi-
cation between cells. In response, synthetic biolo-
gists have employed a build to understand
approach, similar to the initial period of investigation
into network motifs. For instance, the mechanisms
by which cooperators coordinate and select against
cheaters in bacterial quorums are still being eluci-
dated. One emerging hypothesis is that QS machin-
ery co-regulates both intracellular and extracellular
enzyme production (privateand publicgoods,
respectively) and thus limits the cost of cooperation
[278]. Zhang et al. investigated this possibility in B.
subtilis by constructing a circuit composed of the
heterologous Auto-Inducing-Peptide QS system
which allows for senderreceiver communication
but without affecting other aspects of the receiver's
physiology [279]. Their results suggest that in
comparison to the synthetic variant, wild-type strains
demonstrate synergistic coupling of extracellular
905Computing in Living Cells
matrix production and QS which may, in turn, serve
as a private good by allowing receiving cells to be
more responsive to global cell density.
Engineering communication between cells re-
mains challenging as natural QS sensing pathways
often exhibit cross-talk and will require further tuning
and characterization to achieve desired levels of
modularity [280]. To understand this phenomenon,
Wu et al. constructed circuits comprising combina-
tions of auto-inducer, regulator, and promoter for
LuxR/I and LasR/I QS systems, characterized
cross-talk as either signal-based or promoter-based,
and modeled the complex bimodal and trimodal
stability regions that emerge due to cross-talk and
host-circuit interactions [281].
With respect to human health, understanding and
engineering the gut microbiome as an ecosystem of
microbial species in particular holds high therapeutic
potential; studies have implicated the effect the
microbiome has on inflammatory bowel diseases,
obesity, asthma, diabetes, neurological disorders,
behavior, and the metabolism of drugs [282288].
Engineered strains have already been constructed
for usage as sensors to detect small molecule
environmental stimuli in the mammalian gut [289].
Tools have begun to be developed for engineering
species of gut bacteria already well suited for
colonizing the gut; these include members of the
well-represented Bacteroidetes and Firmicutes
[290,291]. For instance, synthetic biologists have
developed a toolkit amenable for engineering of the
commensal Bacteroides thetaiotamicron comprising
characterized promoters, RBS, inducible systems,
and the CRISPRi platform [196]; similar work has
begun to be conducted on members of the Firmi-
cutes [292]. Synthetic biologists have also devel-
oped techniques for the targeted disruption of
endogenous communities; for instance, modular
viral scaffolds and phagemid-delivered CRISPR
enable selective perturbation of bacterial communi-
ties [157,293]. These tools may allow for the targeted
disruption of pathogens within the gut.
The further development of genetic circuits (e.g.,
cell counters) and imaging tools for gut flora will
benefit in vivo efforts to study and understand host
microbiota interactions, interactions between spe-
cies in the gut, and the dynamics of growth
[294,295]. Mathematical approaches to model the
dynamics and evolutionary trajectories of synthetic
ecologies will also be paramount; for an overview of
current methodologies, see Zomorrodi and Segrè
[296].
Challenges for the Future of Synthetic
Biology
In this review, we presented foundational synthetic
biology engineering principles and highlighted in
particular the intersection between synthetic
circuits and the fields of digital and analog
logic, systems biology, and metabolic engineer-
ing. The creation of next-generationsynthetic
networks will likely necessitate the layering of
these circuits into higher-order systems to
achieve predictable behaviors at the organism
and community scale. However, challenges
remain for the forward-engineering of complex
synthetic circuits:
1. As the field of synthetic biology matures,
conceptual models for designing and under-
standing synthetic circuits and techniques
for their construction and characterization
will continue to diversify. Importantly, these
models will become much more central to
the synthetic-biology design cycle when they
become predictive, which has been a major
challenge thus far. Accurate quantification of
various molecules involved in synthetic gene
circuits, such as the precise numbers of
proteins, plasmid DNA copy numbers, and
mRNA numbers in single cells, could en-
hance the predictive power of circuit models.
Toward this end, technologies including
single-cell sequencing and newer develop-
ments such as fluorescent in situ sequenc-
ing may be helpful for synthetic biologists
[297]. Coupling such gene expression data
with metabolomics data (e.g., collected via
mass spectrometry or NMR spectroscopy)
could be important for metabolic engineer-
ing-related circuits or for obtaining a more
complete perspective on the global impact of
synthetic circuits [298,299]. Detailed quanti-
fication strategies for synthetic circuits
should help to determine the major determi-
nants of success or failure in circuit perfor-
mance, and lead to the development of
ranking algorithms for comparing alternative
circuit designs.
2. The current designbuildtest cycle is still
slow and innovative methods are needed
to automate construction, characteriza-
tion, and tuning of parts and circuits.
Employing robotic or acoustic liquid han-
dling systems can help to reduce the
repetitive manual labor that constrains
the speed and scale of the current
designbuildtest cycle and to minimize
human error. Similarly, the increasing
usage of microfluidics-based technologies
can facilitate high-throughput characteriza-
tion of circuit behaviors [144146]. Machine-
906 Computing in Living Cells
learning methodologies for incorporating
findings from high-throughput experiments
into quantitative design principles are
needed.
3. Robust standardization of parts and data
characterization is needed, especially for
those components that cannot be charac-
terized in a high-throughput fashion. This
will enable the porting of parts across labs
and building complex systems by combin-
ing simpler ones. In addition, strategies to
determine and then mitigate undesirable
interactions between parts and their host
would enable greater portability of
designs.
4. It is yet unclear if biological systems can
achieve overall scalability via the design
principles of modularity and orthogonali-
ty, as is the case with electronic devices.
If this is not possible, alternative design
principles that incorporate evolution,
high-throughput circuit construction and
screening, and non-digital computing
need to be explored and tested. A shift
in paradigm from evolving standalone
parts such as DNA-binding proteins,
promoter elements, and enzymes of
interest to evolving circuits and networks
as a whole could be a powerful approach
[152].
5. Finally, the goal of computation in biology is
not toward artificial, general purpose com-
puting but toward enabling sense and
response behaviors relevant to biological
applications. This realization should moti-
vate a more application driven, top-down
approach in designing synthetic gene cir-
cuits to tackle real-world challenges.
In summary, synthetic biologists have made
significant progress over the last 15 years in
establishing a broad suite of gene regulatory parts,
assembling these parts together into higher-order
circuits, widening the applicability of gene circuits
into a range of prokaryotic and eukaryotic systems,
and demonstrating proof-of-concept applications.
We anticipate that expanding interest in the broad
utility of synthetic biology to tackle difficult biomed-
ical, industrial, and environmental problems will
continue to drive growth in the pace, scale,
reliability, and scope of synthetic gene circuit
technologies that can be realized, but emphasize
that synthetic biology remains a nascent field in
which many fundamental challenges and opportu-
nities remain to be solved.
Acknowledgments
We thank members of the Lu laboratory for helpful
discussions. This work was supported by the National
Institutes of Health (DP2 OD008435, P50 GM098792,
1-R21-AI121669-01), the Office of Naval Research
(N00014-13-1-0424), the Defense Threat Reduction
Agency (HDTRA1-15-1-0050, HDTRA1-14-1-0007),
and the National Science Foundation (MCB-1350625).
The project or effort depicted was or is also sponsored by
the Defense Advanced Research Projects Agency
(HR0011-15-C-0091). K.C.M. acknowledges additional
support from the Harvard College Research Program
and the Harvard College PRISE.
Received 4 January 2016;
Received in revised form 13 February 2016;
Accepted 15 February 2016
Available online 22 February 2016
Keywords:
synthetic biology;
digital logic;
analog logic;
systems biology;
metabolic engineering;
memory
Abbreviations used:
QS, quorum sensing; RBS, ribosome-binding sequence;
UTR, untranslated region; MAGE, multiplex-automated
genome engineering.
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