Towards Engineering Biological Systems in a Broader Context

    Towards engineering biological systems in a broader context Ophelia S. Venturelli, Robert G. Egbert, Adam P. Arkin PII: DOI: Reference:

S0022-2836(15)00618-X doi: 10.1016/j.jmb.2015.10.025 YJMBI 64899

To appear in:

Journal of Molecular Biology

Received date: Revised date: Accepted date:

26 August 2015 24 October 2015 28 October 2015

Please cite this article as: Venturelli, O.S., Egbert, R.G. & Arkin, A.P., Towards engineering biological systems in a broader context, Journal of Molecular Biology (2015), doi: 10.1016/j.jmb.2015.10.025

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ACCEPTED MANUSCRIPT Towards engineering biological systems in a broader context Ophelia S. Venturelli13, Robert G. Egbert2 and Adam P. Arkin23 1

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California Institute for Quantitative Biosciences, University of California Berkeley, 2151 Berkeley Way, Berkeley, CA 94704-5230, USA
 2 E.O. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 955-512L, Berkeley, CA 94720, USA 3 Department of Bioengineering, University of California, Berkeley, CA 94720, USA

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HIGHLIGHTS Synthetic biology at the cellular and ecological level holds significant promise to solve imminent global challenges in agriculture, energy and human health To achieve these goals, engineered biological solutions will need to be designed effectively to survive, grow and function in complex environments beyond the laboratory The engineering design space contains trade-offs among the resource limitations to support cellular fitness, implementation of a desired function and mechanisms to constrain the organism to a desired niche Natural biological systems employ multiple strategies at the cellular, population and microbial community levels to continuously adapt to changing environments and to achieve a stable ecological function Engineering efforts should aim to extract natural design principles to develop compact and efficient designs that implement functionally equivalent behaviors

ABSTRACT Significant advances have been made in synthetic biology to program information processing capabilities in cells. While these designs can function predictably in controlled laboratory environments, the reliability of these devices in complex, temporally changing environments has not yet been characterized. As human society faces global challenges in agriculture, human health and energy, synthetic biology should develop predictive design principles for biological systems operating in complex environments. Natural biological systems have evolved mechanisms to overcome innumerable and diverse environmental challenges. Evolutionary design rules should be extracted and adapted to engineer stable and predictable ecological function. We highlight examples of natural biological responses spanning the cellular, population and microbial community levels that show promise in synthetic biology contexts. We argue that synthetic circuits embedded in host organisms or designed ecologies informed by suitable measurement of biotic and abiotic environmental parameters, could be used as engineering substrates to achieve target functions in complex environments. Successful implementation of these methods will broaden the context in which synthetic biological systems can be applied to solve important problems.

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INTRODUCTION Human society is confronting global challenges in agriculture, energy and health owing to resource scarcity, unconstrained population growth, environmental degradation, antibiotic resistance, plant extinction, among numerous other predicaments. Microbes exhibit numerous properties that can be harnessed for engineering. These properties include enzyme catalysis, autocatalysis of self-copies, flexible adaptation to changes in surroundings, and the ability to achieve diverse functionalities on a broad range of temporal and spatial scales. Potential applications include, among others: microbes that mobilize phosphorous and fix nitrogen obviating the dependency on fertilizers; detoxification of water using microbes that protect against infection; extraction of nutrients in the human microbiome; or engineering of plants to grow in extreme or low nutrient environments.

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The goal of synthetic biology is to predictably program cells or interactions among genetically distinct cell populations to achieve a desired goal by engineering biomolecular circuits or manipulating environmental parameters. To advance these objectives, the major approaches include: rational design of molecular information processing systems embedded within a host-cell, engineering of ecological systems using synthetic ecology, or application of evolutionary processes to select for a desired function (Figure 1). The predictable engineering of biological systems poses several challenges: (a) we are limited by basic knowledge of underlying molecular processes; (b) we lack predictable control of molecular pathways; and (c) we have a weak understanding of how to extract generalizable principles that can lead to robust operations in a changeable ecological context[1–3].

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To expand the applications of synthetic biology beyond the laboratory, organisms must survive, maintain functionality and proliferate in complex and uncertain environments. Target environments include open or closed bioreactors, animal-associated microbiomes or plant rhizosphere (Figure 2). When embedded in these environments, organisms will likely confront rapid and unpredictable or recurrent environmental shifts, diverse multi-kingdom resident communities or genetic modifications as a consequence of random mutation. The engineered population will have to balance growth and circuit functionality in spite of environmental uncertainty in an ecological context (Figure 3a). The predictable engineering of stable ecological functions constitutes a major roadblock to developing synthetic biology as a true engineering discipline that bears significant impact extending beyond chemical manufacturing. Since microbes evolved over millions of years to persist in diverse environments containing genetic clones and other organisms, design principles could be extracted and co-opted for engineering. At the cellular-level, regulatory programs are employed to sense the state of the environment, perform information processing, and generate a response to modify internal physiological states. Cell populations can mobilize phenotypic diversification strategies by capitalizing on the underlying noise in molecular networks to hedge bets in the face of uncertain environmental information. This population heterogeneity can also be leveraged to distribute unique tasks among sub-populations to collectively achieve a complex function. Microbial communities partition available resources into metabolic niches that enable specific microbial populations to be competitive winners. To achieve niche separation, microbes exhibit significant diversity in nutrient utilization for energy generation by employing a diverse repertoire of physiological or mutational strategies for growth or survival. In contrast to an isogenic population, this ecological environment has the added advantage that it can expand the range of functionalities and enhance stability and resilience to environmental variability. To transition synthetic biology into complex environments, we posit that the dominant environmental factors and failure modes should be characterized through precise monitoring of biotic and abiotic properties and accurate simulation of these environments in the lab. Future efforts should focus on expanding our knowledge of microbial adaptation mechanisms at the cellular, population and ecological level. Design principles should be inferred based on these systems and applied to develop 2

ACCEPTED MANUSCRIPT novel implementations in target chassis. Target functions include the ability to adapt to specific niches, safeguards against or functionalization of random mutation processes, tactics to promote or defend against invasion of a microbial community, and containment within a bounded region of the environment.

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Cells implement diverse physiological responses to adapt to environmental stimuli In natural environments, cells are continuously bombarded with diverse stimuli including complex mixtures of nutrients, antimicrobials, signaling molecules or abrupt fluctuations in abiotic parameters such as pH or temperature. To grow and survive, cells must accurately sense and selectively respond to specific environmental inputs. In some cases, cells bias their response based on a prior history of environmental conditions [4–6]. To adapt to environmental change, cells can implement direct sensecompute-respond programs [7,8], phenotypic diversification across a population [9,10] or “stochastic sensing” which uses external cues to influence a stochastic outcome [11–13]. To implement directsensing, extracellular signals are transmitted to intracellular pathways that decode the signals and alter the activity of regulatory pathways. Graded responses relay quantitative information via proportional matching of variations in the input and output [14,15], whereas ultrasensitive responses filter input noise below a threshold and amplify a small input change close to the threshold to produce a large output shift[5,16].

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While sense-compute-respond strategies are ubiquitous, there are three major shortcomings that include: (a) metabolic costs [15] to maintain the bio-molecular machinery required for detection of extracellular cues, (b) precise inference about the most probable state of the environment and accurate information transmission to downstream actuators [16] and (c) signal transduction time delays to accumulate a sufficient number of molecules to trigger a cellular response. Catastrophic opportunity costs may result from the commitment to a maladaptive decision due to noisy information or rapid environmental shifts that occur on a faster time scale than the cellular response time. To overcome these limitations, engineering designs should explore the performance advantages of phenotypic diversification strategies for alleviating energetic constraints, achieving a faster response time and obviating the need for accurate information transmission. Some environmental fluctuations are statistically correlated, amenable to the evolution of distinct pathway co-regulation in response to a specific signal [17]. Patterns in the environment that occur frequently enable organisms to develop these anticipatory regulatory networks over evolutionary time to cope with uncertainty. In some cases, environmental fluctuations can be correlated in time or space. In E. coli, the transcriptional response to elevated temperature is highly correlated to the expression of genes that increase fitness in an anaerobic environment. This regulatory link is posited as an anticipatory response to prepare for gut colonization upon entering the oral cavity [17]. Previous work has shown anticipatory regulation of genes in Vibrio cholerae can improve fitness when transitioning from host to stool or water environments[18]. These responses can be adaptive traits selected for via evolution and includes cross-regulation to link statistically correlated environmental signals[19]. Evolutionary re-wiring of regulatory pathways as a consequence of correlated environmental signals has been demonstrated in yeast populations exposed to periodic stress inputs [20] indicating that regulatory networks have evolutionary plasticity on short laboratory time scales. A multi-layer digital logic circuit in E. coli was designed to associate the presence of one chemical signal with the response to a different chemical input through a conditioning phase that recorded memory of simultaneous exposure to both inputs [21]. The authors demonstrated that the duration of input exposure was proportional to the fraction of cells that switched to the memory ON state. Similar tactics could be deployed to engineer synthetic circuits that learn from environmental correlations to improve fitness in predictable environments or record information about temporal patterns. 3

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Functional roles of non-genetic population heterogeneity While some environmental patterns show periodic repetitions [17], others are rare and abrupt and can be catastrophic in the absence of phenotypic heterogeneity [22]. Single-cell heterogeneity is a ubiquitous feature of microbial populations and can reduce the long-term temporal variation in fitness at the expense of the mean fitness level. This cell-to-cell variability can arise due to stochastic fluctuations in network activity, microenvironment differences, single-cell history or cellular age [23].

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There are several other examples that highlight the functional roles of phenotypic diversification in bacterial populations. For instance, persister sub-populations exhibit significantly reduced sensitivity to antibiotic challenges and can display attenuated growth rates prior to the stimulus [24]. While maintenance of a slow-growing fraction of the population may be an immediate fitness disadvantage, this heterogeneity may ensure long-term survival upon an environmental shift. The conditions for which this trade-off provides a benefit for the population has been described theoretically and can be compared to the costs of maintaining sensing capabilities and time delays for inducing a cellular response as described above [25–27]. Stochastic differentiation leads to developmental systems that utilize programmed cell death for population survival. Behaviors encoded by these developmental programs include cannibalism to delay sporulation cell fate decisions [28], fratricide to sample genetic diversity for impending environmental transitions [29], and altruistic cell death to aid in host colonization[30] or to compose [31] or alter [32] biofilm structure. Engineering designs could benefit from exploiting phenotypic heterogeneity since this strategy requires less knowledge of the environmental challenges that the organism will confront in natural environments.

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Controlling genetic diversification Evolvability, or the capacity to adapt to selective pressures through genetic variation, is vital to the persistence of microbes in natural environments [33]. Controlled genetic diversification could be used to facilitate adaptation of engineered organisms in uncertain environments. A population of wild-type E. coli undergoes massive phenotypic diversification in an aging colony with some phenotypes associated with mutator states [34]. This diversification enables sub-populations to outcompete wildtype cells in the structured environment by creating niches for nutrient byproducts [34]. Mutator phenotypes have been shown to increase evolvability in some conditions by sampling beneficial mutations more frequently than wild-type cells [35]. However, global and persistent hypermutation alone is not an optimal evolutionary strategy since the majority of random mutations are deleterious. Contingency loci are hypermutable sequences that facilitate adaptation to new environments by modulating gene expression [36–38]. Genetic variation is generated by mutations in tandem repeats of short nucleotide sequences that undergo insertion and deletion mutations at rates that are significantly higher than arbitrary sequences of the same length [39]. When embedded in promoters or coding sequences, mutations within these repeat elements enable expression diversity by varying transcription efficiency via spacing between the -35 and -10 boxes or introducing translational frame shifts [40,41]. These repeat sequences have been used to fine-tune translational efficiency in the ribosome binding site for engineering microbes [42]. This approach is likely extendable to engineer reversible and tunable genetic variation by targeting control sequences that enhance cell survival in uncertain environments. Other efforts to incorporate evolvability in engineering have focused on two aspects of selective mutagenesis: conditional or local control. A striking example of conditional mutagenesis is an evolvable feedback control system that links a global hypermutation state in E. coli to the output of biosensor that is sensitive to a target metabolite [43]. Two approaches towards local control of mutagenesis include the transcription factor mediated recruitment of mutagenic proteins to genetic loci [44] or engineering of an error-prone extranuclear DNA replicon in yeast [45]. Future focus areas 4

ACCEPTED MANUSCRIPT include introducing biosensors sensitive to environmental cues to control mutagenic processes, narrowing the footprint and controlling the spectrum of mutations produced by localized mutagenesis or evaluating the adaptability of these proof-of-concept systems in natural environments. In sum, control of mutagenesis could be used to generate phenotypic variation in regimes of parameter space by selection of useful targets.

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Beyond random mutagenesis, rational DNA editing has been developed to record memory of environmental cues. Information about the temporal variation of environmental signals such as the maximum time derivative, input pulse duration or the integral may enhance the capabilities of cellular computational. Indeed, information preserved from time responses contains more information, exhibits higher robustness to extrinsic noise and is therefore more accurate compared to digital memory [46]. In one example, dynamic genome editing was used to program a proportional relationship between the duration and magnitude of input exposure and the fraction of modified cells across the population [47]. While population-level memory can be measured externally and controlled via in silico feedback schemes [48], mechanisms to generate single-cell memory would enable the output to function as an input to downstream circuits for in vivo feedback regulation.

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Trade-offs among different design criteria in cellular engineering Trade-offs between the fitness of an engineered organism and resident community members can arise due to the limited availability of environmental resources to sustain growth and survival such as nutrients or access to light (Figure 3a). Intracellular pathways consume energy in environments that are frequently resource limited, leading to strong trade-offs between host-cell fitness and the performance of an engineered function (Figure 3a). These constraints can be further augmented by a weak correlation between gene expression patterns and fitness suggesting that many genes may be sub-optimally regulated [49]. To achieve a stable engineered function within the context of a multispecies community, engineering strategies should attain a balance among different design criteria including fitness, survival and circuit performance (Figure 3b). Optimization of multiple properties inevitably leads to conflicts since the optimal solution for one objective will likely be sub-optimal for a different property (Figure 4). Evolution has repeatedly confronted this multi-objective optimization problem and has identified mechanisms and parameter regimes that can balance distinct tasks [50]. For example, membrane transporter density determines nutrient sequestration capabilities while at the same time permeabilizing the membrane to deleterious toxins [51]. An inverse relationship between growth rate and resistance to antimicrobials was observed across a genetically diverse set of E. coli strains and this variation was attributed to differences in membrane architecture [52]. Previous work in metabolic engineering has demonstrated that membrane transporters can be a critical variable in optimizing biofuel production [53]. Computational modeling indicates that feedback regulation of transporter expression could be used to improve the balance between energetic costs and benefits for metabolic engineering applications [54]. Future research should elucidate the physical and energetic constraints on remodeling of membrane architecture as a major target in engineering designs and identify optimal regulatory schemes for controlling membrane expression. The quantitative shape and magnitude of trade-offs among different properties is a crucial indicator of evolutionary dynamics and can used to predict system behavior and guide engineering designs. The relationship between distinct objectives can be linear or non-linear wherein parameter variation generates incremental improvements in system behavior (e.g. diminishing returns). Due to limited measurement capabilities, these relationships can be difficult to observe or quantify in the laboratory and the natural environment (Figure 4a,b). Further, the strength and shape of a trade-off is dependent on environmental statistics and the presence of other organism populations have evolved 5

ACCEPTED MANUSCRIPT unique survival strategies to cope with physical, chemical and resource limitations and environmental uncertainty.

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Trade-offs between cellular fitness and biomanufacturing Trade-offs between growth and chemical production are pervasive in metabolic engineering. Maximizing product production can reduce biomass generation of the host organism by sequestering key metabolites, competing for transcription or translational machinery, unbalancing co-factors or accumulating toxic intermediates[55]. The majority of industrial processes are batch fermentations in which the majority of production occurs once cells enter stationary phase[56]. As such, an efficient strategy is to focus resources on production of biomass. Then, upon reaching a threshold in cell density, utilization of resources would be focused on a synthesis phase of the target chemical. Temporal regulation of pathway activity using a binary ON/OFF switch can be used to eliminate the metabolic burden of pathway expression during a growth phase. Using this approach, the cell population can attain a sufficient density before redirecting metabolic flux from endogenous cellular processes and towards synthetic pathway expression[57,58].

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While superior to constitutive control, the two-stage control strategy does not allow for continuous adjustment of gene expression in response to environmental change. Feedback regulation can be used to automatically modulate the pathway expression levels over time based on variation in environmental stimuli. Natural biological networks exhibit similar design rules by capitalizing on feedback regulation to respond and adapt to environmental change [8]. The widespread implementation of feedback in engineering applications is limited by the diversity of well-characterized environmental or intracellular sensors and the ability to predictably modify the quantitative parameters of these feedback loops to optimize system performance. Recently, genome-wide transcriptome measurements were used to identify promoters regulated by a toxic intermediate to temporally control gene expression in a metabolic pathway [59]. These dynamically regulated promoters demonstrated improvement in cell growth and product yield by modulating the level of the toxic intermediate. While discovery-driven approaches can uncover novel regulatory connections, predictive models should be developed to tune system properties such as the gain or response time to enhance functionality. Since natural promoters are frequently regulated by unknown combinatorial factors, these regulatory sequences should be extensively characterized across a range of environmental contexts [60]. Towards these goals, rational promoter engineering using the FapR malonyl-CoA-responsive transcriptional regulator was used to design a control switch for regulating fatty acid biosynthesis and consumption [61]. This dynamic control system demonstrated a superior balance of cell growth and product accumulation. Developing biosensors for promoter activation or repression for other central metabolites will enable the dynamic re-distribution of metabolic flux at different stages of metabolism[62]. Interactions between synthetic circuits and host-cell processes mediated by resource allocation Whereas specific regulation of metabolic pathways can re-direct flux in a local region of the network [63], the availability of global resource pools such as the transcriptional and translational machinery can more generally constrain circuit functionality. This demand on resource pools leads to an indirect coupling between host-cell processes and synthetic circuit function. Ribosome production is highly correlated with growth rate and predictive of basal gene expression in cloning strains of E. coli [64,65]. Several lines of evidence point to a strong correlation between the number of rRNA operons and cellular fitness in different environmental conditions [66]. For example, lower numbers of rRNA operons have been shown to provide an advantage in temporally constant environments with low nutrient abundance. This contrasts with the fitness benefit provided by additional rRNA operons in environments that vary between high and low nutrient availability, which suggests that that the 6

ACCEPTED MANUSCRIPT number of rRNA operons plays key role in adaptability to environmental change [67]. Together, these results point to modulation of ribosome availability as a promising control knob for engineering hostcell ecological tactics.

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Natural regulation of ribosome abundance by environment-sensing global feedbacks may not be optimal for engineered circuits. To improve circuit performance and host-cell fitness, novel regulatory schemes to redistribute host-cell resources and mechanisms to sense resource abundance should be developed. Engineering principles to design and compose synthetic circuits are based on modularity assumptions that are violated by indirect linkages between the host-cell and synthetic circuit [68]. Advances should be made to develop predictive computational models that capture the major interactions that link host-cell fitness and synthetic circuit function. Taken together, the dominant coupling mechanisms should be included as a major controllable variable in the system engineering design and optimization cycle.

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Modifying the activity of global regulators can significantly change the partitioning of the proteome. For example, long-term adaptive evolution experiments have identified mutations in RNA polymerase (RNAP) that enhance growth rate by altering genome-wide transcription rates [69]. These data demonstrated a down-regulation of dispensable pathways such as stress and motility and upregulation of genes that limit biomass accumulation. While targeting central hubs such as RNAP can rewire cellular resource allocation, the effects can be pleiotropic and thus difficult to control or predict. Global predictive computational models should be developed to identify key targets and design control strategies[70,71].

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Facing similar challenges, the abundance of distinct sigma factors programs the transcriptional state of the cell by focusing RNAP on specific classes of promoters. Reproduction and survival under stress are energy demanding cellular processes that have opposing consequences on cell state [72]. In E. coli, the amount of resources channeled into growth or stress is determined by the competitive binding of rpoS and rpoD for the core RNAP [73,74]. The ratio of rpoS to rpoD has been shown to be a key determinant of cellular fitness in response to stress or nutrient stimuli [73]. If the RNAP core domain is limiting, molecular titration couples an increase in RNAP composed of one sigma factor to a corresponding decrease in RNAP bound to a different sigma factor. This compact regulatory architecture can simultaneously control activation and repression of different transcriptional programs. Inspired by this regulatory architecture, the phage polymerase T7 was divided into different domains that assembled into a functional T7 protein [75]. This fragmented T7 system could be used to threshold a transcriptional resource budget among different promoters in a synthetic circuit. Linking synthetic circuit activity to global resource availability could improve the predictability, evolutionary stability and performance of these devices. Global physiological state has been shown to dominate gene expression patterns as opposed to specific regulation by transcription factors [76]. A “capacity monitor” consisting of a genomically-integrated constitutive promoter driving a fluorescent protein was used to measure changes in resource availability in E. coli [77]. The mapping between this reporter and different circuit designs was quantified to identify circuits that minimally impacted host-cell physiology. Future work could link the outputs of resource-sensing devices to synthetic circuit activity using feedback regulation. To evaluate different designs, objective functions for the growth and survival of organisms within a bounded region of a target environment should be defined. There is mounting evidence that the digital logic principles that have been adapted from engineering disciplines to synthetic biology can incur significant energetic costs due to the necessity for many components [78–80]. Increasing metabolic demands on the host-cell will lead to degradation of circuit performance and reduced evolutionary stability over time [21]. To overcome these challenges, approaches include identifying functionally equivalent circuit designs that minimize the number of 7

ACCEPTED MANUSCRIPT components, subdividing the circuit among sub-populations using division-of-labor, or using RNA regulation to decrease reliance on translation machinery [81,82]. Alternatively, a different design framework could be applied, such as analog computation which can reduce the number of required parts to build information processing circuits [83]. Functioning within similar constraints, natural biological networks have been shown to employ graded input-output responses as opposed to digital alternatives [15,84].

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Division-of-labor strategies to parallelize disparate functionalities among sub-populations Compartmentalization of component modules among different cell types can be used to overcome resource constraints. This division-of-labor can function to parallelize different tasks such as secretion of key enzymes [85], implementation of invasive strategies in infection [86], or execution of different metabolic functions [87]. For example, a fungal/bacterial consortium leveraged the cellulose deconstruction capabilities of Trichoderma reesei and the genetic tractability of E. coli to jointly produce isobutanol from cellulose [88]. A metabolic interdependence between cellulolytic Actinotalea fermentans and an engineered strain of S. cerevisiae was utilized to express methyl halides from plant biomass [89]. A co-culture of E. coli and S. cerevisiae was developed for the distributed production of natural products. This system combined the biochemical specialization of the bacterium for overproduction of an intermediate with the functionalization of the intermediate via expression of eukaryotic proteins using the yeast [90]. The natural product work has been extended to show that division of labor between two variants of the same species could achieve production titers not realized by a related monoculture [91]. Since the balance among populations determines system functionality, an effective design relies on engineering compositional stability via mutualistic dependencies [90], unique niches [91] or detoxification strategies [89,90].

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Programming of spatiotemporal dynamics using division-of-labor can broaden the functional capabilities of biological systems and has numerous applications in the engineering of biological materials [92]. The combination of local interactions and long-range inhibition due to diffusion gradients can generate spatial patterning. Spatial gradients in quorum sensing signals have been used to program patterns through cell-to-cell communication between sender and receiver E. coli populations [93]. Different patterns were realized by modifying the spatial arrangement and genetic circuit parameters. Complex information processing systems have been constructed by compartmentalizing digital logic devices in distinct spatially separated cell populations [94]. Computational programs were specified by the spatial position of each cell population and quorumsensing signals functioned as “chemical wires” to transmit information between gates. Mechanisms to persist and maintain functionality in an ecological environment A division-of-labor strategy among isogenic populations could be accomplished through the partitioning of tasks among a community of genetically distinct organisms that has some added advantages. In theory, such ecologies are more stable, functionally diverse and resilient to perturbations compared to an isogenic population [95]. Microbial communities can perform functions that cannot be achieved by a single organism population. For example, syntrophic association between two metabolically distinct types of bacteria can enable degradation of a substrate that could not be performed by a single organism due to thermodynamic constraints. This syntrophic interaction can be observed when growth of both organisms is allowed in anaerobic sewage sludge digestors or freshwater lake sediments[96]. Microbial communities have evolved mechanisms to utilize a broad range of different nutrients from the environment. Co-existence of diverse populations can be achieved through niche-partitioning wherein the growth of each member is limited by a unique set of environmental parameters. These resource utilization profiles, including the affinity of each organism to the available substrates and the rate of consumption, significantly shapes the population structure of the ecosystem [97]. The 8

ACCEPTED MANUSCRIPT parallelization of resource consumption due to niche diversity in a microbial community more efficiently utilizes the available resources compared to an isogenic population. As such, these properties could be co-opted by engineering designs.

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While inter- and intra-species interactions are dominated by competition [98], microbes can also can also promote the growth or survival of other organisms through mechanisms such as syntrophy, cross-feeding or waste detoxification. Cellulose utilization is achieved through the synergistic activities of different microbial species [99]. These consortia consist of inter-connected and balanced metabolic connections that can promote the maintenance of diversity and composition over time [100]. A recent study demonstrated that C. debilis protected C. thermocellum from oxygen under aerobic conditions in co-culture, which enabled the oxygen-sensitive C. thermocellum to degrade cellulose for ethanol production[101]. These results highlight how indirect interactions mediated through environmental variables can expand a functional parameter regime. Similar mechanisms of waste detoxification are frequently observed in natural microbial communities and can serve as a reinforcing interaction correlating the fitness of members of a consortium.

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Functionalities are frequently distributed among distinct organisms in a microbial community. According to the Black Queen hypothesis, some organisms have “streamlined” or reduced genomes leading to key dependencies on other organisms in the community. Prochlorococcus is a highly abundance organism in ocean water and relies on other members of the community to detoxify H202, which can reach critically high levels due to photooxidation of dissolved organic carbon [102]. This detoxification process is due to the highly permeable membranes of the functional members that sequester and degrade the H202 from the environment. Dependencies such as H202 degradation among distinct organisms in a microbial community can generate a dense and interconnected microbial interaction network structure. Due to the immense complexity of these time-varying, nonlinear feedback networks, computational modeling approaches can be used to provide insight into the connectivity of these networks, by linking network structure and function, characterizing feedback loops and the response of these networks to environmental perturbations [103,104]. However, these approaches have not yet been applied to the design of microbial assemblages. Population dynamic models should be developed that capture the dominant inter- and intra-species interactions based on a detailed characterization of molecular mechanisms, functionalities and growth behaviors. Harnessing community-level properties for engineering requires strategies for stable integration into an ecosystem. Embedding an engineered organism in a microbial community modifies the trade-off between fitness and desired functionality due to competition with other genotype populatios (Figure 4b) [105]. A key concept in evolutionary theory is an evolutionary stable state wherein deviations in the population structure of the stable state can only decrease the fitness of each member. Related to this notion, an evolutionary stable strategy of a microbial community blocks invasion by organisms that are initially low in abundance compared to the resident organism populations [106,107]. To test these theories, molecular mechanisms underlying inter-species and intra-species interactions that collectively generate evolutionary stable states or strategies should be discovered. Engineered microbes will be forced to interact with other genetically distinct microbial populations due to the prevalence of microbial communities in natural environments. The engineered organism must grow and survive in the presence of resident microbes that have each evolved unique mechanisms for persistence (Figure 3b-3). Designing successful invasive strategies will be necessary for specific applications (Figure 3b-1). In other cases, the goal should be to minimize changes to the population structure upon introduction of the modified organism to establish a stable ecological function (Figure 3b-2). In all scenarios, effective containment strategies of the engineered organism must be developed. 9

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Engineering the stability and functionality of microbial ecologies Microbial communities can be manipulated by: altering the abundance of specific organisms, modifying environmental inputs, or engineering an organism [108] to function as a diagnostic device or actuator [109]. Synthetic ecology is an offshoot of synthetic biology in which the overall approach is to combine different populations to achieve a desired function (Figure 1). Here, “synthetic” indicates that the organisms may not naturally co-exist. Synthetic ecology approaches have been used to dissect the mapping between microbial communities and host-organism phenotypes. A regression model was used to probe the relationship between an unbiased combinatorial sampling of defined microbial communities and host-organism phenotypes [110]. These data could be used to forward engineer microbial communities that generate target host-organism phenotypes. In a separate study, a statistical model was constructed to explore the relationship between environmental inputs and microbe abundance in a mouse-gut synthetic ecology [111]. This approach identified a key diet ingredient that correlated with large variations in the abundance of members of the consortia, demonstrating that control of environmental inputs can be used to manipulate communities [112].

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Achieving a desired community-level function requires the coordination of the activities of individual members by maintaining a stable community composition over time (Figure 3b-2). A key challenge for synthetic ecology is ability to design long-term behaviors at an ecological level. Operating under similar constraints, the human gut microbiome has been shown to exhibit long-term persistence in microbial composition [113]. Natural communities employ several mechanisms to establish stable population structures within a microbial assemblage. Growth limitation on different energy sources provides a simple mechanism for co-existence by minimizing resource competition as a result of distinct ecological niches. Growth promoting positive interactions through cross-feeding can also enable diverse and stable populations. Applying this concept, complimentary deficiencies in amino acid biosynthesis pathways have been used to create co-dependencies between microbe populations through cross-feeding[114–117]. In some designs, synthetic cross-feeding communities exhibited enhanced fitness compared to wild-type and generated community structures that were resistant to invasion[117]. Fitness trade-offs in different environmental regimes can permit co-existence among genotypes. For example, a periodic variation in nutrient abundance can promote co-existence for organisms that thrive in either low or high nutrient regimes due to fitness trade-offs[118]. Niche-specialization can lead to negative frequency-dependent selection of specific genotypes resulting in a decrease in fitness as a function of abundance, which favors the growth of other members of the community. Corroborating this hypothesis, dynamical systems modeling of microbial ecosystems demonstrates that stability can arise when intra-species competition is stronger than inter-species competition[119]. Trade-offs in energetic cost and functional benefit of protein production can reduce the dominance of a single organism through competition network cycles leading to co-existence between species [120]. Finally, spatial heterogeneity has a critical role in shaping the composition and function of microbial communities and can expand the parameter space that supports diverse and stable ecosystems[121]. These mechanisms of co-existence can be used as design principles for engineering stable consortia for target applications including tailored probiotics to promote human health or engineered plant phenotypes such as pathogen resistance. The structure and function of microbial communities can also be engineered through directed evolution (Figure 1). A recent study demonstrated that repeated selection of plant-associated microbiomes for the onset of flowering time altered the composition of the microbial communities [122]. Introduction of these evolved microbial communities into sterile soil of Arabidopsis thalinana and Brassica rapa generated differences in the timing of flowering. This study identified a diversity of genetic loci that could be critical targets for the design of plant-host or microbe [122]. Here, an important challenge lies in the optimization and stacking of traits identified though multiple cycles of 10

ACCEPTED MANUSCRIPT evolution. A recent study demonstrated that iterative evolution of many replicates of a multi-species community generated a small number of disparate community states as opposed to random divergence into a large number of possible states or convergence to a single state [123]. These data suggest that the reproducibility of evolutionary outcomes can be exploited to modify community structure for engineering desired behaviors.

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In contrast to evolutionary methods, rational engineering approaches have been used to manipulate the composition of microbial communities. For example, E. coli strains engineered to produce or scavenge the quorum sensing molecule autoinducer-2 (Al-2) were introduced into mice and the composition of the gut microbiome was measured following Streptomycin treatment [108]. Their results demonstrated a correlation between E. coli that produce Al-2 and an increase in the ratio of Firmicutes to Bacterioidetes. These findings showed that engineered organisms could be used to manipulate the chemical signatures in an environment to bias community structure.

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Engineered microbes have been used to sense and record digital information about the occurrence of environmental stimuli. Recombinases alter the directionality of specific sequences of DNA[124–126] and can be linked to sensing modules to perform computation and encode DNA-based memory. Mimicking natural phenotypic diversity generators derived from multi-site, nested recombinase systems [127–129], a double inversion switch was engineered to implement genetic memory [126]. Noisy regulation of similar single-site recombinase switches[130,131] could be scaled to create multiinput, multi-output diversity generators using orthogonal sets of these memory switches[132].

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A non-genetic bistable memory device was engineered in E. coli to switch states and persist for many cell-generations upon exposure to aTc and the circuit performance was evaluated in a mouse-gut [133]. An E. coli mouse-gut isolate expressing the same circuit components persisted for longer period of time and attained a higher cell density compared to K12 in the microbiome environment. These data demonstrate that the competitive fitness of the microbe-host is a critical variable and advances should be made to develop tools and parts to manipulate the genetics of organisms native to target environments [134]. Engineering evolutionary robustness and containment Engineering organisms to operate and persist in complex environments must balance long-term genetic stability against the risks of environmental release that includes unconstrained propagation or horizontal gene transfer. Sequence design and strain selection are two major factors that affect evolutionary stability. Synthetic circuits encoded on multi-copy plasmids often introduce growth defects by competing for cellular resources [135]. Cells can escape these burdens by disrupting circuit function through insertion elements or recombination-mediated deletion of duplicated genetic components [136]. A sequence analysis tool has recently been developed to evaluate the evolutionary stability of input sequences and identify features that are prone to mutation [137]. Such efforts provide the first steps towards a formal process to incorporate evolutionary stability specifications into synthetic circuit design. Future work should leverage reduced genome strains lacking specific sequences that induce genetic instability [138,139] and integrate circuits on the chromosome [140–143]. A broader deployment of engineered organisms requires safeguards that limit the proliferation of the organisms or the genetic programs they carry. Recent work on biocontainment strategies for engineered microbes has focused on synthetic auxotrophy, novel addiction plasmids, and genetically recoded organisms. Together, these advances show promise as components of an effective toolkit for biocontainment. A multi-pronged approach to biocontainment of plasmid-encoded circuits consisted of separating plasmid replication components and a toxin-antitoxin dependency between the genome 11

ACCEPTED MANUSCRIPT and the plasmid as well as moving an essential gene from the genome to a plasmid [144]. This approach aimed to reduce horizontal gene transfer via environmental plasmid uptake or homologous recombination. However, selection of appropriate toxins to prevent environmental transformants could prove challenging in complex microbial communities.

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A CRISPR device has been engineered to inducibly and selectively degrade plasmid or genomic DNA [145] in an effort to reduce the risk of horizontal gene transfer or to protect industrial trade secrets. A combination of engineered auxotrophy, linkage of essential gene expression to multi-input riboregulation and a methylase/endonuclease addiction module reduced escape frequency below 1.31012 in the absence of chemical inducers [146]. This is far below the NIH-recommended limit of 108 for containment of recombinant DNA [147]. Synthetic auxotrophy based on a common ligand dependence of three essential genes has shown comparably low escape rates [148]. Many of these approaches could be extended to include environmental signals as triggers for cell death or DNA degradation. Successful implementation of these engineering approaches in natural contexts remains to be tested.

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Genomically recoded organisms (GRO) have significant potential for open release with reduced risk of horizontal gene transfer, engineered immunity to environmental phages, or controlled propagation through synthetic auxotrophy. The first GRO was engineered by converting all TAG stop codons with synonymous TAA codons and by deleting release factor 1 in E. coli [149]. A separate study liberated a codon to reassign sense codons for engineered networks that could then be mistranslated in standard genetic contexts or used for incorporation of nonstandard amino acids (NSAA). Further work has used rational design [150] or protein engineering [151] to identify essential gene candidates to construct engineered auxotrophs for biological containment. There is potential to extend these approaches to other organisms based on the significant chromosome re-synthesis effort in yeast [152]. In order to reliably program behaviors for natural contexts, future work must recode the genomes of new host organisms. While exhibiting higher fitness in the target environment, these organisms have fewer well-characterized genetic parts, devices and manipulation tools (though impressive progress has been demonstrated for the human gut microbe B. theta [134]). CONCLUSIONS AND FUTURE OUTLOOK Engineered microbial systems that interact with natural environments including resident communities must be designed to maintain physiological activity and display ecological and genetic stability over a broad range of time scales. Achievement of these objectives requires a detailed understanding of the mechanistic coupling between dominant biotic and abiotic environmental variations and engineered system behaviors. The design-test-build cycle should take into account the key environmental variables that microbes will face beyond the laboratory. A pioneering study in this area systematically characterized the response of transcriptional logic devices in E. coli to industrially relevant environments to identify circuit failure modes [153]. Similar endeavors should be undertaken to identify key parameters that lead to system fragility in natural environments. Future advances for predictable engineering in complex environments should include: (a) effective environmental simulation [154] that enables high-throughput experimentation to explore large parameter spaces and precise measurement and manipulation; (b) expansion of in vivo sensors and control systems for cellular physiology and resource allocation; and (c) improvement of cellular growth and ecosystem computational models that accounts for energetics and microbial growth responses to design stable microbial community composition and function. Novel designs based on natural biological systems that balance the costs of pathway activity with cellular fitness and survivability will expand the range of environments that engineered organisms or ecologies can operate effectively. 12

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ACKNOWLEDGEMENTS We would like to thank Ryan Melnyk, Morgan Price, Harneet Rishi and Nicholas Justice for helpful discussions. This work is supported by the Genome Science program within the Office of Biological and Environmental Research (Project grant number DE-SC008812, Funding Opportunity Announcement DE-FOA-0000640) and the Simons Foundation of the Life Sciences Research Foundation.

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FIGURE LEGENDS Figure 1. Three major approaches to biological system engineering. Rational circuit engineering involves introducing designed molecular pathways into a host organism to implement diverse functionalities that include, among others: sense-compute-respond programs, temporal regulation including anticipatory responses or phenotypic diversification across a population. Synthetic ecology aims to engineer ecological systems by combining organisms that do not necessarily co-occur in nature or by constructing sub-communities to identify a simplified microbial interaction network that can achieve a desired functionality. Laboratory evolution employs iterative cycles of mutation and selection to engineer functionalities of interest.

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Figure 2. Environmental context for engineered organisms spanning the laboratory flask to complex natural communities. Ecological complexity is a function of environmental variation and the level of exposure to natural communities via nutrient feedstocks in controlled environments or direct inoculation of engineered organisms into existing communities. System design specifications are prescribed by temporal and spatial heterogeneity in the environment and the expected persistence time for the functionality of the engineered microbes. Example applications highlight the frontier for future synthetic biology efforts in complex environments. Figure 3. Critical set of intracellular and extracellular interactions that control the target function, growth and ecological stability of an engineered organism. (a) Microbe genotypes X, Y, Z and W compete over a shared nutrient pool (environmental resources) leading to negative growth coupling interactions. W has been engineered to express a synthetic circuit, which competes with the host-cell processes for limited pools of intracellular resources including ribosomes. (b) Three possible population dynamic outcomes: (1) W can exhibit higher fitness than the resident organisms X, Y, Z and thus drive X, Y and Z to low levels (b-1), (2) W can stably integrate into the consortium (b-2) or (3) W can exhibit lower fitness in the target environment compared to the other resident populations, leading to extinction of W over a short time scale (b-3). Figure 4. Relationship between the growth of an organism and desired functionality in a controlled laboratory environment compared to a natural environment. The relationship between fitness and the engineered function will depend on the environmental statistics and the ecological context. (a) Relationship between an engineered function and fitness of a microbial population X in a laboratory environment. In this case, there is a weak trade-off between the desired function and fitness of X. (b) On a plant-host (bottom), the relationship between growth and engineered function is altered due to the presence of other competing populations. Y shares a similar niche to X and more efficiently allocates resources towards fitness compared to X. Due these environmental resource constraints, 13

ACCEPTED MANUSCRIPT there is a strong trade-off between the growth of X and the target functionality performed by X due to the presence of Y.

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