Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells

Extending dynamic and operational range of the biosensor responding to l-carnitine by directed evolution

l-carnitine is a quaternary amine compound essential for eukaryotic metabolism. It is mainly involved in the oxidative decomposition of medium-and long-chain fatty acids and provides energy for the body. Therefore, it is widely used in health care and food additives. As a pivotal transcriptional activator of l-carnitine metabolism, CaiF is notably activated by crotonobetainyl-CoA, a key intermediate product in the carnitine metabolic pathway. Capitalizing on this mechanism, a sophisticated biosensor was ingeniously developed. Nevertheless, it is worth mentioning that the biosensor currently exhibits a relatively restricted detection range, which results in some specific limitations in practical application scenarios. In this paper, we constructed a biosensor based on CaiF and developed a strategy for modifying this biosensor. The structural configuration of CaiF was formulated by computer-aided design, and the DNA binding site was simulated, which was verified by alanine scanning. Functional Diversity-Oriented Volume-Conservative Substitution Strategy of the key sites of CaiF was conducted to extend the dynamic range of the biosensor. The biosensor based on CaiFY47W/R89A, which exhibited a considerably expanded concentration response range, from 10-4 mM-10 mM, was obtained. The response range was 1000-fold wider and the output signal intensity was 3.3-fold higher to that of the control biosensor. These variants may have great value in improving the l-carnitine production process.

Engineering Microbial Consortia as Living Materials: Advances and Prospectives

The field of Engineered Living Materials (ELMs) integrates engineered living organisms into natural biomaterials to achieve diverse objectives. Multiorganism consortia, prevalent in both naturally occurring and synthetic microbial cultures, exhibit complex functionalities and interrelationships, extending the scope of what can be achieved with individual engineered bacterial strains. However, the ELMs comprising microbial consortia are still in the developmental stage. In this Review, we introduce two strategies for designing ELMs constituted of microbial consortia: a top-down strategy, which involves characterizing microbial interactions and mimicking and reconstructing natural ecosystems, and a bottom-up strategy, which entails the rational design of synthetic consortia and their assembly with material substrates to achieve user-defined functions. Next, we summarize technologies from synthetic biology that facilitate the efficient engineering of microbial consortia for performing tasks more complex than those that can be done with single bacterial strains. Finally, we discuss essential challenges and future perspectives for microbial consortia-based ELMs.

A noncommutative combinatorial protein logic circuit controls cell orientation in nanoenvironments

Single-protein-based devices that integrate signal sensing with logical operations to generate functional outputs offer exceptional promise for monitoring and modulating biological systems. Engineering such intelligent nanoscale computing agents is challenging, as it requires the integration of sensor domains into a functional protein via intricate allosteric networks. We incorporate a rapamycin-sensitive sensor (uniRapR) and a blue light-responsive LOV2 domain into human Src kinase, creating a protein device that functions as a noncommutative combinatorial logic circuit. In our design, rapamycin activates Src kinase, causing protein localization to focal adhesions, whereas blue light exerts the reverse effect that inactivates Src translocation. Focal adhesion maturation induced by Src activation reduces cell migration dynamics and shifts cell orientation to align along collagen nanolane fibers. Using this protein device, we reversibly control cell orientation by applying the appropriate input signals, a framework that may be useful in tissue engineering and regenerative medicine.

Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells

During development, cell state transitions are coordinated through changes in the identity of molecular regulators in a cell type‐ and dose‐specific manner. The ability to rationally engineer such transitions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein, we report the generation of synthetic gene circuits that can detect a desired cell state using AND‐like logic integration of endogenous miRNAs (classifiers) and, upon detection, produce fine‐tuned levels of output proteins using an miRNA‐mediated output fine‐tuning technology (miSFITs). Specifically, we created an “hPSC ON” circuit using a model‐guided miRNA selection and circuit optimization approach. The circuit demonstrates robust PSC‐specific detection and graded output protein production. Next, we used an empirical approach to create an “hPSC‐Off” circuit. This circuit was applied to regulate the secretion of endogenous BMP4 in a state‐specific and fine‐tuned manner to control the composition of differentiating hPSCs. Our work provides a platform for customized cell state‐specific control of desired physiological factors in hPSC, laying the foundation for programming cell compositions in hPSC‐derived tissues and beyond.

Episome-Based Gene Expression Modulation Platform in the Model Diatom Phaeodactylum tricornutum

Chemically inducible gene expression systems have been an integral part of the advanced synthetic genetic circuit design and are employed for precise dynamic control over genetically engineered traits. However, the current systems for controlling transgene expression in most algae are limited to endogenous promoters that respond to different environmental factors. We developed a highly efficient, tunable, and reversible episome-based transcriptional control system in the model diatom alga, Phaeodactylum tricornutum. We assessed the time- and dose-response dynamics of each expression system using a reporter protein (eYFP) as a readout. Using our circuit configuration, we found two inducible expression systems with a high dynamic range and confirmed the suitability of an episome expression platform for synthetic biological applications in diatoms. These systems are controlled by the presence of β-estradiol and digoxin. Addition of either chemical to transgenic strains activates transcription with a dynamic range of up to ∼180-fold and ∼90-fold, respectively. We demonstrated that our episome-based transcriptional control systems are tunable and reversible in a dose- and time-dependent manner. Using droplet digital polymerase chain reaction (PCR), we also confirmed that inducer-dependent transcriptional activation starts within minutes of inducer application without any detectable transcript in the uninduced controls. The system described here expands the molecular and synthetic biology toolkits in algae and will facilitate future gene discovery and metabolic engineering efforts.

An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice

[Figure: see text].

Rational gRNA design based on transcription factor binding data

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor (TF) binding data to obtain a better understanding of the interplay between TF binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9–VPR toward binding sites of Gcr1–Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9–VPR was targeted next to a TF binding motif, whereas a downregulation or no change was observed when dCas9 was bound on a TF motif. This suggests a steric competition between dCas9 and the specific TF. Integrating TF binding data, therefore, proved to be useful for designing guide RNAs for CRISPR interference or CRISPR activation applications.

Engineering digitizer circuits for chemical and genetic screens in human cells

Cell-based transcriptional reporters are invaluable in high-throughput compound and CRISPR screens for identifying compounds or genes that can impact a pathway of interest. However, many transcriptional reporters have weak activities and transient responses. This can result in overlooking therapeutic targets and compounds that are difficult to detect, necessitating the resource-consuming process of running multiple screens at various timepoints. Here, we present RADAR, a digitizer circuit for amplifying reporter activity and retaining memory of pathway activation. Reporting on the AP-1 pathway, our circuit identifies compounds with known activity against PKC-related pathways and shows an enhanced dynamic range with improved sensitivity compared to a classical reporter in compound screens. In the first genome-wide pooled CRISPR screen for the AP-1 pathway, RADAR identifies canonical genes from the MAPK and PKC pathways, as well as non-canonical regulators. Thus, our scalable system highlights the benefit and versatility of using genetic circuits in large-scale cell-based screening.

Synthetic gene circuits as tools for drug discovery

Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays.

A platform for post-translational spatiotemporal control of cellular proteins

Mammalian cells process information through coordinated spatiotemporal regulation of proteins. Engineering cellular networks thus relies on efficient tools for regulating protein levels in specific subcellular compartments. To address the need to manipulate the extent and dynamics of protein localization, we developed a platform technology for the target-specific control of protein destination. This platform is based on bifunctional molecules comprising a target-specific nanobody and universal sequences determining target subcellular localization or degradation rate. We demonstrate that nanobody-mediated localization depends on the expression level of the target and the nanobody, and the extent of target subcellular localization can be regulated by combining multiple target-specific nanobodies with distinct localization or degradation sequences. We also show that this platform for nanobody-mediated target localization and degradation can be regulated transcriptionally and integrated within orthogonal genetic circuits to achieve the desired temporal control over spatial regulation of target proteins. The platform reported in this study provides an innovative tool to control protein subcellular localization, which will be useful to investigate protein function and regulate large synthetic gene circuits.

Model-guided design of mammalian genetic programs

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

Multiple Alternative Promoters and Alternative Splicing Enable Universal Transcription-Based Logic Computation in Mammalian Cells

Multi-input logic gene circuits can enable sophisticated control of cell function, yet large-scale synthetic circuitry in mammalian cells has relied on post-transcriptional regulation or recombinase-triggered state transitions. Large-scale transcriptional logic, on the other hand, has been challenging to implement. Inspired by a naturally found regulatory strategy of using multiple alternative promoters, followed by alternative splicing, we developed a scalable and compact platform for transcriptional OR logic using inputs to those promoters. The platform is extended to implement disjunctive normal form (DNF) computations capable of implementing arbitrary logic rules. Specifically, AND logic is implemented at individual promoters using synergistic transcriptional inputs, and NOT logic via microRNA inputs targeting unique exon sequences driven by those promoters. Together, these regulatory programs result in DNF-like logic control of output gene expression. The approach offers flexibility for building complex logic programs in mammalian cells.

Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

One of the key features of a gene circuit is its input/output behavior. A few earlier publications attempted to develop methods to extract this behavior using transient transfection of circuit components in mammalian cells. However, the hitherto developed methods are only suitable for circuit with monomodal output distribution. Moreover, the relationship between the extracted I/O mapping and the "ground truth" that would have obtained with stably-integrated circuits, has not been addressed. Here we explore cell populations easily identifiable in flow cytometry data, namely, the peaks of fluorescent readout distribution in cells binned by the common expression value of the transfection reporter, or marker, gene. Using numerical simulations, we find that the distribution of circuit copy number in these cells deterministically depends on marker fluorescence in the noise-dependent manner. Moreover, we find that this is true also in the case of bi-modal output distribution. Using the peaks of input and output distributions, we are able to reconstruct the I/O mapping of the circuit and relate it to the I/O mapping of the stably-integrated circuit. The reconstruction is enabled by a new computational method we call PFAFF. The method is extensively validated with forward-simulated flow cytometry data from stable and transient transfections, with up to seven different circuits. The results show excellent correlation between the I/O behavior extracted by PFAFF from simulated transient transfection data, and the data simulated for stably integrated circuit.

Engineered systems of inducible anti-repressors for the next generation of biological programming

Traditionally engineered genetic circuits have almost exclusively used naturally occurring transcriptional repressors. Recently, non-natural transcription factors (repressors) have been engineered and employed in synthetic biology with great success. However, transcriptional anti-repressors have largely been absent with regard to the regulation of genes in engineered genetic circuits. Here, we present a workflow for engineering systems of non-natural anti-repressors. In this study, we create 41 inducible anti-repressors. This collection of transcription factors respond to two distinct ligands, fructose (anti-FruR) or D-ribose (anti-RbsR); and were complemented by 14 additional engineered anti-repressors that respond to the ligand isopropyl β-d-1-thiogalactopyranoside (anti-LacI). In turn, we use this collection of anti-repressors and complementary genetic architectures to confer logical control over gene expression. Here, we achieved all NOT oriented logical controls (i.e., NOT, NOR, NAND, and XNOR). The engineered transcription factors and corresponding series, parallel, and series-parallel genetic architectures represent a nascent anti-repressor based transcriptional programming structure.

Transcriptional anti-repressors have been largely absent in the design of regulated genetic circuits. Here, the authors present a workflow of the engineering of non-natural anti-reperssors that can be built into NOT oriented logic gates.

De novo design of protein logic gates

The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo-designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.

The COMET toolkit for composing customizable genetic programs in mammalian cells

Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we develop the COmposable Mammalian Elements of Transcription (COMET)-an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not, to the best of our knowledge, previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells.

Artificial signaling in mammalian cells enabled by prokaryotic two-component system

Augmenting live cells with novel signal transduction capabilities is a key objective in genetic engineering and synthetic biology. We showed earlier that two-component signaling pathways could function in mammalian cells, albeit while losing their ligand sensitivity. Here we show how to transduce small molecule ligands in a dose-dependent fashion into gene expression in mammalian cells using two-component signaling machinery. First, we engineer mutually complementing truncated mutants of a histidine kinase unable to dimerize and phosphorylate the response regulator. Next, we fuse these mutants to protein domains capable of ligand-induced dimerization, which restores the phosphoryl transfer in a ligand-dependent manner. Cytoplasmic ligands are transduced by facilitating mutant dimerization in the cytoplasm, while extracellular ligands trigger dimerization at the inner side of a plasma membrane. These findings point to the potential of two-component regulatory systems as enabling tools for orthogonal signaling pathways in mammalian cells.

Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy

Improving efficacy of oncolytic virotherapy remains challenging due to difficulty increasing specificity and immune responses against cancer and limited understanding of its population dynamics. Here, we construct programmable and modular synthetic gene circuits to control adenoviral replication and release of immune effectors selectively in hepatocellular carcinoma cells in response to multiple promoter and microRNA inputs. By performing mouse model experiments and computational simulations, we find that replicable adenovirus has a superior tumor-killing efficacy than non-replicable adenovirus. We observe a synergistic effect on promoting local lymphocyte cytotoxicity and systematic vaccination in immunocompetent mouse models by combining tumor lysis and secretion of immunomodulators. Furthermore, our computational simulations show that oncolytic virus which encodes immunomodulators can exert a more robust therapeutic efficacy than combinatorial treatment with oncolytic virus and immune effector. Our results provide an effective strategy to engineer oncolytic adenovirus, which may lead to innovative immunotherapies for a variety of cancers.

It is difficult to improve the efficacy of oncolytic virotherapy due to immune system responses and limited understanding of population dynamics. Here the authors use synthetic biology gene circuits to control adenoviral replication and release of immunomodulators in hepatocellular carcinoma cells.

Synthetic developmental biology: build and control multicellular systems

Synthetic biology offers a bottom-up engineering approach that intends to understand complex systems via design-build-test cycles. Embryonic development comprises complex processes that originate at the level of gene regulatory networks in a cell and emerge into collective cellular behaviors with multicellular forms and functions. Here, we review synthetic biology approaches to development that involve building de novo developmental trajectories or engineering control in stem cell-derived multicellular systems. The field of synthetic developmental biology is rapidly growing with the help of recent advances in artificial gene circuits, self-organizing organoids, and controllable tissue microenvironments. The outcome will be a blueprint to decode principles of morphogenesis and to create programmable organoids with novel designs or improved functions.

Biofunctionalized Materials Featuring Feedforward and Feedback Circuits Exemplified by the Detection of Botulinum Toxin A

Feedforward and feedback loops are key regulatory elements in cellular signaling and information processing. Synthetic biology exploits these elements for the design of molecular circuits that enable the reprogramming and control of specific cellular functions. These circuits serve as a basis for the engineering of complex cellular networks, opening the door for numerous medical and biotechnological applications. Here, a similar principle is applied. Feedforward and positive feedback circuits are incorporated into biohybrid polymer materials in order to develop signal-sensing and signal-processing devices. This concept is exemplified by the detection of the proteolytic activity of the botulinum neurotoxin A. To this aim, site-specific proteases are incorporated into receiver, transmitter, and output materials, and their release, diffusion, and/or activation are wired according to a feedforward or a positive feedback circuit. The development of a quantitative mathematical model enables analysis and comparison of the performance of both systems. The flexible design could be easily adapted to detect other toxins or molecules of interest. Furthermore, cellular signaling or gene regulatory pathways could provide additional blueprints for the development of novel biohybrid circuits. Such information-processing, material-embedded biological circuits hold great promise for a variety of analytical, medical, or biotechnological applications.

Biomolecular Assemblies: Moving from Observation to Predictive Design

Biomolecular assembly is a key driving force in nearly all life processes, providing structure, information storage, and communication within cells and at the whole organism level. These assembly processes rely on precise interactions between functional groups on nucleic acids, proteins, carbohydrates, and small molecules, and can be fine-tuned to span a range of time, length, and complexity scales. Recognizing the power of these motifs, researchers have sought to emulate and engineer biomolecular assemblies in the laboratory, with goals ranging from modulating cellular function to the creation of new polymeric materials. In most cases, engineering efforts are inspired or informed by understanding the structure and properties of naturally occurring assemblies, which has in turn fueled the development of predictive models that enable computational design of novel assemblies. This Review will focus on selected examples of protein assemblies, highlighting the story arc from initial discovery of an assembly, through initial engineering attempts, toward the ultimate goal of predictive design. The aim of this Review is to highlight areas where significant progress has been made, as well as to outline remaining challenges, as solving these challenges will be the key that unlocks the full power of biomolecules for advances in technology and medicine.

Sigma Factor-Mediated Tuning of Bacterial Cell-Free Synthetic Genetic Oscillators

Cell-free transcription–translation provides a simplified prototyping environment to rapidly design and study synthetic networks. Despite the presence of a well characterized toolbox of genetic elements, examples of genetic networks that exhibit complex temporal behavior are scarce. Here, we present a genetic oscillator implemented in an E. coli-based cell-free system under steady-state conditions using microfluidic flow reactors. The oscillator has an activator–repressor motif that utilizes the native transcriptional machinery of E. coli: the RNAP and its associated sigma factors. We optimized a kinetic model with experimental data using an evolutionary algorithm to quantify the key regulatory model parameters. The functional modulation of the RNAP was investigated by coupling two oscillators driven by competing sigma factors, allowing the modification of network properties by means of passive transcriptional regulation.

Programmable protein circuits in living cells

Synthetic protein-level circuits could enable engineering of powerful new cellular behaviors. Rational protein circuit design would be facilitated by a composable protein-protein regulation system in which individual protein components can regulate one another to create a variety of different circuit architectures. In this study, we show that engineered viral proteases can function as composable protein components, which can together implement a broad variety of circuit-level functions in mammalian cells. In this system, termed CHOMP (circuits of hacked orthogonal modular proteases), input proteases dock with and cleave target proteases to inhibit their function. These components can be connected to generate regulatory cascades, binary logic gates, and dynamic analog signal-processing functions. To demonstrate the utility of this system, we rationally designed a circuit that induces cell death in response to upstream activators of the Ras oncogene. Because CHOMP circuits can perform complex functions yet be encoded as single transcripts and delivered without genomic integration, they offer a scalable platform to facilitate protein circuit engineering for biotechnological applications.

A Workflow for In Vivo Evaluation of Candidate Inputs and Outputs for Cell Classifier Gene Circuits

Cell classifier gene circuits that integrate multiple molecular inputs to restrict the expression of therapeutic outputs to cancer cells have the potential to result in efficacious and safe cancer therapies. Preclinical translation of the hitherto developments requires creating the conditions where the animal model, the delivery platform, in vivo expression levels of the inputs, and the efficacy of the output, all come together to enable detailed evaluation of the fully assembled circuits. Here we show an integrated workflow that addresses these issues and builds the framework for preclinical classifier studies using the design framework of microRNA (miRNA, miR)-based classifier gene circuits. Specifically, we employ HCT-116 colorectal cancer cell xenograft in an experimental mouse metastatic liver tumor model together with Adeno-associated virus (AAV) vector delivery platform. Novel engineered AAV-based constructs are used to validate in vivo the candidate inputs miR-122 and miR-7 and, separately, the cytotoxic output HSV-TK/ganciclovir. We show that while the data are largely consistent with expectations, crucial insights are gained that could not have been obtained in vitro. The results highlight the importance of detailed stepwise interrogation of the experimental parameters as a necessary step toward clinical translation of synthetic gene circuits.

Multiplexed sgRNA Expression Allows Versatile Single Nonrepetitive DNA Labeling and Endogenous Gene Regulation

The CRISPR/Cas9 system has made significant contributions to genome editing, gene regulation and chromatin studies in recent years. High-throughput and systematic investigations into the multiplexed biological systems require simultaneous expression and coordinated functioning of multiple sgRNAs. However, current cotransfection based sgRNA coexpression systems remain inefficient, and virus-based transfection approaches are relatively costly and labor intensive. Here we established a vector-independent method allowing multiple sgRNA expression cassettes to be assembled in series into a single plasmid. This synthetic biology-based strategy excels in its efficiency, controllability and scalability. Taking the flexibility advantage of this all-in-one sgRNA expressing system, we further explored its applications in single nonrepetitive genomic locus imaging as well as coordinated gene regulation in live cells. With its full potency, our method will facilitate the research in understanding genome structure, function and dynamics.

Programming Morphogenesis through Systems and Synthetic Biology

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids.

An Orthogonal Multi-input Integration System to Control Gene Expression in Escherichia coli

In many biotechnological applications, it is useful for gene expression to be regulated by multiple signals, as this allows the programming of complex behavior. Here we implement, in Escherichia coli, a system that compares the concentration of two signal molecules, and tunes GFP expression proportionally to their relative abundance. The computation is performed via molecular titration between an orthogonal σ factor and its cognate anti-σ factor. We use mathematical modeling and experiments to show that the computation system is predictable and able to adapt GFP expression dynamically to a wide range of combinations of the two signals, and our model qualitatively captures most of these behaviors. We also demonstrate in silico the practical applicability of the system as a reference-comparator, which compares an intrinsic signal (reflecting the state of the system) with an extrinsic signal (reflecting the desired reference state) in a multicellular feedback control strategy.

CAGE: Chromatin Analogous Gene Expression

Self-assembled nucleic acids perform biological, chemical, and mechanical work at the nanoscale. DNA-based molecular machines have been designed here to perform work by reacting with cancer-specific miRNA mimics and then regulating gene expression in vitro by tuning RNA polymerase activity. Because RNA production is topologically restrained, the machines demonstrate chromatin analogous gene expression (CAGE). With modular and tunable design features, CAGE has potential for molecular biology, synthetic biology, and personalized medicine applications.

Model-guided combinatorial optimization of complex synthetic gene networks

Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.