Hysteresis in a synthetic mammalian gene network

Promoter DNA methylation and transcription factor condensation are linked to transcriptional memory in mammalian cells

The regulation of genes can be mathematically described by input-output functions that are typically assumed to be time invariant. This fundamental assumption underpins the design of synthetic gene circuits and the quantitative understanding of natural gene regulatory networks. Here, we found that this assumption is challenged in mammalian cells. We observed that a synthetic reporter gene can exhibit unexpected transcriptional memory, leading to a shift in the dose-response curve upon a second induction. Mechanistically, we investigated the cis-dependency of transcriptional memory, revealing the necessity of promoter DNA methylation in establishing memory. Furthermore, we showed that the synthetic transcription factor's effective DNA binding affinity underlies trans-dependency, which is associated with its capacity to undergo biomolecular condensation. These principles enabled modulating memory by perturbing either cis- or trans-regulation of genes. Together, our findings suggest the potential pervasiveness of transcriptional memory and implicate the need to model mammalian gene regulation with time-varying input-output functions. A record of this paper's transparent peer review process is included in the supplemental information.

Mapping the dynamics of epigenetic adaptation in S. pombe during heterochromatin misregulation

Epigenetic mechanisms enable cells to develop novel adaptive phenotypes without altering their genetic blueprint. Recent studies show histone modifications, such as heterochromatin-defining H3K9 methylation (H3K9me), can be redistributed to establish adaptive phenotypes. We developed a precision-engineered genetic approach to trigger heterochromatin misregulation on-demand in fission yeast. This enabled us to trace genome-scale RNA and H3K9me changes over time in long-term, continuous cultures. Adaptive H3K9me establishes over remarkably slow timescales relative to the initiating stress. We captured dynamic H3K9me redistribution events which depend on an RNA binding complex MTREC, ultimately leading to cells converging on an optimal adaptive solution. Upon stress removal, cells relax to new transcriptional and chromatin states, establishing memory that is tunable and primed for future adaptive epigenetic responses. Collectively, we identify the slow kinetics of epigenetic adaptation that allow cells to discover and heritably encode novel adaptive solutions, with implications for drug resistance and response to infection.

Dynamics and bifurcations in genetic circuits with fibration symmetries

Circuit building blocks of gene regulatory networks (GRN) have been identified through the fibration symmetries of the underlying biological graph. Here, we analyse analytically six of these circuits that occur as functional and synchronous building blocks in these networks. Of these, the lock-on, toggle switch, Smolen oscillator, feed-forward fibre and Fibonacci fibre circuits occur in living organisms, notably Escherichia coli; the sixth, the repressilator, is a synthetic GRN. We consider synchronous steady states determined by a fibration symmetry (or balanced colouring) and determine analytic conditions for local bifurcation from such states, which can in principle be either steady-state or Hopf bifurcations. We identify conditions that characterize the first bifurcation, the only one that can be stable near the bifurcation point. We model the state of each gene in terms of two variables: mRNA and protein concentration. We consider all possible 'admissible' models-those compatible with the network structure-and then specialize these general results to simple models based on Hill functions and linear degradation. The results systematically classify using graph symmetries the complexity and dynamics of these circuits, which are relevant to understand the functionality of natural and synthetic cells.

Classification of 2-node excitatory-inhibitory networks

We classify connected 2-node excitatory-inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory-inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. For each type we give two different classifications. Using results on ODE-equivalence and minimality, we classify the ODE-classes and present a minimal representative for each ODE-class. We also classify all the networks with valence ≤2. These classifications are up to renumbering of nodes and the interchange of 'excitatory' and 'inhibitory' on nodes and arrows. These classifications constitute a first step towards analysing dynamics and bifurcations of excitatory-inhibitory networks. The results have potential applications to biological network models, especially neuronal networks, gene regulatory networks, and synthetic gene networks.

Mapping the dynamics of epigenetic adaptation during heterochromatin misregulation

A classical and well-established mechanism that enables cells to adapt to new and adverse conditions is the acquisition of beneficial genetic mutations. Much less is known about epigenetic mechanisms that allow cells to develop novel and adaptive phenotypes without altering their genetic blueprint. It has been recently proposed that histone modifications, such as heterochromatin-defining H3K9 methylation (H3K9me), normally reserved to maintain genome integrity, can be redistributed across the genome to establish new and potentially adaptive phenotypes. To uncover the dynamics of this process, we developed a precision engineered genetic approach to trigger H3K9me redistribution on-demand in fission yeast. This enabled us to trace genome-scale RNA and chromatin changes over time prior to and during adaptation in long-term continuous cultures. Establishing adaptive H3K9me occurs over remarkably slow time-scales relative to the initiating stress. During this time, we captured dynamic H3K9me redistribution events ultimately leading to cells converging on an optimal adaptive solution. Upon removal of stress, cells relax to new transcriptional and chromatin states rather than revert to their initial (ground) state, establishing a tunable memory for a future adaptive epigenetic response. Collectively, our tools uncover the slow kinetics of epigenetic adaptation that allow cells to search for and heritably encode adaptive solutions, with implications for drug resistance and response to infection.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Competition and evolutionary selection among core regulatory motifs in gene expression control

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Programmable mammalian translational modulators by CRISPR-associated proteins

Translational modulation based on RNA-binding proteins can be used to construct artificial gene circuits, but RNA-binding proteins capable of regulating translation efficiently and orthogonally remain scarce. Here we report CARTRIDGE (Cas-Responsive Translational Regulation Integratable into Diverse Gene control) to repurpose Cas proteins as translational modulators in mammalian cells. We demonstrate that a set of Cas proteins efficiently and orthogonally repress or activate the translation of designed mRNAs that contain a Cas-binding RNA motif in the 5'-UTR. By linking multiple Cas-mediated translational modulators, we designed and built artificial circuits like logic gates, cascades, and half-subtractor circuits. Moreover, we show that various CRISPR-related technologies like anti-CRISPR and split-Cas9 platforms could be similarly repurposed to control translation. Coupling Cas-mediated translational and transcriptional regulation enhanced the complexity of synthetic circuits built by only introducing a few additional elements. Collectively, CARTRIDGE has enormous potential as a versatile molecular toolkit for mammalian synthetic biology.

Modulating gene regulation function by chemically controlled transcription factor clustering

Recent studies have suggested that transcriptional protein condensates (or clusters) may play key roles in gene regulation and cell fate determination. However, it remains largely unclear how the gene regulation function is quantitatively tuned by transcription factor (TF) clustering and whether TF clustering may confer emergent behaviors as in cell fate control systems. Here, to address this, we construct synthetic TFs whose clustering behavior can be chemically controlled. Through single-parameter tuning of the system (i.e., TF clustering propensity), we provide lines of evidence supporting the direct transcriptional activation and amplification of target genes by TF clustering. Single-gene imaging suggests that such amplification results from the modulation of transcriptional dynamics. Importantly, TF clustering propensity modulates the gene regulation function by significantly tuning the effective TF binding affinity and to a lesser extent the ultrasensitivity, contributing to bimodality and sustained response behavior that are reminiscent of canonical cell fate control systems. Collectively, these results demonstrate that TF clustering can modulate the gene regulation function to enable emergent behaviors, and highlight the potential applications of chemically controlled protein clustering.

Transcription factor (TF) condensates appear to be pervasive, yet their roles remain debated. Here, the authors use a synthetic biology approach to show that TF clusters causally amplify transcription and can confer bimodality and “memory”.

Orthogonal control of gene expression in plants using synthetic promoters and CRISPR-based transcription factors

BACKGROUND

The construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression.

RESULTS

A panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants.

CONCLUSIONS

The design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.

Emergent Bistable Switches from the Incoherent Feed-Forward Signaling of a Positive Feedback Loop

Bistability is intrinsically connected to various decision making processes in living systems. The operating principles of a bistable switch, generated from a positive feedback loop, are well understood both in natural and synthetic settings. However, the fate of dynamic modularity of a positive feedback loop is unknown when it is connected to another dynamically modular signaling motif. In order to address this, here we investigate feed-forward signaling of a positive feedback loop to determine the fate of a bistable switch under such signaling. Using the potential energy based high-throughput bifurcation analysis method, we uncover that in addition to the conventional bistability the hybrid motifs generate various emergent bistable switches, namely mushroom and isola switches, which are not produced by the individual motifs. Using random parameter sampling, network perturbation, and phase plane analysis, we establish the design principles of such emergent behaviors. Incoherent feed-forward signaling of a positive feedback loop with distinct regulatory thresholds of the two arms of the feed-forward loop are the key requirements for such emergent behaviors. Our calculations show that the specific types of atypical bistable responses depend on the logic gate configuration of the signals. However, the emergent bistable behaviors of the hybrid networks do not depend on the nature of the positive feedback loop.

Spatial control of gene expression in flies using bacterially derived binary transactivation systems

Controlling gene expression is an instrumental tool for biotechnology, as it enables the dissection of gene function, affording precise spatial-temporal resolution. To generate this control, binary transactivational systems have been used employing a modular activator consisting of a DNA binding domain(s) fused to activation domain(s). For fly genetics, many binary transactivational systems have been exploited in vivo; however, as the study of complex problems often requires multiple systems that can be used in parallel, there is a need to identify additional bipartite genetic systems. To expand this molecular genetic toolbox, we tested multiple bacterially derived binary transactivational systems in Drosophila melanogaster including the p-CymR operon from Pseudomonas putida, PipR operon from Streptomyces coelicolor, TtgR operon from Pseudomonas putida and the VanR operon from Caulobacter crescentus. Our work provides the first characterization of these systems in an animal model in vivo. For each system, we demonstrate robust tissue-specific spatial transactivation of reporter gene expression, enabling future studies to exploit these transactivational systems for molecular genetic studies.

Control of mammalian cell-based devices with genetic programming

Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.

Minimal biophysical model of combined antibiotic action

Phenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.

Modeling the intracellular dynamics of the dengue viral infection and the innate immune response

The interplay between the dengue virus and the innate immune response is not fully understood. Here, we use deterministic and stochastic approaches to investigate the dynamics of the interaction between the interferon-mediated innate immune response and the dengue virus. We aim to develop a quantitative representation of these complex interactions and predict their system-level dynamics. Our simulation results predict bimodal and bistable dynamics that represent viral clearance and virus-producing states. Under normal conditions, we determined that the viral infection outcome is modulated by the innate immune response and the positive-strand viral RNA concentration. Additionally, we tested system perturbations by external stimulation, such as the direct induction of the innate immune response by interferon, and a therapeutic intervention consisting of the direct application of mRNA encoding for several interferon-stimulated genes. Our simulation results suggest optimal regimes for the studied intervention approaches.

Concerted localization-resets precede YAP-dependent transcription

Yes-associated protein 1 (YAP) is a transcriptional regulator with critical roles in mechanotransduction, organ size control, and regeneration. Here, using advanced tools for real-time visualization of native YAP and target gene transcription dynamics, we show that a cycle of fast exodus of nuclear YAP to the cytoplasm followed by fast reentry to the nucleus ("localization-resets") activates YAP target genes. These "resets" are induced by calcium signaling, modulation of actomyosin contractility, or mitosis. Using nascent-transcription reporter knock-ins of YAP target genes, we show a strict association between these resets and downstream transcription. Oncogenically-transformed cell lines lack localization-resets and instead show dramatically elevated rates of nucleocytoplasmic shuttling of YAP, suggesting an escape from compartmentalization-based control. The single-cell localization and transcription traces suggest that YAP activity is not a simple linear function of nuclear enrichment and point to a model of transcriptional activation based on nucleocytoplasmic exchange properties of YAP.

Circuits with broken fibration symmetries perform core logic computations in biological networks

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits.

We show that the core functional logic of genetic circuits arises from a fundamental symmetry breaking of the interactions of the biological network. The idea can be put into a hierarchy of symmetric genetic circuits that reveals their logical functions. We do so through a constructive procedure that naturally reveals a series of building blocks, widely present across species. This hierarchy maps to a progression of fundamental units of electronics, starting with the transistor, progressing to ring oscillators and current-mirror circuits and then to synchronized clocks, switches and finally to memory devices such as latches and flip-flops.

Toward Modeling Context-Specific EMT Regulatory Networks Using Temporal Single Cell RNA-Seq Data

Epithelial-mesenchymal transition (EMT) is well established as playing a crucial role in cancer progression and being a potential therapeutic target. To elucidate the gene regulation that drives the decision making of EMT, many previous studies have been conducted to model EMT gene regulatory circuits (GRCs) using interactions from the literature. While this approach can depict the generic regulatory interactions, it falls short of capturing context-specific features. Here, we explore the effectiveness of a combined bioinformatics and mathematical modeling approach to construct context-specific EMT GRCs directly from transcriptomics data. Using time-series single cell RNA-sequencing data from four different cancer cell lines treated with three EMT-inducing signals, we identify context-specific activity dynamics of common EMT transcription factors. In particular, we observe distinct paths during the forward and backward transitions, as is evident from the dynamics of major regulators such as NF-KB (e.g., NFKB2 and RELB) and AP-1 (e.g., FOSL1 and JUNB). For each experimental condition, we systematically sample a large set of network models and identify the optimal GRC capturing context-specific EMT states using a mathematical modeling method named Random Circuit Perturbation (RACIPE). The results demonstrate that the approach can build high quality GRCs in certain cases, but not others and, meanwhile, elucidate the role of common bioinformatics parameters and properties of network structures in determining the quality of GRCs. We expect the integration of top-down bioinformatics and bottom-up systems biology modeling to be a powerful and generally applicable approach to elucidate gene regulatory mechanisms of cellular state transitions.

Transcriptional programming using engineered systems of transcription factors and genetic architectures

The control of gene expression is an important tool for metabolic engineering, the design of synthetic gene networks, and protein manufacturing. The most successful approaches to date are based on modulating mRNA synthesis via an inducible coupling to transcriptional effectors. Here we present a biological programming structure that leverages a system of engineered transcription factors and complementary genetic architectures. We use a modular design strategy to create 27 non-natural and non-synonymous transcription factors using the lactose repressor topology as a guide. To direct systems of engineered transcription factors we employ parallel and series genetic (DNA) architectures and confer fundamental and combinatorial logical control over gene expression. Here we achieve AND, OR, NOT, and NOR logical controls in addition to two non-canonical half-AND operations. The basic logical operations and corresponding parallel and series genetic architectures represent the building blocks for subsequent combinatorial programs, which display both digital and analog performance.

Successful approaches for controlling gene expression modulate mRNA synthesis by coupling it to inducible transcription effectors. Here the authors design 27 non-natural and non-synonymous transcription factors.

Hysteretic Genetic Circuit for Detection of Proteasomal Degradation in Mammalian Cells

Synthetic hysteretic mammalian gene circuits generating sustained cellular responses to transient perturbations provide important tools to investigate complex cellular behaviors and reprogram cells for a variety of applications, ranging from protein production to cell fate decisions. The design rules of synthetic gene circuits with controlled hysteretic behaviors, however, remain uncharacterized. To identify the criteria for achieving predictable control of hysteresis, we built a genetic circuit for detection of proteasomal degradation (Hys-Deg). The Hys-Deg circuit is based on a tetracycline-controlled transactivator (tTA) variant engineered to interface with the ubiquitin proteasome system (UPS). The tTA variant activates its own expression, generating a positive feedback loop that is triggered by expression of another tTA gene that is constitutively regulated. Guided by predictive modeling, we characterized the hysteretic response of the Hys-Deg circuit. We demonstrated that control of the hysteretic response is achieved by modulating the ratio of expression of constitutive to inducible tTA. We also showed that the system can be finely tuned through dosage of the inducer tetracycline to calibrate the circuit for detection of the desired levels of UPS activation. This study establishes the design rules for building a hysteretic genetic circuit with an autoregulatory feedback loop and provides a synthetic memory module that could be easily integrated into regulatory gene networks to study and engineer complex cellular behaviors.

Designing cell function: assembly of synthetic gene circuits for cell biology applications

Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Load-Dependent Changes in Left Ventricular Structure and Function in a Pathophysiologically Relevant Murine Model of Reversible Heart Failure

BACKGROUND

To better understand reverse left ventricular (LV) remodeling, we developed a murine model wherein mice develop LV remodeling after transverse aortic constriction (TAC) and a small apical myocardial infarct (MI) and undergo reverse LV remodeling after removal of the aortic band.

METHODS AND RESULTS

Mice studied were subjected to sham (n=6) surgery or TAC+MI (n=12). Two weeks post-TAC+MI, 1 group underwent debanding (referred to as heart failure debanding [HF-DB] mice; n=6), whereas the aortic band remained in a second group (heart failure [HF] group; n=6). LV remodeling was evaluated by 2D echocardiography at 1 day, 2 weeks and 6 weeks post-TAC+MI. The hearts were analyzed by transcriptional profiling at 4 and 6 weeks and histologically at 6 weeks. Debanding normalized LV volumes, LV mass, and cardiac myocyte hypertrophy at 6 weeks in HF-DB mice, with no difference in myofibrillar collagen in the HF and HF-DB mice. LV ejection fraction and radial strain improved after debanding; however, both remained decreased in the HF-DB mice relative to sham and were not different from HF mice at 6 weeks. Hemodynamic unloading in the HF-DB mice was accompanied by a 35% normalization of the HF genes at 2 weeks and 80% of the HF genes at 4 weeks.

CONCLUSIONS

Hemodynamic unloading of a pathophysiologically relevant mouse model of HF results in normalization of LV structure, incomplete recovery of LV function, and incomplete reversal of the HF transcriptional program. The HF-DB mouse model may provide novel insights into mechanisms of reverse LV remodeling.

Intracellular Noise Level Determines Ratio Control Strategy Confined by Speed-Accuracy Trade-off

Robust and precise ratio control of heterogeneous phenotypes within an isogenic population is an essential task, especially in the development and differentiation of a large number of cells such as bacteria, sensory receptors, and blood cells. However, the mechanisms of such ratio control are poorly understood. Here, we employ experimental and mathematical techniques to understand the combined effects of signal induction and gene expression stochasticity on phenotypic multimodality. We identify two strategies to control phenotypic ratios from an initially homogeneous population, suitable roughly to high-noise and low-noise intracellular environments, and we show that both can be used to generate precise fractional differentiation. In noisy gene expression contexts, such as those found in bacteria, induction within the circuit's bistable region is enough to cause noise-induced bimodality within a feasible time frame. However, in less noisy contexts, such as tightly controlled eukaryotic systems, spontaneous state transitions are rare and hence bimodality needs to be induced with a controlled pulse of induction that falls outside the bistable region. Finally, we show that noise levels, system response time, and ratio tuning accuracy impose trade-offs and limitations on both ratio control strategies, which guide the selection of strategy alternatives.

Synthetic Bistability and Differentiation in Yeast

Engineered systems that control cellular differentiation and pattern formation are essential for applications like tissue engineering, biomaterial fabrication, and synthetic ecosystems. Synthetic circuits that can take on multiple states have been made to engineer multicellular systems. However, how to use these states to drive interesting cellular behavior remains challenging. Here, we present a cellular differentiation program involving a novel synthetic bistable switch coupled to an antibiotic resistance gene that affects growth in yeast ( S. cerevisiae). The switch is composed of a positive feedback loop involving a novel transcription factor and can be switched ON and OFF via two different transient inducer inputs. By further coupling the bistable switch with an antibiotic resistance gene, we obtained a growth differentiation circuit, where yeast cells can be switched to stable HIGH or LOW growth rate states via transient inducer inputs. This work demonstrates a rationally designed and experimentally validated cellular differentiation behavior in yeast.

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.

A Green-Light-Responsive System for the Control of Transgene Expression in Mammalian and Plant Cells

The ever-increasing complexity of synthetic gene networks and applications of synthetic biology requires precise and orthogonal gene expression systems. Of particular interest are systems responsive to light as they enable the control of gene expression dynamics with unprecedented resolution in space and time. While broadly used in mammalian backgrounds, however, optogenetic approaches in plant cells are still limited due to interference of the activating light with endogenous photoreceptors. Here, we describe the development of the first synthetic light-responsive system for the targeted control of gene expression in mammalian and plant cells that responds to the green range of the light spectrum in which plant photoreceptors have minimal activity. We first engineered a system based on the light-sensitive bacterial transcription factor CarH and its cognate DNA operator sequence CarO from Thermus thermophilus to control gene expression in mammalian cells. The system was functional in various mammalian cell lines, showing high induction (up to 350-fold) along with low leakiness, as well as high reversibility. We quantitatively described the systems characteristics by the development and experimental validation of a mathematical model. Finally, we transferred the system into A. thaliana protoplasts and demonstrated gene repression in response to green light. We expect that this system will provide new opportunities in applications based on synthetic gene networks and will open up perspectives for optogenetic studies in mammalian and plant cells.

Translation initiation events on structured eukaryotic mRNAs generate gene expression noise

Gene expression stochasticity plays a major role in biology, creating non-genetic cellular individuality and influencing multiple processes, including differentiation and stress responses. We have addressed the lack of knowledge about posttranscriptional contributions to noise by determining cell-to-cell variations in the abundance of mRNA and reporter protein in yeast. Two types of structural element, a stem–loop and a poly(G) motif, not only inhibit translation initiation when inserted into an mRNA 5΄ untranslated region, but also generate noise. The noise-enhancing effect of the stem–loop structure also remains operational when combined with an upstream open reading frame. This has broad significance, since these elements are known to modulate the expression of a diversity of eukaryotic genes. Our findings suggest a mechanism for posttranscriptional noise generation that will contribute to understanding of the generally poor correlation between protein-level stochasticity and transcriptional bursting. We propose that posttranscriptional stochasticity can be linked to cycles of folding/unfolding of a stem–loop structure, or to interconversion between higher-order structural conformations of a G-rich motif, and have created a correspondingly configured computational model that generates fits to the experimental data. Stochastic events occurring during the ribosomal scanning process can therefore feature alongside transcriptional bursting as a source of noise.

The Hepato-Hypothalamic-Pituitary-Adrenal-Renal Axis: Mathematical Modeling of Cortisol’s Production, Metabolism, and Seasonal Variation

Cortisol dynamics are governed by the integration of influences from the suprachiasmatic nucleus (SCN), the hypothalamic-pituitary-adrenal (HPA) axis, and metabolic enzymes, such as the 11β–hydroxysteroid dehydrogenase (HSD) family, which are highly expressed in hepatic and renal tissue. The coordinated regulation of cortisol dynamics is essential for the maintenance of a healthy state, and aberrant cortisol circadian rhythms are associated with various pathophysiological conditions. The duration of the light-dark cycle, or photoperiod, which regulates SCN activity, varies seasonally, and the shorter photoperiod winter season is associated with elevated cortisol levels, peak inflammatory disease incidence, and symptom exacerbation. Elevated expression and activity of 11β-HSD1 protein, assumed to also occur during the winter, have been allied with numerous inflammatory conditions. A comprehensive understanding of the communication between the underlying regulatory mechanisms of cortisol as well as how changes in their activity could lead to the development of disease is yet to be elucidated. In this work, we propose the use of a semimechanistic mathematical model to explore the impact of the hepato-hypothalamic-pituitary-adrenal-renal axis in modulating neuroendocrine-immune system dynamics. Our model predicts the predominance of a winter proinflammatory state and that genetic variations could alter 11β-HSD enzyme functionality, rendering certain subpopulations more susceptible to disease as a consequence of HPA axis dysregulation.

Using synthetic biology to explore principles of development

Developmental biology is mainly analytical: researchers study embryos, suggest hypotheses and test them through experimental perturbation. From the results of many experiments, the community distils the principles thought to underlie embryogenesis. Verifying these principles, however, is a challenge. One promising approach is to use synthetic biology techniques to engineer simple genetic or cellular systems that follow these principles and to see whether they perform as expected. As I review here, this approach has already been used to test ideas of patterning, differentiation and morphogenesis. It is also being applied to evo-devo studies to explore alternative mechanisms of development and 'roads not taken' by natural evolution.

Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

DOI:http://dx.doi.org/10.7554/eLife.23702.001

Cells in animals use a process called differentiation to specialize into specific cell types such as skin cells and liver cells. Proteins called transcription factors drive particular steps in differentiation by controlling the activity of specific genes. Many transcription factors interact with each other to form complex networks that regulate gene activity to determine the fate of a cell and control the whole differentiation process. Some individual gene networks can program cells to become any one of several different cell fates, a feature known as multistability.

In the 1950s, a scientist called Conrad Waddington proposed the concept of an “epigenetic landscape” to describe how the fate of a cell is decided as an animal develops. The cell, depicted as a ball, rolls down a rugged landscape and has the option of taking several different routes. Each route will eventually lead to a distinct cell fate. As the ball moves down the hill, the choice of routes and final destinations becomes more limited. Theoretical approaches have been used to understand how gene regulatory networks shape the epigenetic landscape of an animal. However, few studies have experimentally tested the findings of the theoretical approaches and it is not clear how environmental inputs help to determine which path a cell will take.

Although bacteria cells do not generally specialize into particular cell types, bacteria cells can use multistability in transcription factor networks to switch between different behaviors or “states” in response to cues from the environment. Wu et al. used a bacterium called E. coli as a model to investigate how a gene network called MINPA from mammals, which is involved in differentiation and is believed to show multistability, can guide cells to adopt different states. The work combined experimental and mathematical approaches to design, construct and test an artificial version of the MINPA gene network in E. coli.

The experiments showed that MINPA could direct the cells to adopt four different stable states in which the cells produced fluorescent proteins of different colors. With the help of mathematical modeling, Wu et al. charted how the landscape of cell states changed when external chemical cues were applied. Exposing the cells to several cues in particular orders guided the cells to different final states.

The findings of Wu et al. shed new light on how the fate of a cell is determined and provide a theoretical framework for understanding the complex networks that control cell differentiation. This could help develop new ways of directing cell fate that could ultimately be used to generate cells to treat human diseases.

DOI:http://dx.doi.org/10.7554/eLife.23702.002

Steady state statistical correlations predict bistability in reaction motifs

We show that steady state cross-moments and cross-cumulants predict bistability in feedback-regulated reaction networks.

Mammalian synthetic biology for studying the cell

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology.

Mammalian Synthetic Biology: Time for Big MACs

The enabling technologies of synthetic biology are opening up new opportunities for engineering and enhancement of mammalian cells. This will stimulate diverse applications in many life science sectors such as regenerative medicine, development of biosensing cell lines, therapeutic protein production, and generation of new synthetic genetic regulatory circuits. Harnessing the full potential of these new engineering-based approaches requires the design and assembly of large DNA constructs-potentially up to chromosome scale-and the effective delivery of these large DNA payloads to the host cell. Random integration of large transgenes, encoding therapeutic proteins or genetic circuits into host chromosomes, has several drawbacks such as risks of insertional mutagenesis, lack of control over transgene copy-number and position-specific effects; these can compromise the intended functioning of genetic circuits. The development of a system orthogonal to the endogenous genome is therefore beneficial. Mammalian artificial chromosomes (MACs) are functional, add-on chromosomal elements, which behave as normal chromosomes-being replicating and portioned to daughter cells at each cell division. They are deployed as useful gene expression vectors as they remain independent from the host genome. MACs are maintained as a single-copy and can accommodate multiple gene expression cassettes of, in theory, unlimited DNA size (MACs up to 10 megabases have been constructed). MACs therefore enabled control over ectopic gene expression and represent an excellent platform to rapidly prototype and characterize novel synthetic gene circuits without recourse to engineering the host genome. This review describes the obstacles synthetic biologists face when working with mammalian systems and how the development of improved MACs can overcome these-particularly given the spectacular advances in DNA synthesis and assembly that are fuelling this research area.

An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability

Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.

Design of a bistable switch to control cellular uptake

Bistable switches are widely used in synthetic biology to trigger cellular functions in response to environmental signals. All bistable switches developed so far, however, control the expression of target genes without access to other layers of the cellular machinery. Here, we propose a bistable switch to control the rate at which cells take up a metabolite from the environment. An uptake switch provides a new interface to command metabolic activity from the extracellular space and has great potential as a building block in more complex circuits that coordinate pathway activity across cell cultures, allocate metabolic tasks among different strains or require cell-to-cell communication with metabolic signals. Inspired by uptake systems found in nature, we propose to couple metabolite import and utilization with a genetic circuit under feedback regulation. Using mathematical models and analysis, we determined the circuit architectures that produce bistability and obtained their design space for bistability in terms of experimentally tuneable parameters. We found an activation-repression architecture to be the most robust switch because it displays bistability for the largest range of design parameters and requires little fine-tuning of the promoters' response curves. Our analytic results are based on on-off approximations of promoter activity and are in excellent qualitative agreement with simulations of more realistic models. With further analysis and simulation, we established conditions to maximize the parameter design space and to produce bimodal phenotypes via hysteresis and cell-to-cell variability. Our results highlight how mathematical analysis can drive the discovery of new circuits for synthetic biology, as the proposed circuit has all the hallmarks of a toggle switch and stands as a promising design to control metabolic phenotypes across cell cultures.

Toward the First Data Acquisition Standard in Synthetic Biology

This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.

Engineering Gene Circuits for Mammalian Cell-Based Applications

Synthetic gene switches are basic building blocks for the construction of complex gene circuits that transform mammalian cells into useful cell-based machines for next-generation biotechnological and biomedical applications. Ligand-responsive gene switches are cellular sensors that are able to process specific signals to generate gene product responses. Their involvement in complex gene circuits results in sophisticated circuit topologies that are reminiscent of electronics and that are capable of providing engineered cells with the ability to memorize events, oscillate protein production, and perform complex information-processing tasks. Microencapsulated mammalian cells that are engineered with closed-loop gene networks can be implanted into mice to sense disease-related input signals and to process this information to produce a custom, fine-tuned therapeutic response that rebalances animal metabolism. Progress in gene circuit design, in combination with recent breakthroughs in genome engineering, may result in tailored engineered mammalian cells with great potential for future cell-based therapies.

Molecular recordings by directed CRISPR spacer acquisition

The ability to write a stable record of identified molecular events into a specific genomic locus would enable the examination of long cellular histories and have many applications, ranging from developmental biology to synthetic devices. We show that the type I-E CRISPR (clustered regularly interspaced short palindromic repeats)-Cas system of Escherichia coli can mediate acquisition of defined pieces of synthetic DNA. We harnessed this feature to generate records of specific DNA sequences into a population of bacterial genomes. We then applied directed evolution so as to alter the recognition of a protospacer adjacent motif by the Cas1-Cas2 complex, which enabled recording in two modes simultaneously. We used this system to reveal aspects of spacer acquisition, fundamental to the CRISPR-Cas adaptation process. These results lay the foundations of a multimodal intracellular recording device.

Genetic circuit design automation

Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.

Mammalian synthetic biology: emerging medical applications

In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON-OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes.

From noise to synthetic nucleoli: can synthetic biology achieve new insights?

Synthetic biology aims to re-organise and control biological components to make functional devices. Along the way, the iterative process of designing and testing gene circuits has the potential to yield many insights into the functioning of the underlying chassis of cells. Thus, synthetic biology is converging with disciplines such as systems biology and even classical cell biology, to give a new level of predictability to gene expression, cell metabolism and cellular signalling networks. This review gives an overview of the contributions that synthetic biology has made in understanding gene expression, in terms of cell heterogeneity (noise), the coupling of growth and energy usage to expression, and spatiotemporal considerations. We mainly compare progress in bacterial and mammalian systems, which have some of the most-developed engineering frameworks. Overall, one view of synthetic biology can be neatly summarised as "creating in order to understand."

Comparison between the Amount of Environmental Change and the Amount of Transcriptome Change

Cells must coordinate adjustments in genome expression to accommodate changes in their environment. We hypothesized that the amount of transcriptome change is proportional to the amount of environmental change. To capture the effects of environmental changes on the transcriptome, we compared transcriptome diversities (defined as the Shannon entropy of frequency distribution) of silkworm fat-body tissues cultured with several concentrations of phenobarbital. Although there was no proportional relationship, we did identify a drug concentration “tipping point” between 0.25 and 1.0 mM. Cells cultured in media containing lower drug concentrations than the tipping point showed uniformly high transcriptome diversities, while those cultured at higher drug concentrations than the tipping point showed uniformly low transcriptome diversities. The plasticity of transcriptome diversity was corroborated by cultivations of fat bodies in MGM-450 insect medium without phenobarbital and in 0.25 mM phenobarbital-supplemented MGM-450 insect medium after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital, followed by cultivation for 10 hours in 1.0 mM phenobarbital-supplemented MGM-450 insect medium). Interestingly, the transcriptome diversities of cells cultured in media containing 0.25 mM phenobarbital after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital, followed by cultivation for 10 hours in 1.0 mM phenobarbital-supplemented MGM-450 insect medium) were different from cells cultured in media containing 0.25 mM phenobarbital after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital). This hysteretic phenomenon of transcriptome diversities indicates multi-stability of the genome expression system. Cellular memories were recorded in genome expression networks as in DNA/histone modifications.

Robust, tunable genetic memory from protein sequestration combined with positive feedback

Natural regulatory networks contain many interacting components that allow for fine-tuning of switching and memory properties. Building simple bistable switches, synthetic biologists have learned the design principles of complex natural regulatory networks. However, most switches constructed so far are so simple (e.g. comprising two regulators) that they are functional only within a limited parameter range. Here, we report the construction of robust, tunable bistable switches in Escherichia coli using three heterologous protein regulators (ExsADC) that are sequestered into an inactive complex through a partner swapping mechanism. On the basis of mathematical modeling, we accurately predict and experimentally verify that the hysteretic region can be fine-tuned by controlling the interactions of the ExsADC regulatory cascade using the third member ExsC as a tuning knob. Additionally, we confirm that a dual-positive feedback switch can markedly increase the hysteretic region, compared to its single-positive feedback counterpart. The dual-positive feedback switch displays bistability over a 10(6)-fold range of inducer concentrations, to our knowledge, the largest range reported so far. This work demonstrates the successful interlocking of sequestration-based ultrasensitivity and positive feedback, a design principle that can be applied to the construction of robust, tunable, and predictable genetic programs to achieve increasingly sophisticated biological behaviors.

Synthetic Biology: A Unifying View and Review Using Analog Circuits

We review the field of synthetic biology from an analog circuits and analog computation perspective, focusing on circuits that have been built in living cells. This perspective is well suited to pictorially, symbolically, and quantitatively representing the nonlinear, dynamic, and stochastic (noisy) ordinary and partial differential equations that rigorously describe the molecular circuits of synthetic biology. This perspective enables us to construct a canonical analog circuit schematic that helps unify and review the operation of many fundamental circuits that have been built in synthetic biology at the DNA, RNA, protein, and small-molecule levels over nearly two decades. We review 17 circuits in the literature as particular examples of feedforward and feedback analog circuits that arise from special topological cases of the canonical analog circuit schematic. Digital circuit operation of these circuits represents a special case of saturated analog circuit behavior and is automatically incorporated as well. Many issues that have prevented synthetic biology from scaling are naturally represented in analog circuit schematics. Furthermore, the deep similarity between the Boltzmann thermodynamic equations that describe noisy electronic current flow in subthreshold transistors and noisy molecular flux in biochemical reactions has helped map analog circuit motifs in electronics to analog circuit motifs in cells and vice versa via a `cytomorphic' approach. Thus, a body of knowledge in analog electronic circuit design, analysis, simulation, and implementation may also be useful in the robust and efficient design of molecular circuits in synthetic biology, helping it to scale to more complex circuits in the future.

Mammalian designer cells: Engineering principles and biomedical applications

Biotechnology is a widely interdisciplinary field focusing on the use of living cells or organisms to solve established problems in medicine, food production and agriculture. Synthetic biology, the science of engineering complex biological systems that do not exist in nature, continues to provide the biotechnology industry with tools, technologies and intellectual property leading to improved cellular performance. One key aspect of synthetic biology is the engineering of deliberately reprogrammed designer cells whose behavior can be controlled over time and space. This review discusses the most commonly used techniques to engineer mammalian designer cells; while control elements acting on the transcriptional and translational levels of target gene expression determine the kinetic and dynamic profiles, coupling them to a variety of extracellular stimuli permits their remote control with user-defined trigger signals. Designer mammalian cells with novel or improved biological functions not only directly improve the production efficiency during biopharmaceutical manufacturing but also open the door for cell-based treatment strategies in molecular and translational medicine. In the future, the rational combination of multiple sets of designer cells could permit the construction and regulation of higher-order systems with increased complexity, thereby enabling the molecular reprogramming of tissues, organisms or even populations with highest precision.