Next-generation synthetic gene networks

Use of plant colonizing bacteria as chassis for transfer of N₂-fixation to cereals

Engineering cereal crops that are self-supported by nitrogen fixation has been a dream since the 1970s when nitrogenase was transferred from Klebsiella pneumoniae to Escherichia coli. A renewed interest in this area has generated several new approaches with the common aim of transferring nitrogen fixation to cereal crops. Advances in synthetic biology have afforded the tools to rationally engineer microorganisms with traits of interest. Nitrogenase biosynthesis has been a recent target for the application of new synthetic engineering tools. Early successes in this area suggest that the transfer of nitrogenase and other supporting traits to microorganisms that already closely associate with cereal crops is a logical approach to deliver nitrogen to cereal crops.

Evolving robust gene regulatory networks

Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of 'parts' and 'devices'. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.

Applications of synthetic gene networks

Synthetic gene networks have evolved from simple proof-of-concept circuits to complex therapy-oriented networks over the past 15 years. This advancement has greatly facilitated the expansion of the emerging field of synthetic biology. In this review, we highlight the main applications ofsynthetic gene networks in understanding biological design principles, developing biosensors for diagnosis, producing industrial and biomedical compounds, and treating human diseases. Finally, we outline current challenges and future prospects of synthetic gene networks for advancing practical applications.

Synthetic Biology: A Bridge between Artificial and Natural Cells

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.

iBrick: a new standard for iterative assembly of biological parts with homing endonucleases

The BioBricks standard has made the construction of DNA modules easier, quicker and cheaper. So far, over 100 BioBricks assembly schemes have been developed and many of them, including the original standard of BBF RFC 10, are now widely used. However, because the restriction endonucleases employed by these standards usually recognize short DNA sequences that are widely spread among natural DNA sequences, and these recognition sites must be removed before the parts construction, there is much inconvenience in dealing with large-size DNA parts (e.g., more than couple kilobases in length) with the present standards. Here, we introduce a new standard, namely iBrick, which uses two homing endonucleases of I-SceI and PI-PspI. Because both enzymes recognize long DNA sequences (>18 bps), their sites are extremely rare in natural DNA sources, thus providing additional convenience, especially in handling large pieces of DNA fragments. Using the iBrick standard, the carotenoid biosynthetic cluster (>4 kb) was successfully assembled and the actinorhodin biosynthetic cluster (>20 kb) was easily cloned and heterologously expressed. In addition, a corresponding nomenclature system has been established for the iBrick standard.

Regulation of endogenous human gene expression by ligand-inducible TALE transcription factors

The construction of increasingly sophisticated synthetic biological circuits is dependent on the development of extensible tools capable of providing specific control of gene expression in eukaryotic cells. Here, we describe a new class of synthetic transcription factors that activate gene expression in response to extracellular chemical stimuli. These inducible activators consist of customizable transcription activator-like effector (TALE) proteins combined with steroid hormone receptor ligand-binding domains. We demonstrate that these ligand-responsive TALE transcription factors allow for tunable and conditional control of gene activation and can be used to regulate the expression of endogenous genes in human cells. Since TALEs can be designed to recognize any contiguous DNA sequence, the conditional gene regulatory system described herein will enable the design of advanced synthetic gene networks.

Engineering allostery

Allosteric proteins have great potential in synthetic biology, but our limited understanding of the molecular underpinnings of allostery has hindered the development of designer molecules, including transcription factors with new DNA-binding or ligand-binding specificities that respond appropriately to inducers. Such allosteric proteins could function as novel switches in complex circuits, metabolite sensors, or as orthogonal regulators for independent, inducible control of multiple genes. Advances in DNA synthesis and next-generation sequencing technologies have enabled the assessment of millions of mutants in a single experiment, providing new opportunities to study allostery. Using the classic LacI protein as an example, we describe a genetic selection system using a bidirectional reporter to capture mutants in both allosteric states, allowing the positions most crucial for allostery to be identified. This approach is not limited to bacterial transcription factors, and could reveal new mechanistic insights and facilitate engineering of other major classes of allosteric proteins such as nuclear receptors, two-component systems, G protein-coupled receptors, and protein kinases.

Synthetic analog and digital circuits for cellular computation and memory

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene networks that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation.

Multicriteria global optimization for biocircuit design

Background

One of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. While circuits in Biology are complex and subject to natural tradeoffs, most synthetic circuits are simple in terms of the number of regulatory regions, and have been designed to meet a single design criterion.

Results

In this contribution we introduce a multiobjective formulation for the design of biocircuits. We set up the basis for an advanced optimization tool for the modular and systematic design of biocircuits capable of handling high levels of complexity and multiple design criteria. Our methodology combines the efficiency of global Mixed Integer Nonlinear Programming solvers with multiobjective optimization techniques. Through a number of examples we show the capability of the method to generate non intuitive designs with a desired functionality setting up a priori the desired level of complexity.

Conclusions

The methodology presented here can be used for biocircuit design and also to explore and identify different design principles for synthetic gene circuits. The presence of more than one competing objective provides a realistic design setting where every solution represents an optimal trade-off between different criteria.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0113-3) contains supplementary material, which is available to authorized users.

Framework for engineering finite state machines in gene regulatory networks

Finite state machines are fundamental computing devices at the core of many models of computation. In biology, finite state machines are commonly used as models of development in multicellular organisms. However, it remains unclear to what extent cells can remember state, how they can transition from one state to another reliably, and whether the existing parts available to the synthetic biologist are sufficient to implement specified finite state machines in living cells. Furthermore, how complex multicellular behaviors can be realized by multiple cells coordinating their states with signaling, growth, and division is not well understood. Here, we describe a method by which any finite state machine can be built using nothing more than a suitably engineered network of readily available repressing transcription factors. In particular, we show the mathematical equivalence of finite state machines with a Boolean model of gene regulatory networks. We describe how such networks can be realized with a small class of promoters and transcription factors. To demonstrate the effectiveness of our approach, we show that the behavior of the coarse grained ideal Boolean network model approximates a fine grained delay differential equation model of gene expression. Finally, we explore a framework for the design of more complex systems via an example, synthetic bacterial microcolony edge detection, that illustrates how finite state machines could be used together with cell signaling to construct novel multicellular behaviors.

Biological applications of expanded genetic codes

Substantial efforts in the past decade have resulted in the systematic expansion of genetic codes, allowing for the direct ribosomal incorporation of ∼100 unnatural amino acids into bacteria, yeast, mammalian cells, and animals. Here, we illustrate the versatility of expanded genetic codes in biology and bioengineering, focusing on the application of expanded genetic codes to problems in protein, cell, synthetic, and experimental evolutionary biology. As the expanded genetic code field continues to develop, its place as a foundational technology in the whole of biological sciences will solidify.

Advances and computational tools towards predictable design in biological engineering

The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated.

Creation of a gated antibody as a conditionally functional synthetic protein

The ability to conditionally direct antibodies is a potentially powerful application for Synthetic Biology in Medicine. Here we show that control of antibody binding through site-specific, chemical phosphorylation of a recognition domain creates a ‘gated’ antibody (Ab). This displays a crude Boolean logic where binding is induced in an enzyme-AND-antigen dependent manner. This ‘AND-Ab’ is therefore active only in the presence of two biomarker inputs: the simultaneous expression of a (cell surface) antigen and secreted enzyme to generate function in vitro, on cells and in mammalian tissue. Such gated Abs, either alone or in combination, could allow the application of logic strategies to enhance precision in biological interrogation, modulation and in therapy.

The ability to control antibody binding could have important medical implications. Here, the authors present a method to engineer phosphatase-controllable antibodies that bind to a specific recognition site in the presence of two biomarker inputs.

Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions

Fully-connected triads (FCTs), such as the Oct4-Sox2-Nanog triad, have been implicated as recurring transcriptional motifs embedded within the regulatory networks that specify and maintain cellular states. To explore the possible connections between FCT topologies and cell fate determinations, we employed computational network screening to search all possible FCT topologies for multistability, a dynamic property that allows the rise of alternate regulatory states from the same transcriptional network. The search yielded a hierarchy of FCTs with various potentials for multistability, including several topologies capable of reaching eight distinct stable states. Our analyses suggested that complete auto-activation is an effective indicator for multistability, and, when gene expression noise was incorporated into the model, the networks were able to transit multiple states spontaneously. Different levels of stochasticity were found to either induce or disrupt random state transitioning with some transitions requiring layovers at one or more intermediate states. Using this framework we simulated a simplified model of induced pluripotency by including constitutive overexpression terms. The corresponding FCT showed random state transitioning from a terminal state to the pluripotent state, with the temporal distribution of this transition matching published experimental data. This work establishes a potential theoretical framework for understanding cell fate determinations by connecting conserved regulatory modules with network dynamics. Our results could also be employed experimentally, using established developmental transcription factors as seeds, to locate cell lineage specification networks by using auto-activation as a cipher.

Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction

The comprehension of protein and DNA binding in vivo is essential to understand gene regulation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides a global map of the regulatory binding network. Most ChIP-seq analysis tools focus on identifying binding regions from coverage enrichment. However, less work has been performed to infer the physical and regulatory details inside the enriched regions. This research extends a previous blind-deconvolution approach to develop a post-peak-calling algorithm that improves binding site resolution and predicts cooperative interactions. At the core of our new method is a physically motivated model that characterizes the binding signal as an extreme value distribution. This model suggests a mathematical framework to study physical properties of DNA shearing from the ChIP-seq coverage. The model explains the ChIP-seq coverage with two signals: The first considers DNA fragments with only a single binding event, whereas the second considers fragments with two binding events (a double-binding signal). The model incorporates motif discovery and is able to detect multiple sites in an enriched region with single-nucleotide resolution, high sensitivity, and high specificity. Our method improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate < 11% for a set of 47 experimentally validated prokaryotic sites. It also improves resolution of highly enriched regions of large-scale eukaryotic data sets. The double-binding signal provides a novel application in ChIP-seq analysis: the identification of cooperative interaction. Predictions of known cooperative binding sites show a 0.85 area under an ROC curve.

A Novel Synthesizing Genetic Logic Circuit: Frequency Multiplier

This paper presents a novel synthesizing genetic logic circuit design based on an existing synthetic genetic oscillator, which provides a function of frequency multiplier to synthesize a clock signal whose frequency is a multiple of that of the genetic oscillator. In the renowned literature, the synthetic genetic oscillator, known as a repressilator, has been successfully built in Escherichia coli to generate a periodic oscillating phenomenon through three repressive genes repress each other in a chain. On the basis of this fact, our proposed genetic frequency multiplier circuit utilizes genetic Buffers in series with a waveform-shaping circuit to reshape the genetic oscillation signal into a crisp logic clock signal. By regulating different threshold levels in the Buffer, the time length of logic high/low levels in a fundamental sinusoidal wave can be engineered to pulse-width-modulated (PWM) signals with various duty cycles. Integrating some of genetic logic XOR gates and PWM signals from the output of the Buffers, a genetic frequency multiplier circuit can be created and the clock signal with the integer-fold of frequency of the genetic oscillator is generated. The synthesized signal can be used in triggering the downstream digital genetic logic circuits. Simulation results show the applicability of the proposed idea.

Bacteriophage-based synthetic biology for the study of infectious diseases

Since their discovery, bacteriophages have contributed enormously to our understanding of molecular biology as model systems. Furthermore, bacteriophages have provided many tools that have advanced the fields of genetic engineering and synthetic biology. Here, we discuss bacteriophage-based technologies and their application to the study of infectious diseases. New strategies for engineering genomes have the potential to accelerate the design of novel phages as therapies, diagnostics, and tools. Though almost a century has elapsed since their discovery, bacteriophages continue to have a major impact on modern biological sciences, especially with the growth of multidrug-resistant bacteria and interest in the microbiome.

Synthesizing genetic sequential logic circuit with clock pulse generator

Background

Rhythmic clock widely occurs in biological systems which controls several aspects of cell physiology. For the different cell types, it is supplied with various rhythmic frequencies. How to synthesize a specific clock signal is a preliminary but a necessary step to further development of a biological computer in the future.

Results

This paper presents a genetic sequential logic circuit with a clock pulse generator based on a synthesized genetic oscillator, which generates a consecutive clock signal whose frequency is an inverse integer multiple to that of the genetic oscillator. An analogous electronic waveform-shaping circuit is constructed by a series of genetic buffers to shape logic high/low levels of an oscillation input in a basic sinusoidal cycle and generate a pulse-width-modulated (PWM) output with various duty cycles. By controlling the threshold level of the genetic buffer, a genetic clock pulse signal with its frequency consistent to the genetic oscillator is synthesized. A synchronous genetic counter circuit based on the topology of the digital sequential logic circuit is triggered by the clock pulse to synthesize the clock signal with an inverse multiple frequency to the genetic oscillator. The function acts like a frequency divider in electronic circuits which plays a key role in the sequential logic circuit with specific operational frequency.

Conclusions

A cascaded genetic logic circuit generating clock pulse signals is proposed. Based on analogous implement of digital sequential logic circuits, genetic sequential logic circuits can be constructed by the proposed approach to generate various clock signals from an oscillation signal.

Heat-inducible gene expression system by applying alternating magnetic field to magnetic nanoparticles

By combining synthetic biology with nanotechnology, we demonstrate remote controlled gene expression using a magnetic field. Magnetite nanoparticles, which generate heat under an alternating magnetic field, have been developed to label cells. Magnetite nanoparticles and heat-induced therapeutic genes were introduced into tumor xenografts. The magnetically triggered gene expression resulted in tumor growth inhibition. This system shows great potential for controlling target gene expression in a space and time selective manner and may be used for remote control of cell functions via gene expression.

Application of nucleic acid-lipid conjugates for the programmable organisation of liposomal modules

We present a critical review of recent work related to the assembly of multicompartment liposome clusters using nucleic acids as a specific recognition unit to link liposomal modules. The asymmetry in nucleic acid binding to its non-self complementary strand allows the controlled association of different compartmental modules into composite systems. These biomimetic multicompartment architectures could have future applications in chemical process control, drug delivery and synthetic biology. We assess the different methods of anchoring DNA to lipid membrane surfaces and discuss how lipid and DNA properties can be tuned to control the morphology and properties of liposome superstructures. We consider different methods for chemical communication between the contents of liposomal compartments within these clusters and assess the progress towards making this chemical mixing efficient, switchable and chemically specific. Finally, given the current state of the art, we assess the outlook for future developments towards functional modular networks of liposomes.

Dynamics of Gene Silencing in a Live Cell: Stochastic Resonance

Binding of a specific siRNA to the target mRNA in a live cell (human breast cancer cell, MCF-7) is studied by confocal microscopy. The specific siRNA (labeled with a fluorophore, alexa 488) exhibits much higher intensity of fluorescence in the bound state than in the free (unbound) state. It is observed that repeated unbinding and rebinding of siRNA (to target mRNA) occur before gene silencing. 16 273 on-time periods (residence or dwell time of siRNA in bound form) are detected. They follow a strikingly simple pattern. All of the on-time periods are odd-integral multiples of 5.5 ± 0.05 ms. This is ascribed to stochastic resonance.

Stochastic resonance in an intracellular genetic perceptron

Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.

Digital and analog gene circuits for biotechnology

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

Bacterial growth: global effects on gene expression, growth feedback and proteome partition

The function of endogenous as well as synthetic genetic circuits is generically coupled to the physiological state of the cell. For exponentially growing bacteria, a key characteristic of the state of the cell is the growth rate and thus gene expression is often growth-rate dependent. Here we review recent results on growth-rate dependent gene expression. We distinguish different types of growth-rate dependencies by the mechanisms of regulation involved and the presence or absence of an effect of the gene product on growth. The latter can lead to growth feedback, feedback mediated by changes of the global state of the cell. Moreover, we discuss how growth rate dependence can be used as a guide to study the molecular implementation of physiological regulation.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.

Antagonism between binding site affinity and conformational dynamics tunes alternative cis-interactions within Shp2

Protein functions are largely affected by their conformations. This is exemplified in proteins containing modular domains. However, the evolutionary dynamics that define and adapt the conformation of such modular proteins remain elusive. Here we show that cis-interactions between the C-terminal phosphotyrosines and SH2 domain within the protein tyrosine phosphatase Shp2 can be tuned by an adaptor protein, Grb2. The competitiveness of two phosphotyrosines, namely pY542 and pY580, for cis-interaction with the same SH2 domain is governed by an antagonistic combination of contextual amino acid sequence and position of the phosphotyrosines. Specifically, pY580 with the combination of a favorable position and an adverse sequence has an overall advantage over pY542. Swapping the sequences of pY542 and pY580 results in one dominant form of cis-interaction and subsequently inhibits the trans-regulation by Grb2. Thus, the antagonistic combination of sequence and position may serve as a basic design principle for proteins with tunable conformations.

A cellular logic circuit for the detection of bacterial pore-forming toxins

We present a cellular logic circuit for deciphering the profiles of toxin production in B. cereus, using multiple readout techniques based on the pore formation on the cell membrane. This new assay enables the simultaneous detection of seven biomarkers in pathogenic strains from various samples.

D-xylose detection in Escherichia coli by a xylose binding protein-dependent response

A gene circuit for the controlled expression of a marker gene and for the assay of xylose concentration in Escherichia coli has been designed and tested. The xylF coding sequence for the xylose binding protein (XBP) was cloned in pT7T318U downstream from the promoter for xylanase A from B. subtilis (Pbsu), together with the GFP coding sequence (gfp) under the control of the xylF promoter, forming the pT7T3-GFP-XBP construct. GFP fluorescence in Escherichia coli JW3538-1 xylF-transformed with pT7T3-GFP-XBP was approximately 1.4 × higher after 520 min growth in the presence of 5mM xylose than in cells transformed with pT7T3-GFP. Under saturating xylose concentration, flow cytometry analysis showed that all cells resulted in homogeneous populations, and the population with XBP showed a fluorescence greater than that without XBP. Activity of the xylF promoter in cells transformed with pT7T3-GFP-XBP was ≈ 40% higher than with the pT7T3-GFP. No response was observed with arabinose and ribose, showing that the expression effects were specific for xylose, demonstrating the potential use of the gene circuit as a biosensor.

A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits

BACKGROUND

An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit.

RESULTS

We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios.

CONCLUSIONS

These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.

One-pot DNA construction for synthetic biology: the Modular Overlap-Directed Assembly with Linkers (MODAL) strategy

Overlap-directed DNA assembly methods allow multiple DNA parts to be assembled together in one reaction. These methods, which rely on sequence homology between the ends of DNA parts, have become widely adopted in synthetic biology, despite being incompatible with a key principle of engineering: modularity. To answer this, we present MODAL: a Modular Overlap-Directed Assembly with Linkers strategy that brings modularity to overlap-directed methods, allowing assembly of an initial set of DNA parts into a variety of arrangements in one-pot reactions. MODAL is accompanied by a custom software tool that designs overlap linkers to guide assembly, allowing parts to be assembled in any specified order and orientation. The in silico design of synthetic orthogonal overlapping junctions allows for much greater efficiency in DNA assembly for a variety of different methods compared with using non-designed sequence. In tests with three different assembly technologies, the MODAL strategy gives assembly of both yeast and bacterial plasmids, composed of up to five DNA parts in the kilobase range with efficiencies of between 75 and 100%. It also seamlessly allows mutagenesis to be performed on any specified DNA parts during the process, allowing the one-step creation of construct libraries valuable for synthetic biology applications.

Tuning response curves for synthetic biology

Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system's dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components. We begin by reviewing a mathematical formulation that captures a broad class of biological response curves and use this to define a standard set of varieties of tuning: vertical shifting, horizontal scaling, and the like. We then survey the experimental literature, classifying the results into our defined categories, and organizing them by regulatory level: transcriptional, post-transcriptional, and post-translational.

Synergistic Synthetic Biology: Units in Concert

Synthetic biology aims at translating the methods and strategies from engineering into biology in order to streamline the design and construction of biological devices through standardized parts. Modular synthetic biology devices are designed by means of an adequate elimination of cross-talk that makes circuits orthogonal and specific. To that end, synthetic constructs need to be adequately optimized through in silico modeling by choosing the right complement of genetic parts and by experimental tuning through directed evolution and craftsmanship. In this review, we consider an additional and complementary tool available to the synthetic biologist for innovative design and successful construction of desired circuit functionalities: biological synergies. Synergy is a prevalent emergent property in biological systems that arises from the concerted action of multiple factors producing an amplification or cancelation effect compared with individual actions alone. Synergies appear in domains as diverse as those involved in chemical and protein activity, polypharmacology, and metabolic pathway complementarity. In conventional synthetic biology designs, synergistic cross-talk between parts and modules is generally attenuated in order to verify their orthogonality. Synergistic interactions, however, can induce emergent behavior that might prove useful for synthetic biology applications, like in functional circuit design, multi-drug treatment, or in sensing and delivery devices. Synergistic design principles are therefore complementary to those coming from orthogonal design and may provide added value to synthetic biology applications. The appropriate modeling, characterization, and design of synergies between biological parts and units will allow the discovery of yet unforeseeable, novel synthetic biology applications.

Systems approaches for synthetic biology: a pathway toward mammalian design

We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.

Mapping behavioral specifications to model parameters in synthetic biology

With recent improvements of protocols for the assembly of transcriptional parts, synthetic biological devices can now more reliably be assembled according to a given design. The standardization of parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal design specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified design constraints are robustly satisfied. In this work we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction. The method is general and can hence be incorporated in a pipeline for the rational forward design of arbitrary devices in synthetic biology.

Biological signal processing with a genetic toggle switch

Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems.

Inducing chaos in a gene regulatory network by coupling an oscillating dynamics with a hysteresis-type one

In this paper, we investigate the chaotic behavior of a gene regulatory network modeled by four differential equations and seventeen parameters. This network, called [Formula: see text]-system, has been designed to couple in a simple way an oscillating system with one having a bistable switch. After having studied it analytically, we exhibit (by a constructive proof) the mechanism responsible of chaos for a general differential system presenting such a coupling. Namely, given a generic one-parameter family of smooth vector fields on [Formula: see text] presenting a Hopf bifurcation, we prove that under an assumption on the Jacobian at the bifurcation point, we can create such a chaotic system by perturbing the parameter thanks to a hysteresis-type dynamics. Finally, we numerically show that the mechanism highlighted previously takes place in the [Formula: see text]-system, for a particular set of values of its parameters.

Adaptive laboratory evolution -- principles and applications for biotechnology

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

A genome-wide activity assessment of terminator regions in Saccharomyces cerevisiae provides a ″terminatome″ toolbox

The terminator regions of eukaryotes encode functional elements in the 3' untranslated region (3'-UTR) that influence the 3'-end processing of mRNA, mRNA stability, and translational efficiency, which can modulate protein production. However, the contribution of these terminator regions to gene expression remains unclear, and therefore their utilization in metabolic engineering or synthetic genetic circuits has been limited. Here, we comprehensively evaluated the activity of 5302 terminator regions from a total of 5880 genes in the budding yeast Saccharomyces cerevisiae by inserting each terminator region downstream of the P TDH3 - green fluorescent protein (GFP) reporter gene and measuring the fluorescent intensity of GFP. Terminator region activities relative to that of the PGK1 standard terminator ranged from 0.036 to 2.52, with a mean of 0.87. We thus could isolate the most and least active terminator regions. The activities of the terminator regions showed a positive correlation with mRNA abundance, indicating that the terminator region is a determinant of mRNA abundance. The least active terminator regions tended to encode longer 3'-UTRs, suggesting the existence of active degradation mechanisms for those mRNAs. The terminator regions of ribosomal protein genes tended to be the most active, suggesting the existence of a common regulator of those genes. The ″terminatome″ (the genome-wide set of terminator regions) thus not only provides valuable information to understand the modulatory roles of terminator regions on gene expression but also serves as a useful toolbox for the development of metabolically and genetically engineered yeast.

Introduction to focus issue: quantitative approaches to genetic networks

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks using field-programmable gate arrays. Mathematical analyses will be essential for understanding naturally occurring genetic networks in diverse organisms and for providing a foundation for the improved development of synthetic genetic networks.