A synthetic gene-metabolic oscillator

A fast, robust and tunable synthetic gene oscillator

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to “design specs” generated from computational modeling^1–6. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behavior. Several fundamental gene circuits have been developed using this approach, including toggle switches⁷ and oscillators^8–10, and these have been applied in novel contexts such as triggered biofilm development¹¹ and cellular population control¹². Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust, and persistent, with tunable oscillatory periods as fast as 13 minutes. The oscillator was designed using a previously modeled network architecture comprising linked positive and negative feedback loops^1,13. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature, and media source. Computational modeling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

Bacterial quorum sensing: signals, circuits, and implications for biofilms and disease

Communication between bacteria, belonging to the same species or to different species, is mediated through different chemical signals that are synthesized and secreted by bacteria. These signals can either be cell-density related (autoinducers) or be produced by bacteria at different stages of growth, and they allow bacteria to monitor their environment and alter gene expression to derive a competitive advantage. The properties of these signals and the response elicited by them are important in ensuring bacterial survival and propagation in natural environments (e.g., human oral cavity) where hundreds of bacterial species coexist. First, the interaction between a signal and its receptor is very specific, which underlies intraspecies communication and quorum sensing. Second, when multiple signals are synthesized by the same bacterium, the signaling circuits utilized by the different signals are coordinately regulated with distinct overall circuit architecture so as to maximize the overall response. Third, the recognition of a universal communication signal synthesized by different bacterial species (interspecies communication), as well that of signals produced by eukaryotic cells (interkingdom communication), is also integral to the formation of multispecies biofilm communities that are important in infection and disease. The focus of this review is on the principles underlying signal-mediated bacterial communication, with specific emphasis on the potential for using them in two applications-development of synthetic biology modules and circuits, and the control of biofilm formation and infection.

De novo biosynthetic pathways: rational design of microbial chemical factories

Increasing interest in the production of organic compounds from non-petroleum-derived feedstocks, especially biomass, is a significant driver for the construction of new recombinant microorganisms for this purpose. As a discipline, Metabolic Engineering has provided a framework for the development of such systems. Efforts have traditionally been focused, first, on the optimization of natural producers, later progressing towards re-construction of natural pathways in heterologous hosts. To maximize the potential of microbes for biosynthetic purposes, new tools and methodologies within Metabolic Engineering are needed for the proposition and construction of de novo designed pathways. This review will focus on recent advances towards the design and assembly of biosynthetic pathways, and provide a Synthetic Biology perspective for the construction of microbial chemical factories.

Coherence, collective rhythm, and phase difference distribution in populations of stochastic genetic oscillators with cellular communication

An ensemble of stochastic genetic relaxation oscillators via phase-attractive or repulsive cell-to-cell communication are investigated. In the phase-attractive coupling case, it is found that cellular communication can enhance self-induced stochastic resonance as well as collective rhythms, and that different intensities of noise resulting from the fluctuation of intrinsic chemical reactions or the extrinsic environment can induce stochastic limit cycles with different amplitudes for a large cell density. In contrast, in the phase-repulsive coupling case, the distribution of phase differences among the stochastic oscillators can display such characteristic as unimodality, bimodality or polymodality, depending on both noise intensity and cell number, but the modality of phase difference distribution almost keeps invariant for an arbitrary noise intensity as the cell number is beyond a threshold.

Robust, tunable biological oscillations from interlinked positive and negative feedback loops

A simple negative feedback loop of interacting genes or proteins has the potential to generate sustained oscillations. However, many biological oscillators also have a positive feedback loop, raising the question of what advantages the extra loop imparts. Through computational studies, we show that it is generally difficult to adjust a negative feedback oscillator's frequency without compromising its amplitude, whereas with positive-plus-negative feedback, one can achieve a widely tunable frequency and near-constant amplitude. This tunability makes the latter design suitable for biological rhythms like heartbeats and cell cycles that need to provide a constant output over a range of frequencies. Positive-plus-negative oscillators also appear to be more robust and easier to evolve, rationalizing why they are found in contexts where an adjustable frequency is unimportant.

A synthetic Escherichia coli predator-prey ecosystem

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator-prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

OptCircuit: an optimization based method for computational design of genetic circuits

Background

Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components.

Results

In this paper we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. Computational results for a toggle switch example demonstrate the ability of the framework to generate the complete list of circuit designs of varying complexity that exhibit the desired response. Designs identified for a genetic decoder highlight the ability of OptCircuit to suggest circuit configurations that go beyond the ones compatible with digital logic-based design principles. Finally, the results obtained from the concentration band detector example demonstrate the ability of OptCircuit to design circuits whose responses are contingent on the level of external inducer as well as pinpoint parameters for modification to rectify an existing (non-functional) biological circuit and restore functionality.

Conclusion

Our results demonstrate that OptCircuit framework can serve as a design platform to aid in the construction and finetuning of integrated biological circuits.

Dynamical quorum sensing: Population density encoded in cellular dynamics

Mutual synchronization by exchange of chemicals is a mechanism for the emergence of collective dynamics in cellular populations. General theories exist on the transition to coherence, but no quantitative, experimental demonstration has been given. Here, we present a modeling and experimental analysis of cell-density-dependent glycolytic oscillations in yeast. We study the disappearance of oscillations at low cell density and show that this phenomenon occurs synchronously in all cells and not by desynchronization, as previously expected. This study identifies a general scenario for the emergence of collective cellular oscillations and suggests a quorum-sensing mechanism by which the cell density information is encoded in the intracellular dynamical state.

A tunable genetic switch based on RNAi and repressor proteins for regulating gene expression in mammalian cells

Here, we introduce an engineered, tunable genetic switch that couples repressor proteins and an RNAi target design to effectively turn any gene off. We used the switch to regulate the expression of EGFP in mouse and human cells and found that it offers >99% repression as well as the ability to tune gene expression. To demonstrate the system's modularity and level of gene silencing, we used the switch to tightly regulate the expression of diphtheria toxin and Cre recombinase, respectively. We also used the switch to tune the expression of a proapoptotic gene and show that a threshold expression level is required to induce apoptosis. This work establishes a system for tight, tunable control of mammalian gene expression that can be used to explore the functional role of various genes as well as to determine whether a phenotype is the result of a threshold response to changes in gene expression.

Biology by design: reduction and synthesis of cellular components and behaviour

Biological research is experiencing an increasing focus on the application of knowledge rather than on its generation. Thanks to the increased understanding of cellular systems and technological advances, biologists are more frequently asking not only 'how can I understand the structure and behaviour of this biological system?', but also 'how can I apply that knowledge to generate novel functions in different biological systems or in other contexts?' Active pursuit of the latter has nurtured the emergence of synthetic biology. Here, we discuss the motivation behind, and foundational technologies enabling, the development of this nascent field. We examine some early successes and applications while highlighting the challenges involved. Finally, we consider future directions and mention non-scientific considerations that can influence the field's growth.

Designing sequential transcription logic: a simple genetic circuit for conditional memory

The ability to learn and respond to recurrent events depends on the capacity to remember transient biological signals received in the past. Moreover, it may be desirable to remember or ignore these transient signals conditioned upon other signals that are active at specific points in time or in unique environments. Here, we propose a simple genetic circuit in bacteria that is capable of conditionally memorizing a signal in the form of a transcription factor concentration. The circuit behaves similarly to a "data latch" in an electronic circuit, i.e. it reads and stores an input signal only when conditioned to do so by a "read command." Our circuit is of the same size as the well-known genetic toggle switch (an unconditional latch) which consists of two mutually repressing genes, but is complemented with a "regulatory front end" involving protein heterodimerization as a simple way to implement conditional control. Deterministic and stochastic analysis of the circuit dynamics indicate that an experimental implementation is feasible based on well-characterized genes and proteins. It is not known, to which extent molecular networks are able to conditionally store information in natural contexts for bacteria. However, our results suggest that such sequential logic elements may be readily implemented by cells through the combination of existing protein-protein interactions and simple transcriptional regulation.

Synthetic gene circuits: design with directed evolution

Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward. Synthetic circuits generally consist of components optimized to function in their natural context, not in the context of the synthetic circuit. Combining mathematical modeling with directed evolution offers one promising means for addressing this problem. Modeling identifies mutational targets and limits the evolutionary search space for directed evolution, which alters circuit performance without the need for detailed biophysical information. This review examines strategies for integrating modeling and directed evolution and discusses the utility and limitations of available methods.

Single-cell zeroth-order protein degradation enhances the robustness of synthetic oscillator

In Escherichia coli, protein degradation in synthetic circuits is commonly achieved by the ssrA-tagged degradation system. In this work, we show that the degradation kinetics for the green fluorescent protein fused with the native ssrA tag in each cell exhibits the zeroth-order limit of the Michaelis–Menten kinetics, rather than the commonly assumed first-order. When measured in a population, the wide distribution of protein levels in the cells distorts the true kinetics and results in a first-order protein degradation kinetics as a population average. Using the synthetic gene-metabolic oscillator constructed previously, we demonstrated theoretically that the zeroth-order kinetics significantly enlarges the parameter space for oscillation and thus enhances the robustness of the design under parametric uncertainty.

Analysis of a minimal model for p53 oscillations

Oscillatory behaviours in genetic networks are important examples for studying the principles underlying the dynamics of cellular regulation. Recently the team of Alon has reported a surprisingly rich oscillatory response of the p53 tumor suppressor to irradiation stress et al. [Lahav, G., Rosenfeld, N., Sigal, A., Geva-Zatorsky, N., Levine, A.J., Elowitz, M.B., Alon, U., 2004. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat. Genet. 36 (2), 147-150; Geva-Zatorsky, N., Rosenfeld, N., Itzkovitz, S., Milo, R., Sigal, A., Dekel, E., Yarnitzky, T., Liron, Y., Polak, P., Lahav, G., Alon, U., 2006. Oscillations and variability in the p53 system. Mol. Syst. Biol. 2, 2006.0033]. Several models for this system have been proposed by different groups, based essentially on negative feedback loops. In this paper we investigate in detail oscillations and stability in a deterministic time delayed differential model of the core circuit for p53 expression. This model is representative of a class of modelling approaches of this system, based on a "minimal" set of well-established biomolecular regulations. Depending on the protein degradation rates we show the existence of bifurcations between a stable steady state and oscillations both in presence and absence of stress.

Combinatorial promoter design for engineering noisy gene expression

Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO(2) operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose-response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental-computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.

Mammalian synthetic biology: Engineering of sophisticated gene networks

With the recent development of a wide range of inducible mammalian transgene control systems it has now become possible to create functional synthetic gene networks by linking and connecting systems into various configurations. The past 5 years has thus seen the design and construction of the first synthetic mammalian gene regulatory networks. These networks have built upon pioneering advances in prokaryotic synthetic networks and possess an impressive range of functionalities that will some day enable the engineering of sophisticated inter- and intra-cellular functions to become a reality. At a relatively simple level, the modular linking of transcriptional components has enabled the creation of genetic networks that are strongly analogous to the architectural design and functionality of electronic circuits. Thus, by combining components in different serial or parallel configurations it is possible to produce networks that follow strict logic in integrating multiple independent signals (logic gates and transcriptional cascades) or which temporally modify input signals (time-delay circuits). Progressing in terms of sophistication, synthetic transcriptional networks have also been constructed which emulate naturally occurring genetic properties, such as bistability or dynamic instability. Toggle switches which possess "memory" so as to remember transient administered inputs, hysteric switches which are resistant to stochastic fluctuations in inputs, and oscillatory networks which produce regularly timed expression outputs, are all examples of networks that have been constructed using such properties. Initial steps have also been made in designing the above networks to respond not only to exogenous signals, but also endogenous signals that may be associated with aberrant cellular function or physiology thereby providing a means for tightly controlled gene therapy applications. Moving beyond pure transcriptional control, synthetic networks have also been created which utilize phenomena, such as post-transcriptional silencing, translational control, or inter-cellular signaling to produce novel network-based control both within and between cells. It is envisaged in the not-too-distant future that these networks will provide the basis for highly sophisticated genetic manipulations in biopharmaceutical manufacturing, gene therapy and tissue engineering applications.

Perfect sampling of the master equation for gene regulatory networks

We present a perfect sampling algorithm that can be applied to the master equation of gene regulatory networks. The method recasts Gillespie's stochastic simulation algorithm (SSA) in the light of Markov chain Monte Carlo methods and combines it with the dominated coupling from the past (DCFTP) algorithm to provide guaranteed sampling from the stationary distribution. We show how the DCFTP-SSA can be generically applied to genetic networks with feedback formed by the interconnection of linear enzymatic reactions and nonlinear Monod- and Hill-type elements. We establish rigorous bounds on the error and convergence of the DCFTP-SSA, as compared to the standard SSA, through a set of increasingly complex examples. Once the building blocks for gene regulatory networks have been introduced, the algorithm is applied to study properly averaged dynamic properties of two experimentally relevant genetic networks: the toggle switch, a two-dimensional bistable system; and the repressilator, a six-dimensional transcriptional oscillator.

Introduction and Perspectives

Classically, metabolism was investigated by studying molecular characteristics of enzymes and their regulators in isolation. This reductionistic approach successfully established mechanistic relationships with the immediate interacting neighbors and allowed reconstruction of network structures. Severely underdeveloped was the ability to make precise predictions about the integrated operation of pathways and networks that emerged from the typically nonlinear and complex interactions of proteins and metabolites. The burden of metabolic engineering is a consequence of this fact-one cannot yet predict with any certainty precisely what needs to be engineered to produce more complex phenotypes. What was and still is missing are concepts, methods, and algorithms to integrate data and information into a quantitatively coherent whole, as well as theoretical concepts to reliably predict the consequence of environmental stimuli or genetic interventions. This introduction and perspective to Domain 3, Metabolism and Metabolic Fluxes, starts with a brief overview of the panoply of global measurement technologies that herald the dawning of systems biology and whose impact on metabolic research is apparent throughout the Domain 3. In the middle section, applications to Escherichia coli are used to illustrate general concepts and successes of computational methods that approach metabolism as a network of interacting elements, and thus have potential to fill the gap in quantitative data and information integration. The final section highlights prospective focus areas for future metabolic research, including functional genomics, eludication of evolutionary principles, and the integration of metabolism with regulatory networks.

Transient dynamics of genetic regulatory networks

We present an approximation scheme for deriving reaction rate equations of genetic regulatory networks. This scheme predicts the timescales of transient dynamics of such networks more accurately than does standard quasi-steady state analysis by introducing prefactors to the ODEs that govern the dynamics of the protein concentrations. These prefactors render the ODE systems slower than their quasi-steady state approximation counterparts. We introduce the method by examining a positive feedback gene regulatory network, and show how the transient dynamics of this network are more accurately modeled when the prefactor is included. Next, we examine the repressilator, a genetic oscillator, and show that the period, amplitude, and bifurcation diagram defining the onset of the oscillations are better estimated by the prefactor method. Finally, we examine the consequences of the method to the dynamics of reduced models of the phage lambda switch, and show that the switching times between the two states is slowed by the presence of the prefactor that arises from protein multimerization and DNA binding.

A synthetic biology challenge: making cells compute

Advances in biology and engineering have enabled the reprogramming of cells with well-defined functions, leading to the emergence of synthetic biology. Early successes in this nascent field suggest its potential to impact diverse areas. Here, we examine the feasibility of engineering circuits for cell-based computation. We illustrate the basic concepts by describing the mapping of several computational problems to engineered gene circuits. Revolving around these examples and past studies, we discuss technologies and computational methods available to design, test, and optimize gene circuits. We conclude with discussion of challenges involved in a typical design cycle, as well as those specific to cellular computation.

Path to bio-nano-information fusion

This article will discuss the challenges in a new convergent discipline created by the fusion of biotechnology, nanotechnology, and information technology. To illustrate the research challenges, we will begin with an introduction to the nanometer-scale environment in which biology resides, and point out the many important behaviors of matters at that scale. Then we will describe an ideal model system, the cell, for bio-nano-information fusion. Our efforts in advancing this field at the Institute of Cell Mimetic Space Exploration (CMISE) will be introduced here as an example to move toward achieving this goal.

Expanding the metabolic engineering toolbox: more options to engineer cells

Metabolic engineering exploits an integrated, systems-level approach for optimizing a desired cellular property or phenotype; and great strides have been made within this scope and context during the past fifteen years. However, due to limitations in the concepts and techniques, these have relied on a focused, pathway-oriented view. Recent advances in 'omics' technologies and computational systems biology have brought the foundational systems approach of metabolic engineering into focus. At the same time, protein engineering and synthetic biology have expanded the breadth and precision of the methods available to metabolic engineers to improve strain properties. Examples are presented that illustrate this broader perspective of tools and concepts, including a recent approach for global transcriptional machinery engineering (gTME), which has demonstrated the ability to elicit multigenic transcriptional changes that have improved phenotypes compared with single-gene perturbations.

Systems theory of Smad signalling

Transforming growth factor-beta (TGFbeta) signalling is an important regulator of cellular growth and differentiation. The principal intracellular mediators of TGFbeta signalling are the Smad proteins, which upon TGFbeta stimulation accumulate in the nucleus and regulate the transcription of target genes. To investigate the mechanisms of Smad nuclear accumulation, we developed a simple mathematical model of canonical Smad signalling. The model was built using both published data and our experimentally determined cellular Smad concentrations (isoforms 2, 3 and 4). We found in mink lung epithelial cells that Smad2 (8.5-12 x 10(4) molecules cell(-1)) was present in similar amounts to Smad4 (9.3-12 x 10(4) molecules cell(-1)), whereas both were in excess of Smad3 (1.1-2.0 x 10(4) molecules cell(-1)). Variation of the model parameters and statistical analysis showed that Smad nuclear accumulation is most sensitive to parameters affecting the rates of R-Smad phosphorylation and dephosphorylation and Smad complex formation/ dissociation in the nucleus. Deleting Smad4 from the model revealed that rate-limiting phospho-R-Smad dephosphorylation could be an important mechanism for Smad nuclear accumulation. Furthermore, we observed that binding factors constitutively localised to the nucleus do not efficiently mediate Smad nuclear accumulation, if dephosphorylation is rapid. We therefore conclude that an imbalance in the rates of R-Smad phosphorylation and dephosphorylation is likely an important mechanism of Smad nuclear accumulation during TGFbeta signalling.

Genomic signal processing: from matrix algebra to genetic networks

DNA microarrays make it possible, for the first time, to record the complete genomic signals that guide the progression of cellular processes. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment, and drug development. This chapter reviews the first data-driven models that were created from these genome-scale data, through adaptations and generalizations of mathematical frameworks from matrix algebra that have proven successful in describing the physical world, in such diverse areas as mechanics and perception: the singular value decomposition model, the generalized singular value decomposition model comparative model, and the pseudoinverse projection integrative model. These models provide mathematical descriptions of the genetic networks that generate and sense the measured data, where the mathematical variables and operations represent biological reality. The variables, patterns uncovered in the data, correlate with activities of cellular elements such as regulators or transcription factors that drive the measured signals and cellular states where these elements are active. The operations, such as data reconstruction, rotation, and classification in subspaces of selected patterns, simulate experimental observation of only the cellular programs that these patterns represent. These models are illustrated in the analyses of RNA expression data from yeast and human during their cell cycle programs and DNA-binding data from yeast cell cycle transcription factors and replication initiation proteins. Two alternative pictures of RNA expression oscillations during the cell cycle that emerge from these analyses, which parallel well-known designs of physical oscillators, convey the capacity of the models to elucidate the design principles of cellular systems, as well as guide the design of synthetic ones. In these analyses, the power of the models to predict previously unknown biological principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with cell cycle-regulated RNA transcription in yeast. These models may become the foundation of a future in which biological systems are modeled as physical systems are today.

Computer-aided design of modular protein devices: Boolean AND gene activation

Many potentially useful synthetic gene networks require the expression of an engineered gene if and only if two different DNA-binding proteins exist in sufficient concentration. While some natural and engineered systems activate gene expression according to a logical AND-like behavior, they often utilize allosteric or cooperative protein-protein interactions, rendering their components unsuitable for a toolbox of modular parts for use in multiple applications. Here, we develop a quantitative model to demonstrate that a small system of interacting fusion proteins, called a protein device, can activate an engineered gene according to the Boolean AND behavior while using only modular protein domains and DNA sites. The fusion proteins are created from transactivating, DNA-binding, non-DNA binding, and protein-protein interaction domains along with the corresponding peptide ligands. Using a combined kinetic and thermodynamic model, we identify the characteristics of the molecular components and their rates of constitutive production that maximize the fidelity of AND behavior. These AND protein devices facilitate the creation of complex genetic programs and may be used to create gene therapies, biosensors and other biomedical and biotechnological applications that turn on gene expression only when multiple DNA-binding proteins are simultaneously present.

Dynamics of single-cell gene expression

Cellular behavior has traditionally been investigated by utilizing bulk-scale methods that measure average values for a population of cells. Such population-wide studies mask the behavior of individual cells and are often insufficient for characterizing biological processes in which cellular heterogeneity plays a key role. A unifying theme of many recent studies has been a focus on the development and utilization of single-cell experimental techniques that are capable of probing key biological phenomena in individual living cells. Recently, novel information about gene expression dynamics has been obtained from single-cell experiments that draw upon the unique capabilities of fluorescent reporter proteins.

From in vivo to in silico biology and back

The massive acquisition of data in molecular and cellular biology has led to the renaissance of an old topic: simulations of biological systems. Simulations, increasingly paired with experiments, are being successfully and routinely used by computational biologists to understand and predict the quantitative behaviour of complex systems, and to drive new experiments. Nevertheless, many experimentalists still consider simulations an esoteric discipline only for initiates. Suspicion towards simulations should dissipate as the limitations and advantages of their application are better appreciated, opening the door to their permanent adoption in everyday research.

Genetic parts to program bacteria

Genetic engineering is entering a new era, where microorganisms can be programmed using synthetic constructs of DNA encoding logic and operational commands. A toolbox of modular genetic parts is being developed, comprised of cell-based environmental sensors and genetic circuits. Systems have already been designed to be interconnected with each other and interfaced with the control of cellular processes. Engineering theory will provide a predictive framework to design operational multicomponent systems. On the basis of these developments, increasingly complex cellular machines are being constructed to build specialty chemicals, weave biomaterials, and to deliver therapeutics.

Synthetic biology--putting engineering into biology

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis-synthetic biology's system fabrication process-supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

Transcriptional autoregulation by phosphorylated and non-phosphorylated KaiC in cyanobacterial circadian rhythms

Cyanobacteria are the simplest organisms known to exhibit circadian rhythms, which is the fundamental process of homeostasis adapting to daily environmental changes. The cyanobacterial clock gene products, KaiA, KaiB, and KaiC interact with each other, and regulate KaiC phosphorylation and kaiBC expression in a circadian fashion. Molecular genetic study recently proposed that KaiC protein may enhance and repress transcription of clock genes depending on KaiC's phosphorylation status, however, the precise mechanism is still unknown. We developed a mathematical model for the dynamics of cyanobacterial circadian rhythms focusing on the transcriptional regulation by KaiC. We investigated the model using numerical methods, and predicted the transcriptional regulation mechanism by KaiC. We searched for conditions for generating circadian oscillation and concluded that only two mechanisms of the transcriptional regulation are the possible pictures. One is the Transcriptional Repression Model where KaiC represses transcription of the clock genes after phosphorylation, and the other is the Transcriptional Activation Model where KaiC induces transcription after phosphorylation. The Transcriptional Repression Model includes self-repression similarly to the circadian oscillator models that have been proposed previously, and dynamical oscillation is easy to understand. However, the Transcriptional Activation Model does not include any direct repression in its interactive circuit, and is distinct from the previous ideas for circadian clocks. Subsequent computer simulation showed that the Transcriptional Activation Model explains most of the observed mutant phenotypes, and the Transcriptional Repression Model realizes only a half of them. It was also revealed that oscillations in the Transcriptional Activation Model is much more robust against the disruption by cell division or cell elongation than the Transcriptional Repression Model. It suggests that the Transcriptional Activation Model may reflect the essence of the actual transcriptional mechanism of the kai oscillator in cyanobacteria.

Modular approaches to expanding the functions of living matter

The synthesis of increasingly complex unnatural networks embedded in living matter is an emerging theme in synthetic biology. Synthetic networks have allowed the creation of organisms endowed with toggle switches, logic gates, pattern-forming systems, oscillators, cellular sensors, new modes of gene regulation and expanded genetic codes. A common challenge of this work is the addition of specific new functions to complex living organisms. This requires spatial and temporal control of molecular interactions and fluxes to achieve the desired outcomes. Here we review recent successes in this emerging field and discuss strategies for addressing the challenges of increasing network complexity.

Synthetic biology: new engineering rules for an emerging discipline

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development.

Plasticity of the cis-regulatory input function of a gene

The transcription rate of a gene is often controlled by several regulators that bind specific sites in the gene's cis-regulatory region. The combined effect of these regulators is described by a cis-regulatory input function. What determines the form of an input function, and how variable is it with respect to mutations? To address this, we employ the well-characterized lac operon of Escherichia coli, which has an elaborate input function, intermediate between Boolean AND-gate and OR-gate logic. We mapped in detail the input function of 12 variants of the lac promoter, each with different point mutations in the regulator binding sites, by means of accurate expression measurements from living cells. We find that even a few mutations can significantly change the input function, resulting in functions that resemble Pure AND gates, OR gates, or single-input switches. Other types of gates were not found. The variant input functions can be described in a unified manner by a mathematical model. The model also lets us predict which functions cannot be reached by point mutations. The input function that we studied thus appears to be plastic, in the sense that many of the mutations do not ruin the regulation completely but rather result in new ways to integrate the inputs.

A few point mutations in the lac operon of Escherichia coli are sufficient to change the nature of the transcriptional computation.

A bottom-up approach to gene regulation

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

Critical threshold of noise-induced energy transduction in molecular machinery system

Responses of energy transduction of molecular machinery to random perturbation were investigated at the conditions where the system stayed near the bifurcation point. It was found that noise-induced oscillation (NIO) could occur. But how far from bifurcation point could one get the admissible region of NIO? We proposed and demonstrated numerically that there existed a critical threshold of NIO for each fixed noise intensity. Furthermore, it was found that noise intensity was a key factor for the determination of critical threshold. Finally, the detailed bifurcation diagram depending on noise intensity was replotted.

Hourglass model for a protein-based circadian oscillator

Many organisms possess internal biochemical clocks, known as circadian oscillators, which allow them to regulate their biological activity with a 24-hour period. It was recently discovered that the circadian oscillator of photosynthetic cyanobacteria is able to function in a test tube with only three proteins, KaiA, KaiB, and KaiC, and ATP. Biochemical events are intrinsically stochastic, and this tends to desynchronize oscillating protein populations. We propose that stability of the Kai-protein oscillator relies on active synchronization by (i) monomer exchange between KaiC hexamers during the day, and (ii) formation of clusters of KaiC hexamers at night. Our results highlight the importance of collective assembly or disassembly of proteins in biochemical networks, and may help guide design of novel protein-based oscillators.

Transcriptional regulation and metabolism

Understanding organisms from a systems perspective is essential for predicting cellular behaviour as well as designing gene-metabolic circuits for novel functions. The structure, dynamics and interactions of cellular networks are all vital components of systems biology. To facilitate investigation of these aspects, we have developed an integrative technique called network component analysis, which utilizes mRNA expression and transcriptional network connectivity to determine network component dynamics, functions and interactions. This approach has been applied to elucidate transcription factor dynamics in Saccharomyces cerevisiae cell-cycle regulation, detect cross-talks in Escherichia coli two-component signalling pathways, and characterize E. coli carbon source transition. An ultimate test of system-wide understanding is the ability to design and construct novel gene-metabolic circuits. To this end, artificial feedback regulation, cell-cell communication and oscillatory circuits have been constructed, which demonstrate the design principles of gene-metabolic regulation in the cell.

Reconstruction of genetic circuits

The complex genetic circuits found in cells are ordinarily studied by analysis of genetic and biochemical perturbations. The inherent modularity of biological components like genes and proteins enables a complementary approach: one can construct and analyse synthetic genetic circuits based on their natural counterparts. Such synthetic circuits can be used as simple in vivo models to explore the relation between the structure and function of a genetic circuit. Here we describe recent progress in this area of synthetic biology, highlighting newly developed genetic components and biological lessons learned from this approach.