Inteins: A Swiss army knife for synthetic biology

Inteins are proteins found in nature that execute protein splicing. Among them, split inteins stand out for their versatility and adaptability, presenting creative solutions for addressing intricate challenges in various biological applications. Their exquisite attributes, including compactness, reliability, orthogonality, low toxicity, and irreversibility, make them of interest to various fields including synthetic biology, biotechnology and biomedicine. In this review, we delve into the inherent challenges of using inteins, present approaches for overcoming these challenges, and detail their reliable use for specific cellular tasks. We will discuss the use of conditional inteins in areas like cancer therapy, drug screening, patterning, infection treatment, diagnostics and biocontainment. Additionally, we will underscore the potential of inteins in executing basic logical operations with practical implications. We conclude by showcasing their potential in crafting complex genetic circuits for performing computations and feedback control that achieves robust perfect adaptation.

Engineering mammalian cell growth dynamics for biomanufacturing

Precise control over mammalian cell growth dynamics poses a major challenge in biopharmaceutical manufacturing. Here, we present a multi-level cell engineering strategy for the tunable regulation of growth phases in mammalian cells. Initially, we engineered mammalian death phase by employing CRISPR/Cas9 to knockout pro-apoptotic proteins Bax and Bak, resulting in a substantial attenuation of apoptosis by improving cell viability and extending culture lifespan. The second phase introduced a growth acceleration system, akin to a "gas pedal", based on an abscidic acid inducible system regulating cMYC gene expression, enabling rapid cell density increase and cell cycle control. The third phase focused on a stationary phase inducing system, comparable to a "brake pedal". A tetracycline inducible genetic circuit based on BLIMP1 gene led to cell growth cessation and arrested cell cycle upon activation. Finally, we developed a dual controllable system, combining the "gas and brake pedals", enabling for dynamic and precise orchestration of mammalian cell growth dynamics. This work exemplifies the application of synthetic biology tools and combinatorial cell engineering, offering a sophisticated framework for manipulating mammalian cell growth and providing a unique paradigm for reprogramming cell behaviour for enhancing biopharmaceutical manufacturing and further biomedical applications.

A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity

Recent progress in synthetic biology has enabled the design of complex genetic circuits that interface with innate cellular functions, such as gene transcription, and control user-defined outputs. Implementing these genetic networks in mammalian cells, however, is a cumbersome process that requires several steps of optimization and benefits from the use of predictive modeling. Combining deterministic mathematical models with software-based numerical computing platforms allows researchers to quickly design, evaluate, and optimize multiple circuit topologies to establish experimental constraints that generate the desired control systems. In this chapter, we present a systematic approach based on predictive mathematical modeling to guide the design and construction of gene activity-based sensors. This approach enables user-driven circuit optimization through iterations of sensitivity analyses and parameter scans, providing a universal method to engineer sense and respond cells for diverse applications.

Validation and calibration of a novel GEM biosensor for specific detection of Cd2+, Zn2+, and Pb2

BACKGROUND

In this study, we designed a novel genetic circuit sensitive to Cd2+, Zn2+ and Pb2+ by mimicking the CadA/CadR operon system mediated heavy metal homeostasis mechanism of Pseudomonas aeruginosa. The regular DNA motifs on natural operon were reconfigured and coupled with the enhanced Green Fluorescent Protein (eGFP) reporter to develop a novel basic NOT type logic gate CadA/CadR-eGFP to respond metal ions mentioned above. A Genetically Engineered Microbial (GEM)-based biosensor (E.coli-BL21:pJET1.2-CadA/CadR-eGFP) was developed by cloning the chemically synthesised CadA/CadR-eGFP gene circuit into pJET1.2-plasmid and transforming into Escherichia coli (E. coli)-BL21 bacterial cells.

RESULTS

The GEM-based biosensor cells indicated the reporter gene expression in the presence of Cd2+, Zn2+ and Pb2+ either singly or in combination. Further, the same biosensor cells calibrated for fluorescent intensity against heavy metal concentration generated linear graphs for Cd2+, Zn2+ and Pb2+ with the R2 values of 0.9809, 0.9761 and 0.9758, respectively as compared to non-specific metals, Fe3+ (0.0373), AsO43- (0.3825) and Ni2+ (0.8498) making our biosensor suitable for the detection of low concentration of the former metal ions in the range of 1-6 ppb. Furthermore, the GEM based biosensor cells were growing naturally within the concentration range of heavy metals, at 37 °C and optimum pH = 7.0 in the medium, resembling the characteristics of wildtype E.coli.

CONCLUSION

Finally, the novel GEM based biosensor cells developed in this study can be applied for detection of targeted heavy metals in low concentration ranges (1-6 ppb) at normal bacterial physiological conditions.

Plant protoplasts in the age of synthetic biology

Protoplasts, which are plant cells with their cell walls removed, have been used for decades in plant research and have been instrumental in genetic transformation and the study of various aspects of plant physiology and genetics. With the advent of synthetic biology, these individualized plant cells are fundamental to accelerate the "design-build-test-learn" cycle, which is relatively slow in plant research. Despite their potential, challenges remain to expanding the use of protoplasts in synthetic biology. The capacity of individual protoplasts to hybridize to form new varieties, and to regenerate from single cells, creating individuals with new features is underexplored. The main objective of this review is to discuss the use of protoplasts in plant synthetic biology and to highlight the challenges to exploiting protoplast technologies in this new "age of synthetic biology".

A logical way to reprogram plants

Living cells constantly monitor their external and internal environments for changing conditions, stresses or developmental cues. Networks of genetically encoded components sense and process these signals following pre-defined rules in such a way that specific combinations of the presence or absence of certain signals activate suitable responses. Many biological signal integration mechanisms approximate Boolean logic operations, whereby presence or absence of signals are computed as variables with values described as either true or false, respectively. Boolean logic gates are commonly used in algebra and in computer sciences, and have long been recognized as useful information processing devices in electronic circuits. In these circuits, logic gates integrate multiple input values and produce an output signal according to pre-defined Boolean logic operations. Recent implementation of these logic operations using genetic components to process information in living cells has allowed genetic circuits to enable novel traits with decision-making capabilities. Although several literature reports describe the design and use of these logic gates to introduce new functions in bacterial, yeast and mammalian cells, similar approaches in plants remain scarce, likely due to challenges posed by the complexity of plants and the lack of some technological advances, e.g., species-independent genetic transformation. In this mini review, we have surveyed recent reports describing synthetic genetic Boolean logic operators in plants and the different gate architectures used. We also briefly discuss the potential of deploying these genetic devices in plants to bring to fruition a new generation of resilient crops and improved biomanufacturing platforms.

Engineering the human gut commensal Bacteroides thetaiotaomicron with synthetic biology

The role of the microbiome in health and disease is attracting the attention of researchers seeking to engineer microorganisms for diagnostic and therapeutic applications. Recent progress in synthetic biology may enable the dissection of host-microbiota interactions. Sophisticated genetic circuits that can sense, compute, memorize, and respond to signals have been developed for the stable commensal bacterium Bacteroides thetaiotaomicron, dominant in the human gut. In this review, we highlight recent advances in expanding the genetic toolkit for B. thetaiotaomicron and foresee several applications of this species for microbiome engineering. We provide our perspective on the challenges and future opportunities for the engineering of human gut-associated bacteria as living therapeutic agents.

Robust network topologies for temperature-inducible bioswitches

BACKGROUND

Thermoinducible bioswitches are unique in that the all-or-none switch response is triggered by temperature, which is a global factor that impacts all biochemical reaction processes. To date, temperature-inducible bioswitches rely exclusively on special thermal sensing biomolecules of DNA, RNA, proteins and lipids whose conformations are critically temperature dependent.

METHOD

This paper extends the traditional thermal switch by utilizing purposely designed network topologies of biomolecular interactions to achieve the switching function. By assuming the general Arrhenius law for biochemical reactions, we explore the full space of all three-node genetic interaction networks to screen topologies capable of thermal bioswitches. Three target bioswitches, i.e., thermal-inducible Off-On, cold-inducible On-Off, and hybrid Off-On-Off double switches, are considered separately.

CONCLUSIONS

We identify the minimal and core network skeletons that are basic and essential for building robust high-performance bioswitches: three Off-On motifs, three On-Off motifs, and an incoherent feedforward motif for an Off-On-Off double switch. Functional topologies are implicitly preferential in choosing parameter values to achieve the target functions. The scenario of the topology-based bioswitch we propose here is an extension of molecule-based bioswitches and would be valuable in aiding the rational design and synthesis of efficient high-performance thermal bioswitches.

Design and Assembly of Multilevel Transcriptional and Translational Regulators for Stringent Control of Gene Expression

Precise control of gene expression is crucial when reprogramming the behavior of living cells. However, common inducible systems often lack the ability to stringently control gene expression due to the use of a single type of regulator that can be susceptible to unavoidable biomolecular fluctuations. In contrast, multilevel controllers (MLCs) employ several forms of regulation simultaneously to overcome this issue, ensuring a reduced basal expression while minimally affecting the maximum induced expression level that can be achieved. Here, we show how our publicly available genetic toolkit can be used to simplify the assembly of MLCs for the stringent control of gene expression. We demonstrate how new compatible parts can be designed and explain the rapid end-to-end assembly procedure.

RNP-Based Control Systems for Genetic Circuits in Synthetic Biology Beyond CRISPR

Ribonucleoproteins (RNPs) are RNA-protein complexes utilized natively in both prokaryotes and eukaryotes to regulate essential processes within the cell. Over the past few years, many of these native systems have been adapted to provide control over custom genetic targets. Engineered RNP-based control systems allow for fine-tune regulation of desired targets, by providing customizable nucleotide-nucleotide interactions. However, as there have been several engineered RNP systems developed recently, identifying an optimal system for various bioprocesses is challenging. Here, we review the most successful engineered RNP systems and their applications to survey the current state of the field. Additionally, we provide selection criteria to provide users a streamlined method for identifying an RNP control system most useful to their own work. Lastly, we discuss future applications of RNP control systems and how they can be utilized to address the current grand challenges of the synthetic biology community.

Probiotic engineering strategies for the heterologous production of antimicrobial peptides

Engineered probiotic bacteria represent an innovative approach for treating and detecting a wide range of diseases including those caused by infectious agents. Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics for combating antibiotic-resistant infections. These molecules can be delivered orally to the gut by using engineered probiotics, which confer protection against AMP degradation, thus enabling numerous applications including treating drug-resistant enteric pathogens and remodeling the microbiota in real time. Here, we provide an update on the current state of the art on AMP-producing probiotics, discuss methods to enhance gut colonization, and end by outlining future perspectives.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

Characterizing Genetic Parts and Devices Using RNA Sequencing

Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.

Dynamic control in metabolic engineering: Theories, tools, and applications

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.

Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli

Regulation of future RNA and protein numbers is a key process by which cells continuously best fit the environment. In bacteria, RNA and proteins exist in small numbers and their regulatory processes are stochastic. Consequently, there is cell-to-cell variability in these numbers, even between sister cells. Traditionally, the two most studied sources of this variability are gene expression and RNA and protein degradation, with evidence suggesting that the latter is subject to little regulation, when compared to the former. However, time-lapse microscopy and single molecule fluorescent tagging have produced evidence that cell division can also be a significant source of variability due to asymmetries in the partitioning of RNA and proteins. Relevantly, the impact of this noise differs from noise in production and degradation since, unlike these, it is not continuous. Rather, it occurs at specific time points, at which moment it can introduce major fluctuations. Several models have now been proposed that integrate noise from cell division, in addition to noise in gene expression, to mimic the dynamics of RNA and protein numbers of cell lineages. This is expected to be particularly relevant in genetic circuits, where significant fluctuations in one component protein, at specific time moments, are expected to perturb near-equilibrium states of the circuits, which can have long-lasting consequences. Here we review stochastic models coupling these processes in Escherichia coli, from single genes to small circuits.

Engineering grass biomass for sustainable and enhanced bioethanol production

Bioethanol from lignocellulosic biomass is a promising step for the future energy requirements. Grass is a potential lignocellulosic biomass which can be utilised for biorefinery-based bioethanol production. Grass biomass is a suitable feedstock for bioethanol production due to its all the year around production, requirement of less fertile land and noninterference with food system. However, the processes involved, i.e. pretreatment, enzymatic hydrolysis and fermentation for bioethanol production from grass biomass, are both time consuming and costly. Developing the grass biomass in planta for enhanced bioethanol production is a promising step for maximum utilisation of this valuable feedstock and, thus, is the focus of the present review. Modern breeding techniques and transgenic processes are attractive methods which can be utilised for development of the feedstock. However, the outcomes are not always predictable and the time period required for obtaining a robust variety is generation dependent. Sophisticated genome editing technologies such as synthetic genetic circuits (SGC) or clustered regularly interspaced short palindromic repeats (CRISPR) systems are advantageous for induction of desired traits/heritable mutations in a foreseeable genome location in the 1st mutant generation. Although, its application in grass biomass for bioethanol is limited, these sophisticated techniques are anticipated to exhibit more flexibility in engineering the expression pattern for qualitative and qualitative traits. Nevertheless, the fundamentals rendered by the genetics of the transgenic crops will remain the basis of such developments for obtaining biorefinery-based bioethanol concepts from grass biomass. Grasses which are abundant and widespread in nature epitomise attractive lignocellulosic feedstocks for bioethanol production. The complexity offered by the grass cell wall in terms of lignin recalcitrance and its binding to polysaccharides forms a barricade for its commercialization as a biofuel feedstock. Inspired by the possibilities for rewiring the genetic makeup of grass biomass for reduced lignin and lignin-polysaccharide linkages along with increase in carbohydrates, innovative approaches for in planta modifications are forging ahead. In this review, we highlight the progress made in the field of transgenic grasses for bioethanol production and focus our understanding on improvements of simple breeding techniques and post-harvest techniques for development in shortening of lignin-carbohydrate and carbohydrate-carbohydrate linkages. Further, we discuss about the designer lignins which are aimed for qualitable lignins and also emphasise on remodelling of polysaccharides and mixed-linkage glucans for enhancing carbohydrate content and in planta saccharification efficiency. As a final point, we discuss the role of synthetic genetic circuits and CRISPR systems in targeted improvement of cell wall components without compromising the plant growth and health. It is anticipated that this review can provide a rational approach towards a better understanding of application of in planta genetic engineering aspects for designing synthetic genetic circuits which can promote grass feedstocks for biorefinery-based bioethanol concepts.

Synthetic genetic circuits for programmable biological functionalities

Living organisms evolve complex genetic networks to interact with the environment. Due to the rapid development of synthetic biology, various modularized genetic parts and units have been identified from these networks. They have been employed to construct synthetic genetic circuits, including toggle switches, oscillators, feedback loops and Boolean logic gates. Building on these circuits, complex genetic machines with capabilities in programmable decision-making could be created. Consequently, these accomplishments have led to novel applications, such as dynamic and autonomous modulation of metabolic networks, directed evolution of biological units, remote and targeted diagnostics and therapies, as well as biological containment methods to prevent release of engineered microorganisms and genetic materials. Herein, we outline the principles in genetic circuit design that have initiated a new chapter in transforming concepts to realistic applications. The features of modularized building blocks and circuit architecture that facilitate realization of circuits for a variety of novel applications are discussed. Furthermore, recent advances and challenges in employing genetic circuits to impart microorganisms with distinct and programmable functionalities are highlighted. We envision that this review gives new insights into the design of synthetic genetic circuits and offers a guideline for the implementation of different circuits in various aspects of biotechnology and bioengineering.

Cell-free protein synthesis enabled rapid prototyping for metabolic engineering and synthetic biology

Advances in metabolic engineering and synthetic biology have facilitated the manufacturing of many valuable-added compounds and commodity chemicals using microbial cell factories in the past decade. However, due to complexity of cellular metabolism, the optimization of metabolic pathways for maximal production represents a grand challenge and an unavoidable barrier for metabolic engineering. Recently, cell-free protein synthesis system (CFPS) has been emerging as an enabling alternative to address challenges in biomanufacturing. This review summarizes the recent progresses of CFPS in rapid prototyping of biosynthetic pathways and genetic circuits (biosensors) to speed up design-build-test (DBT) cycles of metabolic engineering and synthetic biology.

DNA Assembly with the DATEL Method

Simple and reliable DNA assembly methods have become a critical technique in synthetic biology. Here, we present a protocol of the recently developed DATEL (scarless and sequence-independent DNA assembly method using thermostable exonuclease and ligase) method for the construction of genetic circuits and biological pathways from multiple DNA parts in one tube. DATEL is expected to be an applicable choice for both manual and automated high-throughput assembly of DNA fragments, which will greatly facilitate the rapid progress of synthetic biology and metabolic engineering.

Design and application of genetically-encoded malonyl-CoA biosensors for metabolic engineering of microbial cell factories

Malonyl-CoA is the basic building block for synthesizing a range of important compounds including fatty acids, phenylpropanoids, flavonoids and non-ribosomal polyketides. Centering around malonyl-CoA, we summarized here the various metabolic engineering strategies employed recently to regulate and control malonyl-CoA metabolism and improve cellular productivity. Effective metabolic engineering of microorganisms requires the introduction of heterologous pathways and dynamically rerouting metabolic flux towards products of interest. Transcriptional factor-based biosensors translate an internal cellular signal to a transcriptional output and drive the expression of the designed genetic/biomolecular circuits to compensate the activity loss of the engineered biosystem. Recent development of genetically-encoded malonyl-CoA sensor has stood out as a classical example to dynamically reprogram cell metabolism for various biotechnological applications. Here, we reviewed the design principles of constructing a transcriptional factor-based malonyl-CoA sensor with superior detection limit, high sensitivity and broad dynamic range. We discussed various synthetic biology strategies to remove pathway bottleneck and how genetically-encoded metabolite sensor could be deployed to improve pathway efficiency. Particularly, we emphasized that integration of malonyl-CoA sensing capability with biocatalytic function would be critical to engineer efficient microbial cell factory. Biosensors have also advanced beyond its classical function of a sensor actuator for in situ monitoring of intracellular metabolite concentration. Applications of malonyl-CoA biosensors as a sensor-invertor for negative feedback regulation of metabolic flux, a metabolic switch for oscillatory balancing of malonyl-CoA sink pathway and source pathway and a screening tool for engineering more efficient biocatalyst are also presented in this review. We envision the genetically-encoded malonyl-CoA sensor will be an indispensable tool to optimize cell metabolism and cost-competitively manufacture malonyl-CoA-derived compounds.

A Modular Approach to Building Complex Synthetic Circuits

Efficient assembly of genetic circuits is a critical element in synthetic biology for implementing complex gene expression systems. Here we present an assembly framework that integrates recent developments in DNA recombination technologies and unique nucleotide sequences for modular and reliable construction of complex genetic circuits. All relevant protocols are described in detail. Implementing these protocols relies on readily available reagents and basic molecular biology techniques without the need for specialized equipment.

Synthetic Protein Switches: Theoretical and Experimental Considerations

Synthetic protein switches with tailored response functions are finding increasing applications as tools in basic research and biotechnology. With a number of successful design strategies emerging, the construction of synthetic protein switches still frequently necessitates an integrated approach that combines detailed biochemical and biophysical characterization in combination with high-throughput screening to construct tailored synthetic protein switches. This is increasingly complemented by computational strategies that aim to reduce the need for costly empirical optimization and thus facilitate the protein design process. Successful computational design approaches range from analyzing phylogenetic data to infer useful structural, biophysical, and biochemical information to modeling the structure and function of proteins ab initio. The following chapter provides an overview over the theoretical considerations and experimental approaches that have been successful applied in the construction of synthetic protein switches.

Tools and applications in synthetic biology

Advances in synthetic biology have enabled the engineering of cells with genetic circuits in order to program cells with new biological behavior, dynamic gene expression, and logic control. This cellular engineering progression offers an array of living sensors that can discriminate between cell states, produce a regulated dose of therapeutic biomolecules, and function in various delivery platforms. In this review, we highlight and summarize the tools and applications in bacterial and mammalian synthetic biology. The examples detailed in this review provide insight to further understand genetic circuits, how they are used to program cells with novel functions, and current methods to reliably interface this technology in vivo; thus paving the way for the design of promising novel therapeutic applications.

Putting RNA to work: Translating RNA fundamentals into biotechnological engineering practice

Synthetic biology, in close concert with systems biology, is revolutionizing the field of metabolic engineering by providing novel tools and technologies to rationally, in a standardized way, reroute metabolism with a view to optimally converting renewable resources into a broad range of bio-products, bio-materials and bio-energy. Increasingly, these novel synthetic biology tools are exploiting the extensive programmable nature of RNA, vis-à-vis DNA- and protein-based devices, to rationally design standardized, composable, and orthogonal parts, which can be scaled and tuned promptly and at will. This review gives an extensive overview of the recently developed parts and tools for i) modulating gene expression ii) building genetic circuits iii) detecting molecules, iv) reporting cellular processes and v) building RNA nanostructures. These parts and tools are becoming necessary armamentarium for contemporary metabolic engineering. Furthermore, the design criteria, technological challenges, and recent metabolic engineering success stories of the use of RNA devices are highlighted. Finally, the future trends in transforming metabolism through RNA engineering are critically evaluated and summarized.

Modeling stochastic gene expression in growing cells

Gene expression is an inherently noisy process. Fluctuations arise at many points in the expression of a gene, as all the salient reactions such as transcription, translation, and mRNA degradation are stochastic processes. The fluctuations become important when the cellular copy numbers of the relevant molecules (mRNA or proteins) are low. For regulated genes, a computational complication arises from the fact that protein synthesis rates depend on the concentrations of the transcription factors that regulate the corresponding genes. Because of the growing cell volume, such rates are effectively time-dependent. We deal with the effects of volume growth computationally using a rather simple method: the growth of the cell volume is incorporated in our simulations by stochastically adding small volume elements to the cell volume. As an application of this method we study a gene circuit with positive autoregulation that exhibits bistability. We show how the region of bistability becomes diminished by increasing the effect of noise via a reduced copy number of the regulatory protein. Cell volume determines the region of bistability for different noise strengths. The method is general and can also be applied to other cases where synthesis rates of proteins are regulated and an appropriate analytical description is difficult to achieve.