On the Choice of the Right Plasmid Vector(s) in the Times of Synthetic Biology

ABSTRACT

Plasmid vectors are to this day the fundamental tools in molecular biology, but their selection is often guided by convenience rather than informed choice. This article revisits the architectural and functional features that determine plasmid performance i.e., origins of replication, copy number, cargo capacity, selection markers, and stability systems. We outline how these elements shape host range, expression dynamics, and metabolic burden, particularly as synthetic biology increasingly targets non‐model bacteria. The growing need for reliable, portable vectors has driven the development of broad‐host‐range backbones, streamlined modular architectures such as SEVA, and alternatives to antibiotic‐based selection. We also examine strategies to enhance long‐term stability, including toxin–antitoxin systems and chromosomal integration via mini‐transposons, recombinase‐assisted platforms, and CRISPR‐associated transposases. The convergence of standardization and customization, enabled by advances in DNA synthesis and emerging AI‐assisted plasmid design tools is discussed also. These innovations promise flexible vector engineering tailored to diverse microbial chassis. Yet, a deeper, systems‐level understanding of plasmid–host interactions will be necessary to ensure robust deployment of engineered functions in laboratory, industrial, and environmental settings.

This opinion article discusses the importance of vector design, highlighting how the origin of replication, copy number, selection markers and stabilisation systems affect the performance of genetic constructs. Then, we explore approaches to plasmid design that integrate standardisation, customisation and AI‐driven tools to address the needs of synthetic biology applications.

1. Introduction: Plasmids—What for?

Plasmid vectors are the most widely used tools in molecular biology. It is not unusual that their performance is taken for granted and often considered a routine component of any experimental project. Typically, the choice of a specific plasmid vector is driven by convenience, for example, ‘… we used plasmid X because it was the one available in the Lab …’ rather than by an informed decision. But it happens that such an early choice may significantly influence the success of any (synthetic) biology undertaking.

Let us go back in time. The successful cloning of the precursor hormone somatostatin, followed by the human insulin gene marked a key moment in the history of genetic engineering and biotechnology. These recombinant genes were introduced into Escherichia coli , a common laboratory bacterium, enabling the production of human‐derived proteins in a microbe for the first time (Itakura et al. 1977; Goeddel et al. 1979). These breakthroughs were achieved by using a plasmid, an autonomous replicating bacterial‐derived DNA molecule, that worked as a genetic vehicle allowing it to cross the kingdom barrier. Of course, these advances would not have been possible without other important discoveries, the in vitro construction of recombinant plasmids (Cohen et al. 1973), restriction endonucleases to precisely cut DNA (Kelly Jr and Smith 1970; Morrow and Berg 1972) and DNA ligases for specifically joining fragments of DNA (Mertz and Davis 1972; Lehman 1974). All these discoveries laid the foundation of recombinant DNA technology and modern biotechnology (Watson et al. 1983), in which plasmid vectors played a crucial role. Then, as molecular genetics evolved so did plasmids; they were equipped with multiple cloning sites or polylinkers, a sequence of DNA that contains multiple unique restriction enzyme recognition sites that eased the cloning of heterologous DNA, and functionalised with different antibiotic‐based selection markers and other elements, for example, origin of transfer sequences (oriT) to enable mobilisation to other bacteria through conjugation. Quickly, plasmid vectors became indispensable tools in molecular biology, used for cloning, expression of heterologous DNA and genome engineering, allowing for the manipulation and study of bacteria and eventually, in yeast and many other types of organisms. Later improvements in plasmid design enabled the assembly of multi‐gene pathways and complex genetic circuits. A remarkable highlight in this roadmap, was the assembly of a whole gene set for the production of the antimalarial drug precursor artemisinic acid in engineered yeast (Ro et al. 2006), considered by many the contribution that marked the transition between standard genetic engineering and modern synthetic biology. In reality, synthetic biology emerged as a discipline that brought authentic engineering principles to genetic designs and thus overcame the earlier use of the term engineering in biological systems from being a metaphor to become an authentic methodology (Endy 2005; Cheng and Lu 2012). As the field advances in complexity and expands beyond traditional model organisms, the limitations of classic plasmid vectors become evident. What was once considered a routine step—vector selection—has turned out to be a critical choice in the design process. Plasmids are not mere carriers of heterologous DNA, but they directly influence the stability, functionality and portability of the synthetic constructs. With the integration of multi‐gene operons and regulatory elements, a well‐informed plasmid selection is essential to minimise metabolic burden, prevent unwanted interactions and ensure predictability of expression across multiple organisms. These challenges are especially important when working with non‐model microorganisms, where issues such as host compatibility, stability, expression control and safety must be taken into account. As a consequence, awareness of plasmid architecture and functionality is essential for translating synthetic constructs into robust, scalable and functional biological systems.

Recombinant plasmids (Figure 1) can be classified depending on the intended use into broad categories: (i) cloning vectors, (ii) promoter‐probe devices, (iii) expression systems, (iv) mini‐transposons and (v) genome editing tools. This rough classification focuses on their specific utility. However, plasmid functionality relies on often less conspicuous components that make the difference for their successful performance in given tasks. Such components are inspected separately below, specifically for the sake of picking the right plasmid vectors for synthetic biology applications with Gram‐negative bacteria.

FIGURE 1.

What is in a recombinant plasmid vector? The figure sketches the typical functional composition of a plasmid vector, indicating essential elements (green arrows): Replication origin (yellow), antibiotic selection marker (blue) and cargo (pink), the minimum version of which might be just cloning polylinker. Note also optional additions (blue arrows): Terminator sequences (T₁ in light grey and T₀ in dark grey) that punctuate the parts, an origin of conjugal transfer (oriT) and one or more gadgets (white) that may enhance plasmid functionality (e.g., toxin‐antitoxin systems) for stabilisation and/or maintenance in the absence of external antibiotic selection. The specific position of each of these parts in respect to the others may vary. The one shown here corresponds to the SEVA vector series (http://seva‐plasmids.com/).

2. Will This Plasmid Work in My Bacterium?

While Escherichia coli remains the cornerstone of synthetic biology—especially for cloning and prototyping—it is not always the most suitable bacterial platform for deploying engineered functions in industrial or environmental applications. These scenarios often require non‐model organisms with distinctive metabolic capabilities or ecological traits that make them better suited for specific tasks. Once one leaves the comfort zone of dealing with E. coli and sorties into other bacterial species, the one recurrent question at the time of choosing a given vector is ‘… will plasmid X work in my organism …?’ or ‘… which plasmids should I choose to start playing genetically with microbe X …?’. The issue of working or not is not limited to whether the plasmid can replicate; it also encompasses a range of other factors—different from plasmid architecture proper—which determine the success or failure of the intended application. Out of the various functional parts that shape plasmid performance, that determining replication (ORI) is arguably the most critical. The oriV sequence (V for vegetative) where plasmid replication starts, along with the host‐encoded and/or plasmid‐encoded proteins and non‐coding RNAs that interplay with it determine the host range, copy number, compatibility and stability of the engineered replicon (del Solar et al. 1998). For the sake of engineering, ORIs can be broadly categorised into four types depending on the host range (HR) and its intended use. First, those with a narrow HR; these are typically derived from native plasmids of E. coli (or closely related). Conventional examples include ColE1 (and derivatives), pMB1 (and derivatives), pSC101 and p15A, which are widely used in molecular biology due to their well‐characterised behaviour in E. coli . However, their replication is restricted to a limited set of hosts (mostly Enterobacteria), making them unsuitable to use in most other organisms. The narrow HR of some naturally occurring plasmids has been further exacerbated in the laboratory to become entirely host‐addicted, that is, cannot replicate at all if placed in a different recipient. A thoroughly leveraged example of this second type of ORIs is that of the origin of replication of the R6K plasmid, in which the oriV and the cognate π replication protein (encoded by the pir gene) can be placed in trans (Kolter et al. 1978). Plasmids with such R6K oriV can thus replicate only in specialised E. coli hosts where the pir gene is chromosomally encoded. If such plasmids are moved to another host through transformation or conjugation, the constructs may make it to the new recipient but cannot replicate. This creates the scenario that has been called suicidal plasmid delivery, which has been key for genome engineering techniques (Martínez‐García and de Lorenzo 2011; Wirth et al. 2020) or for chromosomal insertion of mini‐transposon vectors (de Lorenzo et al. 1990; Herrero et al. 1990; Martínez‐García et al. 2014; Zobel et al. 2015; Alejaldre et al. 2023).

3. Beyond E. coli : Broad Host Range and Shuttle Vectors

A third type of ORIs includes an expanding series of broad‐host‐range (BHR) devices that enable plasmid replication across a wide variety of bacterial species, including many Gram‐negative and even some Gram‐positive bacteria. Well‐known examples include the RK2 and pBBR1 origins, quite popular for engineering Gram‐negative bacteria and the quite host‐promiscuous RSF1010 origin. This last replicates not only in typical Gram‐negatives, but also in cyanobacteria and some Gram‐positives (Meyer 2009). BHR vectors have been essential for expanding synthetic biology approaches beyond traditional model organisms as exemplified by their extensive application to new SynBio chassis such as Pseudomonas putida (Nikel et al. 2014). Finally, the so‐called shuttle vectors carry two or more replication origins. These vectors are habitually the result of genetic engineering, as having more than one origin is rare in naturally occurring plasmids. Despite their artificial design, shuttle plasmids have been useful when genetic constructs are easier to assemble in E. coli , but then need to be deployed in another host, sometimes taxonomically distant (e.g., Streptomyces albus , Agrobacterium tumefaciens , Saccharomyces cerevisiae , or mammalian cells). To this end, these vectors typically contain two origins of replication, each compatible with a different host. Typical examples include (i) inter‐bacterial shuttles, for example, pRO1600‐ColE1 for E. coli /Pseudomonas (Schweizer 1991) and the pSEVA shuttles between Gram‐negatives and Streptomyces albus (Gutiérrez et al. 2020) and other Gram‐positives (Aparicio et al. 2022); (ii) bacteria/yeast ( S. cerevisiae ) shuttle vectors with a ColE1 oriV for propagation in E. coli or RK2 oriV for Gram‐negatives, along with a yeast‐specific element, that is, the autonomously replicating sequence (ARS) and sometimes a centromeric sequence (CEN) for stable maintenance (Sikorski and Hieter 1989; Silbert et al. 2021); (iii) bacteria/plant shuttle vectors that replicate in both E. coli and Agrobacterium tumefaciens , facilitating plant transformation (Hajdukiewicz et al. 1994); and (iv) bacterial/mammalian shuttle vectors that may include viral ORIs (e.g., SV40) for episomal replication in mammalian cells, alongside bacterial elements for cloning and amplification (Menck et al. 1987). The main advantage of shuttle vectors is their flexibility: researchers can perform cloning in E. coli and then transfer the recombinant plasmid to a more complex or application‐specific host. Besides, it could allow easily testing the production of compounds of interest in different organisms. However, this dual compatibility comes with downsides: shuttle vectors are larger, which may affect stability. Also, one needs to ensure beforehand the functionality of the selection markers and regulatory elements in each successive host.

4. The Copy Number Dilemma: Yield Versus Metabolic Burden

The next critical factor in plasmid vector choice is copy number, that is, the number of plasmid molecules maintained within a host cell. If the objective is, for example, heterologous expression of one or more genes of interest, the vector copy number embodies a big share of the trade‐off between the production of whatever plasmid‐encoded trait and metabolic burden. Plasmids are typically classified as high (> 30 copies), medium (10–30 copies), or low (< 10 copies). High‐copy‐number plasmids are often preferred for their ability to drive robust protein expression and facilitate cloning and high‐yield protein purification. This is particularly advantageous in applications demanding maximal output. However, this benefit comes at a cost: maintaining numerous plasmid copies imposes a significant metabolic burden on the cell, potentially leading to slow growth (or even stopping it completely), and genetic instability of the construct over time, particularly when mutant variants of the construct gain a selective growth advantage. These issues are especially pronounced when expressing toxic or problematic molecules (such as membrane proteins), or in leaky expression systems, where unintended basal expression from high‐copy plasmids can disrupt the regulatory control of the genetic circuit, ultimately ruining the application. On the contrary, low‐copy‐number plasmids impose a lighter metabolic burden, promoting greater genetic stability and predictable behaviour, particularly in long‐term or tightly regulated experiments. However, their lower gene dosage may limit expression levels, posing a bottleneck in high‐demand systems and increasing the risk of plasmid loss without selective pressure. Achieving this equilibrium often requires iterative tuning of genetic elements of the construct, such as promoters and ribosome binding sites (RBSs), or by selecting an appropriate origin of replication (ORI) to balance copy number and host load to optimise system performance. For a comprehensive overview of the metabolic burden imposed by recombinant plasmids, see Silva et al. (2012) and Snoeck et al. (2024). While many ORIs are compatible with a given host their copy number can vary significantly, not only between different microorganisms but even among closely related bacterial species. This variability underscores the importance of selecting an ORI that is optimal for the intended application, whether for routine cloning or for the inducible expression of complex genetic circuits.

5. Take‐Home Lesson: Pick the Right Origin of Replication

Despite the availability of the diverse ORI types briefly addressed above, achieving true host compatibility remains a significant challenge. The origin of replication is not just a technical parameter (compatibility and copy number); it is a consequential factor that can significantly influence the performance and stability of synthetic constructs. Selecting an ORI and its copy number is critical, as these factors influence not only host compatibility and gene expression levels but also impose metabolic burdens that can affect overall cellular physiology. Even BHR vectors frequently demand host‐specific optimisation, including adjustments to codon usage, regulatory elements and other components to ensure optimal performance. Furthermore, recombinant plasmid‐host interactions can produce unpredictable outcomes, particularly when host defence systems such as restriction‐modification mechanisms or CRISPR‐based immunity recognise and degrade foreign DNA. Expanding host compatibility therefore demands a deeper understanding of these interactions. To address this challenge, it is essential to increase the diversity of ORIs available in plasmid repositories, to reach a broader range of microbial species. This goal can be achieved by characterising natural plasmids from diverse environments and repurposing them into versatile vector systems (Martínez‐García et al. 2015). In parallel, the development of predictive tools that tailor vector design to the genomic and physiological context of the target organism is essential. Resources such as REBASE (Roberts et al. 2022), which provides detailed information on methylation and restriction systems, combined with methylome studies of the host to identify specific patterns, are essential for informed plasmid design. Integrating these insights with strategies to modify plasmid DNA methylation or remove restriction‐sensitive sequences can help overcome host restriction barriers and improve compatibility with replication systems. Additionally, predictive tools that scan plasmid vector sequences for potential CRISPR target sites offer an extra layer of security, enabling proactive adjustments to minimise interference from host defence mechanisms. Integrating these approaches creates a more rational and robust framework for designing vectors that perform reliably across diverse hosts. Building on this foundation, one promising avenue involves the standardised use of broad‐host‐range plasmids across laboratories. Such standardisation would help close the knowledge gap of host‐plasmid dynamics and overcome recurring bottlenecks. Systematic collection of key parameters, such as replication efficiency (e.g., copy number), stability under varying growth conditions and impact on host fitness, can feed into shared databases of host–vector performance. These datasets would enable the development of AI‐driven models to predict plasmid–host compatibility across diverse organisms and guide rational vector optimisation. This approach has the potential to significantly expand the synthetic biology toolbox, making it more robust and accessible for engineering diverse organisms, while enabling multi‐plasmid applications in both model and non‐model systems (Kiattisewee 2025). However, achieving this will be neither straightforward nor immediate; it will require sustained community adoption and iterative refinement over time.

6. DNA Cargo Capacity and Selection Markers

Since vector size limits ease of cloning, plasmid purification and transformation efficiency engineering of multi‐gene pathways and larger DNA constructs ask for vectors with high cargo capacity. Alas, large plasmids are prone to recombination events, structural and segregational instability, which can compromise experimental reproducibility. One avenue to mitigate this setback is the adoption of plasmid vectors (e.g., those of the SEVA collection; Martínez‐García et al. 2022) whose functional DNA segments have been streamlined to minimise their size while maintaining their performance. Yet, no vector can accommodate large DNA inserts without functional trade‐offs. In parallel, host‐level strategies can further reduce instability, including the use of recombination‐deficient strains (e.g., recA mutants), genome‐reduced variants (e.g., MDS42; Pósfai et al. 2006), careful design of genetic constructs and optimisation of growth conditions. Addressing this issue also requires computational tools and databases that can predict and optimise plasmid operation based on cargo characteristics. This is not alien either to the choice of a selection marker, which ensures plasmid maintenance within the host population at the cost of expressing one or more plasmid‐encoded heterologous antibiotic‐resistance proteins. Traditionally, such markers have been the gold standard, offering reliable selection through compounds like ampicillin, kanamycin, chloramphenicol, streptomycin and tetracycline, the most commonly used antibiotics for Gram‐negative bacteria. However, this approach presents several limitations and concerns. First, the limited repertoire of compatible antibiotic markers restricts the number of plasmids that can be stably maintained within a single host, becoming a bottleneck in multi‐plasmid systems for complex synthetic biology applications (Kiattisewee 2025). This issue is particularly relevant in microbes with inherently high antibiotic resistance, such as Pseudomonas aeruginosa and other environmental isolates/species. This limitation could be overcome by using less commonly employed antibiotic selection markers, such as those conferring resistance to trimethoprim (dfr; SEVA selection marker #7), zeocin (Sh ble), apramycin (aac(3)‐IV; SEVA selection marker #8), or hygromycin B (hph), which facilitate both selection and counter‐selection. Second, and more critical, the use of recombinant cells with antibiotic selection‐based plasmids raises serious safety concerns in industrial or environmental applications. One evident risk is the potential dissemination of genes of this type through natural microbial communities by means of horizontal transfer, which could contribute to the spread of antimicrobial resistance. Additionally, in biotechnological products, especially those intended for food or agriculture the mere presence of antibiotic resistance DNA can be problematic from regulatory and legal concerns. The easiest way to overcome this challenge is the adoption of non‐antibiotic markers as alternatives for plasmid selection. Successful instances include resistance to herbicides, and heavy metals, for example, mercury, arsenite (Herrero et al. 1990) and tellurite (Taylor 1999). Among these, tellurite resistance has been effectively used as a robust selection marker in various bacterial hosts due to the toxicity and broad specificity of the resistance mechanism (Sanchez‐Romero et al. 1998). On the other hand, the use of toxic heavy metals poses handling and safety risks for researchers, especially in large‐scale settings, that cannot be ignored. Another strategy to avoid antibiotic markers is engineering metabolic complementation that reinstates essential biosynthetic functions in auxotrophic strains. For instance, plasmid‐encoded pyrF restores uracil biosynthesis in ∆pyrF hosts and enables both positive and negative selection using 5‐fluoroorotic acid (5‐FOA; Galvão and de Lorenzo 2005). Similarly, plasmid‐borne markers such as thyA or glmS can complement corresponding auxotrophies in ∆thyA or ∆glmS strains, respectively (Amrofell et al. 2023). Yet, these alternatives are not without drawbacks either, as their use often requires host strains with metabolic deficiencies, which can limit the range of compatible hosts. Also, this approach may reduce flexibility, in particular when working with wild‐type strains or difficult‐to‐engineer non‐model organisms. The choice of a selection marker must thus balance technical performance and biosafety especially as synthetic biology moves from the lab to real‐world applications. In industrial settings, one must also consider the economic impact, as the cost of antibiotics or defined media can become a significant factor. But can we engineer and utilise plasmid vectors without using selection markers—antibiotic or not?

7. Engineering Genetic Stability

In industrial and environmental applications, maintaining recombinant plasmids over time is critical, especially when selective pressure (e.g., antibiotics or non‐antibiotic‐based) cannot be applied continuously or is undesirable due to biosafety, or cost‐related concerns. Under these conditions, plasmid vectors that have been sequence‐minimised by eliminating non‐essential elements, such as partitioning systems and post‐segregational killing mechanisms, rely on the random distribution of plasmids during cell division. This issue is especially relevant for low copy plasmids that are more susceptible to segregational loss. In this case, plasmid‐free cells gradually increase in the population (without selection), affecting the functionality, efficacy and reliability of the engineered system over time. To address this issue—and even to avoid using antibiotics at all—researchers have incorporated non‐selection‐based maintenance systems, such as toxin‐antitoxin (TA) modules, into plasmid vectors. Among these, the parB locus (hok/sok genes native to plasmid R1; Gerdes et al. 1986), ccdAB (F plasmid; Ogura and Hiraga 1983) and parDE (derived from plasmid RK2; Johnson et al. 1996) are the most common ones. However, the use of TA modules may raise biosafety issues, as accidental horizontal transfer of constructs with such devices might result in a permanent acquisition of the plasmid by undesired recipients. In this respect, it is noteworthy that the inherent role of these TA systems remains unclear. Plasmid maintenance, stress response, or even programmed cell death have been entertained as their tasks (Gerdes et al. 2005; Page and Peti 2016). One way or the other, TA modules offer an effective solution for engineering plasmid stability without external selection pressure. Although plasmid stability strategies are diverse, they may not guarantee long‐term retention of constructs, making chromosomal integration a robust alternative for sustained DNA maintenance without selection pressure. Mini‐transposon systems developed in the late 1980s, introduced vector platforms based on Tn5, Tn10 and mariner transposons that enable random and stable chromosomal integration of DNA (Herrero et al. 1990; Rubin et al. 1999). One key feature of them is that the transposase gene is lost after every insertion, thereby allowing their reutilisation multiple times when other selection markers are available (Martínez‐García et al. 2014) or upon deletion of the markers after every round of transposition (Federici et al. 2025). Other types of transposon vectors—this time derived from Tn7—enabled insertion of the DNA of interest in a specific location of the genome (att Tn7) which is present in the chromosome of many Gram‐negative bacteria (Peters and Craig 2001; Choi et al. 2005). In either case, mini‐transposons are typically delivered to the target genome via conjugation or transformation using plasmids with restricted (R6KoriV, see above) or non‐compatible replication origins, ensuring suicide delivery of the mini‐transposon and selection only of integration events (Kolter et al. 1978).

In addition to mini‐transposons, traditional allelic exchange via homologous recombination enables precise insertion of desired DNA fragments into specific genomic loci, relying on sequence homology and host repair mechanisms. Beyond these classical approaches, additional strategies include broad host range site‐specific systems like RAGE (Recombinase‐Assisted Genome Engineering; Santos et al. 2013), its updated variant CRAGE (Chassis‐independent Recombinase‐Assisted Genome Engineering; Wang et al. 2019) and SAGE (Serine recombinase‐Assisted Genome Engineering; Elmore et al. 2023). These platforms facilitate semi‐targeted chromosomal integration of DNA segments through streamlined processes. Another powerful option is the CRISPR‐Associated Transposase system (CAST; Klompe et al. 2019; Strecker et al. 2019) which combines RNA‐guided targeting with transposase‐mediated integration.

Together, these technologies expand the genome integration toolbox, offering options that range from semi‐random to highly precise insertions across diverse species. As with any approach, genome integration presents its own specific challenges, including lower gene dosage compared to plasmid‐based systems. Random insertion allows for the exploration of the genome expression landscape for candidates that best suit specific needs, but it often requires extra validation steps to confirm the successful integration and precise localisation (such as arbitrary PCR for random insertions; Pratt and Kolter 1998; Martínez‐García et al. 2014) of the genetic implant. Therefore, the decision on whether to follow the plasmid vector roadmap or the genomic integration strategy has to be pondered on a case‐by‐case basis.

As discussed throughout the sections on copy number, origin selection, DNA cargo capacity and genetic stability, these factors are highly interconnected and should be considered collectively. Although their relative importance depends on the application and host context, stability should generally be prioritised first for long‐term or multi‐plasmid systems, followed by copy number and expression level, which must be balanced to avoid excessive metabolic burden.

8. Standardisation Versus Customisation of Plasmids

From its inception in the early 2000s the synthetic biology community has championed the demand for standardised, off‐the‐shelf genetic tools (including plasmid vectors) that can be readily employed in DNA assembly pipelines. As SynBio later expanded into diverse microbial hosts, the need for standardised, interoperable plasmids became increasingly important. One archetypal product of such a drive was the launch in 2013 of the SEVA (Standard European Vector Architecture) plasmid collection (Silva‐Rocha et al. 2013) a ready‐to‐use, reliable and cost‐effective platform that supports a large number of genetic operations across a diverse number of Gram‐negative bacterial species. The modular architecture of these plasmids not only facilitates interoperability and reproducibility through different laboratories but also the sharing and reusing of genetic tools, thereby accelerating research and reducing design errors. Later advances in standardized DNA assembly, like Modular Cloning (MoClo), have gained popularity for their ability to construct large and complex genetic architectures (Engler et al. 2008; Weber et al. 2011) These assembly systems rely on standardised parts, destination vectors and specific assembly rules. Furthermore, the combination of the SEVA standard and MoClo methods allows automatisation, rapid prototyping and combinatorial design of multiple genetic circuits in an unprecedented fashion (Blázquez et al. 2023). Yet, there is one more novelty on the close horizon: fully customised plasmids. With the decreasing cost of DNA manufacture (Hoose et al. 2023) and the many in silico tools for design (Timmons and Densmore 2020; Enghiad et al. 2022; Haines et al. 2022; Nava et al. 2023) all‐synthetic plasmids are becoming more accessible and likely to take over the whole field. We can envision a future where synthetic biologists will be no longer limited to utilise or modify existing vector backbones but fully synthesise a plasmid tailored to meet specific needs. At that stage, researchers could construct new plasmids by customising origins of replication, selection markers, codon usage and other elements at will, especially relevant for non‐model organisms or specialised applications. Alas, we are not there yet and the whole‐synthetic option, especially for large plasmids, is still costly—let alone that the synthesis of error‐free large DNA pieces is still quite a challenge (Hoose et al. 2023). Therefore, we can still argue that standardisation enhances functionality and reproducibility here and now, while plasmid customisation will doubtlessly help optimisation in the future. But, apart from the mere technical challenges of high‐quality DNA production and its cost, can we do better at designing plasmids through their unlimited chemical synthesis?

9. Toward Predictive Plasmid Design: Databases and AI Tools

In more recent times, AI‐powered frameworks such as PlasmidGPT (Shao 2024), CD‐GPT (Zhu et al. 2024) and software platforms and databases like DNAda (Nava et al. 2023), PlasmidMaker (Enghiad et al. 2022), PlasmidScope (Li et al. 2024) and REPP (Timmons and Densmore 2020) herald new ways plasmids will be conceptualised, assembled and annotated. Specifically, these platforms can assist in: (i) automated plasmid design tailored to user‐defined functions or host organisms, (ii) error checking and programmed annotation of genetic parts and (iii) guiding the choice of compatible plasmid components from diverse repositories. These AI tools thus fill the gap between standardisation and customisation by enabling intelligent, modular design workflows that are both flexible and reproducible. As a result, researchers can now—at least theoretically—go through the Design‐Build‐Test‐Learn cycle (DBTL) when engineering genetic constructs with greater efficiency. In fact, one of the main values of such AI‐based resources for plasmid construction‐on‐demand will be the identification of recurring bottlenecks and performance patterns due to physiological or regulatory incompatibilities between BHR constructs and new hosts across different species and experimental conditions. This would not only accelerate the DBTL cycle but also democratise access to advanced synthetic biology tools for laboratories working with non‐model organisms. However, it is important to note that the predictive power of these AI tools is currently limited by the availability and diversity of training data; models often perform well in specific contexts but lack broad applicability across bacterial species. Future progress will depend on expanding datasets and using automation to generate high‐quality, large‐scale data for more robust predictions.

10. Future Perspectives

The earlier role of plasmid vectors in the history of biotechnology deserves to be revisited and upgraded in view of the new needs brought about by contemporary bioengineering. As synthetic biology continues to advance toward more sophisticated and real‐world applications, the design and utilities of plasmid vectors must evolve as well. The future of plasmid vector development lies in the convergence of standardisation, customization, automation and mission‐oriented, AI‐informed design. While standardised architectures such as SEVA provide a reliable backbone for genetic operations across diverse hosts, fully synthetic and modular plasmids tailored to specific experimental needs will become increasingly demanded. The expansion of broad‐host‐range origins of replication, safer non‐antibiotic selection markers and compact backbones will be essential to enable plasmid functionality beyond traditional chassis. In parallel, the development of large‐scale, curated datasets capturing host‐vector interactions will feed machine learning frameworks capable of predicting plasmid behaviour in a given microbial context. Biofoundries will play a critical role in generating these datasets through automated, high‐throughput experimentation, thereby providing the data needed to populate and refine predictive models and databases. These AI‐powered platforms will allow the rational design of vectors with optimised compatibility, copy number, stability and expression dynamics. Plasmid vectors offer clear advantages, including ease of genetic manipulation, portability across different hosts and the ability to modulate gene dosage, making them indispensable tools for research and development. Ongoing efforts in expansion and standardisation are critical to unlock their full potential and ensure interoperability across systems. However, they also present inherent limitations such as potential instability, metabolic burden on the host and dependence on selection pressure, which can limit their long‐term use in industrial processes. Nevertheless, insights gained from plasmid‐based systems will be invaluable for guiding the engineering of stable, genome‐integrated strains that overcome these limitations. Furthermore, as synthetic biology applications move from laboratory settings to environmental, industrial and clinical arenas, the emphasis on biosafety, genetic containment and regulatory compliance will shape the future vector portfolio. This includes genome‐integrating systems, toxin‐antitoxin modules and minimal constructs with reduced ecological risk. Ultimately, the field must embrace a systems‐level approach to plasmid design—one that integrates experimental evidence, host physiology and predictive modelling. By doing so, plasmids will move beyond being mere, somewhat host‐alien implants to become fully rationalised components of robust, portable and stable biological systems capable of addressing complex biotechnological challenges.

Author Contributions

Both Authors developed the contents of this Opinion and wrote the article.

Funding

This work was supported by HORIZON EUROPE Excellent Science (HORIZON‐CL6‐2021‐UE101060625) and Consejo Superior de Investigaciones Científicas, BIOEVO (PIE 202420E024).

Conflicts of Interest

The authors declare no conflicts of interest.

de Lorenzo, V. , and Martínez‐García E.. 2025. “On the Choice of the Right Plasmid Vector(s) in the Times of Synthetic Biology.” Microbial Biotechnology 18, no. 12: e70273. 10.1111/1751-7915.70273.

Data Availability Statement

The authors have nothing to report.