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[EN] Leveraging the synergies between theoretical CS and synthetic biology to create powerful cellular computers and move beyond Turing computation.

Colony formation is key to many ecological and biotechnological processes. In its early stages, colony formation involves the concourse of a number of physical and biological parameters for generation of a distinct 3D structure-the specific influence of which remains unclear. We focused on a thus far neglected aspect of the process, specifically the consequences of the differential pressure experienced by cells in the middle of a colony versus that endured by bacteria located in the growing periphery. This feature was characterized experimentally in the soil bacterium Pseudomonas putida. Using an agent-based model we recreated the growth of microcolonies in a scenario in which pressure was the only parameter affecting proliferation of cells. Simulations exposed that, due to constant collisions with other growing bacteria, cells have virtually no free space to move sideways, thereby delaying growth and boosting chances of overlapping on top of each other. This scenario was tested experimentally on agar surfaces. Comparison between experiments and simulations suggested that the inside/outside differential pressure determines growth, both timewise and in terms of spatial directions, eventually moulding colony shape. We thus argue that-at least in the case studied-mere physical pressure of growing cells suffices to explain key dynamics of colony formation.

The engineering of pre-defined functions in living cells requires increasingly accurate tools as synthetic biology efforts become more ambitious. Moreover, the characterization of the phenotypic performance of genetic constructs demands meticulous measurements and extensive data acquisition for the sake of feeding mathematical models and matching predictions along the design-build-test lifecycle. Here, we developed a genetic tool that eases high-throughput transposon insertion sequencing (TnSeq): the pBLAM1-x plasmid vectors carrying the Himar1 Mariner transposase system. These plasmids were derived from the mini-Tn5 transposon vector pBAMD1-2 and built following modular criteria of the Standard European Vector Architecture (SEVA) format. To showcase their function, we analyzed sequencing results of 60 clones of the soil bacterium Pseudomonas putida KT2440. The new pBLAM1-x tool has already been included in the latest SEVA database release, and here we describe its performance using laboratory automation workflows. Graphical Abstract.

The SEVA platform (https://seva-plasmids.com) was launched one decade ago, both as a database (DB) and as a physical repository of plasmid vectors for genetic analysis and engineering of Gram-negative bacteria with a structure and nomenclature that follows a strict, fixed architecture of functional DNA segments. While the current update keeps the basic features of earlier versions, the platform has been upgraded not only with many more ready-to-use plasmids but also with features that expand the range of target species, harmonize DNA assembly methods and enable new applications. In particular, SEVA 4.0 includes (i) a sub-collection of plasmids for easing the composition of multiple DNA segments with MoClo/Golden Gate technology, (ii) vectors for Gram-positive bacteria and yeast and [iii] off-the-shelf constructs with built-in functionalities. A growing collection of plasmids that capture part of the standard-but not its entirety-has been compiled also into the DB and repository as a separate corpus (SEVAsib) because of its value as a resource for constructing and deploying phenotypes of interest. Maintenance and curation of the DB were accompanied by dedicated diffusion and communication channels that make the SEVA platform a popular resource for genetic analyses, genome editing and bioengineering of a large number of microorganisms.

Laboratory automation deals with eliminating manual tasks in high-throughput protocols. It therefore plays a crucial role in allowing fast and reliable synthetic biology. However, implementing open-source automation solutions often demands experimental scientists to possess scripting skills, and even when they do, there is no standardized toolkit available for their use. To address this, we present the Laboratory Automation Protocol (LAP) Format and Repository. LAPs adhere to a standardized script-based format, enhancing end-user implementation and simplifying further development. With a modular design, LAPs can be seamlessly combined to create customized, target-specific workflows. Furthermore, all LAPs undergo experimental validation, ensuring their reliability. Detailed information is provided within each repository entry, allowing users to validate the LAPs in their own laboratory settings. We advocate for the adoption of the LAP Format and Repository as a community resource, which will continue to expand, improving the reliability and reproducibility of the automation processes.

GENETTA is a software tool that transforms synthetic biology designs into networks using graph theory for analysis and manipulation. By representing complex data as interconnected points, GENETTA allows dynamic customization of visualizations, including interaction networks and parts hierarchies. It can also merge design data from multiple databases, providing a unified perspective. The generated interactive network can be edited by adding nodes and edges, simplifying changes to existing design files. This article presents GENETTA and its features through specific use cases, showcasing its practical applications.

Synthetic biology uses molecular biology to implement genetic circuits that perform computations. These circuits can process inputs and deliver outputs according to predefined rules that are encoded, often entirely, into genetic parts. However, the field has recently begun to focus on using mechanisms beyond the realm of genetic parts for engineering biological circuits. We analyse the use of electrogenic processes for circuit design and present a model for a merged genetic and electrogenetic toggle switch operating in a biofilm attached to an electrode. Computational simulations explore conditions under which bistability emerges in order to identify the circuit design principles for best switch performance. The results provide a basis for the rational design and implementation of hybrid devices that can be measured and controlled both genetically and electronically.

As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.

Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications.

The implementation of Boolean logic circuits in cells have become a very active field within synthetic biology. Although these are mostly focussed on the genetic components alone, the context in which the circuit performs is crucial for its outcome. We characterise 20 genetic NOT logic gates in up to 7 bacterial-based contexts each, to generate 135 different functions. The contexts we focus on are combinations of four plasmid backbones and three hosts, two Escherichia coli and one Pseudomonas putida strains. Each gate shows seven different dynamic behaviours, depending on the context. That is, gates can be fine-tuned by changing only contextual parameters, thus improving the compatibility between gates. Finally, we analyse portability by measuring, scoring, and comparing gate performance across contexts. Rather than being a limitation, we argue that the effect of the genetic background on synthetic constructs expands functionality, and advocate for considering context as a fundamental design parameter.

Synthetic biology aims to develop novel biological systems and increase their reproducibility using engineering principles such as standardization and modularization. It is important that these systems can be represented and shared in a standard way to ensure they can be easily understood, reproduced, and utilized by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterization. Previously, this standard has only been used to represent designs in systems where the same design is implemented in every cell; however, there is also much interest in multicellular systems, in which designs involve a mixture of different types of cells with differing genotype and phenotype. Here, we show how the SBOL standard can be used to represent multicellular systems, and, hence, how researchers can better share designs with the community and reliably document intended system functionality.

Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization-a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during-and not after-translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.

Genetically encoded logic gates, especially inverters-NOT gates-are the building blocks for designing circuits, engineering biosensors, or decision-making devices in synthetic biology. However, the repertoire of inverters readily available for different species is rather limited. In this work, a large whole of NOT gates that was shown to function previously in a specific strain of Escherichia coli, was recreated as broad host range (BHR) collection of constructs assembled in low, medium, and high copy number plasmid backbones of the SEVA (Standard European Vector Architecture) collection. The input/output function of each of the gates was characterized and parametrized in the environmental bacterium and metabolic engineering chassis Pseudomonas putida. Comparisons of the resulting fluorescence cytometry data with those published for the same gates in Escherichia coli provided useful hints on the portability of the corresponding gates. The hereby described inverter package (20 different versions of 12 distinct gates) borne by BHR plasmids thus becomes a toolbox of choice for designing genetic circuitries in a variety of Gram-negative species other than E. coli.

The Synthetic Biology Open Language (SBOL) is an emerging synthetic biology data exchange standard, designed primarily for unambiguous and efficient machine-to-machine communication. However, manual editing of SBOL is generally difficult for nontrivial designs. Here, we describe ShortBOL, a lightweight SBOL scripting language that bridges the gap between manual editing, visual design tools, and direct programming. ShortBOL is a shorthand textual language developed to enable users to create SBOL designs quickly and easily, without requiring strong programming skills or visual design tools.

Biofoundries are highly automated facilities that enable the rapid and efficient design, build, test, and learn cycle of biomanufacturing and engineering biology, which is applicable to both research and industrial production. However, developing a biofoundry platform can be expensive and time consuming. A biofoundry should grow organically, starting from a basic platform but with a vision for automation, equipment interoperability, and efficiency. By thinking about strategies early in the process through process planning, simulation, and optimization, bottlenecks can be identified and resolved. Here, we provide a survey of technological solutions in biofoundries and their advantages and limitations. We explore possible pathways towards the creation of a functional, early-phase biofoundry, and strategies towards long-term sustainability.

We present a scheme for implementing a version of task switching in engineered bacteria, based on the manipulation of plasmid copy numbers. Our method allows for the embedding of multiple computations in a cellular population, whilst minimising resource usage inefficiency. We describe the results of computational simulations of our model, and discuss the potential for future work in this area.

Despite intensive research on the biochemical and regulatory features of the archetypal catabolic TOL system borne by pWW0 of Pseudomonas putida strain mt-2, the physical arrangement and tridimensional logic of the xyl gene expression flow remains unknown. In this work, the spatial distribution of specific xyl mRNAs with respect to the host nucleoid, the TOL plasmid, and the ribosomal pool has been investigated. In situ hybridization of target transcripts with fluorescent oligonucleotide probes revealed that xyl mRNAs cluster in discrete foci, adjacent but clearly separated from the TOL plasmid and the cell nucleoid. Also, they colocalize with ribosome-rich domains of the intracellular milieu. This arrangement was maintained even when the xyl genes were artificially relocated to different chromosomal locations. The same held true when genes were expressed through a heterologous T7 polymerase-based system, which likewise led to mRNA foci outside the DNA. In contrast, rifampin treatment, known to ease crowding, blurred the confinement of xyl transcripts. This suggested that xyl mRNAs exit from their initiation sites to move to ribosome-rich points for translation-rather than being translated coupled to transcription. Moreover, the results suggest the distinct subcellular motion of xyl mRNAs results from both innate properties of the sequences and the physical forces that keep the ribosomal pool away from the nucleoid in P. putida This scenario is discussed within the background of current knowledge on the three-dimensional organization of the gene expression flow in other bacteria and the environmental lifestyle of this soil microorganism.IMPORTANCE The transfer of information between DNA, RNA, and proteins in a bacterium is often compared to the decoding of a piece of software in a computer. However, the tridimensional layout and the relational logic of the cognate biological hardware, i.e., the nucleoid, the RNA polymerase, and the ribosomes, are habitually taken fo

Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications.

The Standard European Vector Architecture 3.0 database (SEVA-DB 3.0, http://seva.cnb.csic.es) is the update of the platform launched in 2013 both as a web-based resource and as a material repository of formatted genetic tools (mostly plasmids) for analysis, construction and deployment of complex bacterial phenotypes. The period between the first version of SEVA-DB and the present time has witnessed several technical, computational and conceptual advances in genetic/genomic engineering of prokaryotes that have enabled upgrading of the utilities of the updated database. Novelties include not only a more user-friendly web interface and many more plasmid vectors, but also new links of the plasmids to advanced bioinformatic tools. These provide an intuitive visualization of the constructs at stake and a range of virtual manipulations of DNA segments that were not possible before. Finally, the list of canonical SEVA plasmids is available in machine-readable SBOL (Synthetic Biology Open Language) format. This ensures interoperability with other platforms and affords simulations of their behaviour under different in vivo conditions. We argue that the SEVA-DB will remain a useful resource for extending Synthetic Biology approaches towards non-standard bacterial species as well as genetically programming new prokaryotic chassis for a suite of fundamental and biotechnological endeavours.

Standards are the basis of technology: they allow rigorous description and exact measurement of properties, reliable reproducibility and a common “language” that enables different communities to work together. Molecular biology was in part created by physicists; yet, the field did not inherit the focus on the quantitation, the definition of system boundaries and the robust, unequivocal language that is characteristic of the other natural sciences. However, synthetic biology (SynBio) increasingly requires scientific, technical, operational and semantic standards for the field to become a full‐fledged engineering discipline with a high level of accuracy in the design, manufacturing and performance of biological artefacts. Although the benefits of adopting standards are clear, the community is still largely reluctant to accept them, owing to concerns about adoption costs and losses in flexibility.