Model-based design in synthetic biology is limited by the lack of quantitative, mechanistically interpretable biopart parameters that remain valid across genetic and physiological contexts. This limitation is particularly acute for transcriptional units, whose expression phenotypes emerge from nonlinear coupling between plasmid copy number, transcription, translation, and host resource allocation. Here we introduce a context- and host-aware framework for the absolute characterisation of gene expression bioparts embedded in combinatorial libraries of constitutive transcriptional units. Our approach leverages a digital twin of Escherichia coli conditioned on experimentally measured growth rate, used as a low-dimensional physiological proxy for cellular state. By embedding this growth-conditioned digital twin in a model-in-the-loop identification strategy, host--circuit interactions are explicitly accounted for and decoupled from intrinsic biopart properties. Using structured combinatorial libraries, we identify biophysically interpretable and transferable parameters for plasmid origins, promoters, and ribosome binding sites. In particular, we uncover an intrinsic translation initiation capacity of ribosome binding sites that remains invariant across genetic contexts and growth conditions, while context-dependent translation rates emerge as physiological projections of this invariant descriptor. This intrinsic parameterisation enables accurate prediction of protein synthesis across diverse host states, supports incremental and patchwork library expansion, and reveals localized failures of modularity that are obscured by phenotype-only characterisation. Together, these results establish a principled link between DNA sequence, intrinsic biopart parameters, and circuit-level phenotypes, providing a scalable and host-aware foundation for predictive design in synthetic biology. ### Competing Interest Statement The authors have declared no competing interest. Ministerio de Ciencia, Innovación y Universidades, PID2023-151077OB-I00, PID2023-146275NB-C22 Universitat Politècnica de València, PAID-01-21397, PAID-06-24

Model-based design in synthetic biology is limited because bioparts are typically characterised by relative metrics that vary across genetic and physiological contexts. To address this, we introduce a host-aware framework for quantitatively characterising bioparts in combinatorial libraries of plasmid-based constitutive expression constructs. The approach integrates a digital twin of Escherichia coli, conditioned on measured growth rate, with model-in-the-loop parameter identification to separate biopart-associated properties from host-dependent effects. Using structured combinatorial libraries, we identify mechanistically interpretable, transferable parameters for plasmid origins, promoters and ribosome binding sites. In particular, we define an intrinsic translation initiation capacity that captures the dominant RBS-associated contribution to translation while context-dependent expression emerges from host physiology and local sequence context. The resulting parameterisation accurately predicts protein synthesis across physiological conditions, supports incremental library expansion, and reveals localised failures of modularity, providing a scalable foundation for predictive host-aware design in synthetic biology. Model-based design in synthetic biology is limited because bioparts are typically characterised by relative metrics that vary across genetic and physiological contexts. Here, using combinatorial libraries of plasmid-based expression constructs and a host-aware E. coli digital twin, the authors identify intrinsic biopart parameters that provide a basis for more predictive model-based design of synthetic gene circuits.

This work presents the identification of microbial batch growth dynamics using experiments performed in Chi.Bio minibioreactors. The objective is to estimate the parameters of a simple nonlinear model describing the growth of one microbial population consuming glucose as the limiting substrate. Optical density measurements are first converted into biomass concentration using a calibration based on equivalent particle counts and dry cell mass. Then, a Monod-type growth model, including an effective maximum biomass concentration, is fitted to batch experiments performed at different initial glucose concentrations.The model parameters are estimated by minimizing the error between the measured biomass trajectories and the simulated model response. The fitted model reproduces the experimental growth curves with good accuracy, achieving an (R2) value of 0.981 anda global RMSE of 0.045 g/L. The results show that the proposed approach can be used to obtain a compact dynamic model of microbial batch growth from Chi.Bio measurements.

Modifying the six-nucleotide Shine-Dalgarno (SD) core motif inside the ribosome binding site (RBS) constitutes a straightforward approach for tuning bacterial translation. However, existing methods for adjusting the effective translation rate (ETR) lack predictability. Even single-nucleotide substitutions can induce substantial alterations in translation efficiency. Moreover, this unpredictability is exacerbated by variations in the leader sequence, spacer region, or coding context. By focusing on the SD core as a key, experimentally tunable determinant of translation initiation in Escherichia coli, we introduce a coarse-grained framework that organizes SD core variants into activity clouds with consistent expression levels. This representation converts a dense sequence-to-phenotype map into an interpretable design space for coarse-grained tuning of expression. In contrast to thermodynamic tools such as the RBS Calculator, which estimate initiation from biophysical parameters, our approach is data-driven and emphasizes (i) interpretable rules over nucleotide positions, (ii) a bidirectional workflow (core → expected ETR range; target ETR → candidate cores), and (iii) simple paths between clouds that suggest minimal sequence edits. Designed with the needs of research teams in mind, our workflow prioritizes fixing the SD core first (i.e., selecting an appropriate activity cloud) to substantially narrow the spread of observed ETRs across constructs. This dampens variability introduced by flanking DNA/RNA context (leader, spacer, local secondary structure), so that subsequent fine-tuning is simpler, cheaper, and more predictable. We validate cloud stability and predictive utility using an independent high-throughput data set. Our approach provides a solid foundation for fast, interpretable coarse control of expression, while fine-grained tuning can then be achieved through flanking-region edits that account for spacing and local structure. Finally, we provide an open web interface and repository, allowing researchers to explore the hierarchy, inspect positional influences, and export candidate cores. Together, these contributions advance the Bonde et al. data set from a static lookup into a portable, actionable map for SD core guided tuning of translation in E. coli, and outline a path to extend the idea to a fully functional framework, with possible applications also to other bacteria.

La evaluación automatizada de tareas prácticas mediante plataformas digitales como MATLAB Grader ofrece ventajas pedagógicas y operativas clave para la enseñanza universitaria de materias técnicas. Este trabajo analiza cómo influye el idioma de instrucción en la eficacia de esta herramienta, comparando el rendimiento de tres grupos: estudiantes en castellano, estudiantes en inglés (EMI) y estudiantes que usaron voluntariamente la plataforma en inglés. Los resultados muestran diferencias significativas en la comprensión del feedback automático, especialmente en preguntas conceptuales, afectadas por el nivel de competencia lingüística. A partir de estos hallazgos se proponen recomendaciones para el diseño de evaluaciones multilingües inclusivas y se plantea su futura transferencia a universidades latinoamericanas.

Heparosan is a natural polymer with unique chemical and biological properties, that holds great promise for biomedical applications. The molecular weight (Mw) and polydispersion index (PDI) are critical factors influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low Mw and low PDI can be challenging, as it requires tight regulation of reaction conditions, enzyme activity, and precursor concentrations. We propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in bacteria. Our strategy involves designing a biomolecular controller that can regulate the expression of genes involved in heparosan biosynthesis. This controller is activated by biosensors that detect heparosan precursors, allowing for fine-tuned control of the polymerization process. Through this approach, we foresee the implementation of this synthetic device, demonstrating the potential to produce low Mw and low PDI heparosan in the probiotic E. coli Nissle 1917 as a biosafe and biosecure biofactory. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development and manufacturing of biomaterials with tailored properties for diverse biomedical applications.

Low-cost mini-bioreactor platforms are becoming increasingly important in synthetic biology and biotechnology for the characterization of genetic constructs and their initial scaling-up to large-scale cultures. Key process variables are the specific growth rate of cells and the synthesis rate of reporter proteins associated with transcriptional units of interest. These variables are of paramount importance for the characterization of gene synthetic circuits. In addition, their on-line estimation can be used for real-time monitoring of cells’ metabolic state and gene circuit dynamic performance, thus allowing for on-line decision taking. In this work, we first describe a procedure for the calibration of absorbance and fluorescence measurements, ensuring standardized and comparable units across different experimental setups and measurement devices. Then, we implement an observer-based software sensor that uses the calibrated on-line measurements to simultaneously estimate the specific growth and protein synthesis rates. We implemented the calibration procedure and software sensor in a Chi.Bio mini-bioreactor platform. The experimental results show very good performance and pave the way for the use of mini-bioreactor platforms in the real-time characterization of gene synthetic circuits under dynamically regulated time-varying complex scenarios.

Heparosan, a natural polymer with unique chemical and biological properties, holds great promise for various biomedical applications. Of particular interest is the production of low molecular weight and low polydisperse heparosan polymers, which offer enhanced functionality and suitability for therapeutic and diagnostic purposes. Polydispersity, a measure of the distribution of molecular weight within a polymer sample, is a critical factor influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low molecular weight and low polydispersity index can be challenging, requiring tight regulation of reaction conditions, enzyme activity, and precursor concentrations. To address this challenge, we propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in Escherichia coli (E. coli). Our strategy involves the design and implementation of a biomolecular controller capable of regulating the expression of genes involved in heparosan biosynthesis using biosensors of both precursors, thereby enabling fine-tuned control over the polymerization process. Through this approach, we successfully envision the implementation of the proposed system, demonstrating the potential to produce heparosan in probiotic E. coli Nissle 1917 with a low Mw and a low PDI that meets the stringent quality standards required for biomedical applications. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development of advanced biomaterials with tailored properties for diverse biomedical applications.

Synthetic Biology, like many other disciplines, is progressing every day. This progress has created the necessity to develop devices for measuring parameters and variables in order to characterize different aspects of genetic circuits. In the market, we can find many devices that fulfill this purpose. One of these devices is Chi.Bio, an open-source mini-bioreactor platform. The platform Chi.Bio allows us to measure process variables, such as fluorescence and absorbance, using its different sensors. Thus, it is necessary to develop protocols and procedures for calibrating data measurements from the Chi.Bio to increase the usability and interpretability of models and estimated parameters of a genetic circuit. Here, we propose a set of protocols and calibration procedures to obtain measurements of fluorescent E. coli cells in Molecules of Equivalent Fluorescein (MEFL) per cell for GFP constructs and we present an implementation of a second-order sliding mode observer to estimate the growth rate of E. coli cells in batch mode of Chi.Bio. The results show that calibrated measures are fundamental for getting reliable values to characterize genetic circuits in Synthetic Biology. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Mathematical modeling is a fundamental aspect of synthetic biology, enabling precise design and analysis of biological systems. To enhance students' understanding of this critical topic, we developed a series of webinars aimed at teaching mathematical modeling to iGEM teams. These webinars were initially created to maintain student engagement during a period of restricted lab access but quickly demonstrated their value as an effective educational tool. The success of these webinars highlighted the suitability of mathematical modeling as a topic well-suited to both onsite and online learning environments. Recognizing this, we expanded the content into a comprehensive syllabus for undergraduate courses in synthetic biology at the Universitat Politècnica de Valencia in Spain and Universidad de las Fuerzas Armadas—ESPE in Ecuador. The course now serves as a core component of synthetic biology education, offering students a robust framework for understanding and applying mathematical models. It includes a series of lectures, practical exercises, and case studies, all designed to deepen students' knowledge and skills in this essential area. To support educators and students, we have also developed a deck of slides and example scripts that provide practical examples and reinforce the concepts taught in the course. This manuscript presents the development, implementation, and impact of these educational initiatives, demonstrating how mathematical modeling can be effectively integrated into synthetic biology curricula to prepare students for real-world challenges in the field.

The Design-Build-Test-Learn (DBTL) cycle is a crucial framework in Synthetic Biology for the development and optimization of biological systems. However, the manual nature of the cycle poses limitations in terms of time and labor. This paper focuses on the application of automation techniques to the DBTL cycle, specifically in the testing and characterization of standard bioparts, which are essential components of genetic circuits. By automating the testing process, throughput, reliability, and reproducibility can be significantly improved. This paper discusses the challenges associated with manual testing methods and explores various automation strategies and technologies that can address these challenges. High-throughput screening methods, laboratory robotics, and data analysis algorithms are key elements in the automation process. As a case study, we utilize the automated DBTL cycle to refactor a biosensor, aiming to enhance its performance. Integrating automation in the DBTL cycle offers numerous advantages, including increased efficiency, standardization, and quality control of bioparts. It also enables the exploration of larger design spaces and rapid prototyping of complex genetic systems. Here, we show the advantages of using the DBTL cycle to refactor a biosensor that presents improved performance and can be readily used in more complex circuits. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.

Synthetic biology aims to program living bacteria cells with artificial genetic circuits for user-defined functions, transforming them into powerful tools with numerous applications in various fields, including oncology. Cancer treatments have serious side effects on patients due to the systemic action of the drugs involved. To address this, new systems that provide localized antitumoral action while minimizing damage to healthy tissues are required. Bacteria, often considered pathogenic agents, have been used as cancer treatments since the early 20th century. Advances in genetic engineering, synthetic biology, microbiology, and oncology have improved bacterial therapies, making them safer and more effective. Here we propose six modules for a successful synthetic biology-based bacterial cancer therapy, the modules include Payload, Release, Tumor-targeting, Biocontainment, Memory, and Genetic Circuit Stability Module. These will ensure antitumor activity, safety for the environment and patient, prevent bacterial colonization, maintain cell stability, and prevent loss or defunctionalization of the genetic circuit.

El ciclo Diseño-Construcción-Prueba-Aprendizaje (DBTL) es un marco crucial en Biología Sintética para el desarrollo y optimización de sistemas biológicos. Sin embargo, la naturaleza manual del ciclo plantea limitaciones en términos de tiempo y mano de obra. Este artículo se centra en la aplicación de técnicas de automatización al ciclo DBTL, concretamente en el ensayo y caracterización de biopartes estándar, que son componentes esenciales de los circuitos genéticos. La automatización del proceso de ensayo puede mejorar significativamente el rendimiento, la fiabilidad y la reproducibilidad. En este artículo se analizan los retos asociados a los métodos de ensayo manuales y se exploran diversas estrategias y tecnologías de automatización que pueden resolverlos. Los métodos de cribado de alto rendimiento, la robótica de laboratorio y los algoritmos de análisis de datos son elementos clave en el proceso de automatización. Se examinan estudios de casos y avances recientes en la automatización del ciclo DBTL para pruebas de biopartes. La integración de la automatización en el ciclo DBTL ofrece numerosas ventajas, como una mayor eficacia, estandarización y control de calidad de las biopartes. También permite la exploración de espacios de diseño más amplios y la creación rápida de prototipos de sistemas genéticos complejos. Este artículo ofrece una revisión exhaustiva del estado actual de la técnica y las perspectivas de futuro en la automatización del ciclo DBTL para el ensayo y la caracterización de biopartes estándar.

The design and construction of genetic systems, in silico, in vitro, or in vivo, often involve the handling of various pieces of DNA that exist in different forms across an assembly process: as a standalone "part" sequence, as an insert into a carrier vector, as a digested fragment, etc. Communication about these different forms of a part and their relationships is often confusing, however, because of a lack of standardized terms. Here, we present a systematic terminology and an associated set of practices for representing genetic parts at various stages of design, synthesis, and assembly. These practices are intended to represent any of the wide array of approaches based on embedding parts in carrier vectors, such as BioBricks or Type IIS methods (e.g., GoldenGate, MoClo, GoldenBraid, and PhytoBricks), and have been successfully used as a basis for cross-institutional coordination and software tooling in the iGEM Engineering Committee.

One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.

Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.

Multiobjective optimization has already been shown to be an appropriate tool to characterize and tune systems subject to multiple trade-offs among competing objectives. Here, we consider the dynamic regulation of a merging metabolic pathway motif. This motif appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids highly appreciated by the pharma, nutraceutical and the cosmetic industries. We present an approach to use multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor in the dynamic regulation of the metabolic pathway. We show how this approach can deal with the trade-offs between performance of the regulated pathway, robustness with respect to perturbations, and stability of the feedback loop. Our results suggest that the strategies for fine-tuning the tradeoffs among performance, robustness, and stability in dynamic pathway regulation are complex and it is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology not only useful but necessary.

One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by certain fluorescent molecules, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units, and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we provide a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression which returns normalized, gain independent, and comparable data, even if the acquisition was done at different device gain levels. The protocol embeds a Lineararity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluatesthe sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, andperform the uncertainty and variability assessment are available in an open access repository.

Plate readers are commonly used to measure cell growth and fluorescence, yet the utility and reproducibility of plate reader data is limited by the fact that it is typically reported in arbitrary or relative units. We have previously established a robust serial dilution protocol for calibration of plate reader measurements of absorbance to estimated bacterial cell count and for green fluorescence from proteins expressed in bacterial cells to molecules of equivalent fluorescein. We now extend these protocols to calibration of red fluorescence to the sulforhodamine-101 fluorescent dye and blue fluorescence to Cascade Blue. Evaluating calibration efficacy via an interlaboratory study, we find that these calibrants do indeed provide comparable precision to the prior calibrants and that they enable effective cross-laboratory comparison of measurements of red and blue fluorescence from proteins expressed in bacterial cells.