Periodic Reporting for period 1 - BioCircus (Improving bioproduction through dynamic regulation circuits)
Reporting period: 2022-06-01 to 2024-11-30
Summary of the context and overall objectives of the project
Bioproduction as the solution to the problem of the current chemical manufacturing industry – Current world’s chemical production is expected to double by 2030. Nevertheless the production of materials, fuels, pharmaceuticals, fertilizers, foods and other types of chemicals is still heavily reliant on traditional chemical synthesis based on unrenewable, polluting, fossil fuels. Using microorganisms to substitute the chemical synthesis and move to greener, bio-based processes is a thriving field known as bioproduction, which moves away from traditional synthesis by improving the production efficiency of some compounds compared to traditional chemical synthesis and producing new compounds with no chemical synthesis route. Bioproduction of chemicals and materials entails a renewable and economically viable alternative, easing the transition towards a circular “bioeconomy”, one of the EU’s political priorities. Synthetic biology (an interdisciplinary field that combines biological, engineering, and computational principles) underpins modern bioproduction.
Bioproduction’s scalability problem – Until recently, biomanufacturing was at a much lower development level than engineering, meaning that researchers had to design and build genetic devices essentially one by one. Fortunately, advances in systems biology, genetic engineering and laboratory automation have allowed researchers to quickly create hundreds of functional producer microbial strains expressing complex metabolic pathways in the laboratory in short periods of time. However, these strains often show poor industrial performance (low production, slow production rate...) under industrial settings. The engineered microorganisms do not adapt well outside laboratory conditions, due to external factors originating from the large-scale bioreactors: pressure, acidity changes, accumulation of toxic metabolites, agitation, nutrient availability and heat transfer among others. The strains are often not responsive to the external stimuli that may arise during industrial fermentation. This stress can cause the strain to stop producing the heterologous pathway or expel it altogether. This leads to production processes that behave poorly when tested during the scale-up phase which prevents the translation of bioproduction projects into economically feasible processes for the industry.
Towards an autonomously regulated bioproduction – Synthetic biology is based on the creation of genetic circuits, i.e. DNA parts or sequences assembled to give new functionalities to the cell. One type of genetic circuit are the so-called whole-cell biosensors, which sense a particular input, typically the presence of a chemical or environmental condition like temperature or pH and react to it in a specific way (e.g. a measurable signal produced by an actuator) (see Fig.1a). Biosensor-based dynamic regulation of microbial production pathways is a strategy used to control genetic circuits based on a feedback loop that regulates the production of some target metabolite to keep its concentration at the desired levels. The biosensor detects the presence of the metabolite and triggers the activation or inhibition of some genes of the metabolic pathway (see Fig.1b) making the system more responsive to possible detrimental conditions. Dynamic regulation is found in nature to control the production of some key molecules and has been finely optimised through evolution. However, there is at present no known methodology that allows to finely tune and optimise synthetic dynamic regulation circuits. A class of biosensors used to engineer dynamic regulation are those based on transcription factors (TF). TF-based circuits will be the foundation of this project. TFs are proteins that can control the expression of genes by binding to specific DNA sequences. Some TFs are triggered after binding to a metabolite. This is the case of the FdeR transcription factor, which is activated after binding to naringenin. Once activated this protein releases itself from the DNA sequence upstream of the target gene activating its expression. Genetic circuits based on the FdeR transcription factor can be used to sense and react to specific concentrations of extracellular or intracellular naringenin concentrations13. When the regulated gene is involved in the naringenin pathway, it can increase or reduce its production (Fig.1b). Even though dynamic regulation circuits can potentially improve the scalability of bioproduction strains, the current state-of-the-art shows that, comparatively to the number biomanufacturing articles, very few biosensors and dynamic regulation circuits have been described.
Objectives:
O1. Build new biosensor circuits that efficiently detect a range of extracellular concentrations (1 μM to 1 mM) of naringenin and other relevant intermediates of the pathway in E. coli [WP1].
O2. Build dynamic regulation circuits that control naringenin production in E. coli in response to the accumulation of intracellular naringenin and intermediates trying at the same time to maximize the yield. Test and compare the robustness (i.e. effective, and autonomous adaptation to changing concentrations of these metabolites) and the production profiles to obtain a dynamic production behaviour [WP2].
O3. Test the developed circuits and models in a pre-industrial setting during the industrial placement. Adjust the methods and models to improve the robustness of the developed circuits in a real-world bioproduction scenario [WP2].
O4. Develop integrated data models using the gathered sensing, bioproduction and robustness data to improve the biosensors and the dynamic control circuits performance over several iterations of the cycle [WP3].
In addition, management, training, and dissemination/exploitation/communication objectives are included: O5. Train the prospective fellow in new scientific, transferable, and transversal skills useful for his career development [WP4] like laboratory automation techniques, machine learning, dynamic characterization of biosensors, management of a biofoundry infrastructure, grant writing, student co-tutoring and organization of dissemination events. O6. Disseminate and communicate project results and outcomes and design an exploitation strategy [WP5]. O7. Managing both the scientific and the financial aspects of the project [WP6].
Bioproduction’s scalability problem – Until recently, biomanufacturing was at a much lower development level than engineering, meaning that researchers had to design and build genetic devices essentially one by one. Fortunately, advances in systems biology, genetic engineering and laboratory automation have allowed researchers to quickly create hundreds of functional producer microbial strains expressing complex metabolic pathways in the laboratory in short periods of time. However, these strains often show poor industrial performance (low production, slow production rate...) under industrial settings. The engineered microorganisms do not adapt well outside laboratory conditions, due to external factors originating from the large-scale bioreactors: pressure, acidity changes, accumulation of toxic metabolites, agitation, nutrient availability and heat transfer among others. The strains are often not responsive to the external stimuli that may arise during industrial fermentation. This stress can cause the strain to stop producing the heterologous pathway or expel it altogether. This leads to production processes that behave poorly when tested during the scale-up phase which prevents the translation of bioproduction projects into economically feasible processes for the industry.
Towards an autonomously regulated bioproduction – Synthetic biology is based on the creation of genetic circuits, i.e. DNA parts or sequences assembled to give new functionalities to the cell. One type of genetic circuit are the so-called whole-cell biosensors, which sense a particular input, typically the presence of a chemical or environmental condition like temperature or pH and react to it in a specific way (e.g. a measurable signal produced by an actuator) (see Fig.1a). Biosensor-based dynamic regulation of microbial production pathways is a strategy used to control genetic circuits based on a feedback loop that regulates the production of some target metabolite to keep its concentration at the desired levels. The biosensor detects the presence of the metabolite and triggers the activation or inhibition of some genes of the metabolic pathway (see Fig.1b) making the system more responsive to possible detrimental conditions. Dynamic regulation is found in nature to control the production of some key molecules and has been finely optimised through evolution. However, there is at present no known methodology that allows to finely tune and optimise synthetic dynamic regulation circuits. A class of biosensors used to engineer dynamic regulation are those based on transcription factors (TF). TF-based circuits will be the foundation of this project. TFs are proteins that can control the expression of genes by binding to specific DNA sequences. Some TFs are triggered after binding to a metabolite. This is the case of the FdeR transcription factor, which is activated after binding to naringenin. Once activated this protein releases itself from the DNA sequence upstream of the target gene activating its expression. Genetic circuits based on the FdeR transcription factor can be used to sense and react to specific concentrations of extracellular or intracellular naringenin concentrations13. When the regulated gene is involved in the naringenin pathway, it can increase or reduce its production (Fig.1b). Even though dynamic regulation circuits can potentially improve the scalability of bioproduction strains, the current state-of-the-art shows that, comparatively to the number biomanufacturing articles, very few biosensors and dynamic regulation circuits have been described.
Objectives:
O1. Build new biosensor circuits that efficiently detect a range of extracellular concentrations (1 μM to 1 mM) of naringenin and other relevant intermediates of the pathway in E. coli [WP1].
O2. Build dynamic regulation circuits that control naringenin production in E. coli in response to the accumulation of intracellular naringenin and intermediates trying at the same time to maximize the yield. Test and compare the robustness (i.e. effective, and autonomous adaptation to changing concentrations of these metabolites) and the production profiles to obtain a dynamic production behaviour [WP2].
O3. Test the developed circuits and models in a pre-industrial setting during the industrial placement. Adjust the methods and models to improve the robustness of the developed circuits in a real-world bioproduction scenario [WP2].
O4. Develop integrated data models using the gathered sensing, bioproduction and robustness data to improve the biosensors and the dynamic control circuits performance over several iterations of the cycle [WP3].
In addition, management, training, and dissemination/exploitation/communication objectives are included: O5. Train the prospective fellow in new scientific, transferable, and transversal skills useful for his career development [WP4] like laboratory automation techniques, machine learning, dynamic characterization of biosensors, management of a biofoundry infrastructure, grant writing, student co-tutoring and organization of dissemination events. O6. Disseminate and communicate project results and outcomes and design an exploitation strategy [WP5]. O7. Managing both the scientific and the financial aspects of the project [WP6].
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
The project focused on advancing biosensor development using transcription factor (TF)-based biosensors, with applications in bioengineering and biomanufacturing. Significant progress was made towards building computational tools and models for biosensor design, optimizing genetic circuits, and improving predictive capabilities for TF-ligand interactions. Key achievements include:
• Development of computational resources to assist researchers in identifying potential TF-ligand pairs, thereby speeding up biosensor circuit prototyping.
• Creation of machine learning (ML)-driven models to predict TF-ligand interactions, although further refinement of specificity is needed.
• A comprehensive analysis of biosensor applications for bioproduction, highlighting the use of biosensors for both screening and dynamic regulation in production systems.
• Development of strategies for biofoundry creation, incorporating automation and AI integration to accelerate biomanufacturing pathways.
• Detailed characterization of a biosensor library, optimizing its performance under various conditions, leading to improved models for biosensor behavior in dynamic environments.
These results have strengthened the understanding of how biosensor circuits function and how they can be effectively applied in both industrial and environmental contexts.
• Development of computational resources to assist researchers in identifying potential TF-ligand pairs, thereby speeding up biosensor circuit prototyping.
• Creation of machine learning (ML)-driven models to predict TF-ligand interactions, although further refinement of specificity is needed.
• A comprehensive analysis of biosensor applications for bioproduction, highlighting the use of biosensors for both screening and dynamic regulation in production systems.
• Development of strategies for biofoundry creation, incorporating automation and AI integration to accelerate biomanufacturing pathways.
• Detailed characterization of a biosensor library, optimizing its performance under various conditions, leading to improved models for biosensor behavior in dynamic environments.
These results have strengthened the understanding of how biosensor circuits function and how they can be effectively applied in both industrial and environmental contexts.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
The project has made significant progress toward delivering scientific impact, achieving several key technological advancements and innovations aligned with its objectives. The primary scientific and technological achievements include:
1. Development of Advanced Biosensor Circuits: The project successfully designed and built new biosensor circuits capable of detecting extracellular concentrations of naringenin and related metabolites within the range of 1 μM to 1 mM. This advancement not only enhances detection sensitivity but also contributes to the field of synthetic biology by providing tools for more efficient metabolic engineering.
2. Real-World Testing and Model Adjustments: The circuits and models were tested in a pre-industrial setting, allowing for practical adjustments that improved their robustness in bioproduction scenarios. This real-world application has increased the project's relevance to industrial processes and demonstrated its potential for scalability.
3. Integrated Data Models: The development of integrated data models has allowed for the analysis and improvement of biosensor and circuit performance through iterative feedback loops. This approach not only enhances the understanding of the biological systems involved but also provides a framework for future research in similar areas.
In addition to these scientific outputs, the project has made strides in contributing to economic and societal impacts, particularly through its potential applications in industrial biotechnology. The innovations in biosensing and metabolic control can lead to more sustainable production methods, aligning with the European Green Deal’s objectives of promoting environmental sustainability and reducing carbon footprints.
1. Development of Advanced Biosensor Circuits: The project successfully designed and built new biosensor circuits capable of detecting extracellular concentrations of naringenin and related metabolites within the range of 1 μM to 1 mM. This advancement not only enhances detection sensitivity but also contributes to the field of synthetic biology by providing tools for more efficient metabolic engineering.
2. Real-World Testing and Model Adjustments: The circuits and models were tested in a pre-industrial setting, allowing for practical adjustments that improved their robustness in bioproduction scenarios. This real-world application has increased the project's relevance to industrial processes and demonstrated its potential for scalability.
3. Integrated Data Models: The development of integrated data models has allowed for the analysis and improvement of biosensor and circuit performance through iterative feedback loops. This approach not only enhances the understanding of the biological systems involved but also provides a framework for future research in similar areas.
In addition to these scientific outputs, the project has made strides in contributing to economic and societal impacts, particularly through its potential applications in industrial biotechnology. The innovations in biosensing and metabolic control can lead to more sustainable production methods, aligning with the European Green Deal’s objectives of promoting environmental sustainability and reducing carbon footprints.