Periodic Reporting for period 1 - KOA (Anticipate disease outbreaks in fish farming)
Okres sprawozdawczy: 2023-12-15 do 2024-10-14
The aquaculture industry faces major challenges from disease management and antibiotic dependency, which threaten productivity and sustainability. KOA Biotech's project targets these issues with two main goals:
- Optimized Pathogen Detection: Developing highly sensitive biosensors to detect pathogens early, enabling timely interventions and reducing antibiotic use.
- Market Alignment: Ensuring the biosensors meet industry needs through stakeholder input for smooth integration into aquaculture practices.
The project’s impact includes not only improved disease management and reduced economic losses for fish farmers but also a shift towards sustainable practices. By decreasing the need for antibiotics, this initiative aims to curb antimicrobial resistance and protect marine ecosystems, supporting both industry growth and environmental resilience.
- Optimized Pathogen Detection: Developing highly sensitive biosensors to detect pathogens early, enabling timely interventions and reducing antibiotic use.
- Market Alignment: Ensuring the biosensors meet industry needs through stakeholder input for smooth integration into aquaculture practices.
The project’s impact includes not only improved disease management and reduced economic losses for fish farmers but also a shift towards sustainable practices. By decreasing the need for antibiotics, this initiative aims to curb antimicrobial resistance and protect marine ecosystems, supporting both industry growth and environmental resilience.
The main activities and outcomes are summarized as follows:
1. Development of the Biosensor Library
o Identify and build specific constructs to create biosensors capable of detecting multiple bacterial pathogens.
o Evaluated biosensor performance under conditions mimicking commercial fish farms, using seawater with known pathogen concentrations.
o Developed a method for preserving biosensors by lyophilisation, ensuring stability for long-term use.
2. Design and Development of Optical Detection System
o Implemented an optical subsystem for uniform illumination and detection of fluorescence at specific wavelengths to capture biosensor responses accurately.
3. Machine Learning and Data Analysis
• Development of image-based detection algorithms:
o Implemented algorithms to extract quantitative information from captured images, including fluorescence intensity (GFP) and optical density (OD).
o Compared performance of KOA Biotech’s proprietary fluorescence detection algorithms with commercially available spectrofluorometers.
• Integration of machine learning models:
o Evaluated multiple machine learning models to select the most appropriate one for pathogen detection based on the dataset characteristics.
4. Data Visualization and Reporting
o Implemented a system for collecting, analyzing, and managing data generated by the device in aquaculture settings.
Outcomes:
• Validated biosensor platform: Successfully developed and validated a biosensor-based pathogen detection system, with high sensitivity and specificity for aquaculture pathogens.
• Functional prototype: Built and tested a fully integrated hardware prototype capable of automated pathogen detection, positioning KOA Biotech closer to market readiness.
1. Development of the Biosensor Library
o Identify and build specific constructs to create biosensors capable of detecting multiple bacterial pathogens.
o Evaluated biosensor performance under conditions mimicking commercial fish farms, using seawater with known pathogen concentrations.
o Developed a method for preserving biosensors by lyophilisation, ensuring stability for long-term use.
2. Design and Development of Optical Detection System
o Implemented an optical subsystem for uniform illumination and detection of fluorescence at specific wavelengths to capture biosensor responses accurately.
3. Machine Learning and Data Analysis
• Development of image-based detection algorithms:
o Implemented algorithms to extract quantitative information from captured images, including fluorescence intensity (GFP) and optical density (OD).
o Compared performance of KOA Biotech’s proprietary fluorescence detection algorithms with commercially available spectrofluorometers.
• Integration of machine learning models:
o Evaluated multiple machine learning models to select the most appropriate one for pathogen detection based on the dataset characteristics.
4. Data Visualization and Reporting
o Implemented a system for collecting, analyzing, and managing data generated by the device in aquaculture settings.
Outcomes:
• Validated biosensor platform: Successfully developed and validated a biosensor-based pathogen detection system, with high sensitivity and specificity for aquaculture pathogens.
• Functional prototype: Built and tested a fully integrated hardware prototype capable of automated pathogen detection, positioning KOA Biotech closer to market readiness.
Key Results:
• Validation of sensor sensitivity: We successfully validated the sensitivity of our whole-cell biosensors, confirming their ability to detect pathogens at early stages.
• Development of a biosensor-integrated device: We progressed in integrating these biosensors into a device, making it the first machine designed to operate with live bacteria. This is a critical advancement that enhances the potential for real-time, automated pathogen detection, which will lead to more efficient and sustainable management practices in aquaculture.
Challenges and Key Needs for Further Success:
• Policy and regulatory challenges: One of the main challenges we face is the lack of specific regulations for machines that operate with whole-cell engineered biosensors. Without clear legislative frameworks, it is difficult to advance the commercialization of such systems. We need policymakers to develop and support regulations that address safety measures and good practices for these types of biotechnological tools.
• Technical challenges in product validation: Another challenge we encountered was the complexity of validating our technology under real infection scenarios. We aim to track naturally occurring infections rather than artificially induced ones, which is more representative of actual farming conditions. This requires gathering sufficient real-world data to ensure the robustness and reliability of our technology.
• IPR and patent support: We have initiated the process of filing a new patent, but we recognize the need for more support in intellectual property rights (IPR) to protect our innovations and secure competitive advantage.
• Funding needs for further R&D: While we have made significant strides, additional funding is crucial to continue our R&D efforts. We require private investment to carry out further product validation, particularly in collaboration with market players, to ensure that the device meets industry demands and can be scaled effectively.
Expected Impact on the European Union:
• Strengthening local fish farming industry: The EU currently imports more than half of the fish consumed by its population, making it one of the largest fish consumers worldwide. Our technology can help local farmers by enabling early disease detection and reducing production losses due to infections. This, in turn, can boost the competitiveness of European aquaculture, promoting sustainable practices and reducing reliance on imports.
• Contribution to EU policy priorities: The outcomes of this project align with the EU’s goals of reducing antibiotic use and promoting sustainable aquaculture practices. By providing farmers with a tool to monitor pathogens early, we can directly contribute to the EU's efforts to enhance food security, reduce environmental impact, and support the growth of a sustainable aquaculture industry.
Policy Recommendations:
• Establishing regulatory frameworks for biosensor technologies: There is an urgent need for EU policymakers to create regulations that address the use of engineered biosensors in agriculture and aquaculture. These frameworks should focus on ensuring the safe use of live bacterial systems, promoting innovation while maintaining safety and environmental standards.
• Validation of sensor sensitivity: We successfully validated the sensitivity of our whole-cell biosensors, confirming their ability to detect pathogens at early stages.
• Development of a biosensor-integrated device: We progressed in integrating these biosensors into a device, making it the first machine designed to operate with live bacteria. This is a critical advancement that enhances the potential for real-time, automated pathogen detection, which will lead to more efficient and sustainable management practices in aquaculture.
Challenges and Key Needs for Further Success:
• Policy and regulatory challenges: One of the main challenges we face is the lack of specific regulations for machines that operate with whole-cell engineered biosensors. Without clear legislative frameworks, it is difficult to advance the commercialization of such systems. We need policymakers to develop and support regulations that address safety measures and good practices for these types of biotechnological tools.
• Technical challenges in product validation: Another challenge we encountered was the complexity of validating our technology under real infection scenarios. We aim to track naturally occurring infections rather than artificially induced ones, which is more representative of actual farming conditions. This requires gathering sufficient real-world data to ensure the robustness and reliability of our technology.
• IPR and patent support: We have initiated the process of filing a new patent, but we recognize the need for more support in intellectual property rights (IPR) to protect our innovations and secure competitive advantage.
• Funding needs for further R&D: While we have made significant strides, additional funding is crucial to continue our R&D efforts. We require private investment to carry out further product validation, particularly in collaboration with market players, to ensure that the device meets industry demands and can be scaled effectively.
Expected Impact on the European Union:
• Strengthening local fish farming industry: The EU currently imports more than half of the fish consumed by its population, making it one of the largest fish consumers worldwide. Our technology can help local farmers by enabling early disease detection and reducing production losses due to infections. This, in turn, can boost the competitiveness of European aquaculture, promoting sustainable practices and reducing reliance on imports.
• Contribution to EU policy priorities: The outcomes of this project align with the EU’s goals of reducing antibiotic use and promoting sustainable aquaculture practices. By providing farmers with a tool to monitor pathogens early, we can directly contribute to the EU's efforts to enhance food security, reduce environmental impact, and support the growth of a sustainable aquaculture industry.
Policy Recommendations:
• Establishing regulatory frameworks for biosensor technologies: There is an urgent need for EU policymakers to create regulations that address the use of engineered biosensors in agriculture and aquaculture. These frameworks should focus on ensuring the safe use of live bacterial systems, promoting innovation while maintaining safety and environmental standards.