DBTL Cycle Advancements: Software Automation Elevates Biotech R&D Workflows

Picture of TeselaGen Team

TeselaGen Team


Biotech research and development (R&D) is undergoing a transformative shift with the integration of automation. This change is redefining the traditional Design-Build-Test-Learn (DBTL) cycle, leading to unprecedented advancements in speed, efficiency, and precision. In this landscape, strategic decisions about deployment — choosing between cloud and on-premises solutions — have become crucial, catering to the varied needs of biotech industries and research environments.

The Evolving DBTL Cycle

Design with Innovation: The intricate Design phase of synthetic biology and biotechnology R&D, encompasses a range of crucial activities, including Protein Design, where researchers might select natural enzymes or design new proteins as necessary; Genetic Design, which involves translating amino acid sequences into coding sequences (CDS), designing ribosome binding sites (RBS), and planning operon architecture; and Assay Design, where biochemical reaction conditions are established. Central to this phase is Assembly Design, a critical step involving the breakdown of plasmids into fragments, laying the groundwork for constructing DNA constructs that are essential for building desired experiments.

Assembly Design requires consideration of factors such as restriction enzyme sites, overhang sequences, and GC content, which are essential for efficient assembly. Traditional manual design methods are often susceptible to errors in this context, leading to failed experiments or unintended consequences. Precision is vital to avoid such costly mistakes and time-consuming troubleshooting. Additionally, as projects scale up in complexity, there’s an increasing need for solutions that can manage numerous variables and large datasets efficiently, without compromising on speed or accuracy.

Automation is playing a key role in addressing these challenges. It brings in advanced software that can generate detailed DNA assembly protocols, tailored to the specific needs of each project. These automated protocols factor in critical elements like the selection of appropriate cloning methods, such as Gibson assembly or Golden Gate cloning, and the strategic arrangement of DNA fragments in an assembly reaction. This level of automation not only enhances precision and reduces errors but also scales efficiently with project complexity. By automating these aspects of the design phase, researchers can achieve higher efficiency and accuracy in their biotech endeavors, paving the way for more successful and innovative outcomes in synthetic biology research.

Build with Precision: In the Build phase of synthetic biology and biotechnology, automation plays a crucial role in enhancing precision and efficiency. Assembling DNA constructs is a complex task requiring high accuracy, as even minor errors can lead to significant deviations in outcomes. Automated liquid handlers, such as those from Labcyte, Tecan, Beckman Coulter, and Hamilton Robotics, are instrumental in this phase. They offer high-precision pipetting, essential for processes like PCR setup, DNA normalization, and plasmid preparation. Additionally, managing high-throughput workflows, particularly plate-based systems, becomes more efficient with these automated solutions. This phase also deals with the complexity of inventory management, ensuring all necessary reagents and components are accurately tracked and utilized.

Further augmenting the Build phase, integration with DNA providers like Twist Bioscience, IDT (Integrated DNA Technologies), and GenScript streamlines the synthesis and assembly process. These collaborations allow for a seamless flow of custom DNA sequences into the automated workflows. On the software front, platforms like TeselaGen’s software orchestrate the entire process, managing protocols and tracking samples across different lab equipment. This sophisticated software support not only enhances the control and monitoring of the workflows but also ensures high-throughput plate management and efficient inventory systems. These technological advancements collectively contribute to a more efficient, scalable, and error-minimized environment, crucial for the successful execution of complex biotech projects.

Test with Speed: In the Test phase of synthetic biology and biotechnology, automation has been pivotal in enhancing the speed and efficiency of sample analysis. High-throughput screening (HTS), a mainstay of this phase, is facilitated by automated liquid handling systems like the Beckman Coulter Biomek Series and the Tecan Freedom EVO series. These systems are crucial for precise and rapid assay setups. Complementing these are automated plate readers and analyzers such as the EnVision Multilabel Plate Reader from PerkinElmer and the BioTek Synergy HTX Multi-Mode Reader, which efficiently assess diverse assay formats.

Robotics and integrated systems further streamline operations, moving samples seamlessly between different stations. Omics technologies also play a significant role, with Next-Generation Sequencing (NGS) platforms like Illumina’s NovaSeq and Thermo Fisher’s Ion Torrent systems providing rapid genotypic analysis. Automated mass spectrometry setups, including Thermo Fisher’s Orbitrap, are instrumental in proteomic analysis, while metabolomics platforms leverage technologies like NMR for comprehensive metabolic profiling.

In this data-intensive phase, the analysis and interpretation of results are critical. Here, TeselaGen’s software platform emerges as a key player. It exemplifies how advanced bioinformatics tools, integrated with AI and machine learning, can effectively manage and analyze the vast datasets generated. TeselaGen’s platform, along with other tools like CLC Genomics Workbench and Geneious, enable researchers to efficiently process sequencing data, identify genetic mutations, and analyze gene expression profiles. The integration of AI aids in pattern recognition and hypothesis generation, particularly useful in phenotypic screenings. This level of automation and sophisticated data analysis is transforming the Test phase, making it not just faster but also more insightful, aiding researchers in swiftly advancing through the biotech R&D pipeline.

Learn with Insight: The Learn phase in synthetic biology and biotechnology is undergoing a significant transformation thanks to automation and machine learning (ML). In this phase, ML algorithms are instrumental in analyzing vast datasets generated from experiments, enabling researchers to uncover complex patterns and insights that are beyond human capability to detect manually. This automation not only accelerates the learning process but also opens new avenues for innovation, as it allows for more sophisticated analyses and predictions based on the accumulated data.

A prime example of this revolution is seen in studies like the optimization of tryptophan metabolism in yeast, as reported in Nature Communications. In this study, where TeselaGen participated, ML models were trained using extensive experimental data to make accurate genotype-to-phenotype predictions. Such applications showcase how ML can guide metabolic engineering by learning from experimental datasets, predicting outcomes, and aiding in the design of more efficient metabolic pathways. This approach represents a paradigm shift in how biological data is interpreted and utilized, paving the way for more targeted and efficient research methodologies in synthetic biology and biotechnology.

Strategic Deployment Choices: Cloud vs. On-Premises

The DBTL cycle is a pivotal framework in synthetic biology and biotech R&D, necessitating the harmonious interplay of people, infrastructure, hardware, and software. Each element is integral to the cycle’s success, especially in an industry context characterized by large data volumes. This complexity gives rise to diverse needs, encompassing scalability as well as regulatory and compliance considerations. Consequently, the flexibility of software deployment paths becomes crucial for meeting these varied requirements. This section will explore and compare the two primary deployment methodologies in this context: on-premises (on-prem) and cloud-based solutions, highlighting how each aligns with the unique demands of the biotech industry.

On-Premises Deployment: On-premises solutions in biotech and synthetic biology R&D offer direct control over IT infrastructure, essential for projects with specific compliance and regulatory requirements. These solutions allow extensive customization, enabling organizations to tailor their systems to unique needs, including specialized configurations. Security is robust, with the advantage of physical control over data. However, scalability and collaboration can be more challenging, as expanding resources requires additional physical infrastructure, and collaboration is limited for non-co-located teams. While initial costs are high due to upfront investment in infrastructure, on-premises solutions can be cost-effective for large-scale, consistent workloads.

Cloud Deployment: Cloud deployment is characterized by its exceptional scalability and facilitation of collaboration, especially for geographically dispersed teams. It offers advanced analytics capabilities and easy access to data and tools, making it ideal for dynamic projects. Cloud services maintain compliance with a wide range of standards and regulations, though specific regulatory compliance may be a concern. Security measures are continuously updated to meet evolving threats. The cost structure is typically pay-as-you-go, reducing upfront costs but potentially leading to higher long-term expenses for data-intensive projects. Cloud deployment provides a flexible and efficient option for a diverse range of biotech research projects.

TeselaGen: Advancing Biotech R&D through Innovative DBTL Cycle Management

Understanding the critical role of software in effectively supporting the automation of the DBTL framework in biotech R&D, a key consideration arises in the strategic deployment of such technology. TeselaGen’s software platform addresses this by offering flexible deployment options, both cloud and on-prem, catering to the diverse security, regulatory and compliance needs within the biotech industry. Importantly, TeselaGen doesn’t just provide deployment flexibility; it supports the entire DBTL cycle end-to-end. This makes it a comprehensive solution for various aspects of biotech research and development, aligning seamlessly with each phase of the DBTL framework.

Design Phase

In the Design phase, TeselaGen’s platform excels with its advanced DNA assembly protocol generation algorithms, particularly effective for large, complex combinatorial libraries. The platform meticulously generates assembly protocols, enhancing the precision and efficiency of genetic construct design. It ensures compatibility among DNA fragments, carefully considering factors such as restriction enzyme sites and GC content.

TeselaGen’s software also excels in optimizing the use of existing lab inventory. When partitioning large DNA sequences for assembly, it intelligently assesses what is already available in the lab, thereby reducing the need for additional DNA synthesis orders. This approach not only proves cost-efficient but also minimizes turnaround times, ensuring a more streamlined and economical design process in synthetic biology and biotech R&D.

Build Phase

In the Build phase, TeselaGen’s platform significantly enhances precision and efficiency. It integrates with automated liquid handlers from Labcyte, Tecan, Beckman Coulter and others, facilitating high-accuracy DNA assembly for tasks like PCR setup and plasmid preparation. This integration, crucial for constructing accurate DNA sequences, is bolstered by TeselaGen’s ability to manage these processes effectively.

Furthermore, TeselaGen partners with DNA synthesis providers such as Twist Bioscience to streamline custom DNA sequence integration into lab workflows. Complemented by robust inventory management capabilities, the platform ensures optimal resource utilization and reduces waste or delays. Additionally, TeselaGen handles plate-based high-throughput workflows, automating their execution while managing lab inventory and freezer space. This positions TeselaGen as a comprehensive LIMS and workflow automation tool, adept at managing the complexities of the Build phase in biotech R&D.

Test Phase

In the Test phase of the DBTL framework in biotechnology, TeselaGen’s platform offers advanced capabilities for managing and analyzing experimental data. The system acts as a centralized hub, efficiently collecting data from various analytical and monitoring equipment, and integrating it seamlessly with the design-build process. This consolidation facilitates the transformation of raw data into a format ready for in-depth analysis, predictive modeling, and machine learning, essential for understanding and optimizing biotechnological processes.

Moreover, TeselaGen ensures standardized data handling with a unified platform for data input, storage, and retrieval, including a RESTful API for programmatic access. This standardization is complemented by features like automatic dataset validation, customizable assay descriptors, and integrated data visualization tools. These capabilities not only streamline data management in the Test phase but also enable researchers to quickly interpret and utilize their findings, accelerating the progression through the DBTL cycle in biotech R&D.

Learn Phase

In the Learn phase of the DBTL cycle, TeselaGen’s Discover Module brings crucial advancements in biotechnology R&D. The module employs predictive models to forecast various biological product phenotypes, leveraging quantitative and qualitative data. This capability is enhanced with advanced embeddings representing DNA, proteins, and chemical compounds, facilitating efficient and accurate training of predictive models. Additionally, generative models in the Discover Module enable the creation of new leads by learning from and sampling the molecular distributions in training datasets, a powerful approach for innovating in biotech research.

Moreover, TeselaGen’s platform incorporates evolutionary models, utilizing Bayesian optimization to guide experimental design based on empirical lab data. This method includes the use of pre-trained models, aiding in navigating complex research landscapes. The Discover Module also allows for the deployment of custom AI models, integrating deep learning techniques for modeling and optimizing biomolecules. This flexibility and advanced modeling capability make TeselaGen a versatile and potent tool in the Learn phase, enabling researchers to extract deep insights and direct future biotechnological endeavors effectively.

The comprehensive capabilities of TeselaGen, spanning the entire DBTL cycle from design to learn, highlight its role as a pivotal solution in the biotech R&D landscape. As we have seen, TeselaGen’s platform not only provides flexibility in deployment but also showcases cutting-edge advancements in each phase of the cycle. This positions TeselaGen at the forefront of the industry’s march towards innovation. It’s clear that AI-powered automation, as demonstrated by TeselaGen, is crucial for groundbreaking developments. TeselaGen’s role in shaping this future underscores the importance of such platforms in tackling the next wave of challenges in synthetic biology and biotechnology.

Embracing the Future of Biotech R&D: The Role of AI-Driven Automation

As we look towards the future of biotech R&D, it’s evident that automation, particularly AI-driven automation, is the cornerstone of major advancements in the field. This evolution is not just about enhancing efficiency; it’s about unlocking new potential in research and development. Platforms like TeselaGen epitomize this revolution, offering end-to-end support for the DBTL cycle and versatile deployment options. TeselaGen stands as a prime example of how sophisticated automation tools are driving innovation and enabling significant breakthroughs in tackling complex biological challenges.

For those aiming to be at the vanguard of synthetic biology and biotechnology, leveraging the power of advanced automation platforms like TeselaGen is essential. These tools represent the next step in a journey towards more profound discoveries and innovations. To explore the cutting-edge capabilities of TeselaGen and how they are shaping the future of biotech R&D, visit teselagen.com. For insights into their on-premises solutions that align with specific regulatory and compliance needs, see landing.teselagen.com/onprem. Embrace these technologies and join the wave of transformative research defining the new era of biotechnology.