The rapid evolution of synthetic biology and protein engineering has positioned biofoundries, automated laboratories for biological construction and analysis, as critical players in biotechnological innovation. As the integration of computational approaches with experimental methodologies becomes increasingly pivotal, this thesis seeks to bridge the gap between experimental strategies, computational techniques, and practical application in the biofoundry setting, enabling the efficient and scalable creation of novel proteins and synthetic biological systems. However, despite the potential of these technologies, several challenges remain. The complexity of biological systems, coupled with the diversity of potential protein structures and functions, makes traditional experimental approaches labor-intensive and often inefficient for data collection. Consequently, there is a pressing need for robust computational frameworks that can complement biofoundry operations, guiding the design process and reducing the trial-and-error inherent in protein engineering.
This thesis has several key objectives:
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- Designing effective experimental strategies: To develop reproducible, standardized, and data centric experimental methods to facilitate integration of computational tools and models for the same. Most current computational models make use of publicly available data is subject to high attrition rate due to publication survior bias, and often don’t work with sufficient experiemental negative data. It is important to design experiments that maximizes the amount of data collected without compromising on quality.
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- Integration of High-Throughput Tools, Computational Models and Biofoundries: To develop and validate computational models and experimental tools that can predict biosensor/protein/DNA activity, stability, and function based on sequence data. By integrating these tools into biofoundry workflows, the design process can be streamlined, predictability of outcomes are improved, and accessible to other biofoundries.
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- Artificial Intelligence Applications: To explore the use of artificial intelligence algorithms in analyzing large datasets generated from high-throughput techniques. By employing techniques such as deep learning, to uncover hidden patterns and correlations that can inform the design of novel proteins with desirable properties.
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- Optimization of Biosensor Activity: To utilize high-throughput experimental and computational tools to optimize various parameters in a sensor protein’s activity, such as signal and background.
Chapter 1 serves as an introduction to the thesis on computational approaches to biofoundry-based protein engineering and synthetic biology, outlining the significance of biofoundries as automated laboratories that streamline the production of biological materials. It discusses key gaps in Biofoundry workflows and software that are essential to be addressed to be able to perform replicable, scalable, and efficient automated experiments. The chapter highlights the integration of computational techniques—such as analytics and machine learning—into biofoundry workflows to enhance efficiency and predictability in a long standing investigation of protein engineering. It also delineates the thesis's objectives, which include developing predictive models, integrating experimental data, and creating optimization algorithms. The chapter concludes with an overview of the subsequent chapters, setting the stage for a detailed exploration of how these computational methods can drive advancements in synthetic biology.
Chapter 2 addresses the development of workflows, software and tools to streamline processes common in synthetic biology and protein engineering using biofoundries. Here the tools for data visualization, analysis, logging, sharing, modelling, machine operatability are exhibited. Case studies illustrate the successful application of these tools in biofoundries, demonstrating their impact on data management and project outcomes. The chapter focuses on tools programmed using Python, with user interfaces developed on Streamlit, which provide a base for further chapters.
Chapter 3 examines the importance of the collection of data when employing AI. It discusses high-throughput methods such as biosensors, which enable real-time monitoring of protein activity; Fluorescence-Activated Cell Sorting (FACS), which facilitates rapid screening of protein variants; and long-read sequencing, which provides one-shot comprehensive genomic information. The chapter emphasizes the integration of data from these diverse sources to enhance AI model training, and discusses different experimental and deep learning strategies, while also addressing challenges related to data variability and quality. The versitality of the method is demonstrated by engineering transcription factor DmpR and enzyme MPH as a proof of concept.
Chapter 4 discusses the innovative use of biofoundries for the rapid production and identification of protein mutants (1-3 site-directed mutations), focusing on an automated workflow that can construct and analyze of more than 2,034 samples simultaneously using Nanopore sequencing technology. The chapter highlights the efficiency of automated systems in mutant construction, which leverage synthetic biology tools for high-throughput experimentation. It emphasizes the advantages of Nanopore sequencing, allowing real-time sequencing of long DNA strands for multiplexed identification of genetic variations. This integration not only accelerates the screening process but also expands opportunities in protein engineering. Challenges such as data analysis and mutant characterization accuracy are acknowledged, while future advancements in sequencing and automation promise to enhance these workflows further. The outcomes of the DmpR and MPH mutant proteins are discussed in this chapter.
Chapter 5 focuses on the critical techniques for assembling long DNA sequences, which are essential for the development of novel proteins or organisms. Here Dsembler, a computational tool aimed to identifying the oligomer combinations to best result in a successful long DNA sequence is introduced. The chapter also discusses the use of structure prediction models to analyse the functionality of predicted proteins.
Chapter 6 presents a comprehensive analysis of the results, aiming to deliver a clear understanding of the overall research findings. The chapter summarizes the overall conclusions of the study, emphasizing the significance of cohesive architecture and interdisciplinary collaboration in advancing biofoundry development.
The integration of computational approaches with biofoundry technologies in protein engineering and synthetic biology holds significant promise for advancing the field:
Utilizing computational models in protein design significantly enhances efficiency by streamlining the design process for engineered proteins. This approach reduces both time and costs, as computational predictions minimize the need for extensive experimental iterations, thereby lowering the financial and temporal resources required for protein engineering projects. Additionally, biofoundries equipped with these computational tools can operate at higher throughput, allowing for the rapid testing of numerous protein variants and ultimately increasing overall productivity.
Artificial intelligence can significantly improve the accuracy and predictability of protein engineering efforts. Advanced algorithms, particularly Deep Learning, enable more reliable inferences of the patterns between genotype and phenotype, resulting in engineered proteins that are more likely to function as intended. Furthermore, with enhanced datasets and models, multiple independent parameters can be targeted simultaneously, increasing the chances of identifying rare yet ideal candidates for specific applications.
The combination of computational methods and biofoundries fosters innovation across a range of applications. In biosensors and diagnostics, the development of novel biosensors using engineered proteins can result in rapid and accurate diagnostic tools for environmental and health monitoring. Additionally, engineered proteins can be designed for bioremediation solutions, addressing critical environmental issues such as plastic degradation and waste management. In therapeutics development, this approach accelerates the discovery of large therapeutic proteins and antibodies, helping to meet urgent healthcare needs, including the development of vaccines and biologics. Moreover, enhanced enzyme design for industrial processes contributes to more efficient manufacturing methods, ultimately reducing waste and energy consumption.
The integration of machine learning in analyzing biofoundry data offers numerous advantages, particularly in uncovering hidden patterns and correlations within large datasets. Deep learning techniques can reveal insights that inform improved design strategies and optimization processes. Additionally, the capacity for adaptive learning enables continuous refinement of computational models based on experimental data, enhancing their accuracy over time and contributing to more effective protein engineering outcomes.
The convergence of computational techniques and biofoundry technologies fosters interdisciplinary collaboration, bringing together scientists from diverse fields such as bioinformatics, molecular biology, and engineering to tackle complex challenges. This cross-disciplinary research leads to innovative solutions and broader perspectives on problem-solving. Additionally, the growing demand for expertise in computational biology and biofoundry technologies enhances educational programs and training initiatives, effectively preparing the next generation of scientists and engineers for future advancements in these fields.