About this Research Topic

Background

Systems biology and synthetic biology have revolutionized our understanding of biological systems and our ability to engineer them for various applications. The International Genetically Engineered Machine (iGEM) competition has been at the forefront of fostering innovation and collaboration in this field for nearly two decades. As we enter a new era of biological engineering, computational approaches have become increasingly crucial in designing, modeling, and analyzing complex biological systems. The integration of computational tools with experimental methodologies has led to significant advancements in areas such as whole-cell modeling, disease simulation, and the development of digital twins. These approaches are not only enhancing our fundamental understanding of biological processes but also paving the way for transformative applications in healthcare, agriculture, and environmental sustainability. Machine learning and artificial intelligence have emerged as powerful tools in systems biology, enabling researchers to extract meaningful insights from vast amounts of biological data. These technologies are facilitating the development of automated data pipelines and streamlined strategies for dynamic model design, striking a balance between realistic complexity and abstracted simplicity. As the field continues to evolve, there is a growing need for interdisciplinary collaboration and education. The next generation of scientists must be equipped with a diverse skill set that spans biology, computer science, engineering, and mathematics. Furthermore, engaging the public through various outreach initiatives is crucial for fostering understanding and support for synthetic biology research and its potential societal impacts.

The iGEM 2023 Collection aims to showcase cutting-edge research and innovative projects at the intersection of systems and synthetic biology approaches. Our goal is to highlight work that demonstrates the power of integrating experimental and computational methodologies to address complex biological challenges. We seek to inspire the next generation of scientists and engineers to push the boundaries of what's possible in biological engineering, while also promoting responsible innovation and ethical considerations in this rapidly advancing field.

Sub-themes include but are not limited to:
1. Computational Tools for Complex Biology: This theme focuses on novel algorithms, software, and computational frameworks designed to aid in the design, modeling, and analysis of complex biological systems. Projects may include tools for genetic circuit design, metabolic engineering optimization, or protein engineering.
2. Machine Learning and AI in Systems Biology: This theme explores the application of advanced machine learning techniques and artificial intelligence to unravel complex biological networks, predict system behavior, and guide experimental design.
3. Whole-Cell and Organ Modeling: This theme encompasses projects aimed at developing comprehensive computational models of cells or organs, with potential applications in drug discovery, disease modeling, and personalized medicine.
4. Synthetic Biology for Sustainability at a Systems Level: This theme highlights projects that leverage synthetic biology approaches to address environmental challenges, such as bioremediation, sustainable materials production, or climate change mitigation

Keywords: iGEM, Systems Biology, Synthetic Biology, Machine Learning, AI, Modeling, Sustainability

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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