AI-Driven Synthetic Biology: From Genetic Design to Biomanufacturing

The aim of this workshop is to provide participants with a strong conceptual and practical foundation in the use of artificial intelligence for synthetic biology, from designing genetic components and biological circuits to optimizing biomanufacturing processes for real-world applications.

• Understand the fundamentals of synthetic biology and its role in modern biotechnology.
• Explore how AI and machine learning are applied in genetic design, DNA sequence analysis, and biological system modeling.
• Learn how AI can assist in designing genes, proteins, metabolic pathways, and synthetic biological circuits.
• Understand the connection between genetic engineering, biological data, and biomanufacturing workflows.
• Explore AI-enabled approaches for optimizing microbial production, enzyme design, and bio-based product development.
• Identify real-world applications of AI-driven synthetic biology in medicine, agriculture, sustainability, and industrial biotechnology.
• Understand the ethical, safety, and regulatory considerations related to synthetic biology and AI-based biological design.

• Introduction to synthetic biology and biological system engineering
• Role of AI, machine learning, and data science in modern biotechnology
• Genetic design workflow: DNA sequences, genes, promoters, proteins, and pathways
• AI for biological data interpretation and sequence-based prediction
• Applications in medicine, agriculture, sustainability, and industrial biotechnology
• Responsible use, biosafety, and ethical considerations in AI-driven synthetic biology

🛠️ Hands-on Activity: Google Colab / Notebook

DNA Sequence Feature Analysis using Python: Participants will learn how to analyze sample DNA sequence data using Python by calculating GC content, sequence length, k-mer patterns, and basic biological features useful for AI-based genetic design.

📅 Day 2: AI for Protein Design, Pathway Engineering, and Biological Prediction

• AI-assisted protein design and protein function prediction
• Introduction to protein sequences, amino acid features, and structure-function relationships
• Machine learning for enzyme selection and pathway optimization
• Generative AI and foundation models in biological design
• AI for synthetic gene circuits and metabolic engineering concepts
• Case studies: enzyme engineering, biomolecule production, biofuels, and sustainable chemicals

🛠️ Hands-on Activity: Google Colab / Notebook

Protein Sequence Property Prediction using Python: Participants will work with sample protein sequence data to extract basic features such as amino acid composition, molecular weight, and sequence patterns, followed by a simple ML-based prediction workflow.

📅 Day 3: AI in Biomanufacturing, Scale-Up, and Industrial Bio-Innovation

• Introduction to biomanufacturing and microbial cell factories
• AI for fermentation monitoring, yield prediction, and process optimization
• Digital twins and smart bioprocessing in synthetic biology
• Data-driven decision-making for scale-up and production efficiency
• AI in quality control, sustainability, and bio-based product development
• Future trends: automated biofoundries, self-driving labs, and intelligent biomanufacturing

🛠️ Hands-on Activity: Google Colab / Notebook

Bioprocess Yield Prediction and Optimization using Python: Participants will use a sample biomanufacturing dataset to build a simple machine learning model for predicting product yield based on process parameters such as temperature, pH, substrate concentration, and incubation time.

• Researchers in biotechnology, synthetic biology, molecular biology, and bioinformatics.
• Ph.D. scholars, postgraduate students, and academicians.
• Industry professionals from biotech, pharma, healthcare, agriculture, and biomanufacturing.
• Data science and bioinformatics learners interested in AI-based biological analysis.
• Professionals exploring protein design, genetic engineering, pathway optimization, and sustainable bioproduction.

• Explain the role of AI in accelerating synthetic biology research and development.
• Describe how genetic parts, pathways, and biological systems can be designed using computational and AI-based approaches.
• Interpret the importance of biological datasets in AI-driven genetic design and biomanufacturing.
• Understand how AI supports faster experimentation, prediction, optimization, and decision-making in biotechnology.
• Recognize the potential of synthetic biology in developing medicines, biofuels, biomaterials, sustainable chemicals, and engineered biological products.
• Apply workshop concepts to research, academic projects, biotechnology innovation, and industry-focused problem-solving.
• Evaluate opportunities and challenges in using AI for responsible and scalable biomanufacturing.