About Workshop
This workshop introduces participants to the emerging field of AI-driven synthetic biology, where artificial intelligence is used to design, analyze, and optimize biological systems. The program focuses on how AI tools and computational approaches can support genetic design, pathway engineering, biological data interpretation, and scalable biomanufacturing. Participants will gain a practical understanding of how synthetic biology and AI together are transforming biotechnology, healthcare, agriculture, sustainability, and industrial bio-based production.
Aim
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.
What Participants Will Learn
- 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.
Structure
>📅 Day 1: Foundations of AI-Driven Synthetic Biology and Genetic 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.
Important Dates
Registration Ends
4: 30 PM IST
Workshop Dates
2026-06-26
5:30 PM IST
5:30 PM IST
What You Will Gain
Outcomes
- 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.
Who Should Attend
- 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.