AI-Powered Synthetic Biology & Microbiome Engineering
Design Smarter Microbes with AI—The Future of Synthetic Biology
About This Course
Synthetic biology enables the rational design of biological systems for applications in healthcare, agriculture, biofuels, and environmental sustainability. At the same time, microbiome engineering is emerging as a powerful frontier, where microbial communities are manipulated to improve human health, crop resilience, and ecosystem restoration. However, designing stable microbial consortia and engineering complex pathways requires handling large datasets, nonlinear interactions, and multi-scale biological complexity.
This workshop introduces AI-powered approaches for synthetic biology and microbiome design, covering machine learning models for gene circuit prediction, metabolic engineering, and microbiome dynamics. Participants will explore dry-lab workflows integrating omics data, genome-scale modeling, and AI tools for strain optimization and microbial community engineering. Practical sessions will emphasize predictive modeling, simulation, and translational applications in precision medicine, industrial biotech, and One Health innovation.
Aim
This workshop aims to train participants in applying artificial intelligence and machine learning to advance synthetic biology and microbiome engineering. It focuses on designing engineered microbial systems, optimizing genetic circuits, and predicting microbiome behavior using data-driven tools. Participants will learn how AI accelerates strain design, metabolic pathway optimization, and community-level microbiome interventions. The program bridges biotechnology, computational biology, and next-generation bioengineering.
Workshop Objectives
- Understand AI applications in synthetic biology and microbiome engineering.
- Learn predictive modeling for gene circuits and engineered pathways.
- Explore AI-driven strain design and metabolic optimization strategies.
- Study microbiome dynamics, stability, and community-level interventions.
- Apply dry-lab AI workflows to real omics and microbiome datasets.
Workshop Structure
Day 1: Synthetic Biology Principles & Nanotechnology in Biological Systems
- Genetic circuit design
- Modular biological parts and chassis engineering
- CRISPR-based programmable systems
- Biofoundries and automation pipelines
- Functional nanomaterials for biosensing and drug delivery
- Nano–bio interfaces and surface engineering
- Smart nanoparticles and responsive systems
- Safety, toxicity, and regulatory considerations
- Tools:Python (Pandas, NumPy), R (tidyverse, phyloseq), QIIME2, Biopython, Galaxy Platform
Day 2: Microbiome Engineering & Computational Integration
- Host–microbiome interactions
- Metabolic pathway reprogramming
- Engineered probiotics and live biotherapeutics
- Agricultural and environmental microbiome manipulation
- Multi-omics integration (genomics, transcriptomics, metabolomics)
- Predictive modeling of microbial ecosystems
- Digital twins for biological systems
- AI-guided strain optimization
Day 3: Translational Workflows & Case Studies
- Scale-up strategies in bio-manufacturing
- Regulatory pathways (FDA, EMA, biosafety frameworks)
- Ethical and biosecurity considerations
- Nano-enabled CRISPR delivery systems
- Engineered microbiomes for gut health
- Synthetic microbes for carbon capture
- Smart nano-biosensors for disease detection
- Industrial fermentation optimization using AI
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/19/2026
IST 7:00 PM
Workshop Dates
02/19/2026 – 02/21/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Apply AI models to synthetic biology and microbiome datasets.
- Understand strain engineering workflows enhanced by predictive analytics.
- Design microbial consortia strategies for health, agriculture, and environment.
- Interpret microbiome behavior using computational and AI-driven approaches.
- Propose scalable engineered biology solutions for real-world applications.
Fee Structure
Student Fee
₹1999 | $70
Ph.D. Scholar / Researcher Fee
₹2999 | $80
Academician / Faculty Fee
₹3999 | $95
Industry Professional Fee
₹4999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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