Next-Gen Bioengineering leading to AI-Powered Synthetic Biology Innovations

This workshop aims to equip participants with advanced data science skills integrating AI, automation, and large-scale data engineering. Attendees will gain hands-on experience in modeling, multi-language data workflows, and cutting-edge machine learning techniques. The program emphasizes end-to-end pipeline design, from statistical foundations to deployment-ready AI systems. Participants will develop practical expertise in handling high-dimensional, multi-terabyte datasets and building predictive models that scale.

• Understand and apply advanced statistical and mathematical foundations for AI modeling.
• Build high-performance AI workflows integrating Python and R for large-scale data.
• Design and implement predictive and recommendation systems using ML and DL techniques.
• Develop end-to-end AI pipelines with deployment-ready visualization and dashboards.
• Apply best practices in optimization, high-dimensional analysis, and distributed computing.

📅 Day 1: The Convergence: AI as the Engine for Synthetic Biology

• Core Objective: Understand the foundations of synthetic biology and explore how artificial intelligence accelerates every step of the Design–Build–Test–Learn (DBTL) cycle.
• Foundations of synthetic biology and the role of AI in modern biological design
• AI-driven acceleration of the Design–Build–Test–Learn (DBTL) cycle
• Conjugate priors, likelihood estimation, and Markov Chain Monte Carlo (MCMC) sampling
• Optimization strategies for AI-enabled synthetic biology workflows
• Custom adaptive optimizer design using Adam and hardware acceleration
• Applications of AI in gene design, protein prediction, cellular engineering, and SynBio workflows

🛠️ Hands-on:

• Hands-on Lab: Visualize a 3D loss surface optimization and write a custom adaptive optimizer using hardware acceleration.
• Hands-on Lab: Explore AI-assisted synthetic biology workflows using gene, protein, and cell-design tools.

🧰 Tools Covered: Matplotlib, AlphaFold, GeneFormer, Cell-Pioneer, SynBioCAD

📅 Day 2: AI-Enabled Protein, Enzyme & Smart Cell Design

• Core Objective: Learn how AI enables the creation of novel proteins, enzymes, genetic circuits, and smart cells for therapeutics, sustainable chemistry, and advanced materials.
• Programming life through AI-driven genetic circuits and smart cell engineering
• Real-world case studies in enzyme design, protein engineering, and programmable cells
• Leveraging R’s statistical depth alongside Python’s production capabilities
• Query optimization, partitioning, clustering, and execution plans in large biological datasets
• Designing new therapeutics using AI-assisted protein and enzyme design approaches
• Role of green chemistry in sustainable biological and chemical innovation
• Applications of nanomaterials and nanochemistry in synthetic biology and bioengineering

🛠️ Hands-on:

• Hands-on Lab: Explore AI-assisted protein structure prediction using AlphaFold Viewer and ESMFold.
• Hands-on Lab: Review AI-designed enzymes that degrade plastics or serve as biosensors.
• Hands-on Lab: Understand AI-driven protein design workflows using ProteinMPNN and EvoDesign.

🧰 Tools Covered: ROSETTA Fold, ESM-3, DiffDock, AlphaFold Viewer, ESMFold, ProteinMPNN, EvoDesign

📅 Day 3: Automation, Self-Driving Labs, Deep Learning & Visualization

• Core Objective: Understand how automation, self-driving laboratories, machine learning, deep learning, and visualization tools are shaping the future of synthetic biology, healthcare, and bioengineering careers.
• Core concepts from molecular biology, biochemistry, and systems biology
• Machine learning models for predicting cell behavior and optimizing genetic circuits
• Biosensors for pathogen detection and programmable probiotic systems for healthcare
• Automation and self-driving labs for faster biological experimentation
• Deep learning methods including ANN and CNN for biomarker analysis and medical image interpretation
• Future-ready skills, tools, and global career roadmap for bio-engineers

🛠️ Hands-on:

• Hands-on Lab: Simulate genetic circuits using Cello and understand AI-supported circuit design workflows.
• Hands-on Lab: Explore synthetic biology platforms such as Benchling, Geneious, and SynBioCAD.
• Hands-on Lab: Use deep learning concepts to analyze biomarkers and medical image data for disease diagnosis and prognosis.

🧰 Tools Covered: Cello, Benchling, Geneious, SynBioCAD, Protein Design AI Tools, GeneNet, Deep Circuit, ANN, CNN

• Undergraduate/postgraduate degree in Computer Science, Data Science, Statistics, AI/ML, Bioinformatics, or related fields.
• Professionals in research, analytics, data engineering, AI development, or IT sectors.
• Data scientists and ML engineers looking to upskill in advanced AI, automation, and predictive modeling.
• Individuals with an interest in end-to-end AI pipelines and high-performance computing.

• Master advanced mathematical/statistical methods for AI applications.
• Gain practical experience in building large-scale AI pipelines using Python and R.
• Develop predictive models and recommendation systems for real-world datasets.
• Handle multi-terabyte datasets efficiently using distributed and parallel processing.
• Build deployment-ready dashboards and visualization tools for enterprise use.