Program Structure

Module 1: Metabolic Engineering and the AI Opportunity

• Industrial targets: biofuels, organic acids, amino acids, enzymes, bioplastics, and specialty chemicals.
• Key performance metrics: titer, yield, productivity, robustness, and consistency.
• Why AI: navigating huge design spaces and reducing experimental cycles.
• Reality check: data quality, biological noise, and limits of prediction.

Module 2: Metabolism Basics and Strain Design Logic

• Central carbon metabolism overview and pathway branching concepts.
• Bottlenecks and byproducts: flux competition and redox/cofactor constraints (high-level).
• Design levers: gene expression tuning, knockouts, pathway introduction, and transport considerations (conceptual).
• Design risks: metabolic burden, toxicity, instability, and adaptive evolution effects.

Module 3: Constraint-Based Modeling and Flux Thinking (High-Level)

• Stoichiometric models and constraints: what they represent and what they miss.
• Flux balance analysis (FBA) intuition: objective functions and feasible solution space.
• Target identification logic: knockout/overexpression hypotheses and pathway alternatives.
• Model validation mindset: aligning predictions with experimental measurements.

Module 4: Omics Data and Feature Engineering for Biodesign

• Omics overview: transcriptomics, proteomics, metabolomics—what each reveals.
• Data preprocessing concepts: normalization, batch effects, missing data, and noise.
• Feature selection: linking genes/metabolites to performance outcomes (conceptual).
• Multi-omics integration: combining datasets responsibly without overfitting.

Module 5: AI/ML Methods for DBTL Acceleration

• Supervised learning: predicting titer/yield from design + process variables (high-level).
• Unsupervised learning: clustering strains/batches and discovering patterns.
• Anomaly detection: identifying failing batches and sensor issues early.
• Active learning / Bayesian optimization concepts: choosing the next best experiment.

Module 6: AI-Guided Pathway and Enzyme Selection (Conceptual)

• Pathway search: selecting routes from substrate to product and ranking options.
• Enzyme selection logic: sequence/function prediction concepts and stability considerations.
• Thermodynamics awareness: feasibility constraints and pathway balancing (overview).
• Risk control: avoiding brittle designs and planning backups.

Module 7: Bioprocess Data, Sensors, and Scale-Up Analytics

• Bioprocess signals: pH, DO, agitation, feed rate, temperature, off-gas—what they indicate.
• Linking process to biology: how conditions affect flux and product formation (conceptual).
• ML for process optimization: yield prediction, soft sensors, and control loop concepts (high-level).
• Scale-up constraints: oxygen transfer, mixing, heat removal, and reproducibility.

Module 8: Evaluation, Reproducibility, and Industrial Readiness

• Benchmarking: comparing to classical baselines and simple heuristics.
• Validation: cross-validation, hold-out strategies, and avoiding leakage (conceptual).
• Documentation: experiment tracking, model cards, and decision logs.
• Quality mindset: critical quality attributes (CQAs) and consistent manufacturing outcomes.

Module 9: Case Studies and Future Trends

• Case studies: AI-guided strain optimization, fermentation anomaly detection, and process setpoint optimization (overview).
• Biofoundries and automation: robotics, high-throughput screening, and closed-loop DBTL.
• Generative design concepts: proposing new designs and ranking by feasibility (high-level).
• Responsible innovation: interpreting results cautiously and planning confirmatory experiments.

Final Project

• Create an AI-Enhanced Metabolic Engineering Blueprint for a chosen product + host (e.g., yeast/bacteria/algae).
• Include: product goal, baseline pathway, model approach (constraint-based + data), ML workflow, experiment prioritization strategy, KPIs, and scale-up considerations.
• Example projects: AI-guided organic acid optimization in yeast, Bayesian optimization for fermentation feed strategy, multi-omics model for yield prediction, or anomaly detection dashboard for bioreactor batches.