Module 1: Foundations of Metabolic Engineering
Basic concepts and scope of metabolic engineering, beginning with an overview of cellular metabolism and metabolic pathway organization. Key biochemical pathways and manipulation of these pathways for biosynthetic applications. Energy and redox balancing, precursor metabolite generation, and regulation of metabolic fluxes. Regulatory mechanisms such as feedback inhibition, catabolite repression, and allosteric control for pathway engineering. Computational tools used for metabolic pathway analysis, including stoichiometric models and basic metabolic flux analysis (MFA).
Module 2: Tools and Strategies in Metabolic Engineering
Methodologies used to modify and optimize metabolic networks. Gene editing techniques, including gene knockouts, knock-ins, and overexpression systems, as well as synthetic biology tools for pathway reconstruction and refactoring. The use of different host organisms such as Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris, CHO cell lines and newer chassis like cyanobacteria and Corynebacterium glutamicum, Aspergillus sps, for pathway efficiency and scalability. Design of new expression systems, promoter engineering, transcription factor engineering, secretion system and signal peptide engineering, Alleviation of ER stress in recombinant protein production systems, Humanisation of Yeast expression systems for therapeutic protein production. Dynamic regulation techniques such as metabolic switches, biosensors, and CRISPR-based transcriptional control for fine-tuning pathway activity.
Module 3: Applications and Emerging Trends
Real-world applications of metabolic engineering in various industries, including pharmaceuticals, food, agriculture, and sustainable energy. Case studies for pathway engineering for the microbial production of biofuels, biodegradable plastics, plant secondary metabolites, therapeutic proteins, growth factors, monoclonal antibodies with special reference to novel and modified expression systems. New genome edititing tools and techniques for metabolic engineering. Systems biology approaches for omics data integration and pathway modeling. Advanced topics such as adaptive laboratory evolution (ALE), genome-scale metabolic models (GEMs), and the application of AI and machine learning in strain design. Regulatory, ethical, and safety considerations in commercial metabolic engineering.