Next-Generation Microbiome Therapeutics: Probiotic Genomics, Prebiotic Interactions & Postbiotic Profiling

The workshop aims to enable participants to assess microbial functional traits, prebiotic–microbe interactions, postbiotic-associated metabolites, and FMT-related microbial community changes using bioinformatics tools and publicly available datasets. It emphasizes data-driven interpretation and evidence-based evaluation of microbiome interventions for next-generation therapeutic development.

• Understand the scientific principles, mechanisms, and therapeutic applications of probiotics, prebiotics, postbiotics, and fecal microbiota transplantation (FMT) in gut microbiome modulation.
• Evaluate probiotic functional traits, genomic characteristics, antimicrobial resistance (AMR), and safety profiles to identify promising probiotic strains.
• Analyze prebiotic–microbe interactions, substrate utilization patterns, and microbial metabolic pathways to understand their roles in gut microbiome modulation.
• Explore postbiotic preparations, microbial metabolites, short-chain fatty acids (SCFAs), and bioactive components associated with gut health and host–microbiome interactions.
• Investigate FMT-associated microbiome restoration by comparing donor–recipient microbiome profiles, microbial diversity, taxonomic composition, and functional changes.
• Apply Google Colab, Python, Pandas, NumPy, Matplotlib, and bioinformatics databases to analyze microbial datasets, visualize microbiome responses, and evaluate microbiome-based therapeutic strategies.

📅 Day 1: Probiotic Discovery, Functional Characterization & Safety Assessment

• Exploring probiotic strain functionality, genome-based characterization, safety assessment, and computational prioritization for microbiome-based therapeutic applications.
• Explore commonly studied probiotic genera, including Lactobacillus, Bifidobacterium, and Bacillus.
• Examine strain-specific functional traits, including acid tolerance, bile resistance, adhesion, and gastrointestinal survival.
• Identify antimicrobial activity, bacteriocin production, and other probiotic-associated functional properties.
• Explore genome-based characterization and functional annotation of candidate probiotic strains.
• Investigate antimicrobial resistance (AMR) genes, virulence-associated factors, and genomic safety indicators.
• Analyze probiotic genomic annotations and functional trait datasets using Python.
• Compare functional characteristics, genomic safety profiles, and available scientific evidence across selected probiotic strains.
• Prioritize potential probiotic candidates using computational scoring, comparative matrices, and data visualization.

🧪 Hands-On Learning Activity:

• Retrieve and analyze probiotic strain datasets, screen AMR and virulence genes, and use Google Colab to develop a Probiotic Functional–Safety Matrix, visualize heatmaps, and rank candidate strains.

🧰 Tools Covered: NCBI Genome | NCBI BLAST | BAGEL4 | AMRFinderPlus | VFDB | Google Colab | Python | Pandas | NumPy | Matplotlib | Seaborn

📅 Day 2: Prebiotic–Microbiome Interactions, Metabolic Pathways & Functional Modulation

• Investigating prebiotic substrate utilization, microbial community responses, metabolic pathways, and computational analysis of prebiotic–microbiome interactions.
• Examine substrate utilization patterns and microbial responses among beneficial gut microorganisms.
• Investigate carbohydrate fermentation, microbial cross-feeding, and substrate-associated metabolic pathways.
• Explore changes in microbial abundance, community composition, and functional potential following prebiotic interventions.
• Examine microbial production of short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate.
• Identify microbial metabolic pathways and functional genes associated with prebiotic utilization and fermentation.
• Analyze prebiotic–microbe interaction datasets using Python, Pandas, and NumPy.
• Visualize microbial response patterns, substrate utilization profiles, and potential metabolic relationships using Matplotlib, Seaborn, and NetworkX.
• Compare prebiotic substrates based on microbial responses, metabolic potential, and reported functional outcomes.

🧪 Hands-On Learning Activity:

• Analyze prebiotic substrates, microbial responses, and metabolic pathways using Google Colab to generate comparative visualizations and construct a Prebiotic–Microbe–Function Interaction Matrix.

🧰 Tools Covered: PubMed | KEGG | NCBI Taxonomy | gutMGene | Google Colab | Python | Pandas | NumPy | Matplotlib | Seaborn | NetworkX

📅 Day 3: Postbiotic Profiling, Fecal Microbiota Transplantation & Integrated Therapeutic Evaluation

• Exploring postbiotic functionality, microbiome restoration through fecal microbiota transplantation (FMT), and integrated computational evaluation of microbiome-based therapeutic strategies.
• Differentiate probiotics, prebiotics, postbiotics, and fecal microbiota transplantation (FMT) based on their composition, mechanisms, and therapeutic applications.
• Explore postbiotic-associated microbial cell components, functional characteristics, and reported host-health effects.
• Understand the principles of FMT, including donor screening, microbial community transfer, safety considerations, and established clinical applications.
• Analyze microbiome intervention datasets using Python to compare microbial abundance, alpha diversity, beta diversity, and functional profiles.
• Compare probiotics, prebiotics, postbiotics, and FMT using intervention-specific functional, safety, clinical-evidence, and microbiome-response parameters.
• Develop an evidence-weighted computational framework to visualize and evaluate microbiome-based therapeutic strategies.

🧪 Hands-On Learning Activity:

• Analyze postbiotic functional evidence and pre-/post-FMT microbiome diversity using Google Colab to generate comparative visualizations and construct an Integrated Microbiome Therapeutic Evaluation Matrix.

🧰 Tools Covered: PubMed | KEGG | HMDB | gutMGene | NCBI SRA | Google Colab | Python | Pandas | NumPy | Matplotlib | Seaborn | Plotly | Scikit-learn

• PhD Scholars & Research Scholars
• Academicians & Faculty Members
• Microbiome & Microbial Ecology Researchers
• Probiotic & Prebiotic Research Scientists
• Bioinformatics & Computational Biology Researchers
• Drug Discovery & Development Scientists
• Biotechnology & Life Science Professionals
• Microbiology & Molecular Biology Researchers
• Nutraceutical & Functional Food Researchers
• Clinical Research Professionals
• Pharmaceutical & Biotechnology Industry Professionals

• Understand the therapeutic applications and mechanisms of probiotics, prebiotics, postbiotics, and FMT in gut microbiome modulation.
• Characterize and assess probiotic strains based on genomic features, functional traits, antimicrobial resistance (AMR), and safety indicators.
• Analyze prebiotic–microbe interactions, fermentation pathways, and SCFA production using bioinformatics databases and computational tools.
• Evaluate postbiotic preparations, microbial metabolites, and bioactive components involved in host–microbiome interactions and metabolic functions.
• Investigate FMT-associated microbiome restoration by comparing donor–recipient profiles, microbial diversity, community composition, and functional changes.
• Apply Google Colab, Python, Pandas, NumPy, Matplotlib, and bioinformatics tools to analyze microbiome datasets, generate heatmaps, and develop an Integrated Microbiome Therapeutic Scorecard for evaluating therapeutic strategies