Plant Metabolomics: Bioactive Compounds, Pathway Analysis & Stress Biomarkers

To equip researchers, academicians and industry professionals with practical skills in plant metabolomics, bioactive compound profiling, metabolic pathway interpretation and AI-assisted stress biomarker discovery using computational tools and publicly available research datasets.

• Understand the principles of targeted and untargeted plant metabolomics and their research applications.

• Explore LC-MS/MS, GC-MS and computational approaches for plant metabolite profiling.

• Analyze plant metabolomics datasets using Python and Google Colab.

• Identify differentially abundant metabolites through statistical and multivariate analysis.

• Investigate bioactive compound biosynthesis and plant metabolic pathways using KEGG and PlantCyc resources.

• Apply pathway enrichment analysis to interpret metabolic changes under environmental stress.

• Use machine learning techniques for candidate stress biomarker identification and classification.

• Connect metabolomics findings with plant stress adaptation, biochemical regulation and crop resilience research.

• Introduction to plant metabolomics: targeted and untargeted approaches.

• Primary and specialized metabolites: phenolics, flavonoids, alkaloids, terpenoids and antioxidants.

• Applications of LC-MS/MS, GC-MS and NMR in plant metabolite profiling.

• Metabolite identification concepts: m/z, retention time, MS/MS spectra and annotation confidence.

• LC-MS feature tables, quality control, missing-value handling and data normalization.

• Differential metabolite analysis using fold change, statistical testing and false discovery rate (FDR).

• Multivariate analysis using Principal Component Analysis (PCA) and metabolite heatmaps.

• Identification of candidate stress-responsive bioactive compounds.

• Plant bioactive metabolite annotation and MS/MS spectral matching principles.

• Overview of GNPS, MassBank, PubChem and plant metabolite databases.

• Current feature-processing and annotation approaches using MZmine and MS-DIAL.

• Biosynthetic pathways of phenylpropanoids, flavonoids, terpenoids and alkaloids.

• Plant metabolic pathway mapping using KEGG and PlantCyc.

• Metabolite set enrichment and pathway overrepresentation analysis.

• Metabolite–pathway networks and functional interpretation of bioactive compound production.

• Pathway-level metabolic reprogramming under abiotic and biotic stress.

Day 3: AI-Based Plant Stress Biomarker Discovery & Predictive Metabolomics

• Metabolic responses to drought, salinity, heat and oxidative stress.

• Stress-associated metabolites: proline, sugars, amino acids, flavonoids and antioxidants.

• Metabolite-based biomarkers for plant stress tolerance and crop resilience.

• Introduction to machine learning in plant metabolomics and multi-omics research.

• Random Forest classification and metabolite feature selection.

• Biomarker ranking, cross-validation and prevention of data leakage.

• ROC-AUC, classification accuracy and model interpretation.

• Integrating metabolic pathways with candidate biomarkers for stress-response research.

This workshop is designed for:

• Researchers & PhD Scholars: Working in plant metabolomics, phytochemistry, plant physiology, molecular biology, stress biology and natural product research.

• Academicians & Faculty Members: Teaching or conducting research in Plant Biotechnology, Botany, Biochemistry, Analytical Chemistry and Agricultural Sciences.

• Plant Biotechnology Professionals: Involved in plant metabolic engineering, bioactive compound research and crop improvement.

• Phytochemistry & Natural Product Researchers: Investigating plant secondary metabolites, medicinal plants, phytochemicals and their biosynthetic pathways.

• Agricultural & Crop Science Researchers: Studying drought tolerance, salinity stress, plant adaptation and stress-resilient crop development.

• Pharmaceutical & Nutraceutical R&D Professionals: Working on plant-derived bioactive compounds, natural product characterization and metabolomics-based research.

• Analytical Chemistry & Metabolomics Professionals: Interested in LC-MS data interpretation, metabolite annotation and computational metabolomics.

• Bioinformatics & Data Science Researchers: Seeking to apply statistical analysis, Python and machine learning to plant metabolomics datasets.

• Postgraduate Students: Pursuing advanced studies in Biotechnology, Botany, Life Sciences, Pharmaceutical Sciences, Agriculture and related disciplines.

Prerequisites: Basic knowledge of plant biology, biochemistry or analytical sciences is recommended. Prior advanced programming experience is not required, as guided hands-on exercises will be conducted using prepared Google Colab notebooks.

Upon successful completion of the workshop, participants will be able to:

• Process, normalize and analyze plant metabolomics datasets.

• Generate PCA plots, volcano plots and metabolite abundance visualizations.

• Identify candidate bioactive compounds and differentially abundant metabolites.

• Perform metabolic pathway enrichment and interpret specialized metabolite biosynthesis.

• Analyze metabolite changes associated with drought, salinity and oxidative stress.

• Develop and evaluate basic machine learning models for plant stress classification.

• Prioritize potential metabolic biomarkers using statistical analysis and feature importance.

• Interpret metabolomics findings for applications in plant biotechnology, natural product research and stress-resilient crop development.