Chapter 11: Phylogenetic Analysis (ML in Pharmacognosy & Biotech Discovery, 2025)
AuthorMrs. Vinny Therissa Mangam, Assistant Professor, Department of Pharmaceutical Analysis, Aditya College of Pharmacy, Aditya University, Surampalem, Andhra Pradesh, IndiaAbstractPhylogenetics represents the computational reconstruction of evolutionary history, transforming the study of life's origins from qualitative morphological observation to precise statistical inference based on molecular data. This section provides a comprehensive framework for understanding phylogeny, detailing how genetic mutations accumulate over time to serve as molecular clocks that measure the divergence between species. The workflow begins with the critical step of Multiple Sequence Alignment (MSA), where algorithms align homologous nucleotides or amino acids to identify informative sites, establishing the foundational dataset for evolutionary analysis. It rigorously compares distinct mathematical approaches for tree construction, contrasting distance-matrix methods like Neighbor-Joining, which offer computational efficiency for large datasets, with character-based methods like Maximum Likelihood and Bayesian Inference, which provide robust statistical testing of evolutionary hypotheses by modeling substitution rates. Finally, the narrative addresses the crucial phase of interpretation and validation, explaining how statistical techniques such as bootstrapping and posterior probabilities assess the reliability of branching patterns. This rigorous methodological approach empowers researchers to accurately trace the lineage of viral pathogens, identify the geographic origins of medicinal plant species, and map the functional evolution of protein families, providing the essential evolutionary context necessary for modern biological research, comparative genomics, and the forensic identification of biological samples.Keywords: Phylogenetics, Multiple Sequence Alignment (MSA), Maximum Likelihood, Bootstrapping, Molecular Clock, Neighbor-JoiningVIEW PDF