Computational Biology

The process of arranging DNA, RNA, or protein sequences to identify regions of similarity that may indicate functional, structural, or evolutionary relationships. Global alignment (Needleman-Wunsch) aligns entire sequences end-to-end, while local alignment (Smith-Waterman) finds the most similar subsequences.

The computational reconstruction of evolutionary relationships among organisms or genes based on molecular data such as DNA or protein sequences. Methods include maximum likelihood, Bayesian inference, and neighbor-joining, producing tree-like diagrams (phylogenies) that depict ancestry and divergence.

The computational determination of the three-dimensional structure of a protein from its amino acid sequence. This includes homology modeling, ab initio methods, and deep learning approaches like AlphaFold, which predict how a polypeptide chain folds into its functional conformation.

The computational process of reconstructing a complete genome sequence from shorter sequencing reads. De novo assembly builds genomes without a reference, while reference-guided assembly maps reads to an existing genome. Algorithms must handle millions to billions of overlapping fragments.

Mathematical and computational models describing how genes, transcription factors, and other molecules interact to control gene expression levels within a cell. These networks capture the logic of cellular decision-making and can be represented as Boolean networks, differential equations, or probabilistic graphical models.

A computational method that simulates the physical movements of atoms and molecules over time by numerically solving Newton's equations of motion. It is used to study protein folding, ligand binding, membrane dynamics, and other biomolecular processes at atomic resolution.

The application of machine learning algorithms, including deep neural networks, random forests, and support vector machines, to identify patterns in genomic data. Tasks include variant calling, gene expression prediction, regulatory element identification, and disease classification from omics data.

An approach that studies biological systems holistically by integrating data from genomics, proteomics, metabolomics, and other high-throughput methods into computational models. It aims to understand emergent properties of biological systems that cannot be predicted from individual components alone.

A statistical model in which a system transitions between hidden states according to probabilistic rules, with each state emitting observable signals. In computational biology, HMMs are widely used for gene finding, protein domain identification, sequence alignment, and chromatin state annotation.

Computational methods for analyzing gene expression data measured at the resolution of individual cells. Techniques include dimensionality reduction (PCA, t-SNE, UMAP), clustering to identify cell types, trajectory inference to model differentiation, and differential expression testing between cell populations.