Computational Biology

Overview

Computational biology is an interdisciplinary field that applies the methods of computer science, mathematics, and statistics to the study of biological systems and data. It develops and uses algorithms, models, and software tools to analyze large and complex datasets, such as genome sequences, gene expression measurements, protein structures, and molecular interactions, and to make predictions about how biological systems behave. By turning biological information into quantitative models, computational biology helps researchers understand the organization and dynamics of living systems, from molecules and cells to whole organisms and populations. It is closely allied with Systems Biology, which seeks to understand how the components of a biological system interact as integrated networks rather than in isolation, and computational approaches are essential for modeling and simulating these networks. Applications span genomics, structural biology, drug discovery, evolutionary analysis, and the modeling of physiological and disease processes. Research relevant to computational and Systems Biology includes the use of computational systemic approaches in areas such as toxicity studies, where modeling is applied to interpret biological data and predict outcomes. Such work illustrates how computational methods support the analysis and prediction of complex biological behavior. This page gathers material relevant to computational biology within the Systems Biology literature.

Research published in this journal

2 peer-reviewed articles, ranked by relevance. Each links to its DOI.

This page summarises published research for orientation; it is not medical or professional advice.

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