"Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships."
This subfield focuses on the development of algorithms and data structures for analyzing biological data, such as DNA sequences or protein structures.
Basics of Molecular Biology: Understanding DNA, RNA, proteins, and how they interact with each other is essential to comprehend computational biology.
Probability and Statistics: Probability theory and statistical analysis are the foundations of computational biology. They are helpful to understand the basics of classification, pattern recognition, and algorithms for machine learning.
Algorithms and Data Structures: Algorithms and data structures are important to store and manipulate complex biological data sets. They also enable the development of efficient computational tools for data analysis and interpretation.
Machine Learning: In computational biology, machine learning is used for classification, clustering, and predictive modelling of biological data. Knowing the different algorithms, their strength and limitations is key to effective application of machine learning techniques.
Sequence Alignment: Sequence alignment is a fundamental tool in computational biology that is used to compare and contrast sequences of nucleotides or amino acids. Different algorithms are used for aligning sequences, each with their own advantages and limitations.
Phylogenetics: Phylogenetics is the study of evolutionary relationships among organisms using both molecular and morphological data. Understanding the principles and methodologies of phylogenetics is crucial to understanding the evolution of life on earth.
Genome Assembly: Genome assembly is the process of reconstructing the complete genome sequence from the raw data. It is important for comparative genomics, understanding genetic variability and structural variation, and identification of new genes and functional elements.
Structural Bioinformatics: Structural bioinformatics is the use of computational methods to analyze, model and simulate the molecular structure and function of biological macromolecules, such as proteins.
Functional Genomics: Functional genomics is the study of the function of genes and how they interact with each other within the context of the whole organism. Computational methods such as CHIPSeq, RNASeq, and proteomics are used to understand the dynamics and regulation of gene expression.
Systems Biology: Systems biology is an interdisciplinary field of science that combines computational and experimental methods to study complex biological systems. Through integrative analysis of molecular data, systems biology aims to understand how biological components interact to generate the emergent properties of the system as a whole.
Sequence Alignment: It is the process of determining similarities and differences between two or more biological sequences to infer functional, evolutionary, or structural relationships.
Genome Assembly: It involves the reconstruction of individual chromosomes or the entire genome from the raw sequencing data.
Gene Prediction: It is the process of identifying the location and structure of genes in genomic sequences.
Phylogenetic Analysis: It is the study of the evolutionary relationships among different organisms based on molecular data.
Protein Structure Prediction: It involves the prediction of the 3D structure of proteins from their amino acid sequences.
Simulation of Biological Systems: It involves the development and use of computational models to simulate biological systems, such as cellular networks, metabolic pathways, and population dynamics.
Drug Design and Discovery: It involves the use of computational methods to design and discover new drugs and therapeutics.
Gene Expression Analysis: It involves the analysis of the patterns and levels of gene expression across different biological conditions to understand the underlying biological processes.
Systems Biology: It involves the study of complex biological systems as a whole, rather than as individual components.
Network Analysis: It involves the analysis of the interactions between genes, proteins, and other biological molecules to infer functional relationships and mechanisms.
Microbiome Analysis: It involves the study of the composition and function of microbial communities in different environments, such as the human gut or soil.
Text Mining and Natural Language Processing: It involves the use of computational techniques to extract and analyze information from scientific literature and other text sources.
"An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics."
"It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers."
"The use of data analysis, mathematical modeling, and computational simulations."
"To understand biological systems and relationships."
"An intersection of computer science, biology, and big data."
"Foundations in applied mathematics, chemistry, and genetics."
"Biological systems and relationships."
"The use of data analysis, mathematical modeling, and computational simulations."
"To build computers."
"An intersection of computer science, biology, and big data."
"Biological systems and relationships."
"Applied mathematics, chemistry, and genetics."
"Mathematical modeling and computational simulations."
"To understand biological systems and relationships."
"An intersection of computer science, biology, and big data."
"It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers."
"The use of data analysis, mathematical modeling, and computational simulations."
"To understand biological systems and relationships."
"Applied mathematics, chemistry, and genetics."