Search results
Results from the WOW.Com Content Network
Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology , however this distinction between the two terms is often ...
Biological data has also been difficult to define, as bioinformatics is a wide-encompassing field. Further, the question of what constitutes as being a living organism has been contentious, as "alive" represents a nebulous term that encompasses molecular evolution, biological modeling, biophysics, and systems biology.
Bioinformatics and computational biology are interdisciplinary fields of research, development and application of algorithms, computational and statistical methods for management and analysis of biological data, and for solving basic biological problems.
The journal Nucleic Acids Research regularly publishes special issues on biological databases and has a list of such databases. The 2018 issue has a list of about 180 such databases and updates to previously described databases. [2] Omics Discovery Index can be used to browse and search several biological databases.
Origin of life.Exactly how, where, and when did life on Earth originate? Which, if any, of the many hypotheses is correct? What were the metabolic pathways used by the earliest life forms?
Popular computational models used in systems biology include process calculi, such as stochastic π-calculus, and constraint-based reconstruction and analysis (COBRA), a paradigm that considers physical, enzymatic, and topological constraints underlying a phenotype in a metabolic network.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Physiomics arose from the imbalance between the amount of data being generated by genome projects and the technological ability to analyze the data on a large scale. [3] As technologies such as high-throughput sequencing were being used to generate large amounts of genomic data, effective methods needed to be designed to experimentally interpret and computationally organize this data. [5]