Computing is Essential to Contemporary Biology

Most biological computing falls into four broad categories:

• Informatics and Data Management

  • What - Collect, store, integrate and make accessible large datasets, which critically depends on agreed upon standards of information and vocabulary(for example, The Gene Ontology).
  
  
  • Why - We need to be able to find what has already been discovered, so we don't repeat studies already done. Make large, and rapidly growing datasets accessible and useable.
  
  
  • How - Database, query, filter, pattern, score, result, ontology, metadata.



• Data Analysis

  • What - Analyze data to find biologically meaningful patterns, patterns.
  
  
  • Why - What's the point of colelcting data if you don't know what it is telling you?
  
  
  • How - Descriptive statistics, plotting, statistical inference, parameter estimation, pattern description and detection.



• Visualization

  • What - Make the meaning of comple data easy to understand and interpret
  
  
  • Why - Many datasets can't be effectively interpreted without visualization.
  
  
  • How - Familiarity with a viewer/viz tool.
  
  
  • The fastest growing area of computing (along with modeling below)


• What - Explicit modeling of biological systems in support of the contemporary scientific method


• Why - Biology is no longer stamp collecting, we need to account for mechanistic cause and effect.


• How - Agent-based simulation, system dynamics, finite difference, deterministic, stocahstic.

As useful tools become available across application domains, modeling has been growing MUCH faster than programming