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Big Data in Caenorhabditis elegans: quo vadis?
Harald Hutter1, Donald Moerman2
1Department of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Molecular Biology of the Cell
|November 7, 2015
Summary
Researchers use Caenorhabditis elegans to explore Big Data in biology. While complete datasets are rare, Big Data reveals correlations for new investigations, though it cannot prove causation.
Area of Science:
- * Developmental Biology
- * Genomics
- * Systems Biology
Background:
- * Defining "Big Data" is challenging; a useful criterion is data completeness.
- * Caenorhabditis elegans (C. elegans) research historically aimed for complete biological descriptions, generating foundational datasets like the nervous system wiring, cell lineage, and genome sequence.
- * Despite this history, truly "complete" large-scale datasets remain scarce due to experimental limitations and the inherent unbounded nature of many biological processes.
Purpose of the Study:
- * To define and explore the utility of Big Data in biological research, using C. elegans as a model system.
- * To investigate the challenges in achieving complete data collection for various biological experiments.
- * To understand the contribution of Big Data to hypothesis generation and problem-solving in multicellular organism development.
Main Methods:
- * Conceptual analysis of Big Data definition and application in biology.
- * Review of existing complete and partial datasets in C. elegans research.
- * Exploration of the potential and limitations of Big Data in identifying correlations versus causation.
Main Results:
- * While foundational complete datasets exist for C. elegans, many crucial biological datasets (e.g., gene mutations, phenotypes, gene expression, protein interactions) are currently partial.
- * Big Data facilitates the discovery of unexpected correlations, driving novel research avenues.
- * Big Data analysis alone cannot establish causal relationships in biological systems.
Conclusions:
- * C. elegans serves as an ideal model for studying Big Data's role in biology due to its simplicity and existing comprehensive datasets.
- * Further research is needed to determine the specific contributions of Big Data to solving complex biological problems and understanding development.
- * The study highlights the ongoing challenge of collecting complete biological data and the need to interpret Big Data findings cautiously regarding causation.
