Title :
How Little Do We Actually Know? On the Size of Gene Regulatory Networks
Author :
Rottger, Richard ; Ruckert, Ulrich ; Taubert, Jan ; Baumbach, Jan
Author_Institution :
Comput. Syst. Biol. Group, Max Planck Institutefor Inf., Saarbrucken, Germany
Abstract :
The National Center for Biotechnology Information (NCBI) recently announced the availability of whole genome sequences for more than 1,000 species. And the number of sequenced individual organisms is growing. Ongoing improvement of DNA sequencing technology will further contribute to this, enabling large-scale evolution and population genetics studies. However, the availability of sequence information is only the first step in understanding how cells survive, reproduce, and adjust their behavior. The genetic control behind organized development and adaptation of complex organisms still remains widely undetermined. One major molecular control mechanism is transcriptional gene regulation. The direct juxtaposition of the total number of sequenced species to the handful of model organisms with known regulations is surprising. Here, we investigate how little we even know about these model organisms. We aim to predict the sizes of the whole-organism regulatory networks of seven species. In particular, we provide statistical lower bounds for the expected number of regulations. For Escherichia coli we estimate at most 37 percent of the expected gene regulatory interactions to be already discovered, 24 percent for Bacillus subtilis, and <;3% human, respectively. We conclude that even for our best researched model organisms we still lack substantial understanding of fundamental molecular control mechanisms, at least on a large scale.
Keywords :
DNA; bioinformatics; biological techniques; cellular biophysics; genetics; genomics; microorganisms; Bacillus subtilis; DNA sequencing technology; Escherichia coli; National Center for Biotechnology Information; bioinformatics; cell reproduction; cell survival; direct juxtaposition; fundamental molecular control mechanisms; gene regulatory networks; genome sequences; transcriptional gene regulation; Bioinformatics; Databases; Estimation; Genomics; Humans; Robustness; Computational biology; network statistics; transcriptional gene regulatory networks.; Bacillus subtilis; Computational Biology; Escherichia coli; Gene Expression Regulation; Gene Regulatory Networks; Humans;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2012.71