How Little Do We Actually Know? On the Size of Gene Regulatory Networks

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.

Counting motifs in the human interactome

“…As the number of genes alone does not fully characterize the biological complexity of living organisms, the scale of physiologically relevant protein and gene interactions are now being investigated to understand the basic biological principles of life678. Although the list of known protein–protein interactions (PPIs) and gene regulatory interactions (GRIs) is expanding at an ever-increasing pace, the human PPI and GRI networks are far from being complete and, hence, their dynamics have yet to be uncovered91011.…”

“…One approach is to infer spurious and missing links in the entire network121314, and then to count motif occurrences. Another approach is to estimate the number of motif occurrences in the interactome from the observed subnetwork data using the same method as that for estimating the size of eukaryotic interactomes91015. If we have the number of occurrences of a motif or its estimate in a network, we can determine whether the motif is over-represented or not, based on how often the motif is seen in a random network with similar structural parameters111617.…”

CMRegNet–An interspecies reference database for corynebacterial and mycobacterial regulatory networks

“…Considering the scarce knowledge we have on regulatory interaction even for heavily studied model organisms [3], the restriction to one model organism poses one of the most prominent limitations of the automated network transfer [23]. Apparently, a regulation can only be transferred to a target organism, when it was experimentally validated in model organisms in the first place.…”

“…Additionally, even for these highly studied model organisms, the currently known regulatory network is far from complete [3]. Until this work, the databases similar to CMRegNet were limited only to one model organism [8–12, 30–32] which was extended to a second model organism for CMRegNet; but nevertheless the amount of information of the target organisms is strictly limited by the available information on the model organisms.…”

“…Potential reasons for this include an inability to simulate the different environmental conditions in which the organism lives in the laboratory and the inherent background noise of the existing wet-lab techniques. Even for the model organism E. coli , only a third of its transcriptional regulatory network (TRN) has been identified, even though a large number of studies with accurate data have been published on this organism [3]. The situation even worsens, when we focus on organisms like Mycobacterium leprae , which is the bacterium with the longest known duplication time and which does not grow in culture medium [4].…”