Models of biological networks have always had gaps, but they are bigger than most researchers realized.
Only in the past few years have biologists begun to recognize the extensive regulatory role of naturally occurring small RNAs. The best known of these endogenous RNAs are chains of 21-23 nucleotides with the rather unfortunate designation of "microRNA" (whose abbreviation, miRNA, is awkwardly similar to the mRNA used for messenger RNA). MicroRNAs arise from sections of DNA whose RNA transcripts contain nearly complementary mirror-image sequences, and so naturally fold back on themselves to form a "stem-loop" structure. Processing by a series of protein complexes liberates one strand from the overlapping section and incorporates it into specialized RNA-protein complexes in the cytoplasm that modify protein production.
Traditionally, systems biologists aiming to unravel gene-regulation networks have relied on the wealth of data from microarrays that measure the mRNA precursors of proteins. By regarding the mRNA abundance as a proxy for the corresponding protein, and looking at how various mRNA levels change with cellular conditions, the researchers construct hypothetical networks of interacting genes. In the graphical representations of these networks, genes are connected by a line or "edge" if the protein product of one seems to act as a transcription factor to change the activity of the other.
MicroRNAs complicate this picture dramatically, although many researchers don't yet incorporate them. Improved tools, often directly sequencing of millions of fragments rather than matching pre-chosen sequences in microarrays, let researchers survey the small RNAs in the cell. In many cases, as in the work of Frank Slack of Yale University described in my latest eBriefing for the New York Academy of Sciences, microRNAs control cellular processes in much the same way as traditional protein transcription factors--in Slack's case extending the lifespan of worms. These regulatory RNAs are a previously unsuspected layer of genetic regulation.
Some of the RNA-protein complexes promote degradation of messenger RNA that is complementary to the bound miRNA. In this case the remaining mRNA could still be a good indicator of a gene's activity, although not of its original transcription. Sometimes accounting for the miRNA might require only a change in the mathematical relationship between genes.
Many miRNAs, however, as well as some transcription factors, act as "master regulators," generating coordinated activity among scores of genes. As a result, these master regulators can effectively change one genetic network into an entirely different one--adding or removing edges. For example, researchers have constructed networks for cancerous cells in which the connections differ markedly from those for their healthy counterparts. Such context-dependent networks may be simple and accurate in specific situations, but they obviously lack important ingredients.
In other cases, a miRNA can continuously vary the activity of genes, rather than being a simple on/off switch. Again, such coordinated response will be hard to capture unless the hidden factor is explicitly identified.
A second type of RNA-protein complex slows (or less often speeds) the translation of complementary messenger RNA. One profound implication is that the measured levels of mRNA may no longer be a good proxy for the levels of the protein produced from it. Indeed, in the few cases where researchers have done the experiments, they have found only weak correlations between the levels of mRNA and the corresponding proteins. Any procedure that depends on these levels being equivalent is on thin ice.
In addition to these quantitative effects, qualitatively new behavior appears when molecules are connected in feedback loops. Systems biologists have catalogued the action of many interesting "motifs." Even two molecules, for example, can act to stabilize concentration--if the feedback around the loop is negative--or act as a bistable switch--if the loop feedback is positive. Clearly, if such motifs are acting in the hidden mRNA layer, no tweaking of the gene-gene interactions will replicate their effects.
All of this reinforces the need for researchers to develop and use as many high-throughput techniques as possible to measure different types of RNA as well as the different states of proteins in cells. The "reverse engineering" of networks never seemed easy. Now it's clear that it's even harder than it seemed.