The origin of the rapid adaptive response of yeast cells to changes in environmental osmolarity has been unclear. Mettetal et al. (p. 482; see the Perspective by Lipan) now show that increases in extracellular osmolarity activate the high-osmolarity glycerol signaling pathway, which changes transcription of particular target genes. By measuring the cellular response to pulses of medium with increased ionic strength, the authors were able to develop a predictive model of the dynamics of this regulatory system. Rather than changes in gene expression, which have often been suggested to be at the core of the response to osmotic shock, the fast response is actually dominated by a nontranscriptional response that probably involves altered glycerol transport.

SYSTEMS BIOLOGY:
Enlightening Rhythms

Ovidiu Lipan^*

We live in a sea of vibrations, detecting them through our senses and forming impressions of our surroundings by decoding information encrypted in these fluctuations. Such periodic phenomena range from circadian oscillations in living cells (1) to acoustic oscillations in the primordial universe (2). Passively observing periodic phenomena is scientifically rewarding, but actively using periodic stimuli to observe the hidden wonders of nature is even more so. On page 482 of this issue, Mettetal et al. (3) report using oscillatory stimuli to decipher how an organism--the yeast Saccharomyces cerevisiae--responds to environmental changes. By constructing a predictive mathematical model for specific signaling pathways (4), they show that oscillatory stimuli can be used to study how networks of proteins and genes are engaged by a living system to control physiological behavior.

Many scientific studies hinged on creating oscillations to study natural systems. The idea of electromagnetic waves was implicit in James Maxwell's theory, but it was Heinrich Hertz's electric oscillators that created and measured their properties, thus confirming light waves as electromagnetic radiation, the most striking victory of 19th-century experimental physics (5).

Although the use of oscillatory stimuli to study how networks of proteins and genes regulate gene expression is theoretically valuable (6), implementation of this procedure is not obvious because the possibilities for constructing genetic oscillators are limited, at present. It takes ingenuity to find a molecular pathway that responds to an oscillatory signal, much less an experimental procedure to create these oscillations. Furthermore, these oscillations must produce detectable responses. Mettetal et al. fulfill these constraints by studying a signaling pathway in yeast that responds to changes in environmental osmolarity. Glycerol is the main yeast osmolyte and its concentration is controlled in part by the high-osmolarity glycerol (HOG) signaling pathway that involves the enzyme Hog1. By adjusting the export rate of glycerol through the cell membrane, yeast maintain osmotic balance.

Variable frequencies. Oscillating signals may unlock the complex organization of organisms.

Mettetal et al. studied three negative-feedback loops of the HOG pathway. One loop controls glycerol concentration through the membrane protein Fps1, and depends on the amount of active Hog1 in the nucleus. A second loop also involves Fps1, but is controlled by osmotic pressure across the cell's membrane and the concentration of intracellular glycerol. A third loop is Hog1 dependent and acts on glycerol concentration by increasing the expression of genes encoding the glycerol-producing proteins Gpd1 and Gpp2.

Which of the three negative-feedback loops dominate the dynamics of this osmo-adaptation system? Can system identification methods, such as those used by robotics engineers, describe the signaling dynamics of the dominant negative-feedback loops? To apply systems engineering methods, an input signal must be created and an output response signal must be recorded. Mettetal et al. varied the concentration of sodium chloride in the cell media, thus exposing cells to square-wave pulses (alternating between two values for an equal amount of time) of osmotic pressure. The output response recorded was the ratio between nuclear-localized, active Hog1 and Hog1 within the entire cell.

For designing complex systems, a success is claimed if the output response of a system to an external input signal can be mathematically predicted. Viewed only in terms of its input and output characteristics, the osmo-adaptation pathway loses its inner biological structure and becomes what is referred to as a "black box." The authors developed a black-box mathematical model for the osmo-adaptation pathway. They estimated parameters of the model from measurements using square-wave pulses of variable frequencies, then validated the predictive power of the model using a step input--one that switches on at a definite time and remains on indefinitely--of sodium chloride. Because gene regulatory networks contain many unknown molecular components, a black-box mathematical model is the best achievable solution in various situations.

The ultimate goal, however, is a mathematical model for a white box, in which all the molecular components and their interactions are known. The road toward this goal is paved with intermediate gray-box models containing some biological inner structures. Toward this end, Mettetal et al. transform the black-box mathematical model into a gray one that successfully incorporates the first two of the three osmo-adaptation feedback loops described. In doing so, they discovered that the dynamics of the osmo-adaptation response are dominated by the fast-acting Hog1-dependent negative feedback loop that does not require a change in gene expression.

The hope is to include other molecular components and feedback loops into a more detailed mathematical model. This will require new techniques to generate molecular input signals--perhaps based on photons rather than chemicals--for tuning gene expression and protein degradation (7, 8). The study by Mettetal et al. is part of a large effort to blend the biological and mathematical structure of living systems and understand living systems not as collections of machine parts, but as stable, complex dynamic organizations (9). Hopefully, 21st-century systems biology will claim victories as striking as those of Maxwell and Hertz.

References

1. J. C. Dunlap et al., Chronobiology: Biological Timekeeping (Sinauer, Sunderland, MA, 2004).
2. D. J. Eisenstein et al., Astrophys. J. 633, 560 (2005).
3. J. T. Mettetal et al., Science 319, 482 (2008).
4. A. V. Oppenheim et al., Signals and Systems (Prentice-Hall, Englewood-Cliffs, NJ, 1983).
5. J. G. O'Hara, D. W. Pricha, Hertz and the Maxwellians: A Study and Documentation of the Discovery of Electromagnetic Wave Radiation, 1873-1894, IEE History of Technology Ser. 8 (Peter Peregrinus and Science Museum of London, 1987).
6. O. Lipan, W. H. Wong, Proc. Natl. Acad. Sci. U.S.A. 102, 7063 (2005).
7. C. Grilly et al., Mol. Syst. Biol. 3, 127 (2007).
8. S. Shimizu-Sato et al., Nature Biotechnol. 20, 1041 (2002).
9. C. R. Woese, Microbiol. Mol. Biol. Rev. 68, 173 (2004).