Box 5.1
Perturbation of Biological Systems

Perturbation of biological systems can be accomplished through a number of genetic mechanisms, such as the following:

  • High-throughput genomic manipulation. Increasingly inexpensive and highly standardized tools are available that enable the disruption, replacement, or modification of essentially any genomic sequence. Furthermore, these tools can operate simultaneously on many different genomic sequences.

  • Systematic gene mutations. Although random gene mutations provide a possible set of perturbations, the random nature of the process often results in nonuniform coverage of possible genotypes—some genes are targeted multiple times, others not at all. A systematic approach can cover all possible genotypes and the coverage of the genome is unambiguous.

  • Gene disruption. While techniques of genomic manipulation and systematic gene mutation are often useful in analyzing the behavior of model organisms such as yeast, they are not practical for application to organisms of greater complexity (i.e., higher eukaryotes). On the other hand, it is often possible to induce disruptions in the function of different genes, effectively silencing (or deleting) them to produce a biologically significant perturbation.

SOURCE: Adapted from T. Ideker, T. Galitski, and L. Hood, “A New Approach to Decoding Life: Systems Biology,” Annual Review of Genomics and Human Genetics 2:343-372, 2001.

necessary to create a number of more refined models, each incorporating a different mechanism underlying the discrepancies in measurement.

With the refined model(s) in hand, a new set of perturbations can be applied to the cell. Note that new perturbations are informative only if they elicit different responses between models, and they are most useful when the predictions of the different models are very different from one another. Nevertheless, a new set of perturbations is required because the predictions of the refined model(s) will generally fit well with the old set of measurements.

The refined model that best accounts for the new set of measurements can then be regarded as the initial model for the next iteration. Through this process, model and measurement are intended to converge in such a way that the model’s predictions mirror biological responses to perturbation. Modeling must be connected to experimental efforts so that experimentalists will know what needs to be determined in order to construct a comprehensive description and, ultimately, a theoretical framework for the behavior of a biological system. Feedback is very important, and it is this feedback, along with the global—or, loosely speaking, genomic-scale—nature of the inquiry that characterizes much of 21st century biology.


In the last decade, mathematical modeling has gained stature and wider recognition as a useful tool in the life sciences. Most of this revolution has occurred since the era of the genome, in which biologists were confronted with massive challenges to which mathematical expertise could successfully be brought to bear. Some of the success, though, rests on the fact that computational power has allowed scientists to explore ever more complex models in finer detail. This means that the mathematician’s talent for abstraction and simplification can be complemented with realistic simulations in which details not amenable to analysis can be explored. The visual real-time simulations of modeled phenomena give

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