218 CATALYZING INQUIRY
however, refers to the ability of a structure or process to persist in the face of perturbations of internal
components or the environment. Those perturbations might include outright component failure, unex-
pected behavior from components or other cooperating systems, stochastic changes in chemical concen-
trations or reaction rates, mutations, or the motion of external biochemical parameters. These sorts of
perturbations, such as stochastic changes of molecular concentrations, are intrinsic to the nature of
biology, from the molecular scale to the ecological.
A robust response to these perturbations generally consists of one of three types: (1) parameter
insensitivity, meaning that a robust process does not depend on a single ideal value of an input; (2)
graceful degradation, in which the level of functionality of the system is indeed lessened by component
failures, but it continues to function; and (3) adaptation, in which internal components reconfigure to
react to a change to maintain the same level of functionality.^32
Kitano notes that robustness is attained in biological systems by using mechanisms well known to
human engineers. He describes four mechanisms or approaches to biological robustness:^33
- System control mechanisms such as negative-feedback and feed-forward control;
- Redundancy, whereby multiple components with equivalent functions are introduced for backup;
- Structural stability, where intrinsic mechanisms are built to promote stability; and
- Modularity, where subsystems are physically or functionally insulated so that failure in one
module does not spread to other parts and lead to system-wide catastrophe.
Kitano then notes that these approaches used in engineering systems are also found in biological
systems, pointing out that “redundancy is seen at the gene level, where it functions in control of the cell
cycle and circadian rhythms, and at the circuit level, where it operates in alternative metabolic path-
ways in E. coli.” Furthermore, engineering approaches have proven to be a useful lens when investigat-
ing biological robustness.
For example, Barkai and Leibler^34 established a model (later confirmed experimentally) to explain
perfect robust adaptation in bacterial chemotaxis, or the ability of bacteria to move toward increased
concentrations of certain ligands. It had long been known that the mechanism responsible for this ability
had several key attributes, among them a high sensitivity to changes in chemical concentration, together
with an ability to adapt to the absolute level of that concentration. Working with the known molecular
makeup of these cells (e.g., the receptors, kinases, and diffusible messenger proteins), Barkai and Leibler
showed that when varied separately, many of the rate constants (such as molecular concentrations of
elements of the signaling network or reaction rates) could be varied by orders of magnitude without
affecting the magnitude of the response.^35
Later work by Yi et al. used the mathematics of control systems to show how the Barkai-Leibler
model was a special case of integral feedback control, a well-studied approach of control theory.^36 In
addition to control theory (including feedback and feed-forward control), many other engineering
approaches are found in biological systems, including redundancy, modularity, purging (quickly elimi-
nating failing components), and spatial compartmentalization.^37
(^32) H. Kitano, “Systems Biology: A Brief Overview,” Science 295(5560):1662-1664, 2002. Available at http://www.sciencemag.
org/cgi/content/abstract/295/5560/1662.
(^33) H. Kitano, “Systems Biology,” 2002.
(^34) N. Barkai and S. Leibler, “Robustness in Simple Biochemical Networks,” Nature 387(6636):913-917, 1997.
(^35) However, the mechanism does not account for the full dynamic range of the sensor patches at a molecular level. (It may be
that some sort of emergent property of the sensor patch as a whole, as opposed to some property of the individual sensor
complexes, is necessary to obtain the full dynamic range. See, for example, T.S. Shimizu, S.V. Aksenov, and D. Bray, “A Spatially
Extended Stochastic Model of the Bacterial Chemotaxis Signaling Pathway,” Journal of Molecular Biology 329(2):291-309, 2003.)
(^36) T.M. Yi, Y. Huang, M.I. Simon, and J. Doyle, “Robust Perfect Adaptation in Bacterial Chemotaxis Through Integral Feedback
Control,” Proceedings of the National Academy of Sciences 97(9):4649-4653, 2000.
(^37) D.C. Krakauer, “Robustness in Biological Systems,” 2003.