On the un-thoughtful use of engineering metaphors 1: robustness (wonkish)

One topic that will merit multiple attempts to address is the limitations to the utility of ideas and metaphors from engineering for understanding the function of biological systems. My starting stance on this is that when exploring the unknown (for example, the quantitative function of intracellular signaling systems) ideas that are imported from other spheres, and their attempted use as metaphors qualifies as among first and best approaches researchers can take.

Moreover, any first use of a term as a metaphor, to compare truths from one domain to knowledge to knowledge in another, is by definition a creative act, one that can lead to new understanding.

But to move from possibility to reality, to actually generate new knowledge, the metaphor-wielder must actually test the idea, not merely assert it as truth.

When people became enthusiastic about the application of ideas from engineering to understand biology (Hartwell et al. 1999, Brent 2000), I think most of us imagined that the ideas would be tested, to see if they led to new knowledge, or not (Brent 2004). I doubt anyone imagined that the such concepts might be imperfectly understood, then asserted, then and re-asserted until meaning was lost.

As a first example of how use of metaphors can fail to generate knowledge, I’ll start with the term robust. And note that a single post can’t begin to convey all of the lack of enlightenment spread by its unquestioned repeptition.

Colloquially, robust means something like rugged. In biology, its use is old, and broadly consistent with colloquial its colloquial meaning. For example, it sometimes stands in opposition to gracile. Our Australopithecus great-great-grand parents are said to be gracile, while our Paranthropus great aunts and uncles were robust, more heavily built, and incidentally able to crush roots and seed grains with their teeth.

For systems biology, I suspect that term robustness came in from control theory. Here, by the 1990s, the term had a fairly precise technical meaning: a control system was said to be robust if its stability (return to starting values), and some level of its performance, were guaranteed in the face of changes or lack of certainty in some system variables within defined ranges (see for example Zhou and Doyle 1997). Note at this point how different this particular meaning is from either the colloquial sense of the term or the pre-existing biological meaning.

In the early crossover uses in biology in the 1990s we see the term robust being used precisely, for example by Leibler and his coworkers (Barkai and Leibler 1997, Alon et al. 1999). At this point there is nothing of the metaphorical at all. When these authors use the term robust, they mean by it what control theorists say it to mean.

But then the other connotations of the term begin to take over, and the metaphor began to take flight. We see early flapping of wings in a review by Hiroaki Kitano (2004), in which robustness “is one of the fundamental and ubiquitously observed systems-level phenomena that cannot be understood by looking at the individual components. A system must be robust to function in unpredictable environments using unreliable components.” Not all ways biological systems that can function in unpredictable environments involve an engineer’s conception of robust control (for example, some biological systems do not return to a baseline, but deviate afterwards from where they began by continued development or by physically moving their entire fleshy envelopes to more predictable environments).

But a greater problem is with the widely shared assertion captured in the 2004 Kitano review, that there is a thing called robustness ubiquitous to biological systems that robustness enables them to function with unreliable components. An ability to function with unreliable parts was not part of the original definition of robust control. It crept in, during or at least around, the time of its importation into biology.

But I believe still more important problem is how the far this assertion of robustness deviates from the colloquial meaning of the word. The result is that the word, by pushing an interesting idea and asserts a statement about the natural world, ignores known truths.

Consider one particularly important, and yet unreliable, biological “component”, DNA. Consider how deeply unrobust a cell’s chromosomal DNA is. It’s easily broken by the tiniest sheer forces, yet it needs to be unwound at frightening speeds, replicated, and stuffed into descendent cells. Its sequence needs to be accurate but one of its bases, cytosine, is so vulnerable to deamination that it made evolutionary sense in vertebrates to generate the male gamets outside the body in cells a few degrees cooler. And, in haploid cells in G1, a single unrepaired double strand break, as might be caused by a single cosmic ray, is death. There is nothing at all here that matches any natural language meaning of the term robust.

For DNA, the idea that the system has a general property, that it is robust, in the colloquial sense of being rugged, is, all things considered, false.

Moreover, for DNA, the idea that there is characteristic robustness, an “emergent”, property of the biological system, in the not-actually-from-control-theory sense allowing “functioning with unreliable components”– that idea also seems mostly. Rather, to protect the fragile DNA molecule, to ensure its accurate replication and segregation into descendent cells, and to protect the soma of from the consequences of fragility, there are dozens of known systems. Repair systems. Proofreading systems. Error-detecting systems. Freeze-until-you’ve-corrected-the-error systems. Suicide-if-continued- nonrepair systems. These known systems use known molecular entities to carry out processes  These processes and entities have names. Repair. UvrA. Proofreading. DnaQ. APC/C. Checkpoints. p53. Apoptosis.

It is from study of these processes and named entities, rather than any wishful assertions based on understanding of control theory or other disciplines, that all epistemically useful statements of how cells can be “robust” or resistant or resiliant to DNA damage have so far come

References

Alon U, Surette MG, Barkai N, Leibler S (1999) Robustness in bacterial chemotaxis. Nature 397: 168–171 PMID: 9923680

Barkai N, Leibler S (1997) Robustness in simple biochemical networks. Nature 387: 913–917  PMID: 9202124

Brent, R. Genomic Biology, Cell, 2000, 100, 169-183.  PMID: 10647941

Brent, R. A partnership between biology and engineering. Nature Biotechnology 2004, 22, 1211-1214.  PMID: 15470452

Carlson, J. M. and Doyle, J. (1999).  Highly optimized tolerance: a mechanism for power laws in designed systems. Phys. Rev. E 60, 1412–1427.

Carlson, J. M. and Doyle, J. Complexity and robustness. Proc. Natl Acad. Sci. USA 99 (Suppl 1), 2538–2545 (2002).  PMID: 11875207

Hartwell LH, Hopfield JJ, Leibler S, Murray AW. (1999).  From molecular to modular cell biology. Nature. 1999 402(6761 Suppl: C47-52. PMID: 10591225

Kitano H. (2004) Biological robustness. Nat Rev Genet. 2004 Nov;5(11):826-837. PMID: 10591225

Zhou, K. and Doyle, J.  (1997) Essentials of robust control. Prentice Hall.   ISBN 0-13-525833-2