PERSPECTIVEStion, have proven extremely fruitful for computer science. Artificial neural networks(ANNs)—which have myriad uses, including classiﬁcation, prediction, and sequential
decision-making—are based on complex
biological networks of neurons, axons, dendrites, and synapses. The learning rules usedby ANNs, which nonlinearly combine signals from different nodes into outputs that are
fed to other nodes or interpreted as computational results, have grown more sophisticatedover time (3). They can still be traced in spirit,
however, to a theory introduced by neuropsychologist Donald Hebb in the 1940s, sometimes described as “neurons that ﬁre together
wire together” (4). Discovery of the molecular basis of natural evolution ultimately
inspired John Holland (5) and other pioneersof genetic algorithms to solve complex problems in combinatorial optimization by using
computers to generate populations of “individuals” that represented solutions, allowing
them to “breed” and then selecting for the“ﬁttest,” or most optimal, solution.
Biological analogies have been especiallyinstructive for developing artiﬁcial immune
systems (AISs). Like their biological namesakes, computer viruses replicate by attaching themselves to a host (a program, or acomputer) and coopting the host’s resources
to copy themselves. The analogy worked wellat a macroscopic level, where it provided
insights into computer virus epidemiology
and helped explain the evolution of different
types of viruses as different storage media andoperating systems rose and fell in prevalence.
It also worked at a microscopic level, where
it became very natural to explore how thedetailed workings of the vertebrate and invertebrate (or “innate”) immune system could
provide computer virus remedies. During
the 1990’s, for instance, IBM Research (6, 7)
and the University of New Mexico (8) developed computer analogs of the immune system that employed “know thyself ” or “knowthine enemy” strategies to recognize unknown
pathogens (computer viruses or intrusions).
Many of these past computational
approaches sought to apply biological ideasthat had been known for decades or centuries.
In contrast, Afek et al. sought to simultaneously solve a pair of contemporary problems:
one computational and one biological. The
computational problem, known as distributively selecting a Maximal Independent Set(MIS), is to develop a mechanism by which
nodes of a large computer or sensor network can work together without any central
coordination to identify a subset of “leader”
nodes that form a backbone through which
messages can be routed efﬁciently. The bio-logical problem is to discover the distributed
mechanism by which, during developmentof a ﬂy’s nervous system, certain precursor
cells are selected to differentiate into sensory
bristles. After showing that the two processesare analogous, Afek et al. conducted a seriesof experiments that revealed the details of aprotein-based inhibition system that results
in a regular pattern of bristles. Based on biological plausibility arguments and experimental results, the authors postulated a small
set of possible selection mechanisms, andthen ran computer simulations that revealed
that just two of those tested could explain theobserved results. They then selected the onethat was more plausible for use in computation, and showed it could produce an MIS
with high probability.
The jury is still out on whether the algorithm derived by Afek et al. accurately mirrors the biological system, but what mattersfrom a computer science perspective is that
it works. Indeed, it has more attractive properties than any that had been conceived bydecades of computer scientists. This result
hearkens back to IBM’s decision to pattern

its AIS on an outmoded “template” theoryof antibody generation, rather than on modern theories of negative and clonal selection,
because the outmoded idea worked better forcomputer viruses. In both cases, harnessing a
biological analogy—and treating it with bothrespect and some skepticism—led to very
effective computer algorithms. Apparently,
to truly profit from the lessons of Mother
Nature, we must be judicious, not slavish, inapplying analogies.
References

1. Y. Afek et al., Science 331, 183 (2011).
2. J. Kephart, D. Chess, IEEE Computer 36, 41 (2003).
3. J. J. Hopﬁeld, Proc. Natl. Acad. Sci. U.S.A. 79, 2554 (1982).
4. D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, 1949).
5. J. Holland, Adaptation in Natural and Artiﬁcial Systems(Univ. Michigan Press, Ann Arbor, MI, 1975).
6. J. Kephart et al., in Proceedings of the 14th International
Joint Conference on Artiﬁcial Intelligence (IJCAI, Montreal, 1995), pp. 985–996.
7. J. Kephart et al., Sci. Am. 276, 56 (1997).
8. S. Forrest et al., in Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy (Los Alamitos,CA, 1994), pp. 202–212.
10.1126/science.1201003

CELL BIOLOGY

The Lives of Proteins
Joshua B. Plotkin
A new method to measure protein dynamics in living cells reveals how protein removal regulates
cell physiology.

Biologists have traditionally studied
gene regulation through a transcriptional lens. This is due in part to theinﬂuence of Jacob and Monod’s early work ontranscription and, more recently, to the availability of high-throughput messenger RNA(mRNA) quantiﬁcation by microarrays andsequencing. Libraries of ﬂuorescently tagged
proteins (1) and advances in mass spectrometry (2) have since allowed quantificationof protein abundances, and revealed that asmuch as 30% of the variation is governed byposttranscriptional processes (3). Still, most
systematic studies have focused on steadystate amounts of mRNAs and proteins. A newtechnique called “bleach chase,” described by
Eden et al. (4) on page 764 of this issue, accurately quantiﬁes protein half-lives in livingcells, an innovation that shifts the focus to theDepartment of Biology and Program in Applied Mathematics
and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA. E-mail: jplotkin@sas.upenn.edu

dynamic role of protein removal in regulatingcell physiology.
There are two distinct mechanisms ofprotein removal: dilution by cell division
and active degradation (see the figure).
Whereas dilution decreases all proteinssimultaneously, degradation can be highly
speciﬁc to individual proteins (such as by
the ubiquitin-proteasome pathway). Dilution is dominant in fast-dividing cells, suchas bacteria, whereas degradation dominatesin slow-dividing cells. As a result, the relative importance of synthesis versus removal
in regulating the stoichiometry of the proteome likely varies by cell type, because
rapid cell division decreases the potential
for speciﬁc removal mechanisms to control
protein amounts. To elucidate the balance
between dilution and degradation in moderately growing cells, Eden et al. applied thebleach-chase technique to quantify the halflives of 100 proteins in human lung cancer
cells that divide daily.

www.sciencemag.org SCIENCE VOL 331 11 FEBRUARY 2011
Published by AAAS

683

Protein removal. Proteins are
Bleach chase is a broadly
either diluted by cell division orProtein
removalapplicable method to quantify
actively degraded (by the proteaprotein turnover in living cells
some, for example). The balance
Degradation
Dilutionwithout the use of compoundsof synthesis versus removal in regto inhibit protein expression
ulating the stoichiometry of the(translation), antibodies, orproteome may vary with cellularradioactive labels. The amountdivision timing.
of a protein that is tagged witha ﬂuorescent protein is monifindings in yeast (1). This is
tored by time-lapse microscopy
somewhat analogous to stratein a population of several hungies that reduce noise in protein
dred cells after a pulse of light
synthesis found among essenrenders some of the ﬂuorescenttial yeast genes and amongprotein irreversibly nonﬂuorescomplex-forming proteins thatcent. The relevant signal is therequire strictly regulated stoiProteasome
deficit of fluorescence in thechiometry (10). Furthermore, if“bleached” population relative
the abundance of a protein must
Governs long-lived proteins
Governs short-lived proteinsto a nonbleached control popurespond rapidly to a stimulus,
in rapidly dividing cells
in slowly dividing cells
lation. This ﬂuorescence deﬁcit
then this demand will selectdecays to zero as bleached profor more dramatic transcripGlobal effectSpecific effectteins are removed. Thus, bleachtional regulation in a long-lived
chase is an elegant inversion ofprotein than in a short-lived
the classic pulse-chase techprotein—a trend that has beennique (5), whose (positive) signal consists of of protein removal in these cells.
observed statistically in yeast (1) but has nota radioactive marker that decays as the tagged
How do protein half-lives change when yet been extended to mammals.
protein is removed.
cell growth is arrested? To study this, Eden
The roles of protein synthesis, degradaThe bleach-chase technique offers several et al. treated cells with either drugs or nutri- tion, and dilution in providing a robust celadvantages. Although mass spectrometry can ent starvation. In all treatments, protein half- lular physiology remain topics of active
increase the throughput of the pulse-chase lives typically increased. Moreover, long- research. Recent work in mammals has
approach (6), it does not allow real-time mon- lived proteins showed the most substantial uncovered remarkable bursts of transcription
itoring of proteins in living cells. Likewise, increases, because the removal of such pro- (11), raising the possibility that long halftechniques based on translation inhibition teins is governed primarily by the rate of lives are selected to buffer protein amounts
involve perturbation of the cell and produce dilution. Remarkably, treated cells did not against overdispersion, especially in essential
relatively noisy estimates of individual half- compensate for the change in division rate or complex-forming proteins. Other work,
lives (1), especially for long-lived proteins. by increased protein degradation, and indi- by contrast, emphasizes the adaptive value
Techniques based on activatable ﬂuorophores rect evidence suggests only partial compen- of noisy expression for bet-hedging against(7) cannot take advantage of existing libraries sation by decreased synthesis. As a result, unpredictable environments in microbial popof proteins tagged by standard ﬂuorophores. growth arrest may lead to pathogenic pro- ulations (12, 13) and for cell differentiation in
Dual-fluorescent techniques share many teome imbalance (9), especially in cells that multicellular organisms (14). However theseadvantages of bleach chase, but their typi- are dividing quickly before treatment. Eden questions are resolved, measurements of procal high-throughput applications have not yet et al. suggest this mechanism to explain why tein dynamics will undoubtedly improve ourproduced quantitative estimates of half-lives some anticancer drugs preferentially kill understanding of how cells operate.
(8). However, the relative accuracy of mean tumor cells. At the same time, the view thatReferences
protein turnover rates measured by Eden et al. perturbing cell division is more detrimental
1. A. Belle, A. Tanay, L. Bitincka, R. Shamir, E. K. O’Shea,in a population of asynchronous cells comes to rapidly dividing cells, as opposed to differProc. Natl. Acad. Sci. U.S.A. 103, 13004 (2006).
at a price—sacrificing the ability to study entiated cells that divide rarely, is also a con2. P. Lu, C. Vogel, R. Wang, X. Yao, E. M. Marcotte, Nat.
Biotechnol. 25, 117 (2007).
variation in protein decay rates across indi- vincing explanation.
3. C. Vogel et al., Mol. Syst. Biol. 6, 40 (2010).
vidual cells (which can be done using a dualTherapeutic implications aside, measure4. E. Eden et al., Science 311, 764 (2011).
ﬂuorescent method) (8).
ments of protein dynamics raise intriguing
5. J. D. Jamieson, G. E. Palade, J. Cell Biol. 34, 57 (1967).
The protein half-lives estimated by Eden questions about evolutionary strategies for6. J. M. Pratt et al., Mol. Cell. Proteomics 1, 579 (2002).
7. G. H. Patterson, J. Lippincott-Schwartz, Science 297,
et al. in cancer cells range from roughly 1 robust cell regulation. Although energeti1873 (2002).
hour to 1 day, versus a mean half-life of 45 cally costly, high levels of synthesis com8. H.-C. S. Yen, Q. Xu, D. M. Chou, Z. Zhao, S. J. Elledge,
min in yeast cells (1), which divide more bined with active, speciﬁc degradation might
Science 322, 918 (2008).
9. H. Liu, J. Krizek, A. Bretscher, Genetics 132, 665 (1992).
rapidly. Removal was dominated by active reduce noise in protein abundances and prodegradation for about half of the proteins duce steady-state amounts that are also more 10. H. B. Fraser, A. E. Hirsh, G. Giaever, J. Kumm, M. B. Eisen,
PLoS Biol. 2, e137 (2004).
studied, whereas removal was dominated by robust to changes in the rate of cell division. 11. A. Raj, C. S. Peskin, D. Tranchina, D. Y. Vargas, S. Tyagi,
dilution for the 10% of proteins that were Such features would be desirable in genes that
PLoS Biol. 4, e309 (2006).
very long-lived; both dilution and degrada- require precise temporal regulation. Indeed, 12. M. Thattai, A. van Oudenaarden, Genetics 167, 523 (2004).
13. A. Eldar, M. B. Elowitz, Nature 467, 167 (2010).
tion played substantial roles for the remain- Eden et al. observed enrichment for cell divi- 14. M. F. Wernet et al., Nature 440, 174 (2006).
ing proteins. These results quantify the bal- sion cycle proteins among the degradation10.1126/science.1202010
ance between the fundamental mechanisms dominated short-lived proteins, similar to684

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