REVIEWS

Understanding systems-levelproperties: timely stories fromthe study of clocks
John B. Hogenesch* and Hiroki R. Ueda‡

Abstract | After several decades dominated by reductionist approaches in biology,
researchers are returning to the study of complex biology with a litany of new and oldtechniques — this paradigm has been termed systems biology. Here we detail how
systems biology is being used to uncover complex systems-level properties of thecircadian clock. These properties include robustness, periodicity and temperaturecompensation. We describe how clock researchers are using systems-biology techniques,
such as genetic perturbations, kinetic luminescence imaging, synthetic biology andmathematical modelling, to untangle these complex properties in mammals, fungi
and bacteria. The strategies developed in the context of circadian clocks may prove
useful for tackling similar problems in other systems.

Temperature compensation
In biological clocks, this is theproperty by which an increaseor decrease in temperature
fails to change the period
length of the circadian rhythm.

*Department ofPharmacology, Penn Center
for Bioinformatics, Penn
Genome Frontiers Institute,
Institute for Translational
Medicine and Therapeutics,
University of Pennsylvania
School of Medicine,Philadelphia, Pennsylvania
19104, USA.
‡
Laboratory for Systems
Biology, Functional Genomics
Unit, Center for
Developmental Biology,
Laboratory for Synthetic
Biology, Quantitative Biology
Center, RIKEN, 2‑2‑3Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo
650‑0047, Japan.
e-mails: hogenesc@mail.
med.upenn.edu; uedah-tky@umin.ac.jp
doi:10.1038/nrg2972
Published online 10 May 2011

The old adage ‘timing is everything’ is just as true forbiology as it is for comedy. In the past decade, researchin biological rhythms has shown that many aspects of our
biology occur with a daily rhythm (reviewed in REF. 1);
we wake up, we are active and we sleep at similar times
each day. Because the environmental changes that
accompany the day are so predictable, nature devised aningenious system, the circadian clock, to anticipate themand regulate physiology and behaviour accordingly.
For example, before we wake up, our heart rate andblood pressure increase in anticipation of this stressful
event (reviewed in REF. 2). Our liver, fat cells and skeletal muscles anticipate that we will eat and maximize
their efficiency in metabolizing, storing and utilizing
energy produced from this food (reviewed in REFS 1–3).
Our mental state and capacity for memory and learning, as well as our state of arousal and onset of sleep,
are likewise clock-regulated (reviewed in REFS 3–6).
Although these examples come from human studies,
similar physiological and behavioural rhythms occur
in most organisms, including cyanobacteria, fungi,
plants and virtually all animals. Anticipation and adaptation to environmental change is a critical feature of
biological systems7.
To accomplish this timing, circadian clocks need: alight-input system that synchronizes the clock to day–night cycles; a biochemical and cellular oscillator tomeasure the passing of time; and output mechanisms

to relay this timing information to the primary systems
that regulate physiology and behaviour. Research inseveral model systems — Synechococcus elongatus PCC
7942 (cyanobacterium), Neurospora crassa, Arabidopsis
thaliana, Drosophila melanogaster, Mus musculus andhumans — uncovered many aspects of these basic
mechanisms of oscillator function. In general, the lightinput systems of bacteria, fungi, plants and animals all
differ owing to their different photoreceptive systems. By
contrast, the circadian clocks in these systems and their
methods of regulating output are strikingly similar.
Clocks also have other interesting properties such astemperature compensation and genetic robustness. As temperature increases, the speed of most biochemical reactions also increases. However, the clock needs to keep
24‑hour time in environments where the temperature
may change tens of degrees during the day and even more
between seasons. Poikilotherms need a circadian clock that
accounts for this. Interestingly, clocks in homeotherms
are also temperature-compensated, pointing to the conserved nature of this feature. A second interesting property is genetic robustness. As clocks are so crucial for life,
organisms need genetic architecture that accommodates
mutations without losing clock function.
What all of these properties have in common is thatthey are produced by systems of genes and proteinsinteracting together rather than being traits conveyed
by single genes. In this Review, we document recent

NATURE REVIEWS | GENETICSVOLUME 12 | JUNE 2011 | 407© 2011 Macmillan Publishers Limited. All rights reserved

REVIEWS
Box 1 | Omics data sets and integration
Omics data
One of the key aspects of systems-biology approaches that distinguish them fromtraditional attempts is the availability of omics data and the opportunities for insightthese data provide. Following an initial perturbation — which can take the form of
RNA interference (RNAi), a genetic knockout or other genetic lesion, changing
environmental conditions (for example, oxygen, time of day or temperature), or small
molecule or metabolite perturbations — multi-parametric data are collected. These
data can take the form of RNA or protein expression, analysis of post-translational
modification, interaction data, metabolite profiling or phenotypic profiling by imaging
or end-point analysis. For example, RNA sequencing (RNA-seq) and chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) are now robust
and widely used to investigate changes in RNA levels, splicing and non-coding RNA
expression (RNA-seq), or histone modifications and transcription factor binding(ChIP–seq). Quantitative mass spectrometry has, likewise, evolved to the point where
genome-wide surveys of protein expression, protein modification and metabolite
profiling can be conducted. High-throughput imaging technologies enablegenome-wide screens of small interfering RNAs, cDNAs and small molecules at definedend points; they even enable kinetic studies. These studies are increasingly being
applied at single-cell and even subcellular levels. After the data are collected, they canbe analysed for patterns and other emergent properties. Two of these properties,
parallel compensation and proportionality, are depicted in FIG. 1.
Data integration
A logical extension of having omics data is integrating them to gain insight into biology.
This process involves multiple steps. Public databases are available to house many
types of omics data — for example, gene expression data, short-read sequence data,proteomics data and even data for high-throughput screening. Most of these data setsrequire some form of conditioning before integration. This can involve specific analyses
on each type of data — for example, protein–protein interaction data may requiresignificant filtering and reciprocal protein–protein interactions before a novel
interaction is considered valid. Also, each of these data sets often has its own analysis
framework. Without understanding these frameworks (for example, which values are
informative) you can contaminate downstream analyses and data integration. Even
determining the identity of each gene or protein can be problematic. Although you
might think this should be straightforward, genes and proteins have synonyms, andtechnology platforms often have their own identifiers, both of which can complicate
integration. Integrating small molecule and metabolite profiling data may also not be
straightforward — there are few one-to-one (small molecule or metabolite to gene)
relationships. One-to-many relationships are more common and one-to-none
findings are very frequent. Finally, there are many ways to integrate these data.
Pathway approaches, Bayesian integration (REFS 74–75 for example), machine learning
approaches (REF. 76 for example), including neural networks77, and many othermethods have been proposed and used in data integration. The choice of
these methods often depends on the question — for example, for new members of awell-characterized pathway, Bayesian integration methods can be effective.

Robustness
The resistance to perturbationby, for example, genetic or
environmental factors.

Poikilotherms
Organisms that do not regulate
their body temperatures.

Homeotherms
Organisms that regulate theirbody temperatures.

progress in understanding three clock properties: genetic
robustness, periodicity and temperature compensation.
To do so, we focus on three different model systems,
namely mammals, cyanobacteria and N. crassa, in whichsignificant progress has been made using a variety ofsystems-biology methods. These include perturbation
analyses, genetics, biochemistry, imaging and conceptualand mathematical modelling.
Why is this a propitious time for this Review?
Although reductionist approaches to biology have held
the field for the past several decades, in the past few years
researchers have begun to grapple with biological complexity. This has been aided by several developments.
First, new technologies to collect omics data started in
the 1990s and have been continuing to develop to thisday. These multiparametric data — for example, DNAarray, high-throughput sequencing and mass spectrometry — are enabling global analysis of biology following
genetic or other perturbations (BOX 1). Second, old andnew modelling approaches (BOX 2) are increasingly being
used to analyse these data and gain insight into the governing principles of biology. Third, there has been a slow
but inexorable realization that minimalist approaches tobiology fail at addressing complexity. ‘One-gene-at-atime’ research is giving way to global network approaches
to study complex biology. The development of centres,
institutes and departments of systems biology provides
evidence that these approaches are reaching critical massin academic institutions and elsewhere. It is our hopethat lessons learned and strategies and techniques usedin tackling complex systems-level properties in clocks
will be useful for researchers interested in tacklingsimilar problems in other systems.

Robustness
The clock takes a licking and keeps on ticking. A key
property of clocks, biological or otherwise, is theirresistance to intrinsic and extrinsic perturbation. What
good would your watch be if it was reset by moving yourarm? Instead, to set your watch, you need to use a precise mechanism that is difficult or impossible to do accidentally. This idea also holds true for biological clocks.
For example, each day your circadian clock is reset byenvironmental cues, the most notable being light. This
process of resetting is called entrainment, which acts tokeep the clock running accurately to a 24‑hour time­scale. Entrainment also allows your body to become
accustomed to new lighting schedules that accompany
changes in seasons or geography (for example, after jet
travel). In response to the change in light onset, the circadian clock shifts to the new light schedule by either
advancing or delaying its phase. The clock is also not
overly sensitive. The mammalian clock is most sensitive to blue light, but at reasonably high intensities anddurations. A late-night snack with short-term refrigerator light is unlikely to substantially shift your circadianclock. Biological clocks have evolved balanced resetting mechanisms that allow synchronization to certain
important environmental cues while ignoring others.
Although environmental changes such as light andtemperature are one way to perturb a system, genetics
provides another. Unlike consumer electronics, which
are hardened to perturbation by precision manufacturing and engineering, our biological parts are subject tomutation and selection. Biological networks need to berobust to most of these mutations and allow sufficient
genetic variation to exist in order to be acted on underselection. Here is a remarkable example: of the ~4,500
genes that have been knocked out in mice, only around
50 are heterozygous lethal. Mammals have evolved to dowithout 50% of the gene activity of 99% of the genome
for all the pathways used between a fertilized egg anda reproductively fit adult mouse. Indeed, evidence of
natural selection is especially evident in the fly clock
system. Variation in two genes, period and timeless, hasbeen shown to regulate temperature compensation anddiapause, respectively 8.

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Box 2 | Mathematical modelling
Various mathematical models are used in the study of circadian clocks. Like Albert
Einstein’s famous quote, “Make everything as simple as possible, but not simpler”, thevalue of a model can be measured by its simplicity of representation and its capability
for explanation and prediction. One of the simplest ways to model circadian clocks isusing geometry78. In this geometrical modelling, a state of the circadian clock isrepresented as a point on a circle, whereas the angle of a point and the radius of a circle
represent a phase and amplitude of circadian oscillation. This simple representation
can explain phase–response curves (the phase-dependent response of the clock to an
external stimulus such as light) and can predict the experimental conditions to induce
the singularity behaviour (transient stopping of circadian clocks). Various timings ofcircadian gene expression can also be modelled in geometry52.
Another way to model circadian clocks is using algebra. For example, temperaturecompensation of circadian clocks can be formulated in an algebraic equation that
explains the balance of the elementary processes involved in circadian oscillations79.
There are two non-exclusive interpretations of this equation, the ‘balance model’ andthe ‘robust model’, which are explained in the main text and shown in FIG. 3.
The analytical approach is a third way to mathematically model clocks. Manymathematical models of this type have been proposed that are based on ordinary
differential equations. They range from simple abstractive models, which explain thebasic kinetics of circadian clocks (for example, the van der Pol model80, the Goodwin
model81, the Kuramoto model82, and so on), to complex detailed models, which explain
molecular details of living organisms (for example, the Goldbeter model83, the Forgermodel84, and so on). Recently, in order to grasp the probabilistic behaviour of circadianclocks, stochastic differential equations have also been used for the mathematicalmodelling of circadian clocks.

Bayesian integration
The use of the Bayesianinference — a statisticalinference where experimental
data is used to infer new or
update prior hypotheses — tointegrate large-scale, genomic
data sets.

Diapause
The suspension of
insect development after
subjection to adverse
environmental conditions.

Rhythmicity
In biological clocks, this is theproperty by which a molecular,
cellular or physiologicalresponse recurs with regularity.

RNA interference(RNAi). A biochemical system incells that governs how dsRNA
can interact with mRNAs
to activate or inhibit theirmessage levels or translation.

Autonomous oscillator
In biological clocks, this isa system in which cellularoscillations — rhythmic
fluctuations with a definable
period length — in genetranscription, reporter geneactivity or physiology persist
in isolated cells.

Approaches to study robustness: genetic manipulation.
The clock community took a systems approach andsystematically deleted clock components as single
and combinatorial gene knockouts. In retrospect, thesefindings were remarkable — not only did they provide
the genetic proof that these genes influenced mammalian
behaviour (a requisite for a core clock gene in animals),
they also illustrated the remarkable robustness of the circadian clock to genetic perturbation. Deletion of mostclock genes in mice results in a clock with a short 11–18
or long 13,19 period length, and usually the discrepancy
is less than 1 hour. Notably, in all cases, returning mice
to a 12/12 hour light/dark schedule resulted in perfectly
adapted mice. In other words, in laboratory conditions ofconstant darkness, complete loss of these genes had modest effects (1–4%) on clock function that would be difficult or impossible to see in normal lighting conditions.
These observations prompted researchers to investigate the genetic redundancy of clock components. In
general, there are two or three mammalian orthologues
for each clock gene conserved in flies (FIG. 1A). We notethat other clock systems — D. melanogaster, N. crassa
and cyanobacteria — do not have this level of clock
gene redundancy. Mammals are more complex and also
have clocks in many tissues rather than just one or a few.
As clock gene expression is tissue specific, we speculatethat orthologues may ensure that at least one member of
each subfamily is present in a tissue to maintain circadian function. To test the effect of knocking out multiple
orthologues, researchers generated compound knockouts and tested the rhythmicity of their locomotor activity in constant darkness. In order to see the endogenous
period length of rhythmicity, laboratory conditions ofconstant darkness or light are necessary; in a normal

lighting scheme, organisms maintain nearly precise
24‑hour time keeping. Whereas cryptochrome 1 (Cry1)–/–and Cry2–/– mice generated short- and long-period-length
phenotypes, respectively, Cry1–/–Cry2–/– mice were arrhythmic13. Period homologue 1 (Per1)–/– and Per2–/– mice both
had short-period-length phenotypes as single knockouts,
but were also arrhythmic when combined20. Single-gene
knockouts of Clock and neuronal PAS domain protein 2(Npas2) both had short-period-length phenotypes, but
again compound double-knockout animals were arrhythmic21. Moreover, none of these paralogous pairs of transcription factors was lethal either as single or double
knockouts. As a comparator, 1 in 5 mammalian genes
is lethal when both copies are knocked out (for further
information, see the Mouse Genome Informatics website). Remarkably, deletion of only one clock gene, Bmal1(also known as Arntl), results in an arrhythmic clock byitself 22. Deletion of a second gene, vasoactive intestinal
peptide receptor 2 (Vipr2), also generates an arrhythmicmouse, but it is not considered a component of the clock
as it neither interacts with clock components nor participates in the timekeeping mechanism23. Thus, deletion ofmost clock genes generates a modest phenotype, whereas
only one, Bmal1, is required for oscillator function. The
mammalian clock gene network, therefore, evolved
to maintain extraordinary robustness to the loss of
individual clock components.
Cellular approaches to studying robustness. In addition toregulating biology and organisms, the circadian clock is acell-autonomous signal transduction pathway. As control
of locomotor activity by the clock is definitely not cellautonomous, cellular models provide a way to focus on
the autonomous signal transduction pathway. We set out
to explore this property of robustness by genetic perturbation using RNA interference (RNAi) in cells24. Because
the clock functions autonomously as a cellular oscillator,
we explored this property in a human cellular model ofthe clock, U2OS cells (an osteosarcoma cell line). Thesecells have robust oscillator function in culture, dozens
to a hundred rhythmic genes, and are amenable to cellbiology techniques including RNAi. Unlike behaviour,
autonomous oscillator models — including dissociatedfibroblasts and suprachiasmatic nucleus (SCN) neurons— indicated that cellular clock function was more fractious than the regulation of behaviour by the clock. For
example, whereas Cry1–/– animals showed a short period
length in locomotor activity, dissociated SCN neurons(and also lung, liver, cornea and fibroblasts) from thesemice were arrhythmic25. Similar results were seen in lungand liver cells from Clock–/– animals26.
We decided to test the requirement of clock factors foroscillator function in this model in dose–response — thatis, sensitivity analysis in a biological system. With RNAi,
by dilution, one can knock down an endogenous gene toany arbitrary level between 100% expression and somewhere near 10%. After knocking down these clock genes,
we looked at the effect on circadian rhythm by using
kinetic imaging to monitor a reporter gene driven by aclock gene promoter. These quantitative perturbations
and phenotyping assays provided critical data for systems

NATURE REVIEWS | GENETICSVOLUME 12 | JUNE 2011 | 409© 2011 Macmillan Publishers Limited. All rights reserved

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Figure 1 | Perturbation modelling— paralogue
0CVWTG4GXKGYU^)GPGVKEU
compensation and proportionality. Animal clock genes
are conserved between flies and humans. In general,
there are two or three mammalian paralogues (mousegenes shown) of each fly gene (A). Perturbation analysis
has uncovered the principles of proportionality (Ba) andparalogue compensation (Bb). Here, proportionality isillustrated in an example in which knockdown (using RNAinterference (RNAi), for example) of Gene A, a transactional
activator, results in decreased levels of its target, Gene B.
Paralogue compensation is shown by an example in whicha transcriptional repressor, R1, is depleted by RNAi andexpression of its target genes increase. In this case, one ofthese targets is its paralogue, R2. A mechanism that can
explain paralogue compensation by repressors is shown
below the graph. In this case, the repressors R1 and R2
both bind to the same response element. Knockdown ofeither gene, therefore, has the potential to regulate R2,
which has this response element in its structural gene.
R1 does not have this element, so knockdown of either
repressor does not result in its induction. Bmal1, also
known as Arntl; Cry, cryptochrome; Npas2, neuronal PAS
domain protein 2; Per, period.

approaches. Similar to results seen in analysis of cells fromknockout animals, knockdown of most clock components
had a bigger effect on the U2OS model than behaviouralrhythmicity in mice. Knockdown of Bmal1, Clock, Cry1
and Cry2, and Per1 all generated either arrhythmic cells
or a clock with such low amplitude that it could not be
observed. Unlike locomotor activity rhythms, analysisof kinetic luminescence imaging showed that knockout of
most clock genes reduced the amplitude of oscillator function. Together, these results were consistent with previous
reports which showed that cellular oscillations were not as
robustly controlled as locomotor activity behaviour.
The U2OS cellular model was also advantageous
for biochemical analysis; we looked at gene expression
changes in clock genes after dose-dependent knockdown of clock components. We focused on conditions
that generated an arrhythmic phenotype (knockdownof Bmal1, Clock, Cry1 and Cry2, and Per1) and madetwo observations: most gene expression changes werelinear and proportional24 (FIG. 1Ba). A 50% knockdown of
Bmal1, for example, resulted in a nearly 50% knockdownof its target gene nuclear receptor subfamily 1, groupD, member 1 Nr1d1 (also known as Rev-erb alpha).
Some changes were still proportional, but nonlinear.
For example, partial knockdown of the transcriptional
repressors CRY1 and CRY2 resulted in a nearly tenfold
induction of their target genes Per2 and RAR-relatedorphan receptor G (Rorg). In addition to this property
of proportionality, we saw several examples of paraloguecompensation24 (FIG. 1Bb). Knockdown of Cry1 resulted
in induction of its paralogue, Cry2. Knockdown of
Nr1d2 (also known as Rev-erb beta) resulted in induction of Nr1d1. Knockdown of Per1 resulted in inductionof both Per2 and Per3. These relationships were unidirectional as knockdown of the other paralogues — forexample, Nr1d2, Cry2, Per2 or Per3 — did not generatecompensation. There were many other non-paralogous
compensatory gene-expression changes as well.
Collectively, these results showed that nearly all perturbations generated complex changes in the clock-gene
network. These properties of the network — proportionality and paralogue compensation — are mechanismsthat explain the robustness of the clock. These properties
are not clock specific; they have been noted in large-scale
analysis of yeast gene knockouts and in meta-analysis
of vertebrate knockout literature27,28. Although theseproperties have not yet been observed in other circadian models, their presence in several model organisms
suggests that they will be. Whereas humans use materials
and design strategies to engineer robustness, nature haseven more ingenious means.
Finally, large-scale studies were recently performed to
examine the response of the cellular oscillator to environmental perturbagens and synthetic small molecules29,30.
These studies demonstrate that the vast majority of conditions and synthetic perturbations do little or nothing
to cellular clock function. Future research will investigate
how the clock can ignore most environmental changes
to keep precise biological time. Moreover, this research
will also uncover its sensitivity and specificity to appropriate environmental stimuli such as light, temperature

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REVIEWS
and activity. For example, in experiments similar to thosedescribed above, one could expose cells to various temperatures, then measure consequences for clock function in
kinetic luminescence imaging and also measure transcriptional responses. The questions that such experiments
could address include: how does the network rewire when
the temperatures get low or high; which temperatures permit clock function and which do not; and are the sametranscriptional circuits used or are there new ones?

Periodicity
A minimal model of circadian clock function — thecyanobacterial clock. The most basic property of thecircadian clock is its oscillatory nature, including its
frequency and amplitude. Although research in several
models has identified clock genes and suggests biochemical mechanisms for oscillator function, none is as
far advanced as S. elongatus PCC 7942, a cyanobacterial
clock model31–33. In an astounding series of experiments,
Kondo and colleagues took just three circadian clock
proteins, KaiA, KaiB and KaiC, mixed them with ATP
and observed circadian oscillations in KaiC phosphorylation34. Moreover, the system was temperature compensated (see more on this concept later) and entrained
to large shifts in temperature. Therefore, this in vitro system constituted a circadian clock. This observation flew
in the face of the dominant paradigm of the transcriptional–post-translational feedback loop (TTL). Most
clock researchers (including ourselves) believed that it
would be impossible to separate the transcriptional component of clock function from its biochemical mechanism. Moreover, O’Neill and Reddy35 recently showedthat human red blood cells, which lack a nucleus, still
have rhythms in conserved antioxidant proteins called
peroxiredoxins. These rhythms entrain to extrinsic stimuli, are temperature compensated and persist in the presence of transcriptional and translational inhibitors. In
bacteria and humans, then, post-translational rhythms
exist in the absence of transcription.
The development of an in vitro system simplified
the study of the cyanobacterial clock. However, systems
approaches to study this model revealed that it was far
from simple.
The three Kai proteins and ATP produced robust
oscillations in KaiC phosphorylation that lasted morethan a week in vitro. Modelling coupled with experiments, a systems-biology paradigm, was used to addresssome of the interesting questions that came from thisobservation. First, how is it possible that oscillations
happened at all (and that the system did not reduce tothe steady state)? Second, how did the various biochemical complexes maintain synchrony with one anotherinstead of becoming desynchronized and cancelling eachother out? Models were, and continue to be, importantin answering these questions by helping researchers todesign, analyse and interpret experiments (BOX 2).
Biochemical studies powered these models. KaiC
functions to both phosphorylate and dephosphorylate
itself 36–38. KaiA and KaiB modulate the function of KaiC
and its phosphorylation status36,39,40. KaiA promotes
the phosphorylation of KaiC at two adjacent residues

near its ATP binding site41,42; one residue is phosphorylated, followed by the other. The phosphorylated formof KaiC has a higher affinity for KaiB, which promotes
dephosphorylation in the same order: site one, then site
two39,40. This process is termed the ‘phosphoform cycle’(REF. 33) (FIG. 2A). However, despite having directionality
and feedback, this structure would reach the steady state— wherein all forms of KaiC would be at similar levelsof phosphorylation — without additional regulatory
steps. The additional regulation is provided by a feedback mechanism in which KaiB has a higher affinity for
KaiC in its phosphorylated state and inhibits the actionof KaiA on KaiC39,40. Temporally, this cycle is maintainedby having more KaiA activity at the beginning of thecycle, thus promoting the formation of KaiA–KaiC
complexes and also phosphorylation of KaiC43,44. Later,as KaiB inhibits KaiA, the situation is reversed.
Molecular synchrony. The fact that three proteins andATP can generate robust oscillations in phosphorylation
is incredible. However, when considering populationsof these complexes in a solution, not only are feedback
events in individual complexes necessary, they needto act in synchrony. Otherwise, variability and noise
would ensure that some complexes progressed fasteror slower through the phosphoform cycle, or would
be out-of-phase with other complexes, resulting in an
asynchronous population. As the population cycles,
individual complexes somehow maintain synchrony
with one another through the phosphoform cycle. A
remarkable example of this property is illustrated by anexperiment from the Kondo laboratory 45, in which samples of Kai protein complexes at different initial phases
were mixed. Samples in the dephosphorylation phase of
KaiC remained in synchrony with the unmixed samples.
However, phosphorylated KaiC samples either slowed
their rate of phosphorylation or underwent dephosphorylation. Ito et al.45 went on to thoroughly test the effect of
initial phosphorylation status and showed that samples
in the dephosphorylated state were dominant over samples in the phosphorylated state. In short, they showedthat these molecular components could synchronize.
Modelling molecular synchrony. Models were put forward to explain this property of molecular synchrony.
An early model, if early is just 3 years ago, was provided
by Rust et al.43. In this model, KaiA is limiting — when
it is inhibited later in the cycle by KaiB, it affects all KaiC
molecules. In this model, KaiA would act as an allo­stericactivator of KaiC auto-phosphorylation, whereas KaiB
would act as an allosteric inhibitor through action on
KaiA. Synchrony, then, is an emergent feature of the
cyanobacterial clockworks. A second idea, monomer
shuttling, came from the Kondo laboratory experimentdiscussed above45. It was known that individual KaiC
complexes formed hexamers, and this study showedthat these KaiC hexamers exchanged monomeric subunits. They postulated that the shuttling occurred preferentially in the dephosphorylation phase of the cycle(FIG. 2B,C), a time in which these complexes are dominant. This monomer shuffling mechanism provides a

NATURE REVIEWS | GENETICSVOLUME 12 | JUNE 2011 | 411© 2011 Macmillan Publishers Limited. All rights reserved

REVIEWS
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transcriptional network of mammalian circadian clocks. A | A hypothetical KaiC phosphoform cycle for the
cyanobacteria circadian clock, with four sequential reactions, is shown. KaiC first auto-phosphorylates T432
(shown as S/pT) and then S431 (shown as pS/pT). Double-phosphorylation (pS/pT) switches KaiC from an autokinase
to an autophosphatase. KaiC auto-dephosphorylates T432 (shown as pS/T) and then S431 (S/T). The completely
dephosphorylated form (S/T) switches KaiC from a phosphatase to a kinase. B | During the KaiC phosphorylation–dephosphorylation cycles, shuffling of KaiC monomers occurs only in the early dephosphorylation phase (pink shadedarea). C | When a KaiC hexamer in a shuffling phase (pink shading) meets another KaiC hexamer, the monomers shuffle
between the hexamers. After KaiC-monomer shuffling (‘shuffling’), phosphorylated KaiC monomers are converted todephosphorylated KaiC monomers (‘switching’). Da | Here, a basic network structure of the transcription circuit of the
mammalian circadian clock is shown, which consists of ‘morning’ (E/E′-box), ‘daytime’ (D-box) and ‘night time’ (RRE)
transcriptional programs. Db | This shows one of the two oscillatory motifs, the ‘delayed negative feedback’ motif.
Dc | This shows the second of the two oscillatory motifs, the ‘repressilator’ motif. Robust periodicity against internal noise
might be conferred by internal coupling of these two oscillatory motifs in mammalian circadian clocks.

straightforward way for individual complexes to communicate their phosphorylation status with one another.
These concepts are not mutually exclusive. Recently, they
were combined to generate an allosteric transition andmonomer shuttling (AMS) model that agrees with many
experimental data46.
What these studies show convincingly is the power ofsystems approaches — combining theoretical and quantitative models with experimental science. Althoughmany of the biochemical and modelling details remainunresolved, systems approaches are providing information on mechanisms. Classical circadian principles suchas synchronization and periodicity are being addressedin a more meaningful way in this model than in others.

That being said, it is important to point out some of
the limitations. Biological models are like politicians:
they change their assumptions to fit the polls (or data).
Moreover, most of these early models focus on in vitro
dynamics and ignore the contributions from the rest of
the cyanobacterial genome and entraining cues from theenvironment. In nature, cyanobacteria are not in constant darkness and the Kai proteins do not function in
isolation with ATP.
Perhaps to address this issue, clock researchers haverecently studied the cyanobacterial oscillator in the context of the cell cycle. In many species, the circadian clock‘gates’ (regulates the timing of) the cell cycle. Single-cell
imaging was used to study the behaviour of the circadian412 | JUNE 2011 | VOLUME 12

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REVIEWS
clock and cell cycle simultaneously 47. Normally, undermoderate lighting conditions, cyanobacteria go throughthe cell cycle only once. When the amount of light (andavailable energy) was increased, cyanobacteria went
through the cell cycle twice, mostly at defined phasesof the clock. The authors speculated that the two systems,
the clock and the cell cycle, work best when they resonate
with one another. Why might this be? A possible answer
is that de-synchrony between the two systems may beenergetically inefficient. Alternatively, dividing at thewrong time may lead to an undesirably high mutation
rate. Nevertheless, these results demonstrate the benefitof whole-organism systems approaches to understandcomplex issues such as coupling and gating.
However, minimal models, such as those used incyanobacterial clock research, are generically useful
in decoding complex systems properties. For example,
Elowitz and Leibler 48 built a ‘repressilator’ in Escherichia
coli to study oscillatory dynamics. This study, and theaccompanying models, examined the system for principles that enable robust oscillations. These included
aspects of transcription, as well as translation, and protein
and mRNA decay rates48–51. Interestingly, a recent study
revealed the existence of repressilator and ‘delayed negative feedback’ motifs in the transcriptional circuit of the
mammalian circadian clock52. The basic transcription network of the mammalian circadian clock consists of ‘morning’ (E/E′-box), ‘daytime’ (D-box) and ‘night time’ (RRE)
transcriptional programs, and forms two oscillatory
motifs (FIG. 2Da). The first motif is a delayed negative feedback motif (FIG. 2Db), in which the morning transcriptional
program activates the daytime, the daytime activates thenight time and the night time represses the morning. The
second motif is a repressilator motif (FIG. 2Dc), in which
the morning transcriptional program represses the nighttime, the night time represses the daytime and the daytime
represses the morning. Robust periodicity against internal noise might be conferred by internal coupling of these
two oscillatory motifs in mammalian circadian clocks.

Repressilator
This is a synthetic networkthat generates stableoscillations in GFP.

Temperature compensation
Changes in temperature occur on a daily basis — it iscolder at night than it is during the day — and on a seasonal basis. Most organisms do not regulate their bodytemperature; therefore, their circadian clocks need toresist changes in temperature to keep 24‑hour time.
This property is called temperature compensation and it
remains one of the three hallmarks of circadian clocks.
Interestingly, human cells also display temperature compensation in vitro53, which suggests that this property may
be intrinsic to the mechanism of circadian clock function
even in homeotherms. Mammals may be warm-blooded,
but there are temperature gradients between the body’s
core and surface54. Temperature may also act as a universal
cue in mammals to help reset peripheral oscillators55.
The property of temperature compensation is arguably best understood in N. crassa (bread mould) which
has a robust circadian clock that functions analogously to
D. melanogaster and mammalian clocks56,57. As in mammals and flies, transcription factors function prominently in the N. crassa clock. Two genes, white collar 1(wc‑1) and wc‑2, act together to activate the transcription of the frequency (frq) gene (FIG. 3a). The FRQ protein and ancillary factors promote the phosphorylationof WC‑1 and WC‑2 to inhibit this activation and, thus,
FRQ acts to repress its own transcription. To relieve thisrepression, FRQ is targeted for degradation by F‑box/
WD repeat-containing protein 1 (FWD‑1), which physically interacts with FRQ to target its degradation in theproteasome. The net result of these biochemical actions
is a 24‑hour clock that drives rhythmic activity of thewhite-collar complex, the frq gene, the FRQ protein andits degradation, and circadian clock output.
Conceptual models of temperature compensation. As afungus, N. crassa is exposed to large fluctuations in temperature, whereas the human brain varies in temperatureby only a few degrees from its normal 37 °C (98.6 °F). A
number of conceptual models have been put forward to
explain temperature compensation. The simplest of thesemodels considers two or more enzymatic reactions with
opposing effects on clock function58. As the function ofmost enzymes speeds up with increasing temperature(within limits), reactions that ‘speed up’ the clock arecounteracted by reactions that increase their rate to slow
down the clock (FIG. 3b, upper panel). Theoretically, themore reactions that are involved, the more precise androbust compensation at this temperature can be. Initialresearch into the temperature compensation of N. crassa
looked at regulation of frq and its product, FRQ. Levelsof FRQ increase with temperature, whereas mRNA levels
do not59–61. Two splice forms of FRQ, long and short, varyin their levels according to temperature; the long form ismore prevalent at higher temperatures and the short form
is more prevalent at lower temperatures62. These observations show the effects of temperature compensation on
the N. crassa clock, but do not show its mechanism.
Systems analysis: genetics plus biochemistry. By incor­
porating classical genetics and biochemical analyses,
Mehra and colleagues63 have recently begun to take asystems approach to studying how N. crassa maintains
constant clock function as temperatures change. They
took advantage of gain-of-function genetics — the isolation of two mutant strains that were better at temperaturecompensation than wild-type N. crassa64. Although thecircadian period length of wild-type N. crassa decreases
as temperature increases from 18 °C to 30 °C, the chrono
mutant displayed almost perfect compensation up to
30 °C. The second mutant, period3, is overcompensatedwith respect to wild-type N. crassa, as its period length
increased rather than decreased when shifted from
18 °C to 25 °C. Cloning these strains revealed that the
mutations are in two subunits of casein kinase 2 (CK-2),
which has previously been implicated in clock function
in other model systems. Reducing gene dosage of thesecomponents in wild-type strains produced phenotypes
that were similar to the original mutants. Importantly,
changing the dosage of several other kinases and phosphatases that have been implicated in clock function did
not alter temperature compensation, so it is not just any
phosphorylation event that can induce the effect.

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REVIEWS
C

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Figure 3 | The Neurospora crassa
circadian clock andtwo non-exclusive models for temperaturecompensation. a | A hypothetical transcriptional
circuit in Neurospora crassa circadian clocks. Twoproteins, white collar 1 (WC‑1) and WC‑2, act together
to activate transcription of the frequency (frq) gene.
The FRQ protein and ancillary factors promote thephosphorylation of WC-1 and WC-2 to inhibit thisactivation (inhibitory arrow). Therefore, FRQ acts torepress its own transcription. To relieve this repression,
FRQ is targeted for degradation by F‑box/WD
repeat-containing protein 1 (FWD‑1), which physicallyinteracts with FRQ to target its degradation (yellow
fragments) in the proteasome. b | Two of the models
that have been proposed to explain temperaturecompensation in circadian clocks are the ‘balance’model and the ‘robust’ (or insensitive) model. The figure
shows a hypothetical circadian oscillator at 25 °C and
35 °C. The balance model (upper right panel) proposesthat there is a balance between more temperaturesensitive period-decelerating enzymatic reactions(blue) and less temperature-sensitive period-accelerating
enzyme reactions (red). The robust model (right lower
panel) proposes that a circadian oscillator consists oftemperature-insensitive enzymatic reactions (green).
These two models are not mutually exclusive with eachother, so a hybrid model may be feasible.

To get at the biochemical mechanism, Mehra et al.
investigated putative CK-2 phosphorylation sites on FRQ.
They showed that, as well as phosphorylating
FRQ in vitro, CK-2 interacts with, and phosporylates, FRQ
in vivo. This phosphorylation was abrogated in strainsthat bore mutations in CK-2 components. Moreover,this phosphorylation was temperature-dependent andaccompanied by changes in FRQ levels. In CK-2 mutants,
the half-life of FRQ increased with temperature, becoming twice as stable compared with wild-type N. crassa.
To establish the link between FRQ phosphorylation
by CK-2 and temperature compensation, they constructed CK-2‑phosphorylation-site mutants by inserting
clusters of mutated sites into the endogenous frq locus.
One of the two tested regions of mutated sites gave asimilar phenotype to a CK-2 temperature compensation mutant and had a longer period length that did notchange in a temperature range of 22–31 °C. In vitro analysis showed that CK-2 directly phosphorylated this regionof FRQ, and that this phosphorylation event resulted ina more stable FRQ. These findings are consistent withthe ‘balance model’ of temperature compensation postulated more than 50 years ago. As temperature rises,
FRQ becomes more stable, but this is counteracted byincreased CK-2 activity. Mutations in either protein candisrupt this relationship and lead to overcompensation.
Although it is possible, even likely, that additional
proteins will also have a role in temperature compensation, these data provide convincing evidence that systems
approaches can begin to reveal its mechanisms. Indeed,
component identification is just the beginning of systems
approaches. Perturbation, synthetic biology and mathematical modelling approaches in N. crassa may yet
reveal regulatory and other mechanisms that contributeto robust temperature compensation.
Alternative models. Although the observations in
N. crassa are consistent with the ‘balance model’, otherobservations from cyanobacteria34,36,65 and mammals66
are consistent with another model that explains temperature compensation: the ‘robust model’ (FIG. 3b, lower
panel). In the robust model, all rate-limiting enzymatic
reactions in the clock are temperature-insensitive. In
fact, in the cyanobacterial clock, rate-limiting reactions
such as phosphorylation, dephosphorylation and ATPase
activities are all temperature-compensated34,36,65. In the
mammalian clock, casein kinase 1 isoform-ε (CSNK1ε)dependent and CSNK1d‑dependent phosphorylation ofPER2 is temperature-compensated in vitro and in cellulo66,
but the temperature sensitivity of other rate-limiting reactions in the mammalian clock remains unknown. These
two models may not be exclusive. For example, we notethat CK-2‑dependent activity is dispensable for temperature compensation of the N. crassa clock, as CK-2 hypomorphic mutants exhibited near-perfect temperaturecompensation63.

Conclusions and future directions
When you think about it, trying to understand any
dynamic process, such as circadian rhythmicity, forces
you to take a systems approach. So, it is not too surprising

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REVIEWS
that when the appropriate tools were available, clockresearchers used them in their studies. What lessons
were learned? In tackling genetic robustness, the community took a systematic approach to component
discovery and validation. Clock researchers knocked
out all components and later used the concept ofthe dose–response to conduct sensitivity analysis on
the circadian clock system. From the systematic study
came the essential data on what broke or modified
clock function, and from this came opportunities to
see the rules that governed the network. In trying
to understand the basic property of rhythmicity, cyanobacterial researchers defined a minimal system — threeproteins and ATP — to study rhythms. Although a leapof faith was required to even start this research, studying a biological process in the simplest system possible
is a central tenet of science. The Kondo laboratory andthe cyanobacterial field have been reaping the rewards— conceptual and quantitative models of the circadianclock — ever since. And in understanding temperature compensation, N. crassa researchers went back tothe basics: careful genetics and biochemistry, and theinterplay between the two disciplines.
So, in a sense, success in taking systems approaches
to understand complex biology starts with the desire to
tackle the biological questions. What is clear is thatafter 15 years of (absolutely necessary) component
discovery, the field is moving back to its roots — theunderstanding of the complex properties that underlie
biological rhythms. Many, maybe the majority, of themost important questions remain unanswered. What
governs period length? What are the molecular and systems details underlying entrainment? In multicellularorganisms, what part does cell-to-cell communication
play in rhythm generation and oscillator function? What
are the transcriptional and post-transcriptional cascadesthat govern circadian output? Are the solutions used in
one model system also used in others? Finally, how do
we take this knowledge and translate it into therapies
for people suffering from circadian sleep disorders,metabolic syndrome, cancer and other pathologicalconditions influenced by our biological clock?

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than circadian clocks; virtually all areas of biology havedynamic components and would benefit from many of
the above concepts and tools. The research community’sbattle with complexity has just begun.

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Acknowledgements

The authors would like to thank members of the Hogeneschand Ueda laboratories for their comments on this Review.
J.B.H. is supported by the National Heart, Lung and Blood
Institute (NHBLI, 1R01HL097800), the National Institute for
Neurological Disorders and Stroke (NINDS, 5R01NS054794)
and the Pennsylvania Commonwealth Health Research Formula
Funds. This work was partly supported by the RIKEN research
grant and the Cell Innovation Program from MEXT, Japan.

Competing interests statement

The authors declare no competing financial interests.

FURTHER INFORMATIONJohn B. Hogenesch’s laboratory homepage: http://bioinf.
itmat.upenn.edu/hogeneschlab
Hiroki R. Ueda’s laboratory homepage: http://www.cdb.
riken.jp/lsb/index.html
Circadian Modelling: http://millar.bio.ed.ac.uk/PEBrown/
CircadianModelling/CircadianModelling.htm
Mouse Genome Informatics: http://www.informatics.jax.
org/phenotypes.shtml
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