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Encoding and Decoding Cellular Information through Signaling Dynamics

2013; Cell Press; Volume: 152; Issue: 5 Linguagem: Inglês

10.1016/j.cell.2013.02.005

1097-4172

Jeremy E. Purvis, Galit Lahav,

Single-cell and spatial transcriptomics

A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this Review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes, and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through the dynamics of signaling molecules and possible applications for the treatment of disease. A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this Review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes, and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through the dynamics of signaling molecules and possible applications for the treatment of disease. A unifying theme in biology is that function is reflected in structure. Consider, for example, the highly specialized structure of a bird’s wing. The sparsely arranged bones and feather patterning create a high surface-to-mass ratio that enables flight. Or examine the folded conformation of an enzyme—its three-dimensional structure indicates which substrate molecules it is capable of binding and which reactions it may catalyze. Perhaps the most prevalent example of a biological structure that predicts physiological function is the genome. By knowing the sequence structure of coding DNA, one can infer whether it encodes a protein domain, a binding site, a conserved motif, or a hairpin structure. These examples demonstrate that functional information is encoded in the structural components of a cell. One may argue that all relevant information is embedded in cellular structures, if only we could measure them in sufficient detail. But is this the only way that biological information may be encoded? Are there aspects of biological function that cannot be discovered by simply looking at static structures? In this Review, we discuss an emerging trend in cell biology that suggests an additional mode for transmitting information in cells—through the dynamics of signaling molecules (Behar and Hoffmann, 2010Behar M. Hoffmann A. Understanding the temporal codes of intra-cellular signals.Curr. Opin. Genet. Dev. 2010; 20: 684-693Crossref PubMed Scopus (129) Google Scholar). Here, dynamics is defined as the shape of the curve describing how the concentration, activity, modification state, or localization of a molecule changes over time (Figure 1A). This mode of signaling encodes information in the frequency, amplitude, duration, or other features of the temporal signal (Figure 1B). It is therefore more rich and complex than transmitting information through the state of a signaling molecule at only a single point in time. We present a broad survey of what is known about the dynamics of different systems across biology, focusing on well-studied systems that have been analyzed using multiple quantitative measurement and perturbation approaches. Through these examples, we extract general principles about the role of dynamics in biology and what advantages may be conferred by transmitting information through the dynamics of signaling molecules. Understanding the dynamics of biological responses requires collecting high-quality time series data. An important consideration when measuring the dynamics of a signal is the appropriate timescale of measurement. Some processes, such as ion transport or calcium release, occur in seconds. Others, including changes in protein levels during the cell cycle, occur over minutes or hours. Changes in some observable phenotypes such as cell morphology or expression of cell-surface markers can take days or longer. Thus, a good understanding of the timescale of a particular system is crucial for determining the appropriate sampling frequency to ensure that critical information is not missed (Figure 1C). For example, when the levels of the phosphorylated kinase ATM (ATM-P) were measured at high frequency during the first hour after DNA damage, the conclusion was that ATM is rapidly phosphorylated and reaches a maximal level within 5 min after damage, followed by a slow decrease (Jazayeri et al., 2006Jazayeri A. Falck J. Lukas C. Bartek J. Smith G.C. Lukas J. Jackson S.P. ATM- and cell cycle-dependent regulation of ATR in response to DNA double-strand breaks.Nat. Cell Biol. 2006; 8: 37-45Crossref PubMed Scopus (874) Google Scholar). When the levels of ATM-P were measured every hour for 10 hr, it became clear that it shows a series of oscillations after DNA damage, an observation that led to a new model for the control of ATM and the tumor suppressor p53 in response to DNA breaks (Batchelor et al., 2008Batchelor E. Mock C.S. Bhan I. Loewer A. Lahav G. Recurrent initiation: a mechanism for triggering p53 pulses in response to DNA damage.Mol. Cell. 2008; 30: 277-289Abstract Full Text Full Text PDF PubMed Scopus (341) Google Scholar). The dynamics of a signal can be measured across a population of cells or in individual cells. The development of fluorescent sensors that allow high-resolution time-lapse imaging in living cells has improved our ability to quantify the dynamics of biological responses in single cells. These include chemical sensors that report activation of a signaling molecule (Welch et al., 2011Welch C.M. Elliott H. Danuser G. Hahn K.M. Imaging the coordination of multiple signalling activities in living cells.Nat. Rev. Mol. Cell Biol. 2011; 12: 749-756Crossref PubMed Scopus (114) Google Scholar) as well as sensors that participate directly in the functional response, such as fluorescent fusion proteins (e.g., Albeck et al., 2008Albeck J.G. Burke J.M. Spencer S.L. Lauffenburger D.A. Sorger P.K. Modeling a snap-action, variable-delay switch controlling extrinsic cell death.PLoS Biol. 2008; 6: 2831-2852Crossref PubMed Scopus (204) Google Scholar; Bakstad et al., 2012Bakstad D. Adamson A. Spiller D.G. White M.R. Quantitative measurement of single cell dynamics.Curr. Opin. Biotechnol. 2012; 23: 103-109Crossref PubMed Scopus (32) Google Scholar). A collective observation from these and additional studies is that individual cells differ widely in their dynamical responses even when challenged with the same stimulus (Cohen et al., 2008Cohen A.A. Geva-Zatorsky N. Eden E. Frenkel-Morgenstern M. Issaeva I. Sigal A. Milo R. Cohen-Saidon C. Liron Y. Kam Z. et al.Dynamic proteomics of individual cancer cells in response to a drug.Science. 2008; 322: 1511-1516Crossref PubMed Scopus (468) Google Scholar; Lee et al., 2009Lee T.K. Denny E.M. Sanghvi J.C. Gaston J.E. Maynard N.D. Hughey J.J. Covert M.W. A noisy paracrine signal determines the cellular NF-kappaB response to lipopolysaccharide.Sci. Signal. 2009; 2: ra65Crossref PubMed Scopus (114) Google Scholar). As a result, the average dynamical behavior of a population often represents a distorted version of individual patterns that can lead to misinterpretations. For example, p53 dynamics in response to DNA damage were originally described as damped oscillations when measured by western blot (Lev Bar-Or et al., 2000Lev Bar-Or R. Maya R. Segel L.A. Alon U. Levine A.J. Oren M. Generation of oscillations by the p53-Mdm2 feedback loop: a theoretical and experimental study.Proc. Natl. Acad. Sci. USA. 2000; 97: 11250-11255Crossref PubMed Scopus (486) Google Scholar). Observation of single cells, however, revealed that these are actually pulses with fixed height and duration (Lahav et al., 2004Lahav G. Rosenfeld N. Sigal A. Geva-Zatorsky N. Levine A.J. Elowitz M.B. Alon U. Dynamics of the p53-Mdm2 feedback loop in individual cells.Nat. Genet. 2004; 36: 147-150Crossref PubMed Scopus (811) Google Scholar). Varying number of pulses and loss of synchronization among individual cells over time led to an apparent widening and shortening of successive pulses in the population (Figure 1D). Similarly, the “switch-like” responses of individual cells to certain signals, such as the mitogen-activated protein (MAP) kinase activity in developing oocytes (Ferrell and Machleder, 1998Ferrell Jr., J.E. Machleder E.M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes.Science. 1998; 280: 895-898Crossref PubMed Scopus (853) Google Scholar) or the cleavage of caspase substrates during apoptosis (Tyas et al., 2000Tyas L. Brophy V.A. Pope A. Rivett A.J. Tavaré J.M. Rapid caspase-3 activation during apoptosis revealed using fluorescence-resonance energy transfer.EMBO Rep. 2000; 1: 266-270Crossref PubMed Scopus (224) Google Scholar), give the appearance of a gradual increase in measurements of an averaged population (Figure 1D). These examples underscore the importance of tracking these responses at the single-cell level. Because tagged reporters represent significant perturbations to the cell, it is important to establish that the introduction of a reporter into a cell line does not alter its dynamical properties. This can be accomplished through control experiments that compare the rates of induction and degradation between the tagged and endogenous proteins using immunoblots or flow cytometry. For example, rapid accumulation of a protein in live cells often appears as distinct subpopulations in flow cytometry because the protein spends relatively little time in the intermediate state (for an example, see Albeck et al., 2008Albeck J.G. Burke J.M. Spencer S.L. Lauffenburger D.A. Sorger P.K. Modeling a snap-action, variable-delay switch controlling extrinsic cell death.PLoS Biol. 2008; 6: 2831-2852Crossref PubMed Scopus (204) Google Scholar). However, overinterpretation of the underlying dynamics from flow cytometry should be avoided because simulations show that even graded individual responses can sometimes lead to bimodal populations (Birtwistle et al., 2012Birtwistle M.R. Rauch J. Kiyatkin A. Aksamitiene E. Dobrzyński M. Hoek J.B. Kolch W. Ogunnaike B.A. Kholodenko B.N. Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise.BMC Syst. Biol. 2012; 6: 109Crossref PubMed Scopus (57) Google Scholar). When fluorescent reporters are used to study cell-to-cell variation, the use of clonal stably transfected cell lines is desirable because transiently transfected cells often express varying amounts of the effector, which may alter the dynamics and cause artificial variation between cells (Barken et al., 2005Barken D. Wang C.J. Kearns J. Cheong R. Hoffmann A. Levchenko A. Comment on “Oscillations in NF-kappaB signaling control the dynamics of gene expression”.Science. 2005; 308: 52Crossref PubMed Google Scholar). One of the first concepts to emerge from studying the temporal behavior of signaling molecules is that different upstream signals can lead to different dynamical patterns of the same molecule. An early example of this behavior was found in the extracellular signal-regulated kinase (ERK) pathway (here, ERK refers to the signaling module comprising both Erk1 and Erk2). It was originally observed that two separate growth factors trigger different cell fates of rat neuronal precursors; nerve growth factor (NGF) leads to differentiation, whereas epidermal growth factor (EGF) leads to cell proliferation. At first glance, one might conclude that a separate signaling pathway is induced in response to each of these stimuli, resulting in different fates. Closer examination, however, revealed that both stimuli activate ERK but with distinct dynamical patterns (Gotoh et al., 1990Gotoh Y. Nishida E. Yamashita T. Hoshi M. Kawakami M. Sakai H. Microtubule-associated-protein (MAP) kinase activated by nerve growth factor and epidermal growth factor in PC12 cells. Identity with the mitogen-activated MAP kinase of fibroblastic cells.Eur. J. 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These observations led to the idea that PC-12 differentiation was not strictly ligand specific but was instead governed by the dynamics of ERK activity (Marshall, 1995Marshall C.J. Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation.Cell. 1995; 80: 179-185Abstract Full Text PDF PubMed Scopus (4230) Google Scholar). Additional signaling molecules have been shown to encapsulate upstream signals in their dynamics. For example, different inflammatory stimuli induce distinct temporal profiles of the transcription factor NF-κB (Figure 2B). Under resting conditions, NF-κB is continuously shuttled between nuclear and cytosolic compartments. Activation of NF-κB by tumor necrosis factor-α (TNFα) results in prolonged occupation in the nucleus and transcription of its negative regulator IκBα. This negative feedback loop generates oscillations of transcriptionally active NF-κB (Hoffmann et al., 2002Hoffmann A. Levchenko A. Scott M.L. Baltimore D. The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation.Science. 2002; 298: 1241-1245Crossref PubMed Scopus (1482) Google Scholar; Nelson et al., 2004Nelson D.E. Ihekwaba A.E. Elliott M. Johnson J.R. Gibney C.A. Foreman B.E. Nelson G. See V. Horton C.A. Spiller D.G. et al.Oscillations in NF-kappaB signaling control the dynamics of gene expression.Science. 2004; 306: 704-708Crossref PubMed Scopus (970) Google Scholar; Sung et al., 2009Sung M.H. Salvatore L. De Lorenzi R. Indrawan A. Pasparakis M. Hager G.L. Bianchi M.E. Agresti A. Sustained oscillations of NF-kappaB produce distinct genome scanning and gene expression profiles.PLoS ONE. 2009; 4: e7163Crossref PubMed Scopus (94) Google Scholar; Tay et al., 2010Tay S. Hughey J.J. Lee T.K. Lipniacki T. Quake S.R. Covert M.W. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing.Nature. 2010; 466: 267-271Crossref PubMed Scopus (608) Google Scholar). In contrast, bacterial lipopolysaccharide (LPS) leads to slower accumulation and a single prolonged wave of NF-κB activity (Covert et al., 2005Covert M.W. Leung T.H. Gaston J.E. Baltimore D. Achieving stability of lipopolysaccharide-induced NF-kappaB activation.Science. 2005; 309: 1854-1857Crossref PubMed Scopus (520) Google Scholar; Lee et al., 2009Lee T.K. Denny E.M. Sanghvi J.C. Gaston J.E. Maynard N.D. Hughey J.J. Covert M.W. A noisy paracrine signal determines the cellular NF-kappaB response to lipopolysaccharide.Sci. Signal. 2009; 2: ra65Crossref PubMed Scopus (114) Google Scholar; Werner et al., 2005Werner S.L. Barken D. Hoffmann A. Stimulus specificity of gene expression programs determined by temporal control of IKK activity.Science. 2005; 309: 1857-1861Crossref PubMed Scopus (412) Google Scholar). In various systems, both the identity and strength of the stimulus have been shown to alter the dynamics of the same protein. One example is the yeast transcription factor Msn2, which responds to stress by translocation to the nucleus (Figure 2C). Recent single-cell studies reveal that, in response to glucose limitation or high osmolarity, nuclear Msn2 shows a transient increase with a dose-dependent duration and fixed amplitude (Hao and O’Shea, 2012Hao N. O’Shea E.K. Signal-dependent dynamics of transcription factor translocation controls gene expression.Nat. Struct. Mol. Biol. 2012; 19: 31-39Crossref Scopus (202) Google Scholar). In contrast, oxidative stress leads to prolonged nuclear Msn2 accumulation with amplitude that increases with higher concentration of H2O2. Closer observation in single cells reveals that, following the initial pulse, glucose limitation and osmotic stress lead to a series of Msn2 bursts. The frequency of these pulses depends on the intensity of the signal in glucose limitation but is not affected by the intensity of the osmotic stress (Hao and O’Shea, 2012Hao N. O’Shea E.K. Signal-dependent dynamics of transcription factor translocation controls gene expression.Nat. Struct. Mol. Biol. 2012; 19: 31-39Crossref Scopus (202) Google Scholar). The tumor suppressor p53 also shows both stimulus- and dose-dependent dynamics (Figure 2D). Double-strand breaks (DSBs) caused by γ-radiation trigger a series of p53 pulses with fixed amplitude and duration. Higher doses of radiation increase the number of pulses without affecting their amplitude or duration (Geva-Zatorsky et al., 2010Geva-Zatorsky N. Dekel E. Cohen A.A. Danon T. Cohen L. Alon U. Protein dynamics in drug combinations: a linear superposition of individual-drug responses.Cell. 2010; 140: 643-651Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar; Lahav et al., 2004Lahav G. Rosenfeld N. Sigal A. Geva-Zatorsky N. Levine A.J. Elowitz M.B. Alon U. Dynamics of the p53-Mdm2 feedback loop in individual cells.Nat. Genet. 2004; 36: 147-150Crossref PubMed Scopus (811) Google Scholar). In contrast, UV triggers a single p53 pulse with a dose-dependent amplitude and duration (Batchelor et al., 2011Batchelor E. Loewer A. Mock C. Lahav G. Stimulus-dependent dynamics of p53 in single cells.Mol. Syst. Biol. 2011; 7: 488Crossref PubMed Scopus (231) Google Scholar). Lastly, stimulus strength affects the dynamics of NF-κB activity. Increasing the concentration of TNFα leads to a shortened delay in NF-κB nuclear translocation (Cheong et al., 2006Cheong R. Bergmann A. Werner S.L. Regal J. Hoffmann A. Levchenko A. Transient IkappaB kinase activity mediates temporal NF-kappaB dynamics in response to a wide range of tumor necrosis factor-alpha doses.J. Biol. Chem. 2006; 281: 2945-2950Crossref PubMed Scopus (102) Google Scholar; Tay et al., 2010Tay S. Hughey J.J. Lee T.K. Lipniacki T. Quake S.R. Covert M.W. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing.Nature. 2010; 466: 267-271Crossref PubMed Scopus (608) Google Scholar), and increasing the frequency of TNFα stimulation leads to smaller amplitude oscillations (Ashall et al., 2009Ashall L. Horton C.A. Nelson D.E. Paszek P. Harper C.V. Sillitoe K. Ryan S. Spiller D.G. Unitt J.F. Broomhead D.S. et al.Pulsatile stimulation determines timing and specificity of NF-kappaB-dependent transcription.Science. 2009; 324: 242-246Crossref PubMed Scopus (434) Google Scholar). The emerging picture from these examples is that the dynamics of a signaling molecule can capture both the identity and quantity of upstream stimuli. Dynamical patterns can also reflect a combination of two or more stimuli administered simultaneously or sequentially. For example, simultaneous treatment with multiple drugs can have an additive effect on the resulting dynamical pattern of downstream signaling proteins; that is, the individual dynamical patterns are effectively superimposed (Geva-Zatorsky et al., 2010Geva-Zatorsky N. Dekel E. Cohen A.A. Danon T. Cohen L. Alon U. Protein dynamics in drug combinations: a linear superposition of individual-drug responses.Cell. 2010; 140: 643-651Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar). In other cases, different stimuli interact synergistically or antagonistically to produce a temporal profile in which certain dynamical features are either enhanced or silenced, respectively (Garmaroudi et al., 2010Garmaroudi F.S. Marchant D. Si X. Khalili A. Bashashati A. Wong B.W. Tabet A. Ng R.T. Murphy K. Luo H. et al.Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection.Proc. Natl. Acad. Sci. USA. 2010; 107: 17053-17058Crossref PubMed Scopus (34) Google Scholar; Werner et al., 2008Werner S.L. Kearns J.D. Zadorozhnaya V. Lynch C. O’Dea E. Boldin M.P. Ma A. Baltimore D. Hoffmann A. Encoding NF-kappaB temporal control in response to TNF: distinct roles for the negative regulators IkappaBalpha and A20.Genes Dev. 2008; 22: 2093-2101Crossref PubMed Scopus (165) Google Scholar). This is often the case for sequentially administered stimuli when cells show desensitization to repeated stimulation (Ashall et al., 2009Ashall L. Horton C.A. Nelson D.E. Paszek P. Harper C.V. Sillitoe K. Ryan S. Spiller D.G. Unitt J.F. Broomhead D.S. et al.Pulsatile stimulation determines timing and specificity of NF-kappaB-dependent transcription.Science. 2009; 324: 242-246Crossref PubMed Scopus (434) Google Scholar). For example, treatment of human platelets with thrombin produces a characteristic temporal pattern of intracellular calcium release. If preceded by treatment with ADP, however, the thrombin-induced pattern is attenuated (Chatterjee et al., 2010Chatterjee M.S. Purvis J.E. Brass L.F. Diamond S.L. Pairwise agonist scanning predicts cellular signaling responses to combinatorial stimuli.Nat. Biotechnol. 2010; 28: 727-732Crossref PubMed Scopus (67) Google Scholar). This implies that dynamics can reflect cellular “memory” to previous stimuli and also suggests crosstalk between pathways. Because the dynamics of various proteins vary with the stimulus, it seems plausible that downstream elements may respond to these different dynamical profiles. In fact, there are a number of examples in which the dynamics of a signaling molecule are associated with, or at least precede specific cellular outcomes. As mentioned previously, the transient activation of ERK in response to EGF allows continued proliferation of neuronal precursors, whereas sustained ERK in response to NGF leads to differentiation of sympathetic-like neurons (Marshall, 1995Marshall C.J. Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation.Cell. 1995; 80: 179-185Abstract Full Text PDF PubMed Scopus (4230) Google Scholar) (Figure 3A). The development of highly sensitive calcium dyes in the 1980s (Grynkiewicz et al., 1985Grynkiewicz G. Poenie M. Tsien R.Y. A new generation of Ca2+ indicators with greatly improved fluorescence properties.J. Biol. Chem. 1985; 260: 3440-3450Abstract Full Text PDF PubMed Scopus (80) Google Scholar) revealed a vast variety of dynamical behaviors of calcium molecules—from oscillations induced by fertilization of mammalian eggs (Malcuit et al., 2006Malcuit C. Kurokawa M. Fissore R.A. Calcium oscillations and mammalian egg activation.J. Cell. Physiol. 2006; 206: 565-573Crossref PubMed Scopus (99) Google Scholar) to noisy spikes observed in the tiny volume of a single human platelet (Heemskerk et al., 2001Heemskerk J.W. Willems G.M. Rook M.B. Sage S.O. Ragged spiking of free calcium in ADP-stimulated human platelets: regulation of puff-like calcium signals in vitro and ex vivo.J. Physiol. 2001; 535: 625-635Crossref PubMed Scopus (60) Google Scholar). Careful study of these behaviors reveals that calcium can activate different responses based solely on its dynamical waveform. A brief spike of calcium induces prolonged activation of NF-κB and JNK that lasts well after the decay in calcium. In contrast, calcium spikes evoke only transient nuclear translocation of NFAT (nuclear factor of activated T cells), whereas prolonged NFAT translocation requires sustained calcium levels (Dolmetsch et al., 1997Dolmetsch R.E. Lewis R.S. Goodnow C.C. Healy J.I. Differential activation of transcription factors induced by Ca2+ response amplitude and duration.Nature. 1997; 386: 855-858Crossref PubMed Scopus (1556) Google Scholar). These results suggest that NFAT may distinguish between different dynamical patterns of calcium. The possibility of such a mechanism has been revived by a recent study showing that two isoforms of NFAT, NFAT1 and NFAT4, show different nuclear localization dynamics in response to static calcium stimulation (Yissachar et al., 2013Yissachar N. Sharar Fischler T. Cohen A.A. Reich-Zeliger S. Russ D. Shifrut E. Porat Z. Friedman N. Dynamic response diversity of NFAT isoforms in individual living cells.Mol. Cell. 2013; 49: 322-330Abstract Full Text Full Text PDF PubMed Scopus (76) Google Scholar). Whether these kinetics are responsible for decoding the calcium signal, however, will require characterizing NFAT1/4 dynamics in response to different calcium dynamics. The dynamics of NF-κB nuclear localization and DNA binding activity control both the specificity and levels of target gene expression (Hoffmann et al., 2002Hoffmann A. Levchenko A. Scott M.L. Baltimore D. The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation.Science. 2002; 298: 1241-1245Crossref PubMed Scopus (1482) Google Scholar; Nelson et al., 2004Nelson D.E. Ihekwaba A.E. Elliott M. Johnson J.R. Gibney C.A. Foreman B.E. Nelson G. See V. Horton C.A. Spiller D.G. et al.Oscillations in NF-kappaB signaling control the dynamics of gene expression.Science. 2004; 306: 704-708Crossref PubMed Scopus (970) Google Scholar). Studies performed in cell populations show that activation of NF-κB in response to TNFα (which produces NF-κB oscillations) induces expression of multiple inflammatory response genes, whereas sustained NF-κB levels induced by LPS lead to similar expression patterns but also induce additional cytokine secretion as well as genes associated with the adaptive immune response (Figure 3B) (Werner et al., 2005Werner S.L. Barken D. Hoffmann A. Stimulus specificity of gene expression programs determined by temporal control of IKK activity.Science. 2005; 309: 1857-1861Crossref PubMed Scopus (412) Google Scholar). The dynamics of p53 have also been associated with specific cellular responses. p53 pulses following γ-irradiation are associated with transient cell-cycle arrest and recovery, whereas a single prolonged pulse after UV radiation precedes apoptosis (Figure 3C) (Purvis et al., 2012Purvis J.E. Karhohs K.W. Mock C. Batchelor E. Loewer A. Lahav G. p53 dynamics control cell fate.Science. 2012; 336: 1440-1444Crossref PubMed Scopus (538) Google Scholar). The high-level conclusion that might arise from these studies is that cells are able to “translate” different dynamical patterns of the same signaling molecule into specific outcomes. However, an important caveat to this claim is that, in addition to altering dynamics, different stimuli also affect other pathway components that may be responsible for the observed changes in downstream responses. This concern must ultimately be addressed through direct and careful perturbation of the dynamics using genetic or pharmacological strategies. The observation that distinct dynamical patterns are correlated with certain cellular responses does not prove that dynamics are the causal agents behind these responses. How can one test whether dynamics are actually driving cellular responses? Similar to the way researchers examine the role of a specific gene by mutating it and testing the resultant behavior, a sound approach to examining the role of dynamics is to artificially perturb the dynamics of the system and test how this affects downstream outcomes. Each method of perturbation offers varying strengths and weaknesses, with the best-characterized systems using multiple approaches. One of the first examples of controlled perturbation of dynamics was used to study the effect of calcium dynamics on gene expression. Alternating treatments of calcium-carrying ionophores and calcium-sequestering chelating agents has been used to study the effect of different calcium frequencies on gene expression. This “patch-clamp” setup has revealed that different frequencies of calcium activate none, some, or all of the transcription factors NF-κB, NFAT, and Oct/OAP (Dolmetsch et al., 1998Dolmetsch R.E. Xu K. Lewis R.S. Calcium oscillations increase the efficiency and specificity of gene expression.Nature. 1998; 392: 933-936Crossref PubMed Scopus (1675) Google Scholar). Similarly, the use of photoactivatable inositol 1,4,5-trisphosphate, the intracellular trigger for calcium release, led to the same striking conclusion: specific frequencies of intracellular calcium release could optimize gene expression (Li et al., 1998Li W. Llopis J. Whitney M. Zlokarnik G. Tsien R.Y. Cell-permeant caged InsP3 ester shows that Ca2+ spike frequency can optimize gene expression.Nature. 1998; 392: 936-941Crossref PubMed Scopus (775) Google Scholar). Though preceded by earlier indications that the dynamics of second messengers are functional (Darmon et al., 1975Darmon M. Brachet P. Da Silva L.H. Chemotactic signals induce cell differentiation in Dictyostelium discoideum.Proc. Natl. Acad. Sci. USA.