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• W2127793379 abstract "Article2 August 2011Open Access Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template Leonidas Bleris Leonidas Bleris Department of Bioengineering and Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA Search for more papers by this author Zhen Xie Zhen Xie Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author David Glass David Glass Princeton University, Princeton, NJ, USA Search for more papers by this author Asa Adadey Asa Adadey University of Maryland Baltimore County, Baltimore, MD, USA Search for more papers by this author Eduardo Sontag Eduardo Sontag Department of Mathematics, Rutgers—The State University of New Jersey Hill Center, Piscataway, NJ, USA Search for more papers by this author Yaakov Benenson Corresponding Author Yaakov Benenson Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH Zürich), Basel, Switzerland Search for more papers by this author Leonidas Bleris Leonidas Bleris Department of Bioengineering and Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA Search for more papers by this author Zhen Xie Zhen Xie Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author David Glass David Glass Princeton University, Princeton, NJ, USA Search for more papers by this author Asa Adadey Asa Adadey University of Maryland Baltimore County, Baltimore, MD, USA Search for more papers by this author Eduardo Sontag Eduardo Sontag Department of Mathematics, Rutgers—The State University of New Jersey Hill Center, Piscataway, NJ, USA Search for more papers by this author Yaakov Benenson Corresponding Author Yaakov Benenson Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH Zürich), Basel, Switzerland Search for more papers by this author Author Information Leonidas Bleris1, Zhen Xie2, David Glass3, Asa Adadey4, Eduardo Sontag5 and Yaakov Benenson 6 1Department of Bioengineering and Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA 2Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA 3Princeton University, Princeton, NJ, USA 4University of Maryland Baltimore County, Baltimore, MD, USA 5Department of Mathematics, Rutgers—The State University of New Jersey Hill Center, Piscataway, NJ, USA 6Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH Zürich), Basel, Switzerland *Corresponding author. Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH Zürich), Mattenstrasse 26, 4058 Basel, Switzerland. Tel.: +41 61 387 3338; Fax: +41 61 387 3994; E-mail: [email protected] Molecular Systems Biology (2011)7:519https://doi.org/10.1038/msb.2011.49 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability. Synopsis Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Indeed, changes in gene dosage are clearly a major source of variation in cells, and yet those changes are very common in both normal processes (sex determination, ploidy change) and disease (gene amplification in cancer). In synthetic networks, the problem is exacerbated due to commonly used transient delivery methods that result in very large cell-to-cell variability in gene dosage. The basic question on gene dosage compensation in nature (Veitia et al, 2008; Acar et al, 2010) and a practical challenge of overcoming sensitivity to DNA copy number in synthetic circuits prompted us to investigate mechanisms to reduce this variability using sophisticated internal regulatory mechanisms. Indeed, the baseline expression unit in many synthetic circuits is an open-loop promoter-ORF combination. We hypothesized that some sort of internal regulation will result in ‘expression units’ whose gene product (i.e. protein) output will depend only mildly on the intracellular concentration of its DNA template. In other words, we searched for architecture that would lead to ‘adaptation’ of the gene product to the amount of DNA template. By examining large body of published work, we found frequent reference to a so-called ‘incoherent feedforward’ network motif (Mangan and Alon, 2003). The canonical three-node incoherent loop contains input, auxiliary regulator, and output nodes. The output is controlled directly by the input and the auxiliary regulator. The latter is also controlled by the input, introducing an additional indirect effect of the input on the output. In incoherent loops, the input controls the auxiliary regulator node in such a way that input's overall indirect action on the output via this node counteracts its direct effect. In a motif named ‘type I incoherent feedforward loop’ (I1-FFL), the input's direct effect is activating, as is its control of the auxiliary node, while the auxiliary node controls the output through repression. One of the most studied properties of such motifs is their transient response to persistent stimulus, that is visually characterized as a ‘bump’ or ‘pulse’ (hence the name ‘pulse generator’) that then goes back to the original steady state of the system (Basu et al, 2004). We hypothesized that changing DNA amount could serve as an input to an incoherent circuit if the auxiliary regulator and the output nodes are coexpressed from this DNA; in other words DNA can be viewed as an ‘activator’ of both the regulator and the output. We conjectured that this might lead to adaptation to changes in DNA template. We designed and simulated in silico a number of network architectures that all exhibit incoherent feedforward connectivity. We also compared them with the well-studied feedback loop circuit that in theory weakens but does not eliminate gene product dependency on the DNA template amount. The schematics of the circuits are shown in Figure 1. Experimental measurement of input–output response of these circuits, or their transfer function, indeed uncovered adaptation of the output to DNA template abundance. Such adaptation has not been observed with feedback loop, as expected. Among various architectures, the post-transcriptional circuits showed faster adaptation, higher absolute expression levels and lower ‘noise’ (Figure 4). We also simulated and measured stochastic variability in the circuits by collecting all the cells with similar input values and statistically analyzing output values in those cells. We found that substantial noise component could not be accounted for by known noise sources, and concluded that the very step of negative regulation, both by a repressor LacI and by a microRNA, significantly increases cell-to-cell variability. This needs to be addressed in further studies. We also found that the negative feedback loop did not result in reduced noise as we expected, yet it did not result in noise increase as in the incoherent motif. This means that there may be effective noise reduction but it is not sufficient to produce narrow distributions of outputs for a given input. We conclude that expression units that incorporate incoherent feedforward control of the gene product provide adaptation to the amount of DNA template and can be superior to simple combinations of constitutive promoters with ORFs. We also emphasize the relevance of our findings to the long-standing question of gene dosage compensation in cells, and note that similar incoherent architectures with microRNA negative regulators have been found in cells, suggesting that their physiological role is to curb variability in gene dosage and/or promoter strength. Introduction Biological networks typically contain small-scale subnetworks of recurring topology called ‘motifs’ (Milo et al, 2002; Balazsi et al, 2005; Ma'ayan et al, 2005). The most common motifs are feedforward and feedback (autoregulatory) loops (Shen-Orr et al, 2002), with the former being largely dominated by an ‘incoherent’ connectivity (Mangan and Alon, 2003) in bacteria (Mangan et al, 2006) and yeast (Lee et al, 2002). Incoherent feedforward motifs were also observed in mammalian cells (Boyer et al, 2005), although to the best of our knowledge their prominence remains to be determined. The canonical three-node incoherent loop contains input, auxiliary regulator, and output nodes. The output is controlled directly by the input and the auxiliary regulator. The latter is also controlled by the input, introducing an additional indirect effect of the input on the output. In incoherent loops, the input controls the auxiliary regulator node in such a way that input's overall indirect action on the output via this node counteracts its direct effect. In a motif named ‘type I incoherent feedforward loop’ (I1-FFL), the input's direct effect is activating, as is its control of the auxiliary node, while the auxiliary node controls the output through repression. Because this motif comprises about 30% of all three-node interaction loops in transcriptional circuits (Mangan et al, 2006), a number of studies have attempted to uncover the reasons behind its prevalence by artificially creating plausible operating scenarios and testing whether the topology is superior to alternative arrangements. One line of research studied transcription factor (TF)-based motifs by experimentally perturbing small molecule TF cofactors. Using this setup, a synthetic incoherent motif was shown to act as pulse generator in cell communication experiments (Basu et al, 2004), while in another study (Mangan et al, 2006) a naturally occurring motif embedded in a galactose utilization pathway produced a faster response to its cognate environmental signal compared with the baseline. Other studies showed that steady-state output levels peak at an intermediate input level, generating a non-monotonic transfer function in synthetic (Entus et al, 2007) and natural (Kaplan et al, 2008) systems. Recently, the topology has been shown to enable a ‘fold-change’ detection in the strength of the input signal when the intensity of the transient response was used as output (Goentoro et al, 2009) (provided that certain scaling transformations preserve the form of the system (Shoval et al, 2010)) and to possess a type of ‘memory’ effect of the intensity of previously seen step signals (Sontag, 2010). Enzyme-based incoherent motifs were identified and classified in a study that sought adaptive properties in a computationally generated library of network topologies in signaling pathways (Ma et al, 2009). In such a motif, enzyme A (input) activates enzyme C (output) as well as enzyme B (auxiliary regulator) that in turn inactivates enzyme C. The enzymatic circuit functions as a pulse generator, similarly to its transcriptional counterpart. Similar architectures had been devised earlier from first principles (Tyson et al, 2003) and analyzed from a theoretical standpoint (Sontag, 2010). Finally, it was mathematically shown that two-component genetic circuits with elements of opposite regulatory activity constitute the minimal requirement for network-dosage invariance (Acar et al, 2010). We propose a novel implementation of the I1-FFL, where the input (black circle in Figure 1A) is a DNA fragment coding for both the auxiliary regulator (brown circle) and the output (red circle), with the regulator negatively affecting the output. We might expect the transient and steady-state input–output relation of our circuits to be similar to those of the previously described systems. However, while enzymatic or TF inputs can be experimentally modulated in single cells in a temporal manner, it is not so with DNA inputs. Therefore, our main interest lies in the steady-state relation between the amount of DNA template and the amount of the output protein. Aforementioned studies showed pulse-like output response, or output adaptation, to abrupt temporal changes in the input, meaning that different input levels generate the same output in the steady state. This leads to a conjecture that in our circuits, the steady-state output levels will not depend on the number of DNA molecules that code for the circuit, a property we also call adaptation. An affirmative answer to this conjecture would add experimental support to the hypothesis (Veitia et al, 2008) that similar circuitry might be employed by cells to implement gene dosage compensation in the context of neuronal homeostasis (Tsang et al, 2007), sex determination (Lucchesi et al, 2005), and ploidy changes. It would also suggest that such circuits could become valuable tools (Holtz and Keasling, 2010) in the construction of increasingly complex synthetic networks (Russell and Aloy, 2008; Cantone et al, 2009; Lu et al, 2009) because they will contribute to overall robustness of the system by decreasing natural variability in the circuits' components. Figure 1.Schematics of the synthetic networks. Pointed and blunt arrows denote activation and repression, respectively. In all constructs, the output protein and the repressor are transcribed from the same bidirectional promoter in the constant and non-limiting presence of the TF rtTA that does not constitute an input to the system. In the diagrams, I indicates input node, IR is the input reporter, A is the auxiliary regulator, and O is the output. (A) Transcriptional type I incoherent feedforward motif (tI1-FFL): the output protein is DsRed and the auxiliary repressor is LacI. The plasmid copy number is reported by the ZsGreen1 fluorescent protein, cotranslated with the LacI protein using IRES. Corresponding control circuit is also shown. (B) Post-transcriptional type I incoherent feedforward motif version I (ptI1-FFLI): the output protein is AmCyan and the auxiliary repressor is a microRNA. Plasmid copy number is reported by the DsRed protein coexpressed with the output. DsRed mRNA also contains an intron coding for the regulator microRNA. Corresponding control circuit is also shown. (C) Post-transcriptional type I incoherent feedforward motif version II (ptI1-FFLII): the output protein is DsRed, repressed by a microRNA processed from the intron in its own mRNA. The input is reported by the divergently expressed AmCyan protein. The control circuit is identical to the one in (B). (D) Transcriptional negative autoregulation motif (tAM): auxiliary repressor LacI becomes the output and represses its own transcription as well as the level of the ZsGreen output reporter contranslated via IRES. The input is reported by a divergently expressed DsRed protein. Download figure Download PowerPoint We tested our circuits in mammalian cells, motivated by their potential relevance to eukaryotic dosage compensation and to mammalian synthetic biology, and in order to explore RNA interference (RNAi) as a negative regulation mechanism. We compared them to a negative autoregulatory motif (Becskei and Serrano, 2000; Isaacs et al, 2003) that had been shown in bacteria to weaken the dependency of the expression level on DNA template amount as well as to reduce protein expression fluctuation. Results Study rationale and circuit details In order to explore the input–output behavior of any given system, and specifically adaptation, one needs to generate a wide range of input values and measure the corresponding outputs. Here, to test our hypothesis regarding I1-FFL adaptation to copy number, we relied on transient transfection of DNA plasmids into mammalian cells that generates large variability in the number of plasmids internalized by individual cells. Single plasmids were used to encode all circuit components in order to minimize irrelevant fluctuations typical of plasmid cotransfection. The circuit's input, that is, the number of plasmid copies in a cell, is determined using a constitutively expressed fluorescent protein. Published literature generally supports the view that in transient transfections, fluorescence depends linearly on the copy number of transfected plasmids (Tseng et al, 1997; Pollard et al, 1998; Cohen et al, 2009; Schwake et al, 2010); in addition, we verified this assertion experimentally as shown below. While strictly speaking, this reporter level also depends on many other potentially fluctuating parameters such as global synthesis and degradation rates, it is the differences in the copy number that are the major source of cell-to-cell variability in transient transfections. Therefore, as a first approximation, the copy number is considered as the sole contributor to the differences in reporter levels. Another fluorescent protein, whose expression is controlled by the input and the auxiliary regulator, serves as the output. We considered three motif variants to test the effect of both transcriptional and post-transcriptional repression by the auxiliary node (Figure 1A–C). In the first variant, a transcriptional repressor LacI coexpressed with the DsRed-monomer output downregulates this output via binding to LacO operator in its promoter, implementing a transcriptional tI1-FFL (Figure 1A). Four LacO mutants are used to quantify the effect of weakened LacI binding on circuit performance, while the plasmid copy number is judged by the level of ZsGreen1 fluorescent reporter that is cotranslated with LacI using an internal ribosome entry site (IRES). Control circuit for this motif contains fully scrambled spacer instead of LacO-binding site. In the second variant, a post-transcriptional type I incoherent feedforward motif version I (ptI1-FFLI; Figure 1B), the auxiliary regulator is a synthetic microRNA (miR-FF3) that targets a complementary RNA sequence fused into the 3′-UTR of the output AmCyan mRNA (Rinaudo et al, 2007). miR-FF3 is processed post-splicing from an intron (Leisner et al, 2010) inserted between two exons coding for a fluorescent protein DsRed-Express. Corresponding control circuit does not contain a target site for miR-FF3, eliminating the RNAi against AmCyan. The third variant of an incoherent motif uses a negative miR regulator that is processed from an intron fused into a protein-coding mRNA; the miR then downregulates this mRNA post-splicing (Figure 1C). This post-transcriptional I1-FFL version II (ptI1-FFLII) utilizes the same DsRed–miR-FF3 fusion construct as ptI1-FFLI but this time the FF3 target is inserted into 3′-UTR of DsRed mRNA itself. In this particular topology, unspliced RNA that is the input for the motif generates the output mRNA as well as the miR regulator of this output. If the regulator acted on the unspliced RNA molecule then the topology would become that of negative autoregulation, but that is not the case here, as the splicing is completed before the miR enters the RNAi pathway. The AmCyan fluorescent protein, divergently expressed from a pTRE promoter, serves as a copy number reporter. Control construct for this last motif is identical to the one used for version I circuit, because RNAi against DsRed is non-functional due to the absence of miR-FF3 target in DsRed 3′-UTR. We compare the behavior of our circuits with the well-studied negative feedback motif (Figure 1D). This transcriptional autoregulatory motif (tAM) was constructed by inverting the promoter region of the tI1-FFL, thus placing the LacO sequences in front of the LacI-coding frame. As a result, the LacI protein represses the transcription of its own mRNA, and the circuit's output is now reflected in the level of ZsGreen1 reporter, while the copy number is judged based on the divergently expressed DsRed protein. Modeling of input–output relation We preceded our experiments with mathematical modeling and computer simulations in order to gain insight into circuits' qualitative behavior (for details, see Supplementary Information). First, we ran numerical simulations using the Matlab SimBiology toolbox. Second, we derived a set of ordinary differential equations based on our current mechanistic understanding of individual interactions in the motifs, and studied the circuits analytically. Numerical simulations show that the output of all incoherent motifs adapts to copy number changes (Figure 2A and B) and the input–output response is well fitted by a ‘rational function’ (Supplementary Figure S1A and B; Supplementary Table S1), where the nominator and the denominator are first-degree polynomials. Weaker inhibition results in slower adaptation, eventually leading to a linear input–output mapping. This is manifested by the gradual increase in EC50 values, that is, input values at which the output reaches half of its saturated value (Figure 2C and D). Figure 2.Simulations for the transcriptional and post-transcriptional (ptI1-FFLI) type I incoherent feedforward motif. (A) Noise-free parametric simulations of the tI1-FFL with increasing binding strength of the LacI inhibition, from weak inhibition case (black curve) to strong inhibition (orange curve). Binding rate constants in 1/(mol*sec) units used to generate the different curves are similarly color coded. (B) Noise-free parametric simulations of the ptI1-FFLI with increasing efficiency of the miRNA inhibition, from weak inhibition (gray curve) to strong inhibition (green curve). Identically color-coded binding rate constant values are shown in 1/(mol*sec) units. (C) Fitted EC50 values of the simulated tI1-FFL response for the binding rate constants in panel (A). (D) Fitted EC50 values of the simulated ptI1-FFLI response for the binding rate constants in panel (B). (E) Two special cases in circuits' response. The orange curve shows a biphasic input–output behavior of the tI1-FFL, while the green curve shows the first derivative of the output in ptI1-FFLI consistent with the transition from a saturated to a proportional response. (F) Noisy simulations of the tI1-FFL for increasing binding of the LacI. The colors correspond to the LacI-binding rate constants in panel (A). (G) The coefficients of variation obtained in the noisy simulations of tI1-FFL. The colors correspond to the LacI-binding rate constants of panel (A). (H) Noisy simulations of the ptI1-FFLI for increasing strength of the miRNA binding. The colors correspond to the binding rate constants in panel (B). (I) Coefficients of variation of the noisy simulations of the ptI1-FFLI. The colors correspond to the miRNA-binding rate constants of panel (B). Download figure Download PowerPoint The qualitative ODE analysis (Supplementary Figure S6) reproduced numerical simulations and provided important additional information about the system. We found that when LacI di- and tetramerization are very fast and essentially irreversible resulting in no detectable degradation of LacI monomers and dimers, and LacI tetramer degradation rate is low, the output (y) depends on the input (x) according to the following Michaelis–Menten form for positive constants V and K, where the constant K is directly proportional to the strength of LacI binding to the LacO operator. In the unrepressed case (K=0), y is simply proportional to x. As a way to model a non-zero mean value of the output when the copy number is zero (important when fitting flow cytometry data), we modify the above formula to the more general form or, using different parameterization, We observe that all the above formulas belong to the category of rational functions that exhibit saturating behavior in agreement with our numerical simulations. When no assumptions are made regarding LacI multi-merization, while still assuming that the degradation of the LacI species is very slow, we obtain Note that this is a ‘biphasic’ function that increases from zero to some maximal value and then decreases to zero (Figure 2E, orange curve). We next analyzed the model of the ptI1-FFLI circuit, and again concluded that the output saturates with increasing input. However, for high copy numbers and low concentrations of RNA-induced silencing complex (RISC), we observe that an increase in the copy number does not lead to an increase in the feedforward inhibition due to saturation of RISC, resulting in the loss of adaptive behavior. We plot a derivative dy/dx of this response (Figure 2E, green curve) with low derivative values at low input values characteristic of adaptation, and high values at high input values indicating linear dependency. Modeling of noisy data Next, we attempted to generate in silico flow cytometry data likely to be obtained in transient transfections by executing the code multiple times, each time corresponding to a single-cell readout. The consecutive runs differed in their initial conditions and the parameter values in order to reflect copy number variability between different cells in transient transfection as well as fluctuations in individual cells (i.e. intrinsic noise). Since the values for neither variability nor fluctuations can be obtained from first principles, we measured them in a number of control experiments. First, copy number distribution was measured using transiently transfected ZsGreen1-encoding plasmid. The amplitude of the measured fluorescence was assigned to correspond to 100 plasmid copies based on our experimental estimation (see below), and the distribution redrawn in copy number units assuming linear relationship between the fluorescence and the copy number (see below). Plasmid copy number was then picked randomly from that distribution and used as an initial condition for each code execution (Supplementary Figure S1C and D). Second, we experimentally measured intrinsic noise in certain components of our system, that is, fluctuations in the relative protein amounts generated from the same DNA template. Two easily identifiable sources of fluctuations are coexpression from the bidirectional promoter and cotranslation from a bicistronic mRNA. In addition, one could expect fluctuations in the negative regulation step by an auxiliary node, meaning that for given steady-state levels of the regulator (e.g. LacI) and its target (e.g. pTRE-LacO-DsRed) the output (e.g. DsRed) levels in different cells will change stochastically due to uncertainty in the exact amount of active and inactive (e.g. bound to LacI) targets. These noisy steps combine to determine the distribution of the output values for a given input reporter value in our circuits. Fluctuations in the bidirectional promoter were measured by coexpressing AmCyan and DsRed from pTRE-Tight promoter (Supplementary Figure S1E); fluctuations in cotranslation were measured by coexpressing DsRed and ZsGreen from a bicistronic mRNA (Supplementary Figure S1F) driven by a constitutive CMV promoter. The data were used to randomize parameter values and simulate a set of stochastic single-cell readouts. Those readouts are then processed as follows: (1) the range of input values is divided into a number of bins of equal width; (2) all ‘cells’ whose simulated input values fall into a particular bin are collected together and assumed to have the same input value, corresponding to the center of a bin; (3) the mean output values of these cells and the coefficient of variance (CV) of these values are used to determine, respectively, the mean circuit response and its intrinsic noise for this input. We apply the same procedure to the experimental data (Supplementary Figure S2A–D). As shown in Supplementary Figure S1H, the CV is ∼0.25 for the bidirectional promoter and ∼0.3 for the IRES. When plugged into the simulation of tI1-FFL (Figure 2F and G), these values generate ∼0.35 variability in the negative control circuit variant without a functional LacO site, and the variability decreases slightly to ∼0.3 when we turn on LacI repression. However, experimental measurement of the negative control circuit showed variability of ∼0.55 (Supplementary Figure S1G and H), indicating that there are additional noise sources even in the absence of negative regulation. One possible reason for this increase is pTRE promoter symmetry breaking with a spacer. Similarly, we simulated ptI1-FFLI taking into account bidirectional promoter noise. We predict CV of ∼0.25 variability regardless of miR efficiency (Figure 2H and I), which compares to the observed CV of ∼0.5 in the negative control circuit. Here, the extra noise could arise from the" @default.
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• W2127793379 title "Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template" @default.
• W2127793379 cites W1590222219 @default.
• W2127793379 cites W1599207601 @default.
• W2127793379 cites W1972183902 @default.
• W2127793379 cites W1985654700 @default.
• W2127793379 cites W1994294376 @default.
• W2127793379 cites W1999777663 @default.
• W2127793379 cites W2007831086 @default.
• W2127793379 cites W2010022403 @default.
• W2127793379 cites W2017270642 @default.
• W2127793379 cites W2029272045 @default.
• W2127793379 cites W2030760849 @default.
• W2127793379 cites W2042035666 @default.
• W2127793379 cites W2044692345 @default.
• W2127793379 cites W2046649943 @default.
• W2127793379 cites W2057993765 @default.
• W2127793379 cites W2059053384 @default.
• W2127793379 cites W2059667986 @default.
• W2127793379 cites W2060339242 @default.
• W2127793379 cites W2070147996 @default.
• W2127793379 cites W2077046573 @default.
• W2127793379 cites W2090732700 @default.
• W2127793379 cites W2106362845 @default.
• W2127793379 cites W2106927126 @default.
• W2127793379 cites W2107575419 @default.
• W2127793379 cites W2109110326 @default.
• W2127793379 cites W2110699703 @default.
• W2127793379 cites W2111759547 @default.
• W2127793379 cites W2112720380 @default.
• W2127793379 cites W2119843070 @default.
• W2127793379 cites W2123973944 @default.
• W2127793379 cites W2125108766 @default.
• W2127793379 cites W2126602684 @default.
• W2127793379 cites W2135541516 @default.
• W2127793379 cites W2141688036 @default.
• W2127793379 cites W2141805742 @default.
• W2127793379 cites W2143258942 @default.
• W2127793379 cites W2148061480 @default.
• W2127793379 cites W2152407435 @default.
• W2127793379 cites W2153624566 @default.
• W2127793379 cites W2158075875 @default.
• W2127793379 cites W2162884852 @default.
• W2127793379 cites W2171280873 @default.
• W2127793379 cites W3103241045 @default.
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