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Plant Physiol. 2010 Feb; 152(2): 411–419.
PMCID: PMC2815853
Focus Issue on Plant Systems Biology

Systems Biology Update: Cell Type-Specific Transcriptional Regulatory Networks

Plant cells use regulatory networks composed of numerous components, such as DNA, RNA, proteins, and small molecules, to regulate multiple biological processes, allowing plants to adapt to changing environments or to respond to developmental cues. The availability of high-throughput experimental methods enables researchers to determine the expression levels for thousands of genes and protein-protein or protein-DNA interactions. Systems biology approaches can allow scientists to integrate these large amounts of information and to understand the properties of these biological systems in specific cells or tissues. Dynamic mathematical modeling approaches used to characterize plant transcriptional networks can reveal emergent properties of these networks. This review highlights some currently available methodologies used to obtain systems-scale data such as laser capture microdissection (LCM), fluorescence-activated cell sorting (FACS), chromatin immunoprecipitation (ChIP)-on-chip, proteomics, and modeling approaches that are most useful to explore plant transcriptional networks at the cellular level. We also provide two examples of transcriptional networks in single cell types and detail how such methods and data sets have been used to map and reveal emergent properties of gene regulatory networks that regulate cell identity specification.

In the 21st century, our species and planet are facing many urgent problems—including our diminishing supply of nonrenewable fuel sources, as well as food and water shortages. However, plant biology has the potential to provide solutions to these global crises. To address these problems, efforts to understand and model how a plant responds to environmental stimuli or genetic manipulation using systems biology approaches were proposed (Raikhel and Coruzzi, 2003). Genome-scale acquisition of data describing gene and protein expression and interactions has been and continues to be collected. To better understand these and other large-scale data sets, systems biology approaches were utilized. Systems biology aims to discover emergent properties that arise from the analysis of interactions between components in a biological system. These properties are revealed by integrating, modeling, and analyzing the interactions between all components using network theory, systematic perturbation, and monitoring of the modeled components, and model refinement to reconcile discrepancies between the experimental observations and the existing model (Ideker et al., 2001; Long et al., 2008). Ideally these approaches are best utilized with the full complement of components that exist within gene regulatory networks. However, systems approaches have also been utilized with small cell type-specific regulatory networks elucidated using molecular genetic approaches. The identity of individual plant cells are specified by and respond to changing environments by connecting numerous components, such as DNA, RNA, proteins, and small molecules, within regulatory networks to coordinate multiple biological functions (Brady et al., 2007; Dinneny et al., 2008).

What types of -omics data are ideally needed to reconstruct plant transcriptional regulatory networks at the cellular level? Transcriptomes are obtained by measuring whole-genome expression using microarrays or short-read sequencing methods (Brady et al., 2006; Iyer-Pascuzzi et al., 2009). Proteomes are obtained by two-dimensional gel electrophoresis and mass spectrometry (Raikhel and Coruzzi, 2003; Baginsky and Gruissem, 2006). Regulatory elements are regions of DNA or RNA where regulators such as transcription factors (TFs) bind preferentially to regulate the expression of target genes. These elements are often identified using computational methods coupled with gene expression data (Long et al., 2008; Priest et al., 2009). The metabolome represents the collection of all metabolites in a biological organism, which are additionally the end products of transcriptional regulation, and which can influence transcription as signaling molecules (Brady et al., 2006; Schauer and Fernie, 2006; Long et al., 2008). Additional signaling molecules like carbohydrates and lipids can be collected, analyzed, and processed at the systems level (Watson, 2006; Aoki-Kinoshita, 2008). Recent reviews have discussed techniques used to identify components for use in plant systems biology such as genome sequencing, microarrays, pull-down assays, the yeast one-hybrid system, and other important tools (Brady et al., 2006; Albert, 2007; Long et al., 2008).

A remaining challenge for plant biologists is to integrate and understand the biological properties that emerge from the interactions of cellular components. Recently, plant biologists have been able to discover components of transcriptional networks in individual cell types using systems biology approaches. These have been highlighted in Table I. Some prominent reviews have additionally been published regarding one or two aspects of systems biology approaches (Alon, 2007; Alvarez-Buylla et al., 2007; Camacho et al., 2007; Busser et al., 2008). Here, we will describe some currently well-used systems approaches to explore plant transcriptional networks at the cellular level. We will also describe two examples that demonstrate how these methods have been used to characterize gene regulatory networks that regulate cell identity specification, and that reveal emergent properties of these networks.

Table I.
Characterization of transcriptional regulatory networks at the cellular level


Plant growth and development depends, to a large degree, on the tissue- or cell-type-specific expression of genes. Although the use of large-scale microarray technology has led to the generation of tremendous amounts of plants gene expression data in whole plants or organs, this data is unlikely to provide information about gene expression in specialized tissue or cell types (Kilian et al., 2007; Goda et al., 2008). More recently, two techniques in conjunction with microarrays were developed to study transcriptional changes in individual cell types and tissues within the plant.


LCM is a powerful tool allowing the rapid and precise isolation of specific populations of cells or even individual cells from a heterogeneous tissue based on established histological identification. LCM combined with DNA microarray analysis was used to identify a high-resolution expression map of the syncytial stage of Arabidopsis (Arabidopsis thaliana) endosperm development at 4 d after pollination (Day et al., 2008). Using this technique, Jiao et al. obtained a cell-type transcriptome atlas that includes 40 cell types from rice (Oryza sativa) shoot, root, and the germinating seed at several developmental stages (Jiao et al., 2009). This method has been used to isolate cell-type populations in maize (Zea mays) and cotton (Gossypium hirsutum; Dembinsky et al., 2007; Ohtsu et al., 2007; Wu et al., 2007; Zhang et al., 2007; Brooks et al., 2009). Cells isolated by this technology have also been used to characterize the cell-type-specific proteome in the maize root. This study defined the distinct molecular events during the specification of cell-cycle-competent pericycle cells prior to their first division, and also demonstrates that pericycle specification and lateral root initiation might be controlled by a different set of genes (Dembinsky et al., 2007).


Researchers have used FACS to isolate RNA from cell-type-specific GFP-marked root populations (Birnbaum et al., 2003, 2005). Using this technique, high spatial resolution gene expression data have been obtained for nearly all cell types in the root. This analysis revealed dominant expression patterns between unrelated cell types and was used to infer novel cellular function (Brady et al., 2007). This method has also been used to examine the response to high salt and iron deficiency in specific cell types and developmental stages of the root (Dinneny et al., 2008). Most responses to salt and iron were cell-type specific and were dependent on environmental conditions. This study revealed that the level of conservation among biological functions enriched in cell types under high salt and low iron conditions varied according to cell type and stress (Dinneny et al., 2008). A cell-type-specific transcriptional network underlying nitrogen responses in the root has also been revealed using FACS that mediates lateral root outgrowth (Gifford et al., 2008).


DNA-binding proteins perform a variety of important functions in cells, including transcriptional regulation, chromosome maintenance, replication, and DNA repair. The interactions between TFs and their DNA-binding sites are an integral part of transcriptional regulatory networks. ChIP is a well-established procedure used to investigate interactions between proteins and DNA (Buck and Lieb, 2004). More importantly, ChIP assays in combination with other methods have been used to determine the interaction between TFs and DNA from a few potential individual targets to genome-wide surveys. Coupled with whole-genome DNA microarrays, ChIP-on-chip (also called ChIP-chip) can provide a whole-genome view of DNA-binding sites for any given protein that occurs in vivo. This powerful procedure has been adapted to identify a set of about 20 genes that are targets of GLABRA3 (GL3) and GL1 in the Arabidopsis trichome (Morohashi and Grotewold, 2009). An additional and less comprehensive method utilizes ChIP in conjunction with quantitative real-time PCR (ChIP-qPCR). This technique combined with FACS has been used to characterize the targets of SHORTROOT (SHR) and SCARECROW TFs that specify the endodermis in the Arabidopsis root (Levesque et al., 2006; Cui et al., 2007). ChIP-qPCR was also used to demonstrate the interaction of TRICHOMELESS1 and the cis-acting regulatory elements of GL1 that regulate trichome cell specification in Arabidopsis that provided, to our knowledge, the first molecular and genetic evidence that an R3 MYB may negatively regulate trichome cell specification in a novel manner by directly suppressing the transcription of GL1 (Wang et al., 2007).


Every cell within the plant contains the same DNA, yet different cells appear committed to specialized tasks. How is this possible? The answer lies in the differential regulation of gene expression and protein synthesis. This process begins with transcription and is completed with protein translation and subsequent posttranslational modification. Thus, proteomic approaches are key to investigating transcriptional networks at the cellular level. Improved high-throughput proteomics techniques has shifted attention to protein profiling, which attempts to identify all proteins that are present in a particular tissue or cell (Baginsky and Gruissem, 2006). Recently, an Arabidopsis proteome map has been established in different organs, developmental stages, and in undifferentiated cultured cells (Baerenfaller et al., 2008). Proteins in gene ontology categories for intracellular protein transport, response to oxidative stress, and toxin catabolic process were represented in their analyses (Baerenfaller et al., 2008). An additional dataset that profiles the proteome of four organs in Arabidopsis also sampled the phosphorylated proteins (Castellana et al., 2008). These databases provide information about genome activity and proteome assembly for plant systems biology approaches. In Arabidopsis, cell type-specific proteome maps have only been established in guard cells. Proteomic methods were used to identify 1,734 unique Arabidopsis guard cell proteins including 336 proteins not previously represented in transcriptome analyses of guard cells and 52 proteins classified as signaling proteins by gene ontology analysis (Zhao et al., 2008b). Two-dimensional PAGE coupled with mass spectrometry has been used to identify proteins enriched in a single root hair cell of soybean (Glycine max). The proteins identified are involved not only in basic cell metabolism but also in functions more specific to the single root hair cell, including water and nutrient uptake, vesicle trafficking, and hormone and secondary metabolism that provided insight into the metabolic activities of a single, differentiated plant cell type (Brechenmacher et al., 2009).


High-throughput microarray technology has become a popular tool for large-scale gene expression analysis. As a result, there are rapidly growing collections of available data sets that can be used for subsequent analysis. Meta-analysis consists of a set of statistical techniques that have been used to combine results from several independent microarray experiments. This technique appears to be a practical solution to maximize the use of data available from each experiment. This method has led to the identification of cell-type transcriptional networks (Hong and Breitling, 2008; Iyer-Pascuzzi et al., 2009). For example, eight direct targets of SHR were identified by combining three different microarray expression datasets. Four of eight putative targets were further confirmed by their binding of SHR by ChIP-qPCR in vivo (Levesque et al., 2006). SHR controls radial patterning of the root by regulating endodermis and stem cell niche specification. SHR is also expressed in the vasculature of the root, and this meta-analysis identified targets of SHR in the vasculature and a new function of SHR in vasculature development. Furthermore, meta-analysis has been used to establish a minimal set of genes uniquely associated with the formation of trichomes controlled by GL1 and/or GL3 in Arabidopsis (Morohashi and Grotewold, 2009). Therefore, meta-analysis provides researchers with a powerful tool to interrogate existing databases for further exploration of transcriptional regulatory networks in specific tissues or cell types.


Mathematical and computational tools can be used on data from multiple sources (including genome-scale data and small-scale data) to dynamically model gene regulatory networks (Alvarez-Buylla et al., 2007; Busser et al., 2008). Network theory can then be used to analyze these models to better understand complex systems that have multiple elements and interactions. These predictive frameworks are an extremely important aspect of systems biology that can greatly improve our understanding of the local dynamics of cell-type-specific transcriptional circuits. Boolean and Bayesian network (BN) models are two commonly used modeling approaches to describe dynamic regulatory networks. A Boolean network is a system of binary-state nodes (representing genes) with edges between each node that represent regulatory interactions. One of two states is assigned (on/off) that represent the status of a gene being active or inactive, respectively. The change in state of each regulated node is generally described by a logical function using the Boolean operators and, or, and not. Boolean models can predict dynamic trends within the system at a particular time point (Albert, 2007; Price and Shmulevich, 2007). A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (Albert, 2007; Price and Shmulevich, 2007). A dynamic BN extends the notion of a BN to model the stochastic evolution of a set of random variables over time (Camacho et al., 2007). A probabilistic Boolean modeling approach has recently been used to analyze the gene regulatory network that underlies Arabidopsis root epidermis patterning (Benitez et al., 2008; Savage et al., 2008). The ability of these modeling approaches to reveal emergent properties of this gene regulatory network in determining cell identity specification will be further discussed.


The appropriate specification and patterning of cell types are fundamental features of plant development. Plant root hairs form an important surface that absorbs water and nutrients, and protects the root from water loss and insect and pathogen invasion. In Arabidopsis, root hair cells are specified in a position-dependent manner. All epidermal cells located above two underlying cortical cells (designated the H position) develop as hair cells and cells located above a single cortical cell (designated the N position) adopt the non-hair fate. The formation of root hair cells and non-hair cells in the epidermis of plant roots provides an excellent model to study cell-type patterning because root epidermal cells are accessible, their developmental process can be accurately analyzed, and they differentiate in a predictable gradient along the root axis (Schiefelbein et al., 2009). While some genome-scale expression data has been collected for root hairs (Brady et al., 2007), primarily small-scale molecular genetics studies have resulted in a well-characterized network regulating hair and non-hair cell specification in the Arabidopsis root. Modeling approaches, however, have addressed the hypothesis that this network specifies cell identity via a mutual support mechanism with WEREWOLF (WER) autoregulation, and lateral movement of CAPRICE (CPC) and GL3 (Fig. 1B; Savage et al., 2008).

Figure 1.
System approaches and two models describing the regulatory network for cell-type patterning in the Arabidopsis root epidermis. A, System approaches used to study the transcriptional networks in root hairs. TFs involved in root hair development are first ...

Factors that act in root hair cell patterning in Arabidopsis include an R2R3 MYB-type TF, WER; a WD-repeat protein, TRANSPARENT TESTA GLABRA1(TTG1); two partially redundant basic helix-loop-helix (bHLH) proteins, GL3 and ENHANCER OF GLABRA3 (EGL3); a homeodomain protein, GL2; and three MYB proteins, CPC, TRIPTYCHON (TRY), and ENHANCER OF TRIPTYCHON AND CAPRICE1 (ETC1). It has been proposed that WER, GL3/EGL3, and TTG1 form a transcriptional complex involved in the promotion of non-hair cell specification (Fig. 1, B and C). This regulatory complex is preferentially expressed in the non-hair cell position, and functions to induce the expression of GL2 that promotes the non-hair fate, while the same complex simultaneously activates the expression of CPC, TRY, and ETC1 in N cells (Ishida et al., 2008; Schiefelbein et al., 2009). CPC, TRY, and ETC1 then move into neighboring epidermal cells where they compete with WER for physical interaction with GL3/EGL3 to repress WER and GL2 expression, resulting in H-cell specification (Tominaga et al., 2007; Ishida et al., 2008). Positional determination of N- or H-cell identity acquisition is also influenced by SCRAMBLED (SCM), a Leu-rich receptor-like kinase that is proposed to interpret a positional signal from the underlying cortical cells (Fig. 1, B and C; Kwak and Schiefelbein, 2008; Schiefelbein et al., 2009). Autoregulation of WER expression in N cells has also been proposed to be necessary for epidermal patterning within this network (Fig. 1C).

Two alternative forms of WER regulation were considered in the modeling of this network using Boolean formalism: (1) local WER self activation in N cells implemented by enhancement of WER transcription by the WER-EGL3/GL3-TTG1 complex or (2) WER transcription activated uniformly in all epidermal cells (Fig. 1C). In this Boolean modeling approach, components were either expressed or not. For example, components with only a single positive input were expressed if their direct positive regulator is expressed. In cases where multiple positive and negative inputs exist, these components were given a time-evolving probability of expression that is correlated with their expression abundance in the cell. A positional bias was implemented by expressing SCM only in H cells, resulting in a lower transcription rate of WER than in the N position. Using a simulated ring of epidermal cells, these models were able to recapitulate wild-type expression patterns within the epidermis. These models were then perturbed according to experimentally characterized mutations in components of the network. In a simulated cpc mutant, however, these two models yielded different expression patterns, with only the mutual support framework, yielding expression patterns similar to experimental observations. To then experimentally demonstrate if WER is able to regulate its own expression, the ability of WER to drive its own expression was characterized in wild type and in a wer mutant. Identical spatial expression patterns were observed, suggesting that WER does not autoregulate its expression. To then determine if WER is expressed uniformly in all epidermal cells early in development, this same reporter was analyzed in wild type and found to be expressed uniformly within the root meristem. These results demonstrate that WER is initially expressed uniformly and its expression is restricted due to SCM expression and CPC movement (Fig. 1C). The autoactivation model network was also unable to recapitulate experimentally determined WER expression patterns in a gl3/egl3 and in a ttg mutant. An additional dynamic gene regulatory modeling approach was used to model Arabidopsis root epidermal patterning where each gene can have one of three expression states (0 = off, 1 = mild expression, 2 = strong expression; Benitez et al., 2007, 2008). This model requires WER autoregulation and was not able to recapitulate the observed WER expression pattern in a scm mutant. The authors further provide an alternative regulatory mechanism to determine cell patterning in the root epidermis via the combined activity of a self-activating loop and another input activating WER. This mechanism, the authors suggest, allows both models to be plausible and recapitulate experimental data.

Patterning root hairs in Arabidopsis have provided us a comprehensive understanding of the transcriptional network to guide the fate of epidermal cells. However, future research will be necessary to address several unclear issues in this regulatory system. For example, the exact regulatory mechanism for intercellular movement of the CPC protein and GL3/EGL3 proteins is still a mystery. Moreover, the full complement of targets of these TFs in root hairs remains to be determined. Studies that incorporate more sophisticated computational or mathematical models that bridge the dynamics of pattern formation and that are informed by further experimentation will further elaborate the gene regulatory network in root epidermal cell patterning.


Trichomes of Arabidopsis are developmentally important because they are involved in temperature control, water regulation, and protection against insect herbivores and UV irradiation. Arabidopsis trichomes are single-celled, branched hair-like structures that differentiate from epidermal cells of leaves, stems, and sepals. In contrast to root hair patterning, which is predictable with respect to the underlying cortex and position-dependent cell fate determination, regular trichome patterning is an example of de novo pattern formation (Ishida et al., 2008). The establishment of a single-celled trichome on the leaf also serves as an excellent model system to study the gene regulatory networks involved in cell fate determination. Systems approaches have been used to elucidate the key regulatory molecular interactions that determine trichome spacing.

Similar components of the gene regulatory network that regulate root hair specification also operate during trichome determination (Zhao et al., 2008a; Schiefelbein et al., 2009). They include an R2R3 MYB TF GL1, the bHLH factors GL3 and EGL3, and the WD40-repeat protein TTG1 that form a trichome-promoting complex GL1-GL3/EGL3-TTG1 (Fig. 2). Two downstream targets of this complex that promote trichome formation are the homeobox TF GL2 and the WRKY regulator, TTG2. The negative regulators TRY, CPC, and ETC1 are initially activated by this activating complex and ETC1 had been demonstrated to move into neighboring cells while CPC and TRY had been hypothesized to also act cell nonautonomously. These inhibitors can compete with GL1 for binding to the GL1-GL3/EGL3 active complex to suppress trichome initiation and differentiation into hairless cells (Fig. 2; Ishida et al., 2008; Zhao et al., 2008a). A modeling study using ordinary differential equations has elucidated the molecular mechanism by which TRY can exert its inhibitory function (Digiuni et al., 2008). The authors determined experimentally that TRY, in addition to interacting physically with GL3, can also physically interact with GL1. Three alternative models were proposed—a single competitive inhibition model in which TRY binds to free GL3 and blocks the formation of the GL1-GL3/EGL3-TTG1 active complex, a double competitive inhibition model that additionally includes the binding of TRY to free GL1, and an uncompetitive inhibition model where TRY binds only to the activator complex and renders it inactive (Fig. 2). These models were run and a series of GL3 and TRY overexpression experiments performed. The resulting experimental data best matched the single competitive inhibition model simulation data and therefore predicts that the interaction between TRY and GL3 is the most relevant for trichome patterning.

Figure 2.
A transcriptional network of cell-type patterning in the Arabidopsis trichome. In trichome cells, the activating complex TTG1-GL3/EGL3-GL1 activates the expression of trichome activators (GL2/TTG2) and single MYB inhibitors in the epidermal cell chosen ...

To further determine the number of targets of the active complex, a recent study identified 20 direct targets of both GL1 and GL3 by using ChIP-chip and genome-wide expression analyses (Morohashi and Grotewold, 2009). Among the GL3/GL1 direct targets, at least four TFs, GL2, TTG2, SCL8, and MYC1 constitute main transcriptional regulators of the network hierarchical structure for trichome differentiation. Temporal induction analyses further determined that many of these genes are expressed early upon GL1 or GL3 induction, suggesting that they play important roles in the early events in differentiation of protodermal cells. For example, SCL8 encodes a GRAS family TF and SCL8 mRNA levels peak sharply within the first few hours of GL3 or GL1 induction, to then level off at quantities similar as found in the absence of the regulators, suggesting a need for SCL8 function at early stages during trichome initiation. MYC1 encodes a bHLH factor closely related to GL3 and EGL3 and MYC1 mRNA also accumulates in the early of trichome initiation. Although SCL8 and MYC1 are implicated to be required for trichome development, their functions remain uncharacterized. Additionally, these methods predict novel regulators of trichome formation—a minimal set of 513 genes were identified to be associated with trichome formation and are downstream targets of this transcriptional regulatory cascade (Morohashi and Grotewold, 2009).


The advent of these large-scale, high-resolution datasets and computational tools provide the means for a better understanding of transcriptional regulatory networks in a cell type or tissue. Methods used to obtain single-cell or cell-type-resolution transcription profiles have indeed become robust enough to facilitate their use. Despite these achievements, however, we still lack sufficient cell-type-specific data needed to completely elucidate these networks including the cell-type-specific small RNA compendium, epigenome, proteome, phosphoproteome, metabolome, and lipidome (Zhang et al., 2006; Henderson and Jacobsen, 2007; Kasschau et al., 2007; Zilberman et al., 2007; Brodersen et al., 2008; Castellana et al., 2008; Cokus et al., 2008; Lister et al., 2008; Zhu, 2008; Popescu et al., 2009). The LCM and cell-sorting approaches have proven efficient to isolate cell-type-specific transcriptomes often via amplification of RNA in up to two cDNA synthesis steps. Unfortunately, however, it is not possible to amplify the proteome, metabolome, or lipidome. Therefore, a limitation exists in obtaining the massive amounts of material needed to generate these types of cell-type-specific data. This limitation is not insurmountable, however, it just requires sufficient plant, financial, and person resources.

In addition, to determine the regulatory logic that underlies these transcriptional regulatory networks in individual cells or cell types, we need to identify the targets of all TFs expressed within a cell type, and their preferential binding sites. The majority of experimental approaches used to elucidate downstream targets of TFs in plants utilize a TF-centered approach, where targets of a single TF of interest are characterized. The targets of all TFs expressed in a cell type need to be determined in an unbiased manner to completely and fully understand the function of these transcriptional networks within the cell. However, to collect this data, tagged versions of all TFs expressed in a cell type ideally under their native promoter would need to be synthesized. An alternate gene-centered approach could be utilized with high-throughput yeast one-hybrid approaches utilizing whole cell-type-specific promoters as bait, and prey TF libraries comprised of TFs expressed in the same cell type (Deplancke et al., 2006; Pruneda-Paz et al., 2009). Studies of this magnitude have been carried out in many other organisms, and have revealed emergent properties of these regulatory networks (Deplancke et al., 2006; Vermeirssen et al., 2007; Wu et al., 2007; Brooks et al., 2009; Jiao et al., 2009). Finally, studies characterizing the nature of these transcriptional interactions (activating or repressing), and the temporal dynamics of these interactions are needed to correctly understand flux within these regulatory networks.

Bioinformatic tools also need to be further developed that enable the user to integrate and visualize these diverse data in the appropriate statistical manner. These will enable the construction of integrated models that provide a predictive framework to better understand plant cell type or tissue development. A further challenge for a plant system biologist is in the generation of modeling tools that allows one to build comprehensive models at the appropriate cellular and temporal scale, such as those currently being developed by the iPlant collaborative. We are certainly at the brink of collecting sufficient transcriptomic data to begin to map the transcriptional regulatory networks that regulate the development and function of plant cells and tissues. However, considerably more data is needed to obtain a comprehensive view of these networks. Once obtained, modeling of these data will enable the improvement of crop yield, biofuel production, and other urgent needs.


The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Siobhan Brady (ude.sivadcu@ydarbs).



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