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1.  Comprehensive systems biology analysis of a 7-month cigarette smoke inhalation study in C57BL/6 mice 
Scientific Data  2016;3:150077.
Smoking of combustible cigarettes has a major impact on human health. Using a systems toxicology approach in a model of chronic obstructive pulmonary disease (C57BL/6 mice), we assessed the health consequences in mice of an aerosol derived from a prototype modified risk tobacco product (pMRTP) as compared to conventional cigarettes. We investigated physiological and histological endpoints in parallel with transcriptomics, lipidomics, and proteomics profiles in mice exposed to a reference cigarette (3R4F) smoke or a pMRTP aerosol for up to 7 months. We also included a cessation group and a switching-to-pMRTP group (after 2 months of 3R4F exposure) in addition to the control (fresh air-exposed) group, to understand the potential risk reduction of switching to pMRTP compared with continuous 3R4F exposure and cessation. The present manuscript describes the study design, setup, and implementation, as well as the generation, processing, and quality control analysis of the toxicology and ‘omics’ datasets that are accessible in public repositories for further analyses.
doi:10.1038/sdata.2015.77
PMCID: PMC4700839  PMID: 26731301
Respiratory system models; Microarray analysis; Systems biology; Lipidomics; Proteomic analysis
2.  An 8-Month Systems Toxicology Inhalation/Cessation Study in Apoe−/− Mice to Investigate Cardiovascular and Respiratory Exposure Effects of a Candidate Modified Risk Tobacco Product, THS 2.2, Compared With Conventional Cigarettes 
Toxicological Sciences  2015;149(2):411-432.
Smoking cigarettes is a major risk factor in the development and progression of cardiovascular disease (CVD) and chronic obstructive pulmonary disease (COPD). Modified risk tobacco products (MRTPs) are being developed to reduce smoking-related health risks. The goal of this study was to investigate hallmarks of COPD and CVD over an 8-month period in apolipoprotein E-deficient mice exposed to conventional cigarette smoke (CS) or to the aerosol of a candidate MRTP, tobacco heating system (THS) 2.2. In addition to chronic exposure, cessation or switching to THS2.2 after 2 months of CS exposure was assessed. Engaging a systems toxicology approach, exposure effects were investigated using physiology and histology combined with transcriptomics, lipidomics, and proteomics. CS induced nasal epithelial hyperplasia and metaplasia, lung inflammation, and emphysematous changes (impaired pulmonary function and alveolar damage). Atherogenic effects of CS exposure included altered lipid profiles and aortic plaque formation. Exposure to THS2.2 aerosol (nicotine concentration matched to CS, 29.9 mg/m3) neither induced lung inflammation or emphysema nor did it consistently change the lipid profile or enhance the plaque area. Cessation or switching to THS2.2 reversed the inflammatory responses and halted progression of initial emphysematous changes and the aortic plaque area. Biological processes, including senescence, inflammation, and proliferation, were significantly impacted by CS but not by THS2.2 aerosol. Both, cessation and switching to THS2.2 reduced these perturbations to almost sham exposure levels. In conclusion, in this mouse model cessation or switching to THS2.2 retarded the progression of CS-induced atherosclerotic and emphysematous changes, while THS2.2 aerosol alone had minimal adverse effects.
doi:10.1093/toxsci/kfv243
PMCID: PMC4725610  PMID: 26609137
atherosclerosis; emphysema; tobacco-heating; LC-MS based proteomics; lipidomics
3.  Effects of Cigarette Smoke, Cessation, and Switching to Two Heat-Not-Burn Tobacco Products on Lung Lipid Metabolism in C57BL/6 and Apoe−/− Mice—An Integrative Systems Toxicology Analysis 
Toxicological Sciences  2015;149(2):441-457.
The impact of cigarette smoke (CS), a major cause of lung diseases, on the composition and metabolism of lung lipids is incompletely understood. Here, we integrated quantitative lipidomics and proteomics to investigate exposure effects on lung lipid metabolism in a C57BL/6 and an Apolipoprotein E-deficient (Apoe−/−) mouse study. In these studies, mice were exposed to high concentrations of 3R4F reference CS, aerosol from potential modified risk tobacco products (MRTPs) or filtered air (Sham) for up to 8 months. The 2 assessed MRTPs, the prototypical MRTP for C57BL/6 mice and the Tobacco Heating System 2.2 for Apoe−/− mice, utilize “heat-not-burn” technologies and were each matched in nicotine concentrations to the 3R4F CS. After 2 months of CS exposure, some groups were either switched to the MRTP or underwent cessation. In both mouse strains, CS strongly affected several categories of lung lipids and lipid-related proteins. Candidate surfactant lipids, surfactant proteins, and surfactant metabolizing proteins were increased. Inflammatory eicosanoids, their metabolic enzymes, and several ceramide classes were elevated. Overall, CS induced a coordinated lipid response controlled by transcription regulators such as SREBP proteins and supported by other metabolic adaptations. In contrast, most of these changes were absent in the mice exposed to the potential MRTPs, in the cessation group, and the switching group. Our findings demonstrate the complex biological response of the lungs to CS exposure and support the benefits of cessation or switching to a heat-not-burn product using a design such as those employed in this study.
doi:10.1093/toxsci/kfv244
PMCID: PMC4725611  PMID: 26582801
modified risk tobacco products; lipidomics; quantitative proteomics; systems toxicology
4.  Transcriptional profiling and targeted proteomics reveals common molecular changes associated with cigarette smoke-induced lung emphysema development in five susceptible mouse strains 
Inflammation Research  2015;64(7):471-486.
Background
Mouse models are useful for studying cigarette smoke (CS)-induced chronic pulmonary pathologies such as lung emphysema. To enhance translation of large-scale omics data from mechanistic studies into pathophysiological changes, we have developed computational tools based on reverse causal reasoning (RCR).
Objective
In the present study we applied a systems biology approach leveraging RCR to identify molecular mechanistic explanations of pathophysiological changes associated with CS-induced lung emphysema in susceptible mice.
Methods
The lung transcriptomes of five mouse models (C57BL/6, ApoE−/−, A/J, CD1, and Nrf2−/−) were analyzed following 5–7 months of CS exposure.
Results
We predicted 39 molecular changes mostly related to inflammatory processes including known key emphysema drivers such as NF-κB and TLR4 signaling, and increased levels of TNF-α, CSF2, and several interleukins. More importantly, RCR predicted potential molecular mechanisms that are less well-established, including increased transcriptional activity of PU.1, STAT1, C/EBP, FOXM1, YY1, and N-COR, and reduced protein abundance of ITGB6 and CFTR. We corroborated several predictions using targeted proteomic approaches, demonstrating increased abundance of CSF2, C/EBPα, C/EBPβ, PU.1, BRCA1, and STAT1.
Conclusion
These systems biology-derived candidate mechanisms common to susceptible mouse models may enhance understanding of CS-induced molecular processes underlying emphysema development in mice and their relevancy for human chronic obstructive pulmonary disease.
Electronic supplementary material
The online version of this article (doi:10.1007/s00011-015-0820-2) contains supplementary material, which is available to authorized users.
doi:10.1007/s00011-015-0820-2
PMCID: PMC4464601  PMID: 25962837
Emphysema; Cigarette smoke; Systems biology; Mouse models; Gene expression; Molecular mechanisms
5.  Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems 
With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation.
Database URL: http://causalbionet.com
doi:10.1093/database/bav030
PMCID: PMC4401337  PMID: 25887162
6.  Enhancement of COPD biological networks using a web-based collaboration interface 
F1000Research  2015;4:32.
The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.
doi:10.12688/f1000research.5984.1
PMCID: PMC4350443  PMID: 25767696
COPD; Chronic Obstructive Pulmonary Disease; network model; signaling pathway; crowdsourcing; crowd verification; jamboree; online collaboration
7.  A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability 
Background
Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of “omics” data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.
Methods
We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis.
Results
Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques.
Conclusions
We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability.
doi:10.1186/1479-5876-12-185
PMCID: PMC4227037  PMID: 24965703
Vascular systems biology; Plaque destabilization; Vascular biology networks; Computational modeling; Atherosclerosis modeling
8.  Systems toxicology approaches enable mechanistic comparison of spontaneous and cigarette smoke-related lung tumor development in the A/J mouse model 
Interdisciplinary Toxicology  2014;7(2):73-84.
The A/J mouse is highly susceptible to lung tumor induction and has been widely used as a screening model in carcinogenicity testing and chemoprevention studies. However, the A/J mouse model has several disadvantages. Most notably, it develops lung tumors spontaneously. Moreover, there is a considerable gap in our understanding of the underlying mechanisms of pulmonary chemical carcinogenesis in the A/J mouse. Therefore, we examined the differences between spontaneous and cigarette smoke-related lung tumors in the A/J mouse model using mRNA and microRNA (miRNA) profiling. Male A/J mice were exposed whole-body to mainstream cigarette smoke (MS) for 18 months. Gene expression interaction term analysis of lung tumors and surrounding non-tumorous parenchyma samples from animals that were exposed to either 300 mg/m3 MS or sham-exposed to fresh air indicated significant differential expression of 296 genes. Ingenuity Pathway Analysis® (IPA®) indicated an overall suppression of the humoral immune response, which was accompanied by a disruption of sphingolipid and glycosaminoglycan metabolism and a deregulation of potentially oncogenic miRNA in tumors of MS-exposed A/J mice. Thus, we propose that MS exposure leads to severe perturbations in pathways essential for tumor recognition by the immune system, thereby potentiating the ability of tumor cells to escape from immune surveillance. Further, exposure to MS appeared to affect expression of miRNA, which have previously been implicated in carcinogenesis and are thought to contribute to tumor progression. Finally, we identified a 50-gene expression signature and show its utility in distinguishing between cigarette smoke-related and spontaneous lung tumors.
doi:10.2478/intox-2014-0010
PMCID: PMC4427718  PMID: 26109882
A/J mouse; lung tumorigenesis; cigarette smoke exposure; gene expression; microRNA (miRNA) expression
9.  On Crowd-verification of Biological Networks 
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks.
Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified.
The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.
doi:10.4137/BBI.S12932
PMCID: PMC3798292  PMID: 24151423
community curation; biological network models; reputation system; Biological Expression Language
10.  Systems Approaches Evaluating the Perturbation of Xenobiotic Metabolism in Response to Cigarette Smoke Exposure in Nasal and Bronchial Tissues 
BioMed Research International  2013;2013:512086.
Capturing the effects of exposure in a specific target organ is a major challenge in risk assessment. Exposure to cigarette smoke (CS) implicates the field of tissue injury in the lung as well as nasal and airway epithelia. Xenobiotic metabolism in particular becomes an attractive tool for chemical risk assessment because of its responsiveness against toxic compounds, including those present in CS. This study describes an efficient integration from transcriptomic data to quantitative measures, which reflect the responses against xenobiotics that are captured in a biological network model. We show here that our novel systems approach can quantify the perturbation in the network model of xenobiotic metabolism. We further show that this approach efficiently compares the perturbation upon CS exposure in bronchial and nasal epithelial cells in vivo samples obtained from smokers. Our observation suggests the xenobiotic responses in the bronchial and nasal epithelial cells of smokers were similar to those observed in their respective organotypic models exposed to CS. Furthermore, the results suggest that nasal tissue is a reliable surrogate to measure xenobiotic responses in bronchial tissue.
doi:10.1155/2013/512086
PMCID: PMC3808713  PMID: 24224167
11.  Systematic Verification of Upstream Regulators of a Computable Cellular Proliferation Network Model on Non-Diseased Lung Cells Using a Dedicated Dataset 
We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.
doi:10.4137/BBI.S12167
PMCID: PMC3733638  PMID: 23926424
cell proliferation; biological network model; reverse causal reasoning
12.  A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue 
Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of inflammatory responses. These responses are especially pronounced in the lung, where pulmonary cells sit at the interface between the body’s internal and external environments. We combined a literature survey with a computational analysis of multiple transcriptomic data sets to construct a computable causal network model (the Inflammatory Process Network (IPN)) of the main pulmonary inflammatory processes. The IPN model predicted decreased epithelial cell barrier defenses and increased mucus hypersecretion in human bronchial epithelial cells, and an attenuated pro-inflammatory (M1) profile in alveolar macrophages following exposure to CS, consistent with prior results. The IPN provides a comprehensive framework of experimentally supported pathways related to CS-induced pulmonary inflammation. The IPN is freely available to the scientific community as a resource with broad applicability to study the pathogenesis of pulmonary disease.
doi:10.4137/BBI.S11509
PMCID: PMC3700945  PMID: 23843693
inflammation; cigarette smoke; network model; gene expression; biological expression language (BEL); reverse causal reasoning (RCR)
13.  Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence 
Towards the development of a systems biology-based risk assessment approach for environmental toxicants, including tobacco products in a systems toxicology setting such as the “21st Century Toxicology”, we are building a series of computable biological network models specific to non-diseased pulmonary and cardiovascular cells/tissues which capture the molecular events that can be activated following exposure to environmental toxicants. Here we extend on previous work and report on the construction and evaluation of a mechanistic network model focused on DNA damage response and the four main cellular fates induced by stress: autophagy, apoptosis, necroptosis, and senescence. In total, the network consists of 34 sub-models containing 1052 unique nodes and 1538 unique edges which are supported by 1231 PubMed-referenced literature citations. Causal node-edge relationships are described using the Biological Expression Language (BEL), which allows for the semantic representation of life science relationships in a computable format. The Network is provided in .XGMML format and can be viewed using freely available network visualization software, such as Cytoscape.
doi:10.4137/BBI.S11154
PMCID: PMC3596057  PMID: 23515068
computable; network model; DNA damage; autophagy; apoptosis; necroptosis; senescence; Biological Expression Language (BEL)
15.  A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue 
BMC Systems Biology  2011;5:168.
Background
Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate.
Results
We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung.
Conclusions
The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.
doi:10.1186/1752-0509-5-168
PMCID: PMC3224482  PMID: 22011616
16.  Construction of a computable cell proliferation network focused on non-diseased lung cells 
BMC Systems Biology  2011;5:105.
Background
Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.
Results
To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.
Conclusions
To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.
doi:10.1186/1752-0509-5-105
PMCID: PMC3160372  PMID: 21722388
17.  In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures 
Toxicology Mechanisms and Methods  2014;24(7):470-487.
Smoking has been associated with diseases of the lung, pulmonary airways and oral cavity. Cytologic, genomic and transcriptomic changes in oral mucosa correlate with oral pre-neoplasia, cancer and inflammation (e.g. periodontitis). Alteration of smoking-related gene expression changes in oral epithelial cells is similar to that in bronchial and nasal epithelial cells. Using a systems toxicology approach, we have previously assessed the impact of cigarette smoke (CS) seen as perturbations of biological processes in human nasal and bronchial organotypic epithelial culture models. Here, we report our further assessment using in vitro human oral organotypic epithelium models. We exposed the buccal and gingival organotypic epithelial tissue cultures to CS at the air–liquid interface. CS exposure was associated with increased secretion of inflammatory mediators, induction of cytochrome P450s activity and overall weak toxicity in both tissues. Using microarray technology, gene-set analysis and a novel computational modeling approach leveraging causal biological network models, we identified CS impact on xenobiotic metabolism-related pathways accompanied by a more subtle alteration in inflammatory processes. Gene-set analysis further indicated that the CS-induced pathways in the in vitro buccal tissue models resembled those in the in vivo buccal biopsies of smokers from a published dataset. These findings support the translatability of systems responses from in vitro to in vivo and demonstrate the applicability of oral organotypical tissue models for an impact assessment of CS on various tissues exposed during smoking, as well as for impact assessment of reduced-risk products.
doi:10.3109/15376516.2014.943441
PMCID: PMC4219813  PMID: 25046638
Air–liquid interface; causal biological network model; oral keratinocytes; organotypic cultures; transcriptomics

Results 1-17 (17)