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

BackgroundMouse 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).ObjectiveIn 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 materialThe online version of this article (doi:10.1007/s00011-015-0820-2) contains supplementary material, which is available to authorized users.

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.

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.

Over the past few years, we constructed a number of biological network models representing comprehensive connectivity maps of fundamental molecular mechanisms regulating cell proliferation, cellular stress, and cell fate in the healthy and inflamed lung and cardiovascular system [1, 2, 3, 4]. These network models are based on causal and correlative biological relationships expressed in Biological Expression Language (BEL). We further developed a method that quantifies network response as a whole in an interpretable manner by integrating these causal networks with systems biology data (e.g. transcriptomics) and could show that quantitative network perturbation was in agreement with experimental endpoint data for many of the mechanistic effects of interest [5]. Recently, we extended our efforts in an attempt to build a comprehensive set of computational models reflecting the biology of cancer hallmarks as described by Hanahan and Weinberg [6] with specific attention to mechanisms occurring in the early stages of non-small cell lung cancer (NSCLC) development and progression. Using a dual approach of curating relevant literature and supplementing this information with experimental data sets from a variety of NSCLC microarray studies, we thus far completed the construction of a “Sustaining Proliferative Signaling/Evading Growth Suppressors” hallmark model that describes multiple autocrine and paracrine signaling pathways responsible for driving continuous growth of tumor cells (e.g. growth factor/growth factor receptor, MAPK, JAK/STAT signaling etc.) and deregulating cell cycle check points, as well as a “Resisting Cell Death” hallmark model combining biological mechanisms indicative of intrinsic and extrinsic apoptosis pathways, necroptosis and autophagy that ensure (lung) tumor maintenance and survival. Processes related to VEGF- and other growth factor-driven angiogenesis, vascular sprouting and tubulogenesis, HIF1A signaling and endothelial cell activation were included in an “Inducing Angiogenesis” hallmark model. The latter is closely connected to the hallmark model “Activating Invasion and Metastasis” which also considers the mechanisms associated with the acquisition of invasive capabilities by e.g. epithelial-mesenchymal transition, the degradation of extracellular matrix, and epithelial and endothelial permeability permitting tumor cell dissemination. We further built a “Deregulating Cellular Energetics” hallmark model reflecting the metabolic switch in tumor cells to aerobic glycolysis including various aspects of hypoxia and autophagy. Our focus at the current time is to address the features associated with tumor immune surveillance as exemplified by the complex interplay between tumor cells and tumor-infiltrating lymphocytes, macrophages, dendritic cells and natural killer cells which could be reflected in an “Avoiding Immune Destruction” hallmark model. As a next step we wish to integrate specific mechanisms that contribute to persistent pro-inflammatory signaling into a “Tumor-promoting Inflammation” hallmark network model. This will be followed by a comprehensive review of the newly constructed models and, if necessary, further augmentation by literature and validation with molecular data. Ultimately, we will employ our previously developed network quantification approach together with a number of publicly available lung cancer data sets to objectively evaluate the predictability of disease mechanisms in silico using transcriptomics data, and we hope that, if successful in this endeavor, various applications from drug development to environmental impact analysis could benefit from employing this portfolio of network models in unraveling disease-specific mechanisms and identifying new therapeutic targets. [1] Westra JW, Schlage WK, Frushour BP et al. (2011). Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst Biol. 5, 105. [2] Gebel S, Lichtner RB, Frushour B et al. (2013). Construction of a computable network model for DNA damage, autophagy, cell death, and senescence. Bioinform Biol Insights 7, 97-117. [3] Westra JW, Schlage WK, Hengstermann A et al. (2013). A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue. Bioinform Biol Insights 7, 167-192. [4] De León H, Boué S, Schlage WK et al. (2014). A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. J Transl Med. 12, 185. [5] Thomson TM, Sewer A, Martin F et al. (2013). Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol Appl Pharmacol. 272(3), 863-878. [6] Hanahan D & Weinberg RA (2011). Hallmarks of Cancer: The Next Generation. Cell 144(5), 646–674. Citation Format: Karsta Luettich, Marja Talikka, Anita Iskandar, Justyna Szostak, Ulrike Kogel, Walter Schlage, Yang Xiang, Vered Katz Ben-Yair, Shay Rotkopf, Brett Fields, Jennifer Park, Julia Hoeng, Manuel Peitsch. Computable cancer hallmarks - The construction of novel computable biological network models reflecting causal mechanisms of cancer hallmarks. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-19.

No abstract