Construction of a computable cell proliferation network focused on non-diseased lung cells

Abstract

BackgroundCritical 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.ResultsTo 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.ConclusionsTo 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.

Cell Metabolomics

“…Several other excellent examples of the application of quantitative metabolomics in cell culture analysis were presented for the analysis of neuroendocrine cancers and in the analysis of cancer cell metabolic phenotype (Costello et al, 2011). Examples of such analysis include profiling of central metabolism in human cancer cells, identification of metabolic fluxes in hepatic cells (Westra et al, 2011), and several other examples that have been reviewed previously (Prigione et al, 2011).…”

Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems

“…Structurally, the CBN collection is organized into the following biological areas (Figure 1): cell response to stress, cell proliferation, cell fate (including DNA damage response, autophagy, senescence, apoptosis and necroptosis), tissue repair and angiogenesis, pulmonary inflammatory processes, vascular inflammatory processes and chronic obstructive pulmonary disease (COPD)-specific networks. Details of these network models have been published previously (7)(8)(9)(10)(11)(12).…”

Enhancement of COPD biological networks using a web-based collaboration interface

“…These networks cover mechanisms of cell proliferation 5 , cell stress 4 , DNA damage, autophagy, cell death and senescence 3 , pulmonary inflammation 6 , and tissue repair and angiogenesis 7 in the non-diseased pulmonary context. To create COPD-relevant networks, these non-diseased networks were enhanced by incorporating COPD mechanisms sourced using a literature and data set approach ( Figure 1) in an iterative approach, as described in detail for the non-diseased network model construction, by a team of subject matter experts in computational biology, molecular biology, inhalation toxicology, and COPD.…”

“…In some cases, we made exceptions and included non-lung cell types for canonical mechanisms for which there was additional evidence from the literature that the relationship was not tissue-specific but could also take place in the lung. Human-specific connections were prioritized, but where human data were not available, knowledge has been augmented with orthologous causal assertions derived from rat and mouse sources included after homologization in the Selventa knowledgebase where human data were not available 5 .…”

“…The 90 previously published non-diseased network models used for the initial substrate included networks involved in cell proliferation 5 , cell stress 4 , DNA damage, apoptosis, senescence, autophagy, necroptosis (DACS) 3 , pulmonary inflammation (IPN) 6 , and tissue repair and angiogenesis (TRAG) 7 . The Endothelial Shear Stress network from the cell stress model was excluded because the focus of the COPD Network was to describe lung biology.…”