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.

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.

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.

Towards the validation of a lung tumorigenesis model with mainstream cigarette smoke inhalation using the A/J mouse.

A generally accepted and validated laboratory model for smoking-associated pulmonary tumorigenesis would be useful for both basic and applied research applications, such as the development of early diagnostic endpoints or the evaluation of modified risk tobacco products, respectively. The A/J mouse is susceptible for developing both spontaneous and induced lung adenomas and adenocarcinomas, and increased lung tumor multiplicities were also observed in previous cigarette smoke inhalation studies. The present study was designed to collect data useful towards the validation of an 18-month mainstream smoke (MS) inhalation model.

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

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.