Systems Biology and Bioinformatics Analysis of Inhaled LPS Mouse Model Data from AstraZeneca

Aim:

• To develop a mechanistic model for LPS induced lung inflammatory responses in the mouse. This should describe cytokine and cell influx dynamics, and other signalling markers, and be useful to predict future data, identify new targets, to plan new experiments, and ideally to translate to human respiratory disease features.
• To do bioinformatics network analysis, machine learning (ML), and identification of modules based on Gene expression data from the same LPS mouse model.
• To combine the two above models in hybrid models, by e.g. i) let mechanistic model simulations feed into ML models to add statistical and omics-level properties, ii) by using mechanistic models to inform covariance structures in the ML models

Background:
AstraZeneca has systematically collected a variety of dynamic and temporal data following inhaled LPS challenge in mice. Examples include cytokine and pro-inflammatory biomarker profiles, pathway specific biomarkers, gene expression data, cell infiltration, etc. in the lung. These data have been collected to support future drug development projects, e.g. to know which model protocol to use for different targets, to guide future experiments, to establish a translation to humans, etc. To aid towards this goal, XHiDE aims to translate the knowledge in these data to interactive computational models, using both mechanistic, ML, and hybrids approaches.

Outcomes:
Three outcomes: i) a mechanistic multicellular model, ii) bioinformatics analysis (network modules and ML) of gene expression data, iii) hybrid combinations of the two types of models

Status: Ongoing

Business partner: AstraZeneca

The project owner is X-HiDE - Örebro University (oru.se)

Contact: