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Systems Biology is an interdisciplinary field that uses computational modelling of complex biological systems using a holistic approach for determining interactions between components of the system.
The Systems Biology Team at RxCelerate incorporates the whole Systems Biology workflow from experimental design and data management through data acquisition, processing and modelling to visualisation and interpretation of the experimental findings.
EXPERIMENTAL DESIGN & DATA MANAGEMENT
At RxCelerate we want to ensure that the quality of all our data is maintained throughout the course of a study. The Systems Biology team work closely with the other teams at RxCelerate to provide:
- Optimisation of experimental conditions i.e. identifying a suitable number of replicates for a biological assay.
- Assessment of the accuracy and precision of a bioanalytical assay.
- Customised databases for both data capture and to provide a framework for future analysis.
DATA ACQUISITION, PROCESSING & MODELLING
The Systems Biology team have experience of a wide range of technology platforms coupled with analysis and computational modelling of the resulting data. These include:
- Targeted LC-MS based metabolomics for assessment of diagnostic and/or prognostic biomarkers of a disease or therapeutic intervention.
- Phenotype Wide Association Studies (PheWAS)1 which can incorporate selection of suitable variants within a target gene, genotyping, phenotyping, data quality control, association testing and studies of potential epistatic effects.
- Multivariate data analysis (MVDA) and machine learning algorithms for classification of case/control disease profiles, correlation of experimental data with other therapeutic markers and outlier detection.
1 Meghaan Ferreira (2018) Adventures in Genetics: Rewriting the Story of Drug Development with PheWAS, Clinical OMICs 5: 12-15. https://www.liebertpub.com/doi/10.1089/clinomi.05.01.11
VISUALISATION & INTERPRETATION
A Systems Biology approach has the potential to incorporate data from different sources including genetic, proteomic, phenotypic and metabolic sources as will as information from previous experiments or the literature. Visualisation of multivariate and multi-platform data is essential for the interpretation of a biological outcome including:
- Data reduction and clustering algorithms to aid visualisation of the variation in a dataset.
- Network analysis to map the relationship between different groups of data.
- Pathway topology and enrichment analysis to identify and score biochemical pathways basedupon the activity of their genetic, protein or metabolite components.
Phenome-wide association studies (PheWAS) investigate the non-hypothesis driven associations between variants of a single gene and a wide range of phenotypes, typically coincident with disease.
At RxCelerate, we use PheWAS for:
- Target validation: To ascertain whether pharmacological modification in Man will have the expected effect.
- Indication selection: Determining what is most likely to be amenable to modification of the target.
- Toxicology: Enabling identification of candidate toxicity pathways that may be linked to the target; allowing focused target-specific investigations to be added to early stage clinical trials, reducing the risk of later (and more costly) failure.