We will systematically analyse large datasets of multiple types to: (i) identify key components affected by age or experimental perturbation; (ii) establish networks of interaction; (iii) develop dynamic computational models based on these networks; (iv) use model selection methods to discriminate between alternative network topologies and generate predictive models. To characterise the data, we will apply an ensemble of methods, including frameworks in R/Bioconductor and toolboxes connected via APIs, algorithms for machine learning and deep learning, mutual information, Bayesian inference and various cluster analysis. Each strategy will be assessed for quality of output, efficiency, ease of use and maintainability. We will develop full interaction networks by employing a different set of multi-strategy workflows on enhanced data sets supplemented with data generated from cell cultures and in-vitro model systems upon perturbation. This will require the development of novel multi-workflows to enable data integration and analysis of temporal relationships across age and/or biological processes. Strategies will employ suites of apps within Cytoscape as well as emerging technologies such as Active Interaction Mapping where network connections are driven by function. The outputs will be used to inform the network topology of computational models of molecular mechanisms and cellular processes.